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We present LidarDM, a novel LiDAR generative model capable of producing realistic, layout-aware, physically plausible, and temporally coherent LiDAR videos. LidarDM stands out with two unprecedented capabilities in LiDAR generative modeling: (i) LiDAR generation guided by driving scenarios, offering significant potential for autonomous driving simulations, and (ii) 4D LiDAR point cloud generation, enabling the creation of realistic and temporally coherent sequences. At the heart of our model is a novel integrated 4D world generation framework. Specifically, we employ latent diffusion models to generate the 3D scene, combine it with dynamic actors to form the underlying 4D world, and subsequently produce realistic sensory observations within this virtual environment. Our experiments indicate that our approach outperforms competing algorithms in realism, temporal coherency, and layout consistency. We additionally show that LidarDM can be used as a generative world model simulator for training and testing perception models.

To curb the initial spread of SARS-CoV-2, many countries relied on nation-wide implementation of non-pharmaceutical intervention measures, resulting in substantial socio-economic impacts. Potentially, subnational implementations might have had less of a societal impact, but comparable epidemiological impact. Here, using the first COVID-19 wave in the Netherlands as a case in point, we address this issue by developing a high-resolution analysis framework that uses a demographically-stratified population and a spatially-explicit, dynamic, individual contact-pattern based epidemiology, calibrated to hospital admissions data and mobility trends extracted from mobile phone signals and Google. We demonstrate how a subnational approach could achieve similar level of epidemiological control in terms of hospital admissions, while some parts of the country could stay open for a longer period. Our framework is exportable to other countries and settings, and may be used to develop policies on subnational approach as a better strategic choice for controlling future epidemics., Comment: 9 pages, 4 figures and Supplementary Information

Despite tremendous advancements in bird's-eye view (BEV) perception, existing models fall short in generating realistic and coherent semantic map layouts, and they fail to account for uncertainties arising from partial sensor information (such as occlusion or limited coverage). In this work, we introduce MapPrior, a novel BEV perception framework that combines a traditional discriminative BEV perception model with a learned generative model for semantic map layouts. Our MapPrior delivers predictions with better accuracy, realism, and uncertainty awareness. We evaluate our model on the large-scale nuScenes benchmark. At the time of submission, MapPrior outperforms the strongest competing method, with significantly improved MMD and ECE scores in camera- and LiDAR-based BEV perception.

This paper categorizes the parameterized complexity of the algorithmic problems Perfect Phylogeny and Triangulating Colored Graphs. We show that they are complete for the parameterized complexity class XALP using a reduction from Tree-chained Multicolor Independent Set and a proof of membership. We introduce the problem Triangulating Multicolored Graphs as a stepping stone and prove XALP-completeness for this problem as well. We also show that, assuming the Exponential Time Hypothesis, there exists no algorithm that solves any of these problems in time $f(k) n^{o(k)}$, where $n$ is the input size, $k$ the parameter, and $f$ any computable function.

We present LiDARGen, a novel, effective, and controllable generative model that produces realistic LiDAR point cloud sensory readings. Our method leverages the powerful score-matching energy-based model and formulates the point cloud generation process as a stochastic denoising process in the equirectangular view. This model allows us to sample diverse and high-quality point cloud samples with guaranteed physical feasibility and controllability. We validate the effectiveness of our method on the challenging KITTI-360 and NuScenes datasets. The quantitative and qualitative results show that our approach produces more realistic samples than other generative models. Furthermore, LiDARGen can sample point clouds conditioned on inputs without retraining. We demonstrate that our proposed generative model could be directly used to densify LiDAR point clouds. Our code is available at: https://www.zyrianov.org/lidargen/, Comment: ECCV 2022. Version 2 comments: Corrected typos

pyspeckit is a toolkit and library for spectroscopic analysis in Python. We describe the pyspeckit package and highlight some of its capabilities, such as interactively fitting a model to data, akin to the historically widely-used splot function in IRAF. pyspeckit employs the Levenberg-Marquardt optimization method via the mpfit and lmfit implementations, and important assumptions regarding error estimation are described here. Wrappers to use pymc and emcee as optimizers are provided. A parallelized wrapper to fit lines in spectral cubes is included. As part of the astropy affiliated package ecosystem, pyspeckit is open source and open development and welcomes input and collaboration from the community., Comment: Version 1.0 of pyspeckit is released alongside this paper; https://pyspeckit.readthedocs.io/en/latest/

Resolved kinematical information, such as from molecular gas in star forming regions, is obtained from spectral line observations. However, these observations often contain multiple line-of-sight components, making estimates harder to obtain and interpret. We present a fully automatic method that determines the number of components along the line of sight, or the spectral multiplicity, through Bayesian model selection. The underlying open-source framework, based on nested sampling and conventional spectral line modeling, is tested using the large area ammonia maps of NGC 1333 in Perseus molecular cloud obtained by the Green Bank Ammonia Survey (GAS). Compared to classic approaches, the presented method constrains velocities and velocity dispersions in a larger area. In addition, we find that the velocity dispersion distribution among multiple components did not change substantially from that of a single fit component analysis of the GAS data. These results showcase the power and relative ease of the fitting and model selection method, which makes it a unique tool to extract maximum information from complex spectral data., Comment: 8 pages, 4 figures, accepted for publication in the Astrophysical Journal Letters

