Cohomological non-rigidity of generalized real Bott manifolds of height 2

We investigate when two generalized real Bott manifolds of height 2 have isomorphic cohomology rings with Z/2 coefficients and also when they are diffeomorphic. It turns out that cohomology rings with Z/2 coefficients do not distinguish those manifolds up to diffeomorphism in general. This gives a counterexample to the cohomological rigidity problem for real toric manifolds posed in \cite{ka-ma08}. We also prove that generalized real Bott manifolds of height 2 are diffeomorphic if they are homotopy equivalent

Fairway: A Way to Build Fair ML Software

Machine learning software is increasingly being used to make decisions that affect people's lives. But sometimes, the core part of this software (the learned model), behaves in a biased manner that gives undue advantages to a specific group of people (where those groups are determined by sex, race, etc.). This "algorithmic discrimination" in the AI software systems has become a matter of serious concern in the machine learning and software engineering community. There have been works done to find "algorithmic bias" or "ethical bias" in the software system. Once the bias is detected in the AI software system, the mitigation of bias is extremely important. In this work, we a)explain how ground-truth bias in training data affects machine learning model fairness and how to find that bias in AI software,b)propose a methodFairwaywhich combines pre-processing and in-processing approach to remove ethical bias from training data and trained model. Our results show that we can find bias and mitigate bias in a learned model, without much damaging the predictive performance of that model. We propose that (1) test-ing for bias and (2) bias mitigation should be a routine part of the machine learning software development life cycle. Fairway offers much support for these two purposes.Comment: ESEC/FSE'20: The 28th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineerin

Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatment

Automated data-driven decision making systems are increasingly being used to assist, or even replace humans in many settings. These systems function by learning from historical decisions, often taken by humans. In order to maximize the utility of these systems (or, classifiers), their training involves minimizing the errors (or, misclassifications) over the given historical data. However, it is quite possible that the optimally trained classifier makes decisions for people belonging to different social groups with different misclassification rates (e.g., misclassification rates for females are higher than for males), thereby placing these groups at an unfair disadvantage. To account for and avoid such unfairness, in this paper, we introduce a new notion of unfairness, disparate mistreatment, which is defined in terms of misclassification rates. We then propose intuitive measures of disparate mistreatment for decision boundary-based classifiers, which can be easily incorporated into their formulation as convex-concave constraints. Experiments on synthetic as well as real world datasets show that our methodology is effective at avoiding disparate mistreatment, often at a small cost in terms of accuracy.Comment: To appear in Proceedings of the 26th International World Wide Web Conference (WWW), 2017. Code available at: https://github.com/mbilalzafar/fair-classificatio

5-dimensional contact SO(3)-manifolds and Dehn twists

In this paper the 5-dimensional contact SO(3)-manifolds are classified up to equivariant contactomorphisms. The construction of such manifolds with singular orbits requires the use of generalized Dehn twists. We show as an application that all simply connected 5-manifoldswith singular orbits are realized by a Brieskorn manifold with exponents (k,2,2,2). The standard contact structure on such a manifold gives right-handed Dehn twists, and a second contact structure defined in the article gives left-handed twists.Comment: 16 pages, 1 figure; simplification of arguments by restricting classification to coorientation preserving contactomorphism

Fairness in Algorithmic Decision Making: An Excursion Through the Lens of Causality

As virtually all aspects of our lives are increasingly impacted by algorithmic decision making systems, it is incumbent upon us as a society to ensure such systems do not become instruments of unfair discrimination on the basis of gender, race, ethnicity, religion, etc. We consider the problem of determining whether the decisions made by such systems are discriminatory, through the lens of causal models. We introduce two definitions of group fairness grounded in causality: fair on average causal effect (FACE), and fair on average causal effect on the treated (FACT). We use the Rubin-Neyman potential outcomes framework for the analysis of cause-effect relationships to robustly estimate FACE and FACT. We demonstrate the effectiveness of our proposed approach on synthetic data. Our analyses of two real-world data sets, the Adult income data set from the UCI repository (with gender as the protected attribute), and the NYC Stop and Frisk data set (with race as the protected attribute), show that the evidence of discrimination obtained by FACE and FACT, or lack thereof, is often in agreement with the findings from other studies. We further show that FACT, being somewhat more nuanced compared to FACE, can yield findings of discrimination that differ from those obtained using FACE.Comment: 7 pages, 2 figures, 2 tables.To appear in Proceedings of the International Conference on World Wide Web (WWW), 201

Hypoxia and metabolic inhibitors alter the intracellular ATP:ADP ratio and membrane potential in human coronary artery smooth muscle cells.

