Cellular and Secretory Proteins of the Salivary Glands of \u3cem\u3eSciara coprophila\u3c/em\u3e During the Larval-pupal Transformation

The cellular and secretory proteins of the salivary gland of Sciara coprophila during the stages of the larval-pupal transformation were examined by electrophoresis in 0.6 mm sheets of polyacrylamide gel with both SDS-continuous and discontinuous buffer systems. After SDS-electrophoresis, all electrophoretograms of both reduced and nonreduced proteins from single glands stained with Coomassie brilliant blue revealed a pattern containing the same 25 bands during the stages of the larval-pupal transformation. With the staining procedures used in this study, qualitative increases and decreases were detected in existing proteins and enzymes. There was no evidence, however, for the appearance of new protein species that could be correlated with the onset of either pupation or gland histolysis. Electrophoretograms of reduced samples of anterior versus posterior gland parts indicated that no protein in the basic pattern of 25 bands was unique to either the anterior or posterior gland part. Electrophoretograms of reduced samples of secretion collected from either actively feeding or cocoon -building animals showed an electrophoretic pattern containing up to six of the 25 protein fractions detected in salivary gland samples, with varied amounts of these same six proteins in electrophoretograms of secretion samples from a given stage. Zymograms of non-specific esterases in salivary gland samples revealed a progressive increase in the amount of esterase reaction produce in one major band and some decrease in the second major band during later stages of the larval-pupal transformation

Inferring Robot Task Plans from Human Team Meetings: A Generative Modeling Approach with Logic-Based Prior

We aim to reduce the burden of programming and deploying autonomous systems to work in concert with people in time-critical domains, such as military field operations and disaster response. Deployment plans for these operations are frequently negotiated on-the-fly by teams of human planners. A human operator then translates the agreed upon plan into machine instructions for the robots. We present an algorithm that reduces this translation burden by inferring the final plan from a processed form of the human team's planning conversation. Our approach combines probabilistic generative modeling with logical plan validation used to compute a highly structured prior over possible plans. This hybrid approach enables us to overcome the challenge of performing inference over the large solution space with only a small amount of noisy data from the team planning session. We validate the algorithm through human subject experimentation and show we are able to infer a human team's final plan with 83% accuracy on average. We also describe a robot demonstration in which two people plan and execute a first-response collaborative task with a PR2 robot. To the best of our knowledge, this is the first work that integrates a logical planning technique within a generative model to perform plan inference.Comment: Appears in Proceedings of the Twenty-Seventh AAAI Conference on Artificial Intelligence (AAAI-13

Evaluation of Labeling Strategies for Rotating Maps

We consider the following problem of labeling points in a dynamic map that allows rotation. We are given a set of points in the plane labeled by a set of mutually disjoint labels, where each label is an axis-aligned rectangle attached with one corner to its respective point. We require that each label remains horizontally aligned during the map rotation and our goal is to find a set of mutually non-overlapping active labels for every rotation angle $\alpha \in [0, 2\pi)$ so that the number of active labels over a full map rotation of 2$\pi$ is maximized. We discuss and experimentally evaluate several labeling models that define additional consistency constraints on label activities in order to reduce flickering effects during monotone map rotation. We introduce three heuristic algorithms and compare them experimentally to an existing approximation algorithm and exact solutions obtained from an integer linear program. Our results show that on the one hand low flickering can be achieved at the expense of only a small reduction in the objective value, and that on the other hand the proposed heuristics achieve a high labeling quality significantly faster than the other methods.Comment: 16 pages, extended version of a SEA 2014 pape

Explaining Machine Learning Classifiers through Diverse Counterfactual Explanations

