A New Scenario of Solar Modulation Model during the Polarity Reversing

When the Galactic Cosmic Rays (GCRs) entering the heliosphere, they encounter the solar wind plasma, and their intensity is reduced, so-called solar modulation. The modulation is caused by the combination of a few factors, such as particle energies, solar activity and solar disturbance. In this work, a 2D numerical method is adopted to simulate the propagation of GCRs in the heliosphere with SOLARPROP, and to overcome the time-consuming issue, the machine learning technique is also applied. With the obtained proton local interstellar spectra (LIS) based on the observation from Voyager 1 and AMS-02, the solar modulation parameters during the solar maximum activity of cycle 24 have been found. It shows the normalization and index of the diffusion coefficient indeed reach a maximal value in February 2014. However, after taking into account the travel time of particles with different energies, the peak time was found postponed to November 2014 as expected. The nine-month late is so-called time lag.Comment: 10 pages, 8 figure

A New Scenario of Solar Modulation Model during the Polarity Reversing

When entering the heliosphere, galactic cosmic rays (GCRs) will encounter the solar wind plasma, reducing their intensity. This solar modulation effect is strongly affected by the structure of the solar wind and the heliospheric magnetic field (HMF). To address the effect during the solar maximum of cycle 24, we study the solar modulation under a scenario in which the weights for A = ±1 are determined by the structure of HMF, and the traveling time of GCRs simulated by SOLARPROP is taken into account. We then fit the cosmic-ray proton data provided by AMS-02 and Voyager in the energy range 4 MeV–30 GeV, and confirm that the modulation time lag in this model is about 9 months, which is consistent with the previous studies. This model incorporates a more realistic description of the polarity reversing and provides a more reliable estimation of the solar modulation effect during the maximum activity period

ClusterSeg : a crowd cluster pinpointed nucleus segmentation framework with cross-modality datasets

The detection and segmentation of individual cells or nuclei is often involved in image analysis across a variety of biology and biomedical applications as an indispensable prerequisite. However, the ubiquitous presence of crowd clusters with morphological variations often hinders successful instance segmentation. In this paper, nuclei cluster focused annotation strategies and frameworks are proposed to overcome this challenging practical problem. Specifically, we design a nucleus segmentation framework, namely ClusterSeg, to tackle nuclei clusters, which consists of a convolutional-transformer hybrid encoder and a 2.5-path decoder for precise predictions of nuclei instance mask, contours, and clustered-edges. Additionally, an annotation-efficient clustered-edge pointed strategy pinpoints the salient and error-prone boundaries, where a partially-supervised PS-ClusterSeg is presented using ClusterSeg as the segmentation backbone. The framework is evaluated with four privately curated image sets and two public sets with characteristic severely clustered nuclei across a variety range of image modalities, e.g., microscope, cytopathology, and histopathology images. The proposed ClusterSeg and PS-ClusterSeg are modality-independent and generalizable, and superior to current state-of-the-art approaches in multiple metrics empirically. Our collected data, the elaborate annotations to both public and private set, as well the source code, are released publicly at https://github.com/lu-yizhou/ClusterSeg