ATP-sensitive potassium (KATP) channels couple cellular metabolism to excitability, making them ideal candidate sensors for hypoxic vasodilation. However, it is still unknown whether cellular nucleotide levels are affected sufficiently to activate vascular KATP channels during hypoxia. To address this fundamental issue, we measured changes in the intracellular ATP:ADP ratio using the biosensors Perceval/PercevalHR, and membrane potential using the fluorescent probe DiBAC4(3) in human coronary artery smooth muscle cells (HCASMCs). ATP:ADP ratio was significantly reduced by exposure to hypoxia. Application of metabolic inhibitors for oxidative phosphorylation also reduced ATP:ADP ratio. Hyperpolarization caused by inhibiting oxidative phosphorylation was blocked by either 10 µM glibenclamide or 60 mM K+. Hyperpolarization caused by hypoxia was abolished by 60 mM K+ but not by individual K+ channel inhibitors. Taken together, these results suggest hypoxia causes hyperpolarization in part by modulating K+ channels in SMCs

Making Fair ML Software using Trustworthy Explanation

Machine learning software is being used in many applications (finance, hiring, admissions, criminal justice) having a huge social impact. But sometimes the behavior of this software is biased and it shows discrimination based on some sensitive attributes such as sex, race, etc. Prior works concentrated on finding and mitigating bias in ML models. A recent trend is using instance-based model-agnostic explanation methods such as LIME to find out bias in the model prediction. Our work concentrates on finding shortcomings of current bias measures and explanation methods. We show how our proposed method based on K nearest neighbors can overcome those shortcomings and find the underlying bias of black-box models. Our results are more trustworthy and helpful for the practitioners. Finally, We describe our future framework combining explanation and planning to build fair software.Comment: New Ideas and Emerging Results (NIER) track; The 35th IEEE/ACM International Conference on Automated Software Engineering; Melbourne, Australi

Bioenergetic profile of human coronary artery smooth muscle cells and effect of metabolic intervention

Bioenergetics of artery smooth muscle cells is critical in cardiovascular health and disease. An acute rise in metabolic demand causes vasodilation in systemic circulation while a chronic shift in bioenergetic profile may lead to vascular diseases. A decrease in intracellular ATP level may trigger physiological responses while dedifferentiation of contractile smooth muscle cells to a proliferative and migratory phenotype is often observed during pathological processes. Although it is now possible to dissect multiple building blocks of bioenergetic components quantitatively, detailed cellular bioenergetics of artery smooth muscle cells is still largely unknown. Thus, we profiled cellular bioenergetics of human coronary artery smooth muscle cells and effects of metabolic intervention. Mitochondria and glycolysis stress tests utilizing Seahorse technology revealed that mitochondrial oxidative phosphorylation accounted for 54.5% of ATP production at rest with the remaining 45.5% due to glycolysis. Stress tests also showed that oxidative phosphorylation and glycolysis can increase to a maximum of 3.5 fold and 1.25 fold, respectively, indicating that the former has a high reserve capacity. Analysis of bioenergetic profile indicated that aging cells have lower resting oxidative phosphorylation and reduced reserve capacity. Intracellular ATP level of a single cell was estimated to be over 1.1 mM. Application of metabolic modulators caused significant changes in mitochondria membrane potential, intracellular ATP level and ATP:ADP ratio. The detailed breakdown of cellular bioenergetics showed that proliferating human coronary artery smooth muscle cells rely more or less equally on oxidative phosphorylation and glycolysis at rest. These cells have high respiratory reserve capacity and low glycolysis reserve capacity. Metabolic intervention influences both intracellular ATP concentration and ATP:ADP ratio, where subtler changes may be detected by the latter