Miracle-dz/UniFAR-FTUs huggingface dataset:
A simple model for the outcomes of collisions between exhaled aerosol droplets and airborne particulate matter: Towards an understanding of the influence of air pollution on airborne viral transmission (preprint)

A model that predicts the outcome of collisions between droplets and particles in terms of the distribution of the droplet volume post-collision is lacking, in contrast to the case for droplet-droplet interactions. Taking existing models that successfully predict the outcomes (coalescence, stretching or reflexive separation) and post-separation characteristics (sizes, numbers and velocities of the resulting droplets) of droplet-droplet collisions and adapting them to take into account an inextensible, non-deformable particle with varying wettability characteristics, a new model is presented for droplet-particle collisions. The predictions of the new model agree well with experimental observations of droplet-particle collisions in low-viscosity regimes. The model is then applied to the case of collisions between respiratory aerosols generated by breath, speech, cough and sneeze and ambient airborne particulate material (PM) in order to assess the potential contribution of these interactions to the enhanced transmission of pathogens contained in the aerosol, including COVID-19. The results show that under realistic conditions it is possible for aerosol-PM collisions to enrich the pathogen content of smaller (and so more persistent) aerosol fractions, and to transfer pathogens to the surface of PM particles that can travel deep into the respiratory tract. In the context of better knowledge of the size and velocity distributions of respiratory aerosols, this model may be used to predict the extent to which high ambient PM levels may contribute to airborne infection by pathogens such as COVID-19.
Multicontact formulation for non-conservative field theories

A new geometric structure inspired by multisymplectic and contact geometries, which we call multicontact structure, is developed to describe non-conservative classical field theories. Using the differential forms that define this multicontact structure as well as other geometric elements that are derived from them while assuming certain conditions, we can introduce, on the multicontact manifolds, the variational field equations which are stated using sections, multivector fields, and Ehresmann connections on the adequate fiber bundles. Furthermore, it is shown how this multicontact framework can be adapted to the jet bundle description of classical field theories; the field equations are stated in the Lagrangian and the Hamiltonian formalisms both in the regular and the singular cases.
Pruning Neural Networks via Coresets and Convex Geometry: Towards No Assumptions

Pruning is one of the predominant approaches for compressing deep neural networks (DNNs). Lately, coresets (provable data summarizations) were leveraged for pruning DNNs, adding the advantage of theoretical guarantees on the trade-off between the compression rate and the approximation error. However, coresets in this domain were either data-dependent or generated under restrictive assumptions on both the model's weights and inputs. In real-world scenarios, such assumptions are rarely satisfied, limiting the applicability of coresets. To this end, we suggest a novel and robust framework for computing such coresets under mild assumptions on the model's weights and without any assumption on the training data. The idea is to compute the importance of each neuron in each layer with respect to the output of the following layer. This is achieved by a combination of L\"{o}wner ellipsoid and Caratheodory theorem. Our method is simultaneously data-independent, applicable to various networks and datasets (due to the simplified assumptions), and theoretically supported. Experimental results show that our method outperforms existing coreset based neural pruning approaches across a wide range of networks and datasets. For example, our method achieved a $62\%$ compression rate on ResNet50 on ImageNet with $1.09\%$ drop in accuracy.
ViT-DD: Multi-Task Vision Transformer for Semi-Supervised Driver Distraction Detection

Ensuring traffic safety and mitigating accidents in modern driving is of paramount importance, and computer vision technologies have the potential to significantly contribute to this goal. This paper presents a multi-modal Vision Transformer for Driver Distraction Detection (termed ViT-DD), which incorporates inductive information from training signals related to both distraction detection and driver emotion recognition. Additionally, a self-learning algorithm is developed, allowing for the seamless integration of driver data without emotion labels into the multi-task training process of ViT-DD. Experimental results reveal that the proposed ViT-DD surpasses existing state-of-the-art methods for driver distraction detection by 6.5% and 0.9% on the SFDDD and AUCDD datasets, respectively.
RGB-Event Fusion for Moving Object Detection in Autonomous Driving

