An Effective Strategy to Build Up a Balanced Test Suite for Spectrum-Based Fault Localization

During past decades, many automated software faults diagnosis techniques including Spectrum-Based Fault Localization (SBFL) have been proposed to improve the efficiency of software debugging activity. In the field of SBFL, suspiciousness calculation is closely related to the number of failed and passed test cases. Studies have shown that the ratio of the number of failed and passed test case has more significant impact on the accuracy of SBFL than the total number of test cases, and a balanced test suite is more beneficial to improving the accuracy of SBFL. Based on theoretical analysis, we proposed an PNF (Passed test cases, Not execute Faulty statement) strategy to reduce test suite and build up a more balanced one for SBFL, which can be used in regression testing. We evaluated the strategy making experiments using the Siemens program and Space program. Experiments indicated that our PNF strategy can be used to construct a new test suite effectively. Compared with the original test suite, the new one has smaller size (average 90% test case was reduced in experiments) and more balanced ratio of failed test cases to passed test cases, while it has the same statement coverage and fault localization accuracy

Fe3O4/Au magnetic nanoparticle amplification strategies for ultrasensitive electrochemical immunoassay of alfa-fetoprotein

Ning Gan1*, Haijuan Jin1*, Tianhua Li1, Lei Zheng21The State Key Laboratory Base of Novel Functional Materials and Preparation Science, Faculty of Material Science and Chemical Engineering, Ningbo University, Ningbo, 2Department of Laboratory Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, People's Republic of China *Both authors contributed equally to this workBackground: The purpose of this study was to devise a novel electrochemical immunosensor for ultrasensitive detection of alfa-fetoprotein based on Fe3O4/Au nanoparticles as a carrier using a multienzyme amplification strategy.Methods and results: Greatly enhanced sensitivity was achieved using bioconjugates containing horseradish peroxidase (HRP) and a secondary antibody (Ab2) linked to Fe3O4/Au nanoparticles (Fe3O4/Au-HRP-Ab2) at a high HRP/Ab2 ratio. After a sandwich immunoreaction, the Fe3O4/Au-HRP-Ab2 captured on the electrode surface produced an amplified electrocatalytic response by reduction of enzymatically oxidized hydroquinone in the presence of hydrogen peroxide. The high content of HRP in the Fe3O4/Au-HRP-Ab2 could greatly amplify the electrochemical signal. Under optimal conditions, the reduction current increased with increasing alfa-fetoprotein concentration in the sample, and exhibited a dynamic range of 0.005–10 ng/mL with a detection limit of 3 pg/mL.Conclusion: The amplified immunoassay developed in this work shows good precision, acceptable stability, and reproducibility, and can be used for detection of alfa-fetoprotein in real samples, so provides a potential alternative tool for detection of protein in the laboratory. Furthermore, this immunosensor could be regenerated by simply using an external magnetic field.Keywords: Fe3O4/Au nanoparticles, alfa-fetoprotein, sandwich immunoassay, electrochemical immunosenso

PEANUT: A Human-AI Collaborative Tool for Annotating Audio-Visual Data

Audio-visual learning seeks to enhance the computer's multi-modal perception leveraging the correlation between the auditory and visual modalities. Despite their many useful downstream tasks, such as video retrieval, AR/VR, and accessibility, the performance and adoption of existing audio-visual models have been impeded by the availability of high-quality datasets. Annotating audio-visual datasets is laborious, expensive, and time-consuming. To address this challenge, we designed and developed an efficient audio-visual annotation tool called Peanut. Peanut's human-AI collaborative pipeline separates the multi-modal task into two single-modal tasks, and utilizes state-of-the-art object detection and sound-tagging models to reduce the annotators' effort to process each frame and the number of manually-annotated frames needed. A within-subject user study with 20 participants found that Peanut can significantly accelerate the audio-visual data annotation process while maintaining high annotation accuracy.Comment: 18 pages, published in UIST'2

Preimplantation Genetic Screening: An Effective Testing for Infertile and Repeated Miscarriage Patients?

Aneuploidy in pregnancy is known to increase with advanced maternal age (AMA) and associate with repeated implantation failure (RIF), and repeated miscarriage (RM). Preimplantation genetic screening (PGS) has been introduced into clinical practice, screening, and eliminating aneuploidy embryos, which can improve the chance of conceptions for infertility cases with poor prognosis. These patients are a good target group to assess the possible benefit of aneuploidy screening. Although practiced widely throughout the world, there still exist some doubts about the efficacy of this technique. Recent randomized trials were not as desirable as we expected, suggesting that PGS needs to be reconsidered. The aim of this review is to discuss the efficacy of PGS