RIXA - Explaining Artificial Intelligence in Natural Language

Natural language is the instinctive form of communication humans use among each other. Recently large language models have drastically improved and made natural language interfaces viable for all kinds of applications. We argue that the use of natural language is a great tool to make explainable artificial intelligence (XAI) accessible to end users. We present our concept and work in progress implementation of a new kind of XAI dashboard that uses a natural language chat. We specify 5 design goals for the dashboard and show the current state of our implementation. The natural language chat is the main form of interaction for our new dashboard. Through it the user should be able to control all important aspects of our dashboard. We also define success metrics we want to use to evaluate our work. Most importantly we want to conduct user studies because we deem them to be the best method of evaluation for end-user-centered application

Fixed-time command filtered output feedback control for twin-roll inclined casting system with prescribed performance

The article investigates the issue of fixed-time control with adaptive output feedback for a twin-roll inclined casting system (TRICS) with disturbance. First, by using the mean value theorem, the nonaffine functions are decoupled to simplify the system. Second, radial basis function neural networks (RBFNNs) are introduced to approximate an unknown term, and a nonlinear neural state observer is created to handle the effects of unmeasured states. Then, the backstepping design framework is combined with prescribed performance and command filtering techniques to demonstrate that the scheme proposed in this article guarantees system performance within a fixed-time. The control design parameters determine the upper bound of settling time, regardless of the initial state of the system. Meanwhile, it ensures that all signals in the closed-loop system (CLS) remain bounded, and it can also maintain the tracking error within a predefined range within a fixed time. Finally, simulation results assert the effectiveness of the method

LiSum: Open Source Software License Summarization with Multi-Task Learning

Open source software (OSS) licenses regulate the conditions under which users can reuse, modify, and distribute the software legally. However, there exist various OSS licenses in the community, written in a formal language, which are typically long and complicated to understand. In this paper, we conducted a 661-participants online survey to investigate the perspectives and practices of developers towards OSS licenses. The user study revealed an indeed need for an automated tool to facilitate license understanding. Motivated by the user study and the fast growth of licenses in the community, we propose the first study towards automated license summarization. Specifically, we released the first high quality text summarization dataset and designed two tasks, i.e., license text summarization (LTS), aiming at generating a relatively short summary for an arbitrary license, and license term classification (LTC), focusing on the attitude inference towards a predefined set of key license terms (e.g., Distribute). Aiming at the two tasks, we present LiSum, a multi-task learning method to help developers overcome the obstacles of understanding OSS licenses. Comprehensive experiments demonstrated that the proposed jointly training objective boosted the performance on both tasks, surpassing state-of-the-art baselines with gains of at least 5 points w.r.t. F1 scores of four summarization metrics and achieving 95.13% micro average F1 score for classification simultaneously. We released all the datasets, the replication package, and the questionnaires for the community

Transportation Density Reduction Caused by City Lockdowns Across the World during the COVID-19 Epidemic: From the View of High-resolution Remote Sensing Imagery

As the COVID-19 epidemic began to worsen in the first months of 2020, stringent lockdown policies were implemented in numerous cities throughout the world to control human transmission and mitigate its spread. Although transportation density reduction inside the city was felt subjectively, there has thus far been no objective and quantitative study of its variation to reflect the intracity population flows and their corresponding relationship with lockdown policy stringency from the view of remote sensing images with the high resolution under 1m. Accordingly, we here provide a quantitative investigation of the transportation density reduction before and after lockdown was implemented in six epicenter cities (Wuhan, Milan, Madrid, Paris, New York, and London) around the world during the COVID-19 epidemic, which is accomplished by extracting vehicles from the multi-temporal high-resolution remote sensing images. A novel vehicle detection model combining unsupervised vehicle candidate extraction and deep learning identification was specifically proposed for the images with the resolution of 0.5m. Our results indicate that transportation densities were reduced by an average of approximately 50% (and as much as 75.96%) in these six cities following lockdown. The influences on transportation density reduction rates are also highly correlated with policy stringency, with an R^2 value exceeding 0.83. Even within a specific city, the transportation density changes differed and tended to be distributed in accordance with the city's land-use patterns. Considering that public transportation was mostly reduced or even forbidden, our results indicate that city lockdown policies are effective at limiting human transmission within cities.Comment: 14 pages, 7 figures, submitted to IEEE JSTAR