Moving Object Detection (MOD) is a critical vision task for successfully achieving safe autonomous driving. Despite plausible results of deep learning methods, most existing approaches are only frame-based and may fail to reach reasonable performance when dealing with dynamic traffic participants. Recent advances in sensor technologies, especially the Event camera, can naturally complement the conventional camera approach to better model moving objects. However, event-based works often adopt a pre-defined time window for event representation, and simply integrate it to estimate image intensities from events, neglecting much of the rich temporal information from the available asynchronous events. Therefore, from a new perspective, we propose RENet, a novel RGB-Event fusion Network, that jointly exploits the two complementary modalities to achieve more robust MOD under challenging scenarios for autonomous driving. Specifically, we first design a temporal multi-scale aggregation module to fully leverage event frames from both the RGB exposure time and larger intervals. Then we introduce a bi-directional fusion module to attentively calibrate and fuse multi-modal features. To evaluate the performance of our network, we carefully select and annotate a sub-MOD dataset from the commonly used DSEC dataset. Extensive experiments demonstrate that our proposed method performs significantly better than the state-of-the-art RGB-Event fusion alternatives. The source code and dataset are publicly available at: https://github.com/ZZY-Zhou/RENet.
Deviation of geodesics and particle trajectories in a gravitational wave of the Bianchi type VI universe

For the Bianchi type VI universe, exact solutions of the equation of geodesic deviation in a strong primordial gravitational wave in a privileged coordinate system are obtained. The solutions refer to Shapovalov’s gravitational-wave models of spacetime and allow the existence of complete integrals of the Hamilton–Jacobi equation for test particles. For all the solutions obtained, the analytical form of the tidal acceleration vector in a strong primordial gravitational wave is obtained. An explicit form of the coordinate transformation, an explicit form of the metric of the primordial gravitational wave of the Bianchi type VI universe, and the form of the tidal acceleration vector in the laboratory synchronous coordinate system are obtained. The synchronous coordinate system is associated with a freely falling observer and allows the observer to separate time and spatial coordinates, as well as to synchronize time at different points in space. The presented mathematical approach can be applied both in the general theory of relativity and in modified theories of gravity.
Double Shells of the Planetary Nebula NGC 7009 Minor Axis

We analyzed the minor-axis spectra of the elliptical planetary nebula (PN) NGC 7009 observed with Keck HIRES with a 0. ″862 × 10″ slit placed at about 7. ″5 and 10″ away from the center and a 0. ″862 × 14″ slit at the center. The mean densities derived from the integrated [S ii] 6716/6731 Å fluxes along the Keck HIRES slit length indicate a density range of 103.7–104.1 cm−3, while the local densities derived from the slit spectral images show a large local density variation of about 102.8–104.6 cm−3: local densities vary more substantially than values integrated over the line of sight. The expansion rates of the main and outer shells obtained by [S ii] are about 21.7 and 30.0 km s−1, respectively. The kinematic results of the [S ii] spectral lines correspond to the outermost regions of the two shells and are not representative of the whole PN but are closely related to the other emission lines observed in the shell gas. We conclude that the density contrast leads to the formation of the inner shell, while the change in ionization state leads to the formation of the outer shell. We suggest that the inner main and outer shells result from two successive major ejections. The physical conditions of the central star must have been different when these shells first formed.
Report of the Topical Group on Wave Dark Matter for Snowmass 2021

There is a strong possibility that the particles making up the dark matter in the Universe have a mass below 1 eV and in many important situations exhibit a wave-like behavior. Amongst the candidates the axion stands out as particularly well motivated but other possibilities such as axion-like particles, light scalars and light vectors, should be seriously investigated with both experiments and theory. Discovery of any of these dark matter particles would be revolutionary. The wave-like nature opens special opportunities to gain precise information on the particle properties a well as astrophysical information on dark matter shortly after a first detection. To achieve these goals requires continued strong support for the next generations of axion experiments to probe significant axion parameter space this decade and to realize the vision of a definitive axion search program in the next 20 years. This needs to be complemented by strong and flexible support for a broad range of smaller experiments, sensitive to the full variety of wave-like dark matter candidates. These have their own discovery potential but can also be the test bed for future larger scale searches. Strong technological support not only allows for the optimal realization of the current and near future experiments but new technologies such as quantum measurement and control can also provide the next evolutionary jump enabling a broader and deeper sensitivity. Finally, a theory effort ranging from fundamental model building over investigating phenomenological constraints to the conception of new experimental techniques is a cornerstone of the current rapid developments in the search for wave-like dark matter and should be strengthened to have a solid foundation for the future.
Neural-Symbolic Entangled Framework for Complex Query Answering

