Construction of Wave Dark Matter Halos: Numerical Algorithm and Analytical Constraints

We present a wave generalization of the classic Schwarzschild method for constructing self-consistent halos -- such a halo consists of a suitable superposition of waves instead of particle orbits, chosen to yield a desired mean density profile. As an illustration, the method is applied to spherically symmetric halos. We derive an analytic relation between the particle distribution function and the wave superposition amplitudes, and show how it simplifies in the high energy (WKB) limit. We verify the stability of such constructed halos by numerically evolving the Schrodinger-Poisson system. The algorithm provides an efficient and accurate way to simulate the time-dependent halo substructures from wave interference. We use this method to construct halos with a variety of density profiles, all of which have a core from the ground-state wave function, though the core-halo relation need not be the standard one.

3D RegNet: Deep Learning Model for COVID-19 Diagnosis on Chest CT Image

In this paper, a 3D-RegNet-based neural network is proposed for diagnosing the physical condition of patients with coronavirus (Covid-19) infection. In the application of clinical medicine, lung CT images are utilized by practitioners to determine whether a patient is infected with coronavirus. However, there are some laybacks can be considered regarding to this diagnostic method, such as time consuming and low accuracy. As a relatively large organ of human body, important spatial features would be lost if the lungs were diagnosed utilizing two dimensional slice image. Therefore, in this paper, a deep learning model with 3D image was designed. The 3D image as input data was comprised of two-dimensional pulmonary image sequence and from which relevant coronavirus infection 3D features were extracted and classified. The results show that the test set of the 3D model, the result: f1 score of 0.8379 and AUC value of 0.8807 have been achieved.

Epitaxial growth and magnetic characterization of EuSe thin films with various crystalline orientations

We report different growth modes and corresponding magnetic properties of thin EuSe films grown by molecular beam epitaxy on BaF2, Pb1-xEuxSe, GaAs, and Bi2Se3 substrates. We show that EuSe growth predominantly in (001) orientation on GaAs(111) and Bi2Se3, but along (111) crystallographic direction on BaF2 (111) and Pb1-xEuxSe (111). High-resolution transmission electron microscopy measurements reveal an abrupt and highly crystalline interface for both (001) and (111) EuSe films. In agreement with previous studies, ordered magnetic phases include antiferromagnetic, ferrimagnetic, and ferromagnetic phases. In contrast to previous studies, we found strong hysteresis for the antiferromagnetic-ferrimagnetic transition. An ability to grow epitaxial films of EuSe on Bi2Se3 and of Bi2Se3 on EuSe enables further investigation of interfacial exchange interactions between various phases of an insulating metamagnetic material and a topological insulator.

Unsupervised Action Segmentation with Self-supervised Feature Learning and Co-occurrence Parsing

Temporal action segmentation is a task to classify each frame in the video with an action label. However, it is quite expensive to annotate every frame in a large corpus of videos to construct a comprehensive supervised training dataset. Thus in this work we explore a self-supervised method that operates on a corpus of unlabeled videos and predicts a likely set of temporal segments across the videos. To do this we leverage self-supervised video classification approaches to perform unsupervised feature extraction. On top of these features we develop CAP, a novel co-occurrence action parsing algorithm that can not only capture the correlation among sub-actions underlying the structure of activities, but also estimate the temporal trajectory of the sub-actions in an accurate and general way. We evaluate on both classic datasets (Breakfast, 50Salads) and emerging fine-grained action datasets (FineGym) with more complex activity structures and similar sub-actions. Results show that our method achieves state-of-the-art performance on all three datasets with up to 22% improvement, and can even outperform some weakly-supervised approaches, demonstrating its effectiveness and generalizability.

SiamMOT: Siamese Multi-Object Tracking

In this paper, we focus on improving online multi-object tracking (MOT). In particular, we introduce a region-based Siamese Multi-Object Tracking network, which we name SiamMOT. SiamMOT includes a motion model that estimates the instances movement between two frames such that detected instances are associated. To explore how the motion modelling affects its tracking capability, we present two variants of Siamese tracker, one that implicitly models motion and one that models it explicitly. We carry out extensive quantitative experiments on three different MOT datasets: MOT17, TAO-person and Caltech Roadside Pedestrians, showing the importance of motion modelling for MOT and the ability of SiamMOT to substantially outperform the state-of-the-art. Finally, SiamMOT also outperforms the winners of ACM MM20 HiEve Grand Challenge on HiEve dataset. Moreover, SiamMOT is efficient, and it runs at 17 FPS for 720P videos on a single modern GPU. Codes are available in url{https://github.com/amazon-research/siam-mot}.