Targeting oncogenic miR-335 inhibits growth and invasion of malignant astrocytoma cells

<p>Abstract</p> <p>Background</p> <p>Astrocytomas are the most common and aggressive brain tumors characterized by their highly invasive growth. Gain of chromosome 7 with a hot spot at 7q32 appears to be the most prominent aberration in astrocytoma. Previously reports have shown that microRNA-335 (miR-335) resided on chromosome 7q32 is deregulated in many cancers; however, the biological function of miR-335 in astrocytoma has yet to be elucidated.</p> <p>Results</p> <p>We report that miR-335 acts as a tumor promoter in conferring tumorigenic features such as growth and invasion on malignant astrocytoma. The miR-335 level is highly elevated in C6 astrocytoma cells and human malignant astrocytomas. Ectopic expression of miR-335 in C6 cells dramatically enhances cell viability, colony-forming ability and invasiveness. Conversely, delivery of antagonist specific for miR-335 (antagomir-335) to C6 cells results in growth arrest, cell apoptosis, invasion repression and marked regression of astrocytoma xenografts. Further investigation reveals that miR-335 targets disheveled-associated activator of morphogenesis 1(Daam1) at posttranscriptional level. Moreover, silencing of endogenous Daam1 (siDaam1) could mimic the oncogenic effects of miR-335 and reverse the growth arrest, proapoptotic and invasion repression effects induced by antagomir-335. Notably, the oncogenic effects of miR-335 and siDAAM1 together with anti-tumor effects of antagomir-335 are also confirmed in human astrocytoma U87-MG cells.</p> <p>Conclusion</p> <p>These findings suggest an oncogenic role of miR-335 and shed new lights on the therapy of malignant astrocytomas by targeting miR-335.</p

Predictive value of remnant-like particle cholesterol in the prediction of long-term AF recurrence after radiofrequency catheter ablation

ObjectiveThe relationship between remnant-like particle cholesterol (RLP-C) levels and the progression of atrial fibrillation (AF) is not known. This research aimed to explore the association of RLP-C with long-term AF recurrence events post-radiofrequency catheter ablation (RFCA) of AF.MethodsIn total 320 patients with AF who were subjected to the first RFCA were included in this research. Baseline information and laboratory data of patients were retrospectively collected, and a 1-year follow-up was completed. The follow-up endpoint was defined as an AF recurrence event occurring after 3 months. Afterward, a multivariate Cox regression model was constructed to analyze the risk factors that affect AF recurrence.ResultsAF recurrence occurred in 103 patients (32.2%) within 3–12 months after RFCA. Based on the multivariate Cox regression analysis, Early recurrence (ER) [hazard ratio (HR) =1.57, 95% confidence interval (CI): 1.04–2.36, P = 0.032)], coronary artery disease (CAD) (HR = 2.03, 95% CI: 1.22–3.38, P = 0.006), left atrium anterior-posterior diameter (LAD) (HR = 1.07, 95% CI: 1.03–1.10, P &lt; 0.001), triglyceride (TG) (HR = 1.51, 95% CI: 1.16–1.96, P = 0.002), low-density lipoprotein cholesterol (LDL-C) (HR = 0.74, 95% CI: 0.55–0.98, P = 0.036), and RLP-C (HR = 0.75 per 0.1 mmol/L increase, 95% CI: 0.68–0.83, P &lt; 0.001) were linked to the risk of AF recurrence. Among them, the relationship between RLP-C and AF recurrence was found for the first time. The predictive value of RLP-C for AF recurrence was analyzed utilizing receiver operating characteristic (ROC) curves [area under the curve (AUC) = 0.81, 95% CI: 0.77–0.86, P &lt; 0.001]. Subsequently, the optimal threshold value of RLP-C was determined to be 0.645 mmol/L with a sensitivity of 87.4% and a specificity of 63.6% based on the Youden index. Additionally, Kaplan–Meier analysis indicated a lower AF recurrence rate in the &gt;0.645 mmol/L group than in the ≤0.645 mmol/L group (Log-rank P &lt; 0.001).ConclusionLow levels of RLP-C are associated with a higher risk of AF recurrence post-RFCA, suggesting that RLP-C may be a biomarker that helps to identify long-term AF recurrence