Answering complex queries over knowledge graphs (KG) is an important yet challenging task because of the KG incompleteness issue and cascading errors during reasoning. Recent query embedding (QE) approaches to embed the entities and relations in a KG and the first-order logic (FOL) queries into a low dimensional space, answering queries by dense similarity search. However, previous works mainly concentrate on the target answers, ignoring intermediate entities' usefulness, which is essential for relieving the cascading error problem in logical query answering. In addition, these methods are usually designed with their own geometric or distributional embeddings to handle logical operators like union, intersection, and negation, with the sacrifice of the accuracy of the basic operator - projection, and they could not absorb other embedding methods to their models. In this work, we propose a Neural and Symbolic Entangled framework (ENeSy) for complex query answering, which enables the neural and symbolic reasoning to enhance each other to alleviate the cascading error and KG incompleteness. The projection operator in ENeSy could be any embedding method with the capability of link prediction, and the other FOL operators are handled without parameters. With both neural and symbolic reasoning results contained, ENeSy answers queries in ensembles. ENeSy achieves the SOTA performance on several benchmarks, especially in the setting of the training model only with the link prediction task.
Cosmic ray anisotropy study with the AMANDA Neutrino Telescope

Cosmic rays are particles and radiation hitting the Earth from space. Among them, protons make the largest part, but also heavier nuclei, electrons, photons and neutrinos are present. The bulk of cosmic rays are believed to origine from inside the galaxy, possible sources are supernova remnants. Still several questions remain open about origin, propagation, intensity distribution and its variety. This work presents the search for anisotropies within the arrival directions of the cosmic radiation and its possible variation over time. The data used in this work was recorded as background radiation by the AMANDA-experiment, a neutrino telescope located at the geographic South pole, between 2000 and 2006. For the complete period of data, a one-dimensional as well as a two-dimensional analysis are carried out. Furthermore, for each single year the data is analysed and the examination of a possible variation over time is executed. The results show a clear anisotropy signal consistent with results from other experiments. A variation of this signal over the observed time period could not be seen.
Density-aware NeRF Ensembles: Quantifying Predictive Uncertainty in Neural Radiance Fields

We show that ensembling effectively quantifies model uncertainty in Neural Radiance Fields (NeRFs) if a density-aware epistemic uncertainty term is considered. The naive ensembles investigated in prior work simply average rendered RGB images to quantify the model uncertainty caused by conflicting explanations of the observed scene. In contrast, we additionally consider the termination probabilities along individual rays to identify epistemic model uncertainty due to a lack of knowledge about the parts of a scene unobserved during training. We achieve new state-of-the-art performance across established uncertainty quantification benchmarks for NeRFs, outperforming methods that require complex changes to the NeRF architecture and training regime. We furthermore demonstrate that NeRF uncertainty can be utilised for next-best view selection and model refinement.
Report of the Topical Group on Dark Energy and Cosmic Acceleration: Complementarity of Probes and New Facilities for Snowmass 2021

The mechanism(s) driving the early- and late-time accelerated expansion of the Universe represent one of the most compelling mysteries in fundamental physics today. The path to understanding the causes of early- and late-time acceleration depends on fully leveraging ongoing surveys, developing and demonstrating new technologies, and constructing and operating new instruments. This report presents a multi-faceted vision for the cosmic survey program in the 2030s and beyond that derives from these considerations. Cosmic surveys address a wide range of fundamental physics questions, and are thus a unique and powerful component of the HEP experimental portfolio.
Non-invasive Localization of the Ventricular Excitation Origin Without Patient-specific Geometries Using Deep Learning

Cardiovascular diseases account for 17 million deaths per year worldwide. Of these, 25% are categorized as sudden cardiac death, which can be related to ventricular tachycardia (VT). This type of arrhythmia can be caused by focal activation sources outside the sinus node. Catheter ablation of these foci is a curative treatment in order to inactivate the abnormal triggering activity. However, the localization procedure is usually time-consuming and requires an invasive procedure in the catheter lab. To facilitate and expedite the treatment, we present two novel localization support techniques based on convolutional neural networks (CNNs) that address these clinical needs. In contrast to existing methods, our approaches were designed to be independent of the patient-specific geometry and directly applicable to surface ECG signals, while also delivering a binary transmural position. Moreover, one of the method's outputs can be interpreted as several ranked solutions. The CNNs were trained on a dataset containing only simulated data and evaluated both on simulated test data and clinical data. On a novel large and open simulated dataset, the median test error was below 3 mm. The median localization error on the unseen clinical data ranged from 32 mm to 41 mm without optimizing the pre-processing and CNN to the clinical data. Interpreting the output of one of the approaches as ranked solutions, the best median error of the top-3 solutions decreased to 20 mm on the clinical data. The transmural position was correctly detected in up to 82% of all clinical cases. These results demonstrate a proof of principle to utilize CNNs to localize the activation source without the intrinsic need for patient-specific geometrical information. Furthermore, providing multiple solutions can assist physicians in identifying the true activation source amongst more than one possible location. With further optimization to clinical data, these methods have high potential to accelerate clinical interventions, replace certain steps within these procedures and consequently reduce procedural risk and improve VT patient outcomes.
Impact of large-mass constraints on the properties of neutron stars

