Color, composition, and thermal environment of Kuiper Belt object (486958) Arrokoth

The outer Solar System object (486958) Arrokoth (provisional designation 2014 MU₆₉) has been largely undisturbed since its formation. We study its surface composition using data collected by the New Horizons spacecraft. Methanol ice is present along with organic material, which may have formed through irradiation of simple molecules. H₂O ice is not detected. This composition indicates hydrogenation of CO-rich ice and/or energetic processing of CH₄+H₂O ices in the cold, outer edge of the early Solar System. There are only small regional variations in color and spectra across the surface, suggesting Arrokoth formed from a homogeneous or well-mixed reservoir of solids. Microwave thermal emission from the winter night side is consistent with a mean brightness temperature of 29 ± 5 K

An algorithm to identify automorphisms which arise from self-induced interval exchange transformations

We give an algorithm to determine if the dynamical system generated by a positive automorphism of the free group can also be generated by a self-induced interval exchange transformation. The algorithm effectively yields the interval exchange transformation in case of success.Comment: 26 pages, 8 figures. v2: the article has been reorganized to make for a more linear read. A few paragraphs have been added for clarit

Ages of the Pliocene-Pleistocene Alexandra and Ngatutura Volcanics, western North Island, New Zealand, and some geological implications

The Alexandra and Ngatutura Volcanics are the two southernmost of the Pliocene-Quaternary volcanic fields of western and northern North Island, New Zealand, northwest of Taupo Volcanic Zone TVZ. The Ngatutura Basalts are an alkalic basaltic field comprising monogenetic volcanoes. The Alexandra Volcanics consist of three basaltic magma series: an alkalic (Okete Volcanics), calcalkalic (Karioi, Pirongia, Kakepuku, and Te Kawa Volcanics), and a minor potassic series. Twenty new K-Arages are presented for the Alexandra Volcanics and 9 new ages for the Ngatutura Basalts. Ages of the Alexandra Volcanics range from 2.74 to 1 .60 Ma, and the ages of all three magma series overlap. Ages of the Ngatutura Basalts range from 1 .83 to 1.54 Ma. Each basaltic field has a restricted time range and there is a progressive younging in age of the basaltic fields of western North Island from the Alexandra Volcanics in the south, to Ngatutura, to South Auckland, and then to the Auckland field in the north. Neither of the Alexandra nor Ngatutura Volcanics shows any younging direction of their volcanic centres or any age pattern within their fields, and there is no systematic variation in age with rock composition. Any correlation of age with degree of erosion of volcanic cones is invalid for these basaltic fields; instead, the degree of erosion may be controlled by the lithology of the cones and possibly by the extent of preservation offered by the thick cover deposits of the Kauroa, Hamilton, and younger tephra beds. Stratigraphic relations have enabled the earliest member of the Kauroa Ash Formation to be dated at 2.3 Ma. This formation represents a series of widespread rhyolitic plinian and ignimbrite eruptions probably derived from TVZ and initiated during the Late Pliocene

Color, composition, and thermal environment of Kuiper Belt object (486958) Arrokoth

The outer Solar System object (486958) Arrokoth (provisional designation 2014 MU₆₉) has been largely undisturbed since its formation. We study its surface composition using data collected by the New Horizons spacecraft. Methanol ice is present along with organic material, which may have formed through irradiation of simple molecules. H₂O ice is not detected. This composition indicates hydrogenation of CO-rich ice and/or energetic processing of CH₄+H₂O ices in the cold, outer edge of the early Solar System. There are only small regional variations in color and spectra across the surface, suggesting Arrokoth formed from a homogeneous or well-mixed reservoir of solids. Microwave thermal emission from the winter night side is consistent with a mean brightness temperature of 29 ± 5 K

On Generating Plausible Counterfactual and Semi-Factual Explanations for Deep Learning

There is a growing concern that the recent progress made in AI, especially regarding the predictive competence of deep learning models, will be undermined by a failure to properly explain their operation and outputs. In response to this disquiet counterfactual explanations have become massively popular in eXplainable AI (XAI) due to their proposed computational psychological, and legal benefits. In contrast however, semifactuals, which are a similar way humans commonly explain their reasoning, have surprisingly received no attention. Most counterfactual methods address tabular rather than image data, partly due to the nondiscrete nature of the latter making good counterfactuals difficult to define. Additionally generating plausible looking explanations which lie on the data manifold is another issue which hampers progress. This paper advances a novel method for generating plausible counterfactuals (and semifactuals) for black box CNN classifiers doing computer vision. The present method, called PlausIble Exceptionality-based Contrastive Explanations (PIECE), modifies all exceptional features in a test image to be normal from the perspective of the counterfactual class (hence concretely defining a counterfactual). Two controlled experiments compare this method to others in the literature, showing that PIECE not only generates the most plausible counterfactuals on several measures, but also the best semifactuals.Comment: 4 figures, 9 page

Play MNIST For Me! User Studies on the Effects of