To probe the initial conditions for high-mass star and cluster formation, we investigate the properties of dense filaments within the infrared dark cloud G035.39-00.33 (IRDC G035.39) in a combined Very Large Array (VLA) and the Green Bank Telescope (GBT) mosaic tracing the NH3 (1,1) and (2,2) emission down to 0.08 pc scales. Using agglomerative hierarchical clustering on multiple line-of-sight velocity component fitting results, we identify seven extended velocity-coherent components in our data, likely representing spatially coherent physical structures, some exhibiting complex gas motions. The velocity gradient magnitude distribution peaks at its mode of 0.35 km/s/pc and has a long tail extending into higher values of 1.5 - 2 km/s/pc, and is generally consistent with those found toward the same cloud in other molecular tracers and with the values found towards nearby low-mass dense cloud cores at the same scales. Contrary to observational and theoretical expectations, we find the non-thermal ammonia line widths to be systematically narrower (by about 20%) than those of N2H+ (1-0) line transition observed with similar resolution. If the observed ordered velocity gradients represent the core envelope solid-body rotation, we estimate the specific angular momentum to be about 2 x 10^21 cm^2/s, similar to the low-mass star-forming cores. Together with the previous finding of subsonic motions in G035.39, our results demonstrate high levels of similarity between kinematics of a high-mass star-forming IRDC and the low-mass star formation regime., Comment: 22 pages, 14 figures, accepted to ApJ

Magnetic field is one of the key agents that play a crucial role in shaping molecular clouds and regulating star formation, yet the complete information on the magnetic field is not well constrained due to the limitations in observations. We study the magnetic field in the massive infrared dark cloud G035.39-00.33 from dust continuum polarization observations at 850 $\micron$ with SCUBA-2/POL-2 at JCMT. The magnetic field tends to be perpendicular to the densest part of the main filament (F$_{M}$), whereas it has a less defined relative orientation in the rest of the structure, where it tends to be parallel to some diffuse regions. A mean plane-of-the-sky magnetic field strength of $\sim$50 $\mu$G for F$_{M}$ is obtained using Davis-Chandrasekhar-Fermi method. Based on $^{13}$CO (1-0) line observations, we suggest a formation scenario of F$_{M}$ due to large-scale ($\sim$10 pc) cloud-cloud collision. Using additional NH$_3$ line data, we estimate that F$_{M}$ will be gravitationally unstable if it is only supported by thermal pressure and turbulence. The northern part of F$_{M}$, however, can be stabilized by a modest additional support from the local magnetic field. The middle and southern parts of F$_{M}$ are likely unstable even if the magnetic field support is taken into account. We claim that the clumps in F$_{M}$ may be supported by turbulence and magnetic fields against gravitational collapse. Finally, we identified for the first time a massive ($\sim$200 M$_{\sun}$), collapsing starless clump candidate, "c8", in G035.39-00.33. The magnetic field surrounding "c8" is likely pinched, hinting at an accretion flow along the filament., Comment: Published on ApJ, 27 pages

High-mass star forming regions are typically thought to be dominated by supersonic motions. We present combined Very Large Array and Green Bank Telescope (VLA+GBT) observations of NH$_3$ (1,1) and (2,2) in the infrared dark cloud (IRDC) G035.39-00.33, tracing cold and dense gas down to scales of 0.07 pc. We find that, in contrast to previous similar studies of IRDCs, more than a third of the fitted ammonia spectra show subsonic non-thermal motions (mean line width of 0.71 $\mathrm{km~s^{-1}}$), and the sonic Mach number distribution peaks around $\mathcal{M} = 1$. As possible observational and instrumental biases would only broaden the line profiles, our results provide strong upper limits to the actual value of $\mathcal{M}$, further strengthening our findings of narrow line widths. This finding calls for a reevaluation of the role of turbulent dissipation and subsonic regions in massive-star and cluster formation. Based on our findings in G035.39, we further speculate that the coarser spectral resolution used in the previous VLA NH$_3$ studies may have inhibited the detection of subsonic turbulence in IRDCs. The reduced turbulent support suggests that dynamically important magnetic fields of the 1 mG order would be required to support against possible gravitational collapse. Our results offer valuable input into the theories and simulations that aim to recreate the initial conditions of high-mass star and cluster formation., Comment: 5 pages, 3 figures; accepted for publication in A&A Letters; arxiv revision matches accepted version

Aims. Infrared dark clouds represent the earliest stages of high-mass star formation. Detailed observations of their physical conditions on all physical scales are required to improve our understanding of their role in fueling star formation. Methods. We investigate the large-scale structure of the IRDC G035.39-00.33, probing the dense gas with the classical ammonia thermometer. This allows us to put reliable constraints on the temperature of the extended, pc-scale dense gas reservoir and to probe the magnitude of its non-thermal motions. Available far-infrared observations can be used in tandem with the observed ammonia emission to estimate the total gas mass contained in G035.39-00.33. Results. We identify a main velocity component as a prominent filament, manifested as an ammonia emission intensity ridge spanning more than 6 pc, consistent with the previous studies on the Northern part of the cloud. A number of additional line-of-sight components are found, and a large scale, linear velocity gradient of ~0.2 km s$^{-1}$ pc$^{-1}$ is found along the ridge of the IRDC. In contrast to the dust temperature map, an ammonia-derived kinetic temperature map, presented for the entirety of the cloud, reveals local temperature enhancements towards the massive protostellar cores. We show that without properly accounting for the line of sight contamination, the dust temperature is 2-3 K larger than the gas temperature measured with NH$_3$. Conclusions. While both the large scale kinematics and temperature structure are consistent with that of starless dark filaments, the kinetic gas temperature profile on smaller scales is suggestive of tracing the heating mechanism coincident with the locations of massive protostellar cores., Comment: 11 pages, 10 figures, accepted to A&A