The Isotonic Mechanism for Exponential Family Estimation

In 2023, the International Conference on Machine Learning (ICML) required authors with multiple submissions to rank their submissions based on perceived quality. In this paper, we aim to employ these author-specified rankings to enhance peer review in machine learning and artificial intelligence conferences by extending the Isotonic Mechanism (Su, 2021, 2022) to exponential family distributions. This mechanism generates adjusted scores closely align with the original scores while adhering to author-specified rankings. Despite its applicability to a broad spectrum of exponential family distributions, this mechanism's implementation does not necessitate knowledge of the specific distribution form. We demonstrate that an author is incentivized to provide accurate rankings when her utility takes the form of a convex additive function of the adjusted review scores. For a certain subclass of exponential family distributions, we prove that the author reports truthfully only if the question involves only pairwise comparisons between her submissions, thus indicating the optimality of ranking in truthful information elicitation. Lastly, we show that the adjusted scores improve dramatically the accuracy of the original scores and achieve nearly minimax optimality for estimating the true scores with statistical consistecy when true scores have bounded total variation

The Reproducibility of Lists of Differentially Expressed Genes in Microarray Studies

Reproducibility is a fundamental requirement in scientific experiments and clinical contexts. Recent publications raise concerns about the reliability of microarray technology because of the apparent lack of agreement between lists of differentially expressed genes (DEGs). In this study we demonstrate that (1) such discordance may stem from ranking and selecting DEGs solely by statistical significance (P) derived from widely used simple t-tests; (2) when fold change (FC) is used as the ranking criterion, the lists become much more reproducible, especially when fewer genes are selected; and (3) the instability of short DEG lists based on P cutoffs is an expected mathematical consequence of the high variability of the t-values. We recommend the use of FC ranking plus a non-stringent P cutoff as a baseline practice in order to generate more reproducible DEG lists. The FC criterion enhances reproducibility while the P criterion balances sensitivity and specificity

The ICML 2023 Ranking Experiment: Examining Author Self-Assessment in ML/AI Peer Review

We conducted an experiment during the review process of the 2023 International Conference on Machine Learning (ICML), asking authors with multiple submissions to rank their papers based on perceived quality. In total, we received 1,342 rankings, each from a different author, covering 2,592 submissions. In this paper, we present an empirical analysis of how author-provided rankings could be leveraged to improve peer review processes at machine learning conferences. We focus on the Isotonic Mechanism, which calibrates raw review scores using the author-provided rankings. Our analysis shows that these ranking-calibrated scores out-perform the raw review scores in estimating the ground truth “expected review scores” in terms of both squared and absolute error metrics. Furthermore, we propose several cautious, low-risk applications of the Isotonic Mechanism and author-provided rankings in peer review, including supporting senior area chairs in overseeing area chairs’ recommendations, assisting in the selection of paper awards, and guiding the recruitment of emergency reviewers

The open banking era:An optimal model for the emergency fund

The COVID-19 outbreak has negatively impacted the income of many bank users. Many users without emergency funds had difficulty coping with this unexpected event and had to use credit or apply to the government for bailout funds. Therefore, it is necessary to develop spending plans and deposit plans based on transaction data of users to assist them in saving sufficient emergency funds to cope with unexpected events. In this paper, an emergency fund model is proposed, and two optimization algorithms are applied to solve the optimal solution of the model. Secondly, an early warning mechanism is proposed, i.e. an unexpected prevention index and a consumption index are proposed to measure the ability of users to cope with unexpected events and the reasonableness of their expenditure respectively, which provides early warning to users. Finally, the model is experimented with real bank users and the performance of the model is analysed. The experiments show that compared to the no-planning scenario, the model helps users to save more emergency funds to cope with unexpected events, furthermore, the proposed model is real-time and sensitive.</p

Bio-Inspired Autonomous Learning Algorithm With Application to Mobile Robot Obstacle Avoidance

Spiking Neural Networks (SNNs) are often considered the third generation of Artificial Neural Networks (ANNs), owing to their high information processing capability and the accurate simulation of biological neural network behaviors. Though the research for SNNs has been quite active in recent years, there are still some challenges to applying SNNs to various potential applications, especially for robot control. In this study, a biologically inspired autonomous learning algorithm based on reward modulated spike-timing-dependent plasticity is proposed, where a novel rewarding generation mechanism is used to generate the reward signals for both learning and decision-making processes. The proposed learning algorithm is evaluated by a mobile robot obstacle avoidance task and experimental results show that the mobile robot with the proposed algorithm exhibits a good learning ability. The robot can successfully avoid obstacles in the environment after some learning trials. This provides an alternative method to design and apply the bio-inspired robot with autonomous learning capability in the typical robotic task scenario

Knowledge, attitudes, behaviors, and information needs of women vaccinated with the HPV vaccine regarding cervical cancer prevention: a cross-sectional study