VidTr: Video Transformer Without Convolutions

We introduce Video Transformer (VidTr) with separable-attention for video classification. Comparing with commonly used 3D networks, VidTr is able to aggregate spatio-temporal information via stacked attentions and provide better performance with higher efficiency. We first introduce the vanilla video transformer and show that transformer module is able to perform spatio-temporal modeling from raw pixels, but with heavy memory usage. We then present VidTr which reduces the memory cost by 3.3$times$ while keeping the same performance. To further compact the model, we propose the standard deviation based topK pooling attention, which reduces the computation by dropping non-informative features. VidTr achieves state-of-the-art performance on five commonly used dataset with lower computational requirement, showing both the efficiency and effectiveness of our design. Finally, error analysis and visualization show that VidTr is especially good at predicting actions that require long-term temporal reasoning. The code and pre-trained weights will be released.

Fast dissipation of Colliding Alfven Waves in a Magnetically Dominated Plasma

Magnetic energy around compact objects often dominates over plasma rest mass, and its dissipation can power the object luminosity. We describe a dissipation mechanism which works faster than magnetic reconnection. The mechanism involves two strong Alfven waves with anti-aligned magnetic fields $boldsymbol{B}_1$ and $boldsymbol{B}_2$ that propagate in opposite directions along background magnetic field $boldsymbol{B}_0$ and collide. The collision forms a thin current sheet perpendicular to $boldsymbol{B}_0$, which absorbs the incoming waves. The current sheet is sustained by electric field $boldsymbol{E}$ breaking the magnetohydrodynamic condition $E<B$ and accelerating particles to high energies. We demonstrate this mechanism with kinetic plasma simulations using a simple setup of two symmetric plane waves with amplitude $A=B_1/B_0=B_2/B_0$ propagating in a uniform $boldsymbol{B}_0$. The mechanism is activated when $A>1/2$. It dissipates a large fraction of the wave energy, $f=(2A-1)/A^2$, reaching $100%$ when $A=1$. The plane geometry allows one to see the dissipation process in a one-dimensional simulation. We also perform two-dimensional simulations, enabling spontaneous breaking of the plane symmetry by the tearing instability of the current sheet. At moderate $A$ of main interest the tearing instability is suppressed. Dissipation transitions to normal, slower, magnetic reconnection at $Agg 1$. The fast dissipation described in this paper may occur in various objects with perturbed magnetic fields, including magnetars, jets from accreting black holes, and pulsar wind nebulae.

Learning-based Prediction and Uplink Retransmission for Wireless Virtual Reality (VR) Network

Wireless Virtual Reality (VR) users are able to enjoy immersive experience from anywhere at anytime. However, providing full spherical VR video with high quality under limited VR interaction latency is challenging. If the viewpoint of the VR user can be predicted in advance, only the required viewpoint is needed to be rendered and delivered, which can reduce the VR interaction latency. Therefore, in this paper, we use offline and online learning algorithms to predict viewpoint of the VR user using real VR dataset. For the offline learning algorithm, the trained learning model is directly used to predict the viewpoint of VR users in continuous time slots. While for the online learning algorithm, based on the VR users actual viewpoint delivered through uplink transmission, we compare it with the predicted viewpoint and update the parameters of the online learning algorithm to further improve the prediction accuracy. To guarantee the reliability of the uplink transmission, we integrate the Proactive retransmission scheme into our proposed online learning algorithm. Simulation results show that our proposed online learning algorithm for uplink wireless VR network with the proactive retransmission scheme only exhibits about 5% prediction error.

Oscillations and Random Walk of the Soliton Core in a Fuzzy Dark Matter Halo

A Fuzzy Dark Matter (FDM) halo consists of a soliton core close to the center and an NFW-like density profile in the outer region. Previous investigations found that the soliton core exhibits temporal oscillations and random walk excursions around the halo center. Analyzing a set of numerical simulations, we show that both phenomena can be understood as the results of wave interference -- a suitable superposition of the ground (solitonic) state and excited states in a fixed potential suffices to account for the main features of these phenomena. Such an eigenmode analysis can shed light on the evolution of a satellite halo undergoing tidal disruption. As the outer halo is stripped away, reducing the amplitudes of the excited states, the ground state evolves adiabatically. This suggests diminished soliton oscillations and random walk excursions, an effect to consider in deducing constraints from stellar heating.