Assessing Impulsivity in Chinese: Elaborating Validity of BIS Among Male Prisoners

This study was carried out to test the factor structure and psychometric properties of the Barratt Impulsiveness Scale–Version 11 (BIS-11), and its short versions (the eight-item and 15-item BIS) in a sample of 424 Chinese male prisoners (M = 31.26, SD = 7.43, age = 18-52 years). Confirmatory factor analysis (CFAs) indicated that the single-factor model of BIS with eight items (BIS-8) and the three-factor model of BIS with 15 items (BIS-15) fit the data well. In addition, the item response theory (IRT) approach confirmed the construct and items for the BIS-8 with good discrimination, threshold parameters, and test information curve. Correlations with psychopathic traits, antisocial personality disorder, and aggression suggested that the performance of the eight-item BIS was comparable with that of the original 30-item BIS in measuring general impulsivity.This research was supported by grants from the National Natural Science Foundation of China (Grant 31400904) and Guangzhou University’s 2017 training program for top-notch young people (Grant BJ201715)

Acquiring Weak Annotations for Tumor Localization in Temporal and Volumetric Data

Creating large-scale and well-annotated datasets to train AI algorithms is crucial for automated tumor detection and localization. However, with limited resources, it is challenging to determine the best type of annotations when annotating massive amounts of unlabeled data. To address this issue, we focus on polyps in colonoscopy videos and pancreatic tumors in abdominal CT scans; both applications require significant effort and time for pixel-wise annotation due to the high dimensional nature of the data, involving either temporary or spatial dimensions. In this paper, we develop a new annotation strategy, termed Drag&Drop, which simplifies the annotation process to drag and drop. This annotation strategy is more efficient, particularly for temporal and volumetric imaging, than other types of weak annotations, such as per-pixel, bounding boxes, scribbles, ellipses, and points. Furthermore, to exploit our Drag&Drop annotations, we develop a novel weakly supervised learning method based on the watershed algorithm. Experimental results show that our method achieves better detection and localization performance than alternative weak annotations and, more importantly, achieves similar performance to that trained on detailed per-pixel annotations. Interestingly, we find that, with limited resources, allocating weak annotations from a diverse patient population can foster models more robust to unseen images than allocating per-pixel annotations for a small set of images. In summary, this research proposes an efficient annotation strategy for tumor detection and localization that is less accurate than per-pixel annotations but useful for creating large-scale datasets for screening tumors in various medical modalities.Comment: Published in Machine Intelligence Researc

Progress and outlook on advanced fly scans based on Mamba

Development related to PandABox-based fly scans is an important part of the active work on Mamba, the software framework for beamline experiments at the High Energy Photon Source (HEPS); presented in this paper is the progress of our development, and some outlook for advanced fly scans based on knowledge learned during the process. By treating fly scans as a collaboration between a few loosely coupled subsystems - motors / mechanics, detectors / data processing, sequencer devices like PandABox - systematic analyses of issues in fly scans are conducted. Interesting products of these analyses include a general-purpose software-based fly-scan mechanism, a general way to design undulator-monochromator fly scans, a sketch of how to practically implement online tuning of fly-scan behaviours based on processing of the data acquired, and many more. Based on the results above, an architectural discussion on >=10kHz fly scans is given.Comment: 10 pages, 6 figures, accepted by Synchrotron Rad. New