Post-hoc explanations of machine learning models are crucial for people to understand and act on algorithmic predictions. An intriguing class of explanations is through counterfactuals, hypothetical examples that show people how to obtain a different prediction. We posit that effective counterfactual explanations should satisfy two properties: feasibility of the counterfactual actions given user context and constraints, and diversity among the counterfactuals presented. To this end, we propose a framework for generating and evaluating a diverse set of counterfactual explanations based on determinantal point processes. To evaluate the actionability of counterfactuals, we provide metrics that enable comparison of counterfactual-based methods to other local explanation methods. We further address necessary tradeoffs and point to causal implications in optimizing for counterfactuals. Our experiments on four real-world datasets show that our framework can generate a set of counterfactuals that are diverse and well approximate local decision boundaries, outperforming prior approaches to generating diverse counterfactuals. We provide an implementation of the framework at https://github.com/microsoft/DiCE.Comment: 13 page

Directional gene flow and ecological separation in Yersinia enterocolitica

Yersinia enterocolitica is a common cause of food-borne gastroenteritis worldwide. Recent work defining the phylogeny of the genus Yersinia subdivided Y. enterocolitica into six distinct phylogroups. Here, we provide detailed analyses of the evolutionary processes leading to the emergence of these phylogroups. The dominant phylogroups isolated from human infections, PG3–5, show very little diversity at the sequence level, but do present marked patterns of gain and loss of functions, including those involved in pathogenicity and metabolism, including the acquisition of phylogroup-specific O-antigen loci. We tracked gene flow across the species in the core and accessory genome, and show that the non-pathogenic PG1 strains act as a reservoir for diversity, frequently acting as donors in recombination events. Analysis of the core and accessory genome also suggested that the different Y. enterocolitica phylogroups may be ecologically separated, in contrast to the long-held belief of common shared ecological niches across the Y. enterocolitica species

Discussion: Non-uniqueness of flow liquefaction line for loose sand

published_or_final_versio

On Human Predictions with Explanations and Predictions of Machine Learning Models: A Case Study on Deception Detection

Humans are the final decision makers in critical tasks that involve ethical and legal concerns, ranging from recidivism prediction, to medical diagnosis, to fighting against fake news. Although machine learning models can sometimes achieve impressive performance in these tasks, these tasks are not amenable to full automation. To realize the potential of machine learning for improving human decisions, it is important to understand how assistance from machine learning models affects human performance and human agency. In this paper, we use deception detection as a testbed and investigate how we can harness explanations and predictions of machine learning models to improve human performance while retaining human agency. We propose a spectrum between full human agency and full automation, and develop varying levels of machine assistance along the spectrum that gradually increase the influence of machine predictions. We find that without showing predicted labels, explanations alone slightly improve human performance in the end task. In comparison, human performance is greatly improved by showing predicted labels (>20% relative improvement) and can be further improved by explicitly suggesting strong machine performance. Interestingly, when predicted labels are shown, explanations of machine predictions induce a similar level of accuracy as an explicit statement of strong machine performance. Our results demonstrate a tradeoff between human performance and human agency and show that explanations of machine predictions can moderate this tradeoff.Comment: 17 pages, 19 figures, in Proceedings of ACM FAT* 2019, dataset & demo available at https://deception.machineintheloop.co

Inferring team task plans from human meetings: A generative modeling approach with logic-based prior

We aim to reduce the burden of programming and deploying autonomous systems to work in concert with people in time-critical domains such as military field operations and disaster response. Deployment plans for these operations are frequently negotiated on-the-fly by teams of human planners. A human operator then translates the agreed-upon plan into machine instructions for the robots. We present an algorithm that reduces this translation burden by inferring the final plan from a processed form of the human team's planning conversation. Our hybrid approach combines probabilistic generative modeling with logical plan validation used to compute a highly structured prior over possible plans, enabling us to overcome the challenge of performing inference over a large solution space with only a small amount of noisy data from the team planning session. We validate the algorithm through human subject experimentations and show that it is able to infer a human team's final plan with 86% accuracy on average. We also describe a robot demonstration in which two people plan and execute a first-response collaborative task with a PR2 robot. To the best of our knowledge, this is the first work to integrate a logical planning technique within a generative model to perform plan inference.United States. Dept. of Defense. Assistant Secretary of Defense for Research & Engineering (United States. Air Force Contract FA8721-05-C-0002