GANS for Sequences of Discrete Elements with the Gumbel-softmax Distribution

Generative Adversarial Networks (GAN) have limitations when the goal is to generate sequences of discrete elements. The reason for this is that samples from a distribution on discrete objects such as the multinomial are not differentiable with respect to the distribution parameters. This problem can be avoided by using the Gumbel-softmax distribution, which is a continuous approximation to a multinomial distribution parameterized in terms of the softmax function. In this work, we evaluate the performance of GANs based on recurrent neural networks with Gumbel-softmax output distributions in the task of generating sequences of discrete elements

Causal Reasoning for Algorithmic Fairness

In this work, we argue for the importance of causal reasoning in creating fair algorithms for decision making. We give a review of existing approaches to fairness, describe work in causality necessary for the understanding of causal approaches, argue why causality is necessary for any approach that wishes to be fair, and give a detailed analysis of the many recent approaches to causality-based fairness

Operationalizing Complex Causes: A Pragmatic View of Mediation

We examine the problem of causal response estimation for complex objects (e.g., text, images, genomics). In this setting, classical \emph{atomic} interventions are often not available (e.g., changes to characters, pixels, DNA base-pairs). Instead, we only have access to indirect or \emph{crude} interventions (e.g., enrolling in a writing program, modifying a scene, applying a gene therapy). In this work, we formalize this problem and provide an initial solution. Given a collection of candidate mediators, we propose (a) a two-step method for predicting the causal responses of crude interventions; and (b) a testing procedure to identify mediators of crude interventions. We demonstrate, on a range of simulated and real-world-inspired examples, that our approach allows us to efficiently estimate the effect of crude interventions with limited data from new treatment regimes

A generative model for electron paths

Chemical reactions can be described as the stepwise redistribution of electrons in molecules. As such, reactions are often depicted using “arrow-pushing” diagrams which show this movement as a sequence of arrows. We propose an electron path prediction model (ELECTRO) to learn these sequences directly from raw reaction data. Instead of predicting product molecules directly from reactant molecules in one shot, learning a model of electron movement has the benefits of (a) being easy for chemists to interpret, (b) incorporating constraints of chemistry, such as balanced atom counts before and after the reaction, and (c) naturally encoding the sparsity of chemical reactions, which usually involve changes in only a small number of atoms in the reactants. We design a method to extract approximate reaction paths from any dataset of atom-mapped reaction SMILES strings. Our model achieves excellent performance on an important subset of the USPTO reaction dataset, comparing favorably to the strongest baselines. Furthermore, we show that our model recovers a basic knowledge of chemistry without being explicitly trained to do so.EPSR

Barking up the right tree: An approach to search over molecule synthesis DAGs

When designing new molecules with particular properties, it is not only important what to make but crucially how to make it. These instructions form a synthesis directed acyclic graph (DAG), describing how a large vocabulary of simple building blocks can be recursively combined through chemical reactions to create more complicated molecules of interest. In contrast, many current deep generative models for molecules ignore synthesizability. We therefore propose a deep generative model that better represents the real world process, by directly outputting molecule synthesis DAGs. We argue that this provides sensible inductive biases, ensuring that our model searches over the same chemical space that chemists would also have access to, as well as interpretability. We show that our approach is able to model chemical space well, producing a wide range of diverse molecules, and allows for unconstrained optimization of an inherently constrained problem: maximize certain chemical properties such that discovered molecules are synthesizable

Deep Manifold Traversal: Changing Labels with Convolutional Features

Many tasks in computer vision can be cast as a "label changing" problem, where the goal is to make a semantic change to the appearance of an image or some subject in an image in order to alter the class membership. Although successful task-specific methods have been developed for some label changing applications, to date no general purpose method exists. Motivated by this we propose deep manifold traversal, a method that addresses the problem in its most general form: it first approximates the manifold of natural images then morphs a test image along a traversal path away from a source class and towards a target class while staying near the manifold throughout. The resulting algorithm is surprisingly effective and versatile. It is completely data driven, requiring only an example set of images from the desired source and target domains. We demonstrate deep manifold traversal on highly diverse label changing tasks: changing an individual's appearance (age and hair color), changing the season of an outdoor image, and transforming a city skyline towards nighttime

