FedQAS : Privacy-Aware Machine Reading Comprehension with Federated Learning

Machine reading comprehension (MRC) of text data is a challenging task in Natural Language Processing (NLP), with a lot of ongoing research fueled by the release of the Stanford Question Answering Dataset (SQuAD) and Conversational Question Answering (CoQA). It is considered to be an effort to teach computers how to "understand" a text, and then to be able to answer questions about it using deep learning. However, until now, large-scale training on private text data and knowledge sharing has been missing for this NLP task. Hence, we present FedQAS, a privacy-preserving machine reading system capable of leveraging large-scale private data without the need to pool those datasets in a central location. The proposed approach combines transformer models and federated learning technologies. The system is developed using the FEDn framework and deployed as a proof-of-concept alliance initiative. FedQAS is flexible, language-agnostic, and allows intuitive participation and execution of local model training. In addition, we present the architecture and implementation of the system, as well as provide a reference evaluation based on the SQuAD dataset, to showcase how it overcomes data privacy issues and enables knowledge sharing between alliance members in a Federated learning setting

FedBot : Enhancing Privacy in Chatbots with Federated Learning

Chatbots are mainly data-driven and usually based on utterances that might be sensitive. However, training deep learning models on shared data can violate user privacy. Such issues have commonly existed in chatbots since their inception. In the literature, there have been many approaches to deal with privacy, such as differential privacy and secure multi-party computation, but most of them need to have access to users' data. In this context, Federated Learning (FL) aims to protect data privacy through distributed learning methods that keep the data in its location. This paper presents Fedbot, a proof-of-concept (POC) privacy-preserving chatbot that leverages large-scale customer support data. The POC combines Deep Bidirectional Transformer models and federated learning algorithms to protect customer data privacy during collaborative model training. The results of the proof-of-concept showcase the potential for privacy-preserving chatbots to transform the customer support industry by delivering personalized and efficient customer service that meets data privacy regulations and legal requirements. Furthermore, the system is specifically designed to improve its performance and accuracy over time by leveraging its ability to learn from previous interactions.CC BY 4.0This work is funded by Uppsala University in Sweden on scalable federated learning research and supported by theUniversity of Skövde. The authors also would like to thank SNIC for providing cloud resources</p

Outcomes of Richter’s syndrome in patients with chronic lymphocytic leukemia (CLL) treated with platinum or anthracycline based chemotherapy with or without a BTK inhibitor (BTKi)

e19511 Background: Richter’s syndrome (RS) is a rare complication of chronic lymphocytic leukemia (CLL) and the median survival is generally poor. Evidence is limited regarding the biology of the disease, treatment and outcomes. Methods: We retrospectively reviewed pts diagnosed (dx) with pathologically confirmed RS who presented at the University of Miami Sylvester Cancer Center between 2011 and 2020. Informed consent was provided through IRB-approved protocols. Descriptive statistics were utilized and overall survival (OS) was calculated from RS diagnosis to death or last follow-up by Kaplan-Meier. Results: 33 patients with RS, including 87.9% diffuse large B-cell lymphoma-type RS and 12.1% Hodgkin lymphoma-type RS were identified. Median time from CLL dx to RS transformation was 37months (m). Most patients presented with elevated LDH 75%, bulky lymphadenopathy 66%, and adverse genetic features, such as TP53 disruption 52% or complex karyotype 81%. FISH at RS dx: del(17p) 37%, del(11q) 17%, trisomy 12 58%. ORR to first line therapy was 75.8% (55% CR, 27% PR), but of those who achieved CR, 44% relapsed. Median PFS (mPFS) was 10m for the anthracycline-based chemotherapy group (ABC) with a CR of 50%, compared to 15.5 m for the platinum-based chemotherapy group (PBC) and a CR of 60%. Similar OS were observed regardless of the chemotherapy regimen with median OS (mOS) of 60 m in the ABC group vs 59.5 m in the PBC group. Notably, the addition of a BTKi to chemoimmunotherapy lead to a mOS of more than 10 years in BTKi-naïve pts compared to 5.1 years in BTKi-exposed pts and 1.8 years in pts who did not receive a BTKi for RS treatment. Conclusions: Patients who develop RS often have high risk CLL, especially complex cytogenetics. Although a large proportion of pts with RS respond to frontline therapy, almost half relapse. While mOS is similar for anthracycline and platinum-based chemotherapies, platinum-based chemotherapy can lead to superior CR and PFS. BTKi-naïve pts who receive a BTKi with chemotherapy for RS treatment have superior mOS

The Effects of Desk Cycles in Elementary Children's Classroom Physical Activity: A Feasibility Study

Children who are overweight or obese exhibit a significant number of health concerns and are more likely to exhibit mental health and academic-related concerns. Thus, finding ways in schools to help them with multiple barriers is ideal. DeskCycles TM are one possible means for mitigating some of these concerns. There were three major objectives to this study: (a) To evaluate whether the use of DeskCycles TM increases child physical activity, (b) To determine if DeskCycles TM are feasible to use in the classroom for both teachers and students, and (c) To evaluate whether student on-task behavior improves or worsens from student use of DeskCycles TM . Twenty-eight 5 th grade students from two classrooms were randomly assigned to treatment/control conditions wherein the treatment classroom engaged in the use of DeskCycles TM in their classroom. Both treatment and control classrooms wore wrist accelerometers to measure physical activity. They completed an 11-item feasibility survey regarding the DeskCycles TM and the intervention teacher participated in a feasibility interview. Results showed no significant differences in physical activity by group, and on-task behavior was significantly lower as time passed during the study. The teacher indicated more barriers to using the DeskCycles TM, while the students were divided on their perceptions of feasibility. DeskCycles TM were not an effective intervention for physical activity or behavioral outcomes in this classroom setting. It appears that DeskCycles TM may not be a beneficial part of the solution for accruing classroom physical activity because they did not generate more physical activity, and students did not demonstrate better behavior while using them

