FairNN- Conjoint Learning of Fair Representations for Fair Decisions

In this paper, we propose FairNN a neural network that performs joint feature representation and classification for fairness-aware learning. Our approach optimizes a multi-objective loss function in which (a) learns a fair representation by suppressing protected attributes (b) maintains the information content by minimizing a reconstruction loss and (c) allows for solving a classification task in a fair manner by minimizing the classification error and respecting the equalized odds-based fairness regularized. Our experiments on a variety of datasets demonstrate that such a joint approach is superior to separate treatment of unfairness in representation learning or supervised learning. Additionally, our regularizers can be adaptively weighted to balance the different components of the loss function, thus allowing for a very general framework for conjoint fair representation learning and decision making.Comment: Code will be availabl

Network interventions for managing the COVID-19 pandemic and sustaining economy.

Sustaining economic activities while curbing the number of new coronavirus disease 2019 (COVID-19) cases until effective vaccines or treatments become available is a major public health and policy challenge. In this paper, we use agent-based simulations of a network-based susceptible-exposed-infectious-recovered (SEIR) model to investigate two network intervention strategies for mitigating the spread of transmission while maintaining economic activities. In the simulations, we assume that people engage in group activities in multiple sectors (e.g., going to work, going to a local grocery store), where they interact with others in the same group and potentially become infected. In the first strategy, each group is divided into two subgroups (e.g., a group of customers can only go to the grocery store in the morning, while another separate group of customers can only go in the afternoon). In the second strategy, we balance the number of group members across different groups within the same sector (e.g., every grocery store has the same number of customers). The simulation results show that the dividing groups strategy substantially reduces transmission, and the joint implementation of the two strategies could effectively bring the spread of transmission under control (i.e., effective reproduction number ≈ 1.0)

COVID-19 symptoms at hospital admission vary with age and sex: results from the ISARIC prospective multinational observational study

Background: The ISARIC prospective multinational observational study is the largest cohort of hospitalized patients with COVID-19. We present relationships of age, sex, and nationality to presenting symptoms. Methods: International, prospective observational study of 60 109 hospitalized symptomatic patients with laboratory-confirmed COVID-19 recruited from 43 countries between 30 January and 3 August 2020. Logistic regression was performed to evaluate relationships of age and sex to published COVID-19 case definitions and the most commonly reported symptoms. Results: ‘Typical’ symptoms of fever (69%), cough (68%) and shortness of breath (66%) were the most commonly reported. 92% of patients experienced at least one of these. Prevalence of typical symptoms was greatest in 30- to 60-year-olds (respectively 80, 79, 69%; at least one 95%). They were reported less frequently in children (≤ 18 years: 69, 48, 23; 85%), older adults (≥ 70 years: 61, 62, 65; 90%), and women (66, 66, 64; 90%; vs. men 71, 70, 67; 93%, each P < 0.001). The most common atypical presentations under 60 years of age were nausea and vomiting and abdominal pain, and over 60 years was confusion. Regression models showed significant differences in symptoms with sex, age and country. Interpretation: This international collaboration has allowed us to report reliable symptom data from the largest cohort of patients admitted to hospital with COVID-19. Adults over 60 and children admitted to hospital with COVID-19 are less likely to present with typical symptoms. Nausea and vomiting are common atypical presentations under 30 years. Confusion is a frequent atypical presentation of COVID-19 in adults over 60 years. Women are less likely to experience typical symptoms than men

Matrix-Induced Adipose-Derived Mesenchymal Stem Cells Implantation for Knee Articular Cartilage Repair. Two Years Follow-up.

Objective. The aim of this study was to evaluate the efficacy of cartilage repair using matrix-induced adipose-derived mesenchymal stem cells (MSCs) for focal chondral knee lesions.Materials and Methods. Twenty consecutive patients treated for symptomatic full-thickness chondral defects were prospectively followed for two years. All patients underwent a single-stage procedure consisting in filling each defect with autologous culture-expanded mesenchymal stem cells embedded in a trimmed-to-fit commercially available biodegradable matrix. Knee-related function was evaluated based on subjective scores given by two self-reported questionnaires (KOOS and IKDC).  Results. The data analysis recorded significant improvements (p<0.001) in all the values. The mean preoperative scores in the subscales of KOOS, as well as in the IKDC subjective score were constantly increased during the follow-up period with statistical significant differences at 6, 12 and 24 months review.Conclusions. Matrix-induced adipose-derived mesenchymal stem cells implantation is an efficient and safe single-staged cell-based procedure to manage full-thickness focal chondral lesions of the knee. 

Comparative Evaluation of Machine Learning Inference Machines on Edge-class Devices

Computer science and engineering have evolved rapidly over the last decade offering innovative Machine Learning frameworks and high-performance hardware devices. Executing data analytics at the edge promises to transform the mobile computing paradigm by bringing intelligence next to the end user. However, it remains an open question to explore if, and to what extent, today's Edge-class devices can support ML frameworks and which is the best configuration for efficient task execution. This paper provides a comparative evaluation of Machine Learning inference machines on Edge-class compute engines. The testbed consists of two hardware compute engines (i.e., CPU-based Raspberry Pi 4 and Google Edge TPU accelerator) and two inference machines (i.e., TensorFlow-Lite and Arm NN). Through an extensive set of experiments in our bespoke testbed, we compared three setups using TensorFlow-Lite ML framework, in terms of accuracy, execution time, and energy efficiency. Based on the results, an optimized configuration of the workload parameters can increase accuracy by 10%, and in addition, the class of the Edge compute engine in combination with the inference machine affects execution time by 86% and power consumption by almost 145%.</p

Stem cells for the treatment of early to moderate osteoarthritis of the knee: a systematic review

Abstract Purpose Mesenchymal stem cells (MSCs) present a valuable treatment option for knee osteoarthritis with promising results. The purpose of the present study was to systematically review the clinical and functional outcomes following mesenchymal stem cell application focusing on early to moderate knee osteoarthritis. Methods A systematic search was done using the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses guidelines in Pubmed, Scopus, Web of Science, and Cochrane Library databases. All Studies published between 2017 and March 2023 on patients treated with single mesenchymal stem cell injection for Kellgren‐Lawrence grade I—III knee osteoarthritis reported on clinical and functional outcomes were included. Results Twelve articles comprising 539 patients and 576 knees treated with a single intraarticular injection of MSCs for knee osteoarthritis were included in the current systematic review. In eligible studies, the reported outcomes were improved concerning patient‐reported outcomes measures, knee function, pain relief, and quality of patient's life. Conclusion Based on high‐level evidence studies, single intraarticular injection of MSCs is a safe, reliable, and effective treatment option for Kellgren‐Lawrence grade I—III knee osteoarthritis. However, the lack of homogeneity in the included studies and the variance in MSCs sources and preparations should be noted. Level of evidence III

FairNN: Conjoint learning of fair representations for fair decisions

In this paper, we propose FairNN a neural network that performs joint feature representation and classification for fairness-aware learning. Our approach optimizes a multi-objective loss function which (a) learns a fair representation by suppressing protected attributes (b) maintains the information content by minimizing the reconstruction loss and (c) allows for solving a classification task in a fair manner by minimizing the classification error and respecting the equalized odds-based fairness regularizer. Our experiments on a variety of datasets demonstrate that such a joint approach is superior to separate treatment of unfairness in representation learning or supervised learning. Additionally, our regularizers can be adaptively weighted to balance the different components of the loss function, thus allowing for a very general framework for conjoint fair representation learning and decision making