The maximum mass of a nonrotating neutron star, MTOV, plays a very important role in deciphering the structure and composition of neutron stars and in revealing the equation of state (EOS) of nuclear matter. Although with a large-error bar, the recent mass estimate for the black-widow binary pulsar PSR J0952-0607, i.e. M = 2.35 ± 0.17 M⊙, provides the strongest lower bound on MTOV and suggests that neutron stars with very large masses can in principle be observed. Adopting an agnostic modelling of the EOS, we study the impact that large masses have on the neutron-star properties. In particular, we show that assuming MTOV ≳ 2.35 M⊙ constrains tightly the behaviour of the pressure as a function of the energy density and moves the lower bounds for the stellar radii to values that are significantly larger than those constrained by the NICER measurements, rendering the latter ineffective in constraining the EOS. We also provide updated analytic expressions for the lower bound on the binary tidal deformability in terms of the chirp mass and show how larger bounds on MTOV lead to tighter constraints for this quantity. In addition, we point out a novel quasi-universal relation for the pressure profile inside neutron stars that is only weakly dependent from the EOS and the maximum-mass constraint. Finally, we study how the sound speed and the conformal anomaly are distributed inside neutron stars and show how these quantities depend on the imposed maximum-mass constraints.
MoVQ: Modulating Quantized Vectors for High-Fidelity Image Generation

Although two-stage Vector Quantized (VQ) generative models allow for synthesizing high-fidelity and high-resolution images, their quantization operator encodes similar patches within an image into the same index, resulting in a repeated artifact for similar adjacent regions using existing decoder architectures. To address this issue, we propose to incorporate the spatially conditional normalization to modulate the quantized vectors so as to insert spatially variant information to the embedded index maps, encouraging the decoder to generate more photorealistic images. Moreover, we use multichannel quantization to increase the recombination capability of the discrete codes without increasing the cost of model and codebook. Additionally, to generate discrete tokens at the second stage, we adopt a Masked Generative Image Transformer (MaskGIT) to learn an underlying prior distribution in the compressed latent space, which is much faster than the conventional autoregressive model. Experiments on two benchmark datasets demonstrate that our proposed modulated VQGAN is able to greatly improve the reconstructed image quality as well as provide high-fidelity image generation.
Differentiable Topology-Preserved Distance Transform for Pulmonary Airway Segmentation

Detailed pulmonary airway segmentation is a clinically important task for endobronchial intervention and treatment of peripheral located lung cancer lesions. Convolutional Neural Networks (CNNs) are promising tools for medical image analysis but have been performing poorly for cases when existing a significant imbalanced feature distribution, which is true for the airway data as the trachea and principal bronchi dominate most of the voxels whereas the lobar bronchi and distal segmental bronchi occupy a small proportion. In this paper, we propose a Differentiable Topology-Preserved Distance Transform (DTPDT) framework to improve the performance of airway segmentation. A Topology-Preserved Surrogate (TPS) learning strategy is first proposed to balance the training progress within-class distribution. Furthermore, a Convolutional Distance Transform (CDT) is designed to identify the breakage phenomenon with superior sensitivity and minimize the variation of the distance map between the predictionand ground-truth. The proposed method is validated with the publically available reference airway segmentation datasets. The detected rate of branch and length on public EXACT'09 and BAS datasets are 82.1%/79.6% and 96.5%/91.5% respectively, demonstrating the reliability and efficiency of the method in terms of improving the topology completeness of the segmentation performance while maintaining the overall topology accuracy.
SD-Based Low-Complexity Precoder Design for Gaussian MIMO Wiretap Channels

This paper considers a Gaussian multi-input multi-output (MIMO) multiple access wiretap (MAC-WT) channel, where an eavesdropper (Eve) wants to extract the confidential information of all users. Assuming that both the legitimate receiver and Eve jointly decode their interested messages, we aim to maximize the sum secrecy rate of the system by precoder design. Although this problem could be solved by first using the iterative majorization minimization (MM) based algorithm to get a sequence of convex log-determinant optimization subproblems and then using some general tools, e.g., the interior point method, to deal with each subproblem, this strategy involves quite high computational complexity. Therefore, we propose a simultaneous diagonalization based low-complexity (SDLC) method to maximize the secrecy rate of a simple one-user wiretap channel, and then use this method to iteratively optimize the covariance matrix of each user. Simulation results show that in contrast to the existing approaches, the SDLC scheme achieves similar secrecy performance but requires much lower complexity.