FedBot : Enhancing Privacy in Chatbots with Federated Learning

Chatbots are mainly data-driven and usually based on utterances that might be sensitive. However, training deep learning models on shared data can violate user privacy. Such issues have commonly existed in chatbots since their inception. In the literature, there have been many approaches to deal with privacy, such as differential privacy and secure multi-party computation, but most of them need to have access to users' data. In this context, Federated Learning (FL) aims to protect data privacy through distributed learning methods that keep the data in its location. This paper presents Fedbot, a proof-of-concept (POC) privacy-preserving chatbot that leverages large-scale customer support data. The POC combines Deep Bidirectional Transformer models and federated learning algorithms to protect customer data privacy during collaborative model training. The results of the proof-of-concept showcase the potential for privacy-preserving chatbots to transform the customer support industry by delivering personalized and efficient customer service that meets data privacy regulations and legal requirements. Furthermore, the system is specifically designed to improve its performance and accuracy over time by leveraging its ability to learn from previous interactions.CC BY 4.0This work is funded by Uppsala University in Sweden on scalable federated learning research and supported by theUniversity of Skövde. The authors also would like to thank SNIC for providing cloud resources</p

Trends in Female Representation at Plastic Surgery Meetings: A Move Toward Gender Equity at the Podium

Anecdotally, female plastic surgeons are disproportionately underrepresented as speakers, moderators, and panelists at national and regional plastic surgery meetings. No studies have attempted to quantify female representation at Plastic Surgery The Meeting (PSTM). The objective of our study is to examine trends in female participation at PSTM. Names of participating plastic surgeons and their conference positions were obtained from PSTM meeting programs between 2015-2020. Conference positions included instructor, lead, lecturer, moderator, panelist, or other. Presentations were grouped as the following: conference/symposium; general session; instructional course; and lab. An automated gender assignment tool (gender-api.com) was used to determine the gender of participants. Descriptive statistics and trend analyses using Cochran-Armitage trend tests were performed. Between 2015-2020, 3,382 individuals (602 females, 17.8%) presented at PSTM in one of the instructional or moderating roles. Female presenters at PSTM increased from 60 (12.4%) in 2015, to 155 (26.5%) by 2020. The results for the proportion of females presenting in the general session and the instructional courses were statistically significant (p < .0001; p =.029), demonstrating a positive linear trend in the female proportions over the years. From 2015 to 2020, the proportions of females holding positions as moderators, panelists, and “other” increased significantly (p = .011; p = .011; p < .0001). Although female participation at PSTM has shown substantial growth over the last five years, there still exists a considerable gender imbalance. Notably, females were less likely to hold prominent positions, such as instructors, leads, or lecturers

Clinical outcomes of cancer patients with COVID-19: A systematic review and meta-analysis

e18600 Background: Patients with coronavirus disease 2019 (COVID-19) and cancer have worse clinical outcomes compared to those without cancer. Primary studies have examined this population, but most had small sample sizes and conflicting results. Prior meta-analyses exclude most US and European data or only examine mortality. The present meta-analysis evaluates the prevalence of several clinical outcomes in cancer patients with COVID-19, including new emerging data from Europe and the US. Methods: A systematic search of PubMED, medRxiv, JMIR and Embase by two independent investigators included peer-reviewed papers and preprints up to July 8, 2020. The primary outcome was mortality. Other outcomes were ICU and non-ICU admission, mild, moderate and severe complications, ARDS, invasive ventilation, stable, and clinically improved rates. Study quality was assessed through the Newcastle–Ottawa scale. Random effects model was used to derive prevalence rates, their 95% confidence intervals (CI) and 95% prediction intervals (PI). Results: Thirty-four studies (N = 4,371) were included in the analysis. The mortality prevalence rate was 25.2% (95% CI: 21.1–29.7; 95% PI: 9.8-51.1; I 2 = 85.4), with 11.9% ICU admissions (95% CI: 9.2-15.4; 95% PI: 4.3-28.9; I 2 = 77.8) and 25.2% clinically stable (95% CI: 21.1-29.7; 95% PI: 9.8-51.1; I 2 = 85.4). Furthermore, 42.5% developed severe complications (95% CI: 30.4-55.7; 95% PI: 8.2-85.9; I 2 = 94.3), with 22.7% developing ARDS (95% CI: 15.4-32.2; 95% PI: 5.8-58.6; I 2 = 82.4), and 11.3% needing invasive ventilation (95% CI: 6.7-18.4; 95% PI: 2.3-41.1; I 2 = 79.8). Post-follow up, 49% clinically improved (95% CI: 35.6-62.6; 95% PI: 9.8-89.4; I 2 = 92.5). All outcomes had large I 2 , suggesting high levels of heterogeneity among studies, and wide PIs indicating high variability within outcomes. Despite this variability, the mortality rate in cancer patients with COVID-19, even at the lower end of the PI (9.8%), is higher than the 2% mortality rate of the non-cancer with COVID-19 population, but not as high as what other meta-analyses conclude, which is around 25%. Conclusions: Patients with cancer who develop COVID-19 have a higher probability of mortality compared to the general population with COVID-19, but possibly not as high as previous studies have shown. A large proportion of them developed severe complications, but a larger proportion recovered. Prevalence of mortality and other outcomes published in prior meta-analyses did not report prediction intervals, which compromises the clinical utilization of such results