BackgroundCervical cancer poses a serious threat to women’s health globally, especially in China. HPV vaccination and screening are crucial prevention and control measures. However, the screening coverage among Chinese women remains low, and there is a need to better understand the knowledge, attitudes, behaviors, and information needs of Chinese HPV-vaccinated women regarding cervical cancer prevention to optimize prevention and control strategies.ObjectivesTo explore the knowledge, attitudes, behaviors, and information needs of women vaccinated with the HPV vaccine regarding cervical cancer prevention.MethodsThis cross-sectional study was conducted using a convenience sampling method from October 1 to December 30, 2023. A questionnaire survey was administered to 439 women vaccinated with the HPV vaccine at the Shu Shan District Community Health Service Center in Hefei, Anhui Province. The survey tool was self-designed. Data were analyzed using descriptive statistics, chi-square tests, and binary logistic regression.ResultsThe average age of the 439 participants was 27.82 ± 6.42 years. The average cervical cancer prevention knowledge score was 35.01 ± 5.76. 434 (98.9%) women held a positive attitude towards cervical cancer screening, and 320 (72.9%) women had undergone cervical cancer screening after receiving the HPV vaccine. Educational levels such as college (OR = 2.995, 95%CI: 1.233–7.279, p = 0.015), bachelor’s degree (OR = 3.694, 95%CI: 1.718–7.943, p = 0.001), and postgraduate and above (OR = 4.826, 95%CI: 2.176–10.707, p &lt; 0.001), as well as occupation as medical workers (OR = 4.660, 95%CI: 2.292–9.474, p &lt; 0.001), were associated with higher knowledge of prevention and treatment scores. Individuals aged 26–35 years (OR = 7.431, 95%CI: 2.856–19.331, p &lt; 0.001), 36–45 years (OR = 11.466, 95%CI: 2.279–57.694, p = 0.003), married individuals (OR = 4.307, 95%CI: 1.455–12.750, p = 0.008), and participants who had received health education related to cervical cancer prevention (OR = 2.125, 95%CI: 1.169–3.863, p = 0.013) and possessed good knowledge of prevention (OR = 16.770, 95%CI: 8.667–32.451, p &lt; 0.001) were more inclined to undergo cervical cancer screening. Among the 254 participants who had received health education, 34.2% still had unmet information needs regarding cervical cancer prevention, and 29.5% hoped to receive health education services from professionals.ConclusionChinese HPV-vaccinated women have a good understanding of cervical cancer prevention and a positive attitude and behavior towards cervical cancer screening. However, their knowledge of cervical cancer screening is not sufficient, and their information needs have not been fully met

EEG-based emotion classification using a deep neural network and sparse autoencoder

Emotion classification based on brain–computer interface (BCI) systems is an appealing research topic. Recently, deep learning has been employed for the emotion classifications of BCI systems and compared to traditional classification methods improved results have been obtained. In this paper, a novel deep neural network is proposed for emotion classification using EEG systems, which combines the Convolutional Neural Network (CNN), Sparse Autoencoder (SAE), and Deep Neural Network (DNN) together. In the proposed network, the features extracted by the CNN are first sent to SAE for encoding and decoding. Then the data with reduced redundancy are used as the input features of a DNN for classification task. The public datasets of DEAP and SEED are used for testing. Experimental results show that the proposed network is more effective than conventional CNN methods on the emotion recognitions. For the DEAP dataset, the highest recognition accuracies of 89.49% and 92.86% are achieved for valence and arousal, respectively. For the SEED dataset, however, the best recognition accuracy reaches 96.77%. By combining the CNN, SAE, and DNN and training them separately, the proposed network is shown as an efficient method with a faster convergence than the conventional CNN

Octreotide Alleviates Autophagy by Up-Regulation of MicroRNA-101 in Intestinal Epithelial Cell Line Caco-2

Background: Intestinal mucositis is a common side-effect after anti-cancer therapy, which may greatly restrict the therapeutic effects. We aimed to explore the functional role of octreotide (OCT) in lipopolysaccharide (LPS)-induced autophagy of human intestinal epithelial cells as well as the underlying mechanisms. Methods: Cell viability and expression of proteins related to autophagy, AMPK and the mTOR pathway in LPS-treated Caco-2 cells were determined by CCK-8 assay and Western blot analysis, respectively. Effects of OCT on LPS-induced alterations as well as miR-101 expression were measured. Then, miR-101 was aberrantly expressed, and whether OCT alleviated LPS-induced autophagy through miR-101 was tested. Next, whether TGF-β-activated kinase 1 (TAK1) was involved in the regulation of miR-101 in LPS-induced autophagy was studied. Effects of OCT on monolayer permeability and tight junction level were analyzed via measuring transepithelial electrical resistance (TEER) and expression of tight junction proteins. Results: LPS reduced cell viability and increased autophagy through activating AMPK and inhibiting the mTOR pathway in Caco-2 cells. OCT alleviated LPS-induced alterations and repressed degradation of autophagosome. Then, we found that OCT affected autophagy through up-regulating miR-101 in LPS-treated cells. Moreover, miR-101-induced inactivation of AMPK and activation of the mTOR pathway in LPS-treated cells were reversed by inhibition of TAK1 phosphorylation. Finally, we found miR-101 was up-regulated in differentiated cells, and OCT protected the monolayer permeability and tight junction level. Conclusion: OCT repressed autophagy through miR-101-mediated inactivation of TAK1, along with inactivation of AMPK and activation of the mTOR pathway in LPS-treated Caco-2 cells