Generative Adversarial Networks for Mitigating Biases in Machine Learning Systems

In this paper, we propose a new framework for mitigating biases in machine learning systems. The problem of the existing mitigation approaches is that they are model-oriented in the sense that they focus on tuning the training algorithms to produce fair results, while overlooking the fact that the training data can itself be the main reason for biased outcomes. Technically speaking, two essential limitations can be found in such model-based approaches: 1) the mitigation cannot be achieved without degrading the accuracy of the machine learning models, and 2) when the data used for training are largely biased, the training time automatically increases so as to find suitable learning parameters that help produce fair results. To address these shortcomings, we propose in this work a new framework that can largely mitigate the biases and discriminations in machine learning systems while at the same time enhancing the prediction accuracy of these systems. The proposed framework is based on conditional Generative Adversarial Networks (cGANs), which are used to generate new synthetic fair data with selective properties from the original data. We also propose a framework for analyzing data biases, which is important for understanding the amount and type of data that need to be synthetically sampled and labeled for each population group. Experimental results show that the proposed solution can efficiently mitigate different types of biases, while at the same time enhancing the prediction accuracy of the underlying machine learning model

Mutations in Epigenetic Modifiers in Myeloid Malignancies and the Prospect of Novel Epigenetic-Targeted Therapy

In the recent years, the discovery of a series of mutations in patients with myeloid malignancies has provided insight into the pathogenesis of myelodysplastic syndromes (MDSs), myeloproliferative neoplasms (MPNs), and acute myeloid leukemia (AML). Among these alterations have been mutations in genes, such as IDH1/2, TET2, DNMT3A, and EZH2, which appear to affect DNA and/or histone lysine methylation. Large clinical correlative studies are beginning to decipher the clinical importance, prevalence, and potential prognostic significance of these mutations. Additionally, burgeoning insight into the role of epigenetics in the pathogenesis of myeloid malignancies has prompted increased interest in development of novel therapies which target DNA and histone posttranslational modifications. DNA demethylating agents have been demonstrated to be clinically active in a subset of patients with MDS and AML and are used extensively. However, newer, more specific agents which alter DNA and histone modification are under preclinical study and development and are likely to expand our therapeutic options for these diseases in the near future. Here, we review the current understanding of the clinical importance of these newly discovered mutations in AML and MDS patients. We also discuss exciting developments in DNA methyltransferase inhibitor strategies and the prospect of novel histone lysine methyltransferase inhibitors

Recent advances in the treatment of acute myeloid leukemia

Acute myeloid leukemia (AML) is a disorder with significant molecular and clinical heterogeneity. Although there have been clear advances in the identification of somatic genetic and epigenetic alterations present in the malignant cells of patients with AML, translating this knowledge into an integrated view with an impact on the clinical treatment of AML has been slower to evolve. Recent clinical advances in the treatment of AML include studies demonstrating the benefit of dose-intense daunorubicin therapy in induction chemotherapy for patients of any age. We also review use of the DNA methyltransferase inhibitor azacitidine for treatment of AML in elderly patients as well as a study of global patterns of DNA methylation in patients with AML. Lastly, we review a recent assessment of the role of allogeneic hematopoietic stem cell transplantation in AML in first complete remission

Towards Bilateral Client Selection in Federated Learning Using Matching Game Theory

Federated Learning (FL) is a novel distributed privacy-preserving learning paradigm, which enables the collaboration among several devices. However, selecting the participants that would contribute to this collaborative training is highly challenging. Adopting a random selection strategy would entail substantial problems due to the heterogeneity in terms of data quality and resources across the participants. To overcome this problem, we propose an intelligent client selection approach for federated learning on IoT devices using matching game theory. Our solution involves the design of: (1) preference functions for the client IoT devices and federated servers to allow them to rank each other according to several criteria such as accuracy and price, and (2) intelligent matching algorithms that take into account the preferences of both parties in their design. Based on our simulation findings, our strategy surpasses the VanillaFL selection approach in terms of maximizing both the revenues of the client devices and accuracy of the global federated learning model

FedMint: Intelligent Bilateral Client Selection in Federated Learning with Newcomer IoT Devices

Federated Learning (FL) is a novel distributed privacy-preserving learning paradigm, which enables the collaboration among several participants (e.g., Internet of Things devices) for the training of machine learning models. However, selecting the participants that would contribute to this collaborative training is highly challenging. Adopting a random selection strategy would entail substantial problems due to the heterogeneity in terms of data quality, and computational and communication resources across the participants. Although several approaches have been proposed in the literature to overcome the problem of random selection, most of these approaches follow a unilateral selection strategy. In fact, they base their selection strategy on only the federated server’s side, while overlooking the interests of the client devices in the process. To overcome this problem, we present in this paper FedMint, an intelligent client selection approach for federated learning on IoT devices using game theory and bootstrapping mechanism. Our solution involves the design of: (1) preference functions for the client IoT devices and federated servers to allow them to rank each other according to several factors such as accuracy and price, (2) intelligent matching algorithms that take into account the preferences of both parties in their design, and (3) bootstrapping technique that capitalizes on the collaboration of multiple federated servers in order to assign initial accuracy value for the newly connected IoT devices. We compare our approach against the VanillaFL selection process as well as other state-of-the-art approach and showcase the superiority of our proposal

Hepatic venous outflow obstruction after living donor liver transplantation managed with ectopic placement of a foley catheter: A case report

AbstractIntroductionThe early hepatic venous outflow obstruction (HVOO) is a rare but serious complication after liver transplantation, which may result in graft loss. We report a case of early HVOO after living donor liver transplantation, which was managed by ectopic placement of foley catheter.PresentationA 51 years old male patient with end stage liver disease received a right hemi-liver graft. On the first postoperative day the patient developed impairment of the liver functions. Doppler ultrasound (US) showed absence of blood flow in the right hepatic vein without thrombosis. The decision was to re-explore the patient, which showed torsion of the graft upward and to the right side causing HVOO. This was managed by ectopic placement of a foley catheter between the graft and the diaphragm and the chest wall. Gradual deflation of the catheter was gradually done guided by Doppler US and the patient was discharged without complications.DiscussionMechanical HVOO results from kinking or twisting of the venous anastomosis due to anatomical mismatch between the graft and the recipient abdomen. It should be managed surgically by repositioning of the graft or redo of venous anastomosis. Several ideas had been suggested for repositioning and fixation of the graft by the use of Sengstaken–Blakemore tubes, tissue expanders, and surgical glove expander.ConclusionWe report the use of foley catheter to temporary fix the graft and correct the HVOO. It is a simple and safe way, and could be easily monitored and removed under Doppler US without any complications