Counterfactual Fairness

Machine learning can impact people with legal or ethical consequences when it is used to automate decisions in areas such as insurance, lending, hiring, and predictive policing. In many of these scenarios, previous decisions have been made that are unfairly biased against certain subpopulations, for example those of a particular race, gender, or sexual orientation. Since this past data may be biased, machine learning predictors must account for this to avoid perpetuating or creating discriminatory practices. In this paper, we develop a framework for modeling fairness using tools from causal inference. Our definition of counterfactual fairness captures the intuition that a decision is fair towards an individual if it the same in (a) the actual world and (b) a counterfactual world where the individual belonged to a different demographic group. We demonstrate our framework on a real-world problem of fair prediction of success in law school

β-1,3-Glucan-Induced Host Phospholipase D Activation Is Involved in Aspergillus fumigatus Internalization into Type II Human Pneumocyte A549 Cells

The internalization of Aspergillus fumigatus into lung epithelial cells is a process that depends on host cell actin dynamics. The host membrane phosphatidylcholine cleavage driven by phospholipase D (PLD) is closely related to cellular actin dynamics. However, little is known about the impact of PLD on A. fumigatus internalization into lung epithelial cells. Here, we report that once germinated, A. fumigatus conidia were able to stimulate host PLD activity and internalize more efficiently in A549 cells without altering PLD expression. The internalization of A. fumigatus in A549 cells was suppressed by the downregulation of host cell PLD using chemical inhibitors or siRNA interference. The heat-killed swollen conidia, but not the resting conidia, were able to activate host PLD. Further, β-1,3-glucan, the core component of the conidial cell wall, stimulated host PLD activity. This PLD activation and conidia internalization were inhibited by anti-dectin-1 antibody. Indeed, dectin-1, a β-1,3-glucan receptor, was expressed in A549 cells, and its expression profile was not altered by conidial stimulation. Finally, host cell PLD1 and PLD2 accompanied A. fumigatus conidia during internalization. Our data indicate that host cell PLD activity induced by β-1,3-glucan on the surface of germinated conidia is important for the efficient internalization of A. fumigatus into A549 lung epithelial cells

In vivo expression of innate immunity markers in patients with mycobacterium tuberculosis infection

<p>Abstract</p> <p>Background</p> <p>Toll-like receptors (TLRs), Coronin-1 and Sp110 are essential factors for the containment of <it>Mycobacterium tuberculosis </it>infection. The purpose of this study was to investigate the <it>in vivo </it>expression of these molecules at different stages of the infection and uncover possible relationships between these markers and the state of the disease.</p> <p>Methods</p> <p>Twenty-two patients with active tuberculosis, 15 close contacts of subjects with latent disease, 17 close contacts of subjects negative for mycobacterium antigens and 10 healthy, unrelated to patients, subjects were studied. Quantitative mRNA expression of Coronin-1, Sp110, TLRs-1,-2,-4 and -6 was analysed in total blood cells <it>vs </it>an endogenous house-keeping gene.</p> <p>Results</p> <p>The mRNA expression of Coronin-1, Sp110 and TLR-2 was significantly higher in patients with active tuberculosis and subjects with latent disease compared to the uninfected ones. Positive linear correlation for the expression of those factors was only found in the infected populations.</p> <p>Conclusions</p> <p>Our results suggest that the up-regulation of Coronin-1 and Sp110, through a pathway that also includes TLR-2 up-regulation may be involved in the process of tuberculous infection in humans. However, further studies are needed, in order to elucidate whether the selective upregulation of these factors in the infected patients could serve as a specific molecular marker of tuberculosis.</p

Phospholipase D signaling: orchestration by PIP2 and small GTPases

Hydrolysis of phosphatidylcholine by phospholipase D (PLD) leads to the generation of the versatile lipid second messenger, phosphatidic acid (PA), which is involved in fundamental cellular processes, including membrane trafficking, actin cytoskeleton remodeling, cell proliferation and cell survival. PLD activity can be dramatically stimulated by a large number of cell surface receptors and is elaborately regulated by intracellular factors, including protein kinase C isoforms, small GTPases of the ARF, Rho and Ras families and, particularly, by the phosphoinositide, phosphatidylinositol 4,5-bisphosphate (PIP2). PIP2 is well known as substrate for the generation of second messengers by phospholipase C, but is now also understood to recruit and/or activate a variety of actin regulatory proteins, ion channels and other signaling proteins, including PLD, by direct interaction. The synthesis of PIP2 by phosphoinositide 5-kinase (PIP5K) isoforms is tightly regulated by small GTPases and, interestingly, by PA as well, and the concerted formation of PIP2 and PA has been shown to mediate receptor-regulated cellular events. This review highlights the regulation of PLD by membrane receptors, and describes how the close encounter of PLD and PIP5K isoforms with small GTPases permits the execution of specific cellular functions