A TV-Gaussian prior for infinite-dimensional Bayesian inverse problems and its numerical implementations

Many scientific and engineering problems require to perform Bayesian inferences in function spaces, in which the unknowns are of infinite dimension. In such problems, choosing an appropriate prior distribution is an important task. In particular we consider problems where the function to infer is subject to sharp jumps which render the commonly used Gaussian measures unsuitable. On the other hand, the so-called total variation (TV) prior can only be defined in a finite dimensional setting, and does not lead to a well-defined posterior measure in function spaces. In this work we present a TV-Gaussian (TG) prior to address such problems, where the TV term is used to detect sharp jumps of the function, and the Gaussian distribution is used as a reference measure so that it results in a well-defined posterior measure in the function space. We also present an efficient Markov Chain Monte Carlo (MCMC) algorithm to draw samples from the posterior distribution of the TG prior. With numerical examples we demonstrate the performance of the TG prior and the efficiency of the proposed MCMC algorithm

A Universal Unbiased Method for Classification from Aggregate Observations

In conventional supervised classification, true labels are required for individual instances. However, it could be prohibitive to collect the true labels for individual instances, due to privacy concerns or unaffordable annotation costs. This motivates the study on classification from aggregate observations (CFAO), where the supervision is provided to groups of instances, instead of individual instances. CFAO is a generalized learning framework that contains various learning problems, such as multiple-instance learning and learning from label proportions. The goal of this paper is to present a novel universal method of CFAO, which holds an unbiased estimator of the classification risk for arbitrary losses -- previous research failed to achieve this goal. Practically, our method works by weighing the importance of each label for each instance in the group, which provides purified supervision for the classifier to learn. Theoretically, our proposed method not only guarantees the risk consistency due to the unbiased risk estimator but also can be compatible with arbitrary losses. Extensive experiments on various problems of CFAO demonstrate the superiority of our proposed method

The utility of high-frequency 18 MHz ultrasonography for preoperative evaluation of acral melanoma thickness in Chinese patients

BackgroundDespite the increasing use of preoperative ultrasound evaluation for melanoma, there is limited research on the use of this technique for Acral Melanoma (AM).MethodsThis retrospective study analyzed the electronic medical records of patients who underwent preoperative evaluation for cutaneous melanoma maximum thickness using an 18 MHz probe and histopathological examination between December 2017 and March 2021 at the Department of Dermatology in Xiangya Hospital, Central South University.ResultsA total of 105 patients were included in the study. The mean tumor thickness was 3.9 mm (s.d., 2.3), with 63% of the specimens showing ulceration and 44 patients showing lymph node metastasis. The results showed a good correlation between the high-frequency ultrasonography (HFUS) and histopathological thickness measurements, with a Spearman’s correlation coefficient of 0.83 [(95% CI 0.73–0.90) (P < 0.001)]. The positive predictive value (PPV) of sonography in identifying tumor thickness was also found to be high.ConclusionOur study suggests that high-frequency 18 MHz ultrasonography is an effective tool for the preoperative evaluation of AM thickness. The HFUS measurements correlated well with the histopathological thickness measurements, making it a valuable and reliable method for clinicians to assess the thickness of melanoma lesions preoperatively

Explanation of hand, foot, and mouth disease cases in Japan using Google Trends before and during the COVID-19: infodemiology study

[Background] Coronavirus Disease 2019 (COVID-19) pandemic affects common diseases, but its impact on hand, foot, and mouth disease (HFMD) is unclear. Google Trends data is beneficial for approximate real-time statistics and because of ease in access, is expected to be used for infection explanation from an information-seeking behavior perspective. We aimed to explain HFMD cases before and during COVID-19 using Google Trends. [Methods] HFMD cases were obtained from the National Institute of Infectious Diseases, and Google search data from 2009 to 2021 in Japan were downloaded from Google Trends. Pearson correlation coefficients were calculated between HFMD cases and the search topic “HFMD” from 2009 to 2021. Japanese tweets containing “HFMD” were retrieved to select search terms for further analysis. Search terms with counts larger than 1000 and belonging to ranges of infection sources, susceptible sites, susceptible populations, symptoms, treatment, preventive measures, and identified diseases were retained. Cross-correlation analyses were conducted to detect lag changes between HFMD cases and search terms before and during the COVID-19 pandemic. Multiple linear regressions with backward elimination processing were used to identify the most significant terms for HFMD explanation. [Results] HFMD cases and Google search volume peaked around July in most years, excluding 2020 and 2021. The search topic “HFMD” presented strong correlations with HFMD cases, except in 2020 when the COVID-19 outbreak occurred. In addition, the differences in lags for 73 (72.3%) search terms were negative, which might indicate increasing public awareness of HFMD infections during the COVID-19 pandemic. The results of multiple linear regression demonstrated that significant search terms contained the same meanings but expanded informative search content during the COVID-19 pandemic. [Conclusions] The significant terms for the explanation of HFMD cases before and during COVID-19 were different. Awareness of HFMD infections in Japan may have improved during the COVID-19 pandemic. Continuous monitoring is important to promote public health and prevent resurgence. The public interest reflected in information-seeking behavior can be helpful for public health surveillance

Robotic Assistance in Coordination of Patient Care

We conducted a study to investigate trust in and dependence upon robotic decision support among nurses and doctors on a labor and delivery floor. There is evidence that suggestions provided by embodied agents engender inappropriate degrees of trust and reliance among humans. This concern is a critical barrier that must be addressed before fielding intelligent hospital service robots that take initiative to coordinate patient care. Our experiment was conducted with nurses and physicians, and evaluated the subjects’ levels of trust in and dependence on high- and low-quality recommendations issued by robotic versus computer-based decision support. The support, generated through action-driven learning from expert demonstration, was shown to produce high-quality recommendations that were ac- cepted by nurses and physicians at a compliance rate of 90%. Rates of Type I and Type II errors were comparable between robotic and computer-based decision support. Furthermore, em- bodiment appeared to benefit performance, as indicated by a higher degree of appropriate dependence after the quality of recommendations changed over the course of the experiment. These results support the notion that a robotic assistant may be able to safely and effectively assist in patient care. Finally, we conducted a pilot demonstration in which a robot assisted resource nurses on a labor and delivery floor at a tertiary care center.National Science Foundation (U.S.) (Grant 2388357

Research on the influence of a high proportion of wind power connected to the receiving power grid on the system power angle stability

With the increasing proportion of wind power integration, the effect on the stability of the system power angle cannot be ignored. In this paper, based on the different power characteristics of direct-drive wind generators before a fault and after its clearance, the system model of the receiving-end grid with thermal units replaced by wind turbines is simplified. The influence of the increase in the replacement ratio of wind power in the receiving-end grid on the transfer impedance between the sending end and receiving end is analyzed. Based on the equal area rule, the influence of the replacement ratio k within the receiving-end grid, power grid operation mode, and wind power integration point on the system power angle stability is analyzed. It is concluded that the stability of the system’s power angle will first get better and then deteriorate with the increase in the replacement ratio of wind power, the system can bear a larger proportion of wind turbines under the low-load operation mode, and the system’s power angle of the replacement of wind power with equal capacity in the load center region is relatively better. The aforementioned conclusions are verified by simulation with real data from a bulk power system in China. Therefore, the method and conclusion can also be used to study the power angle stability of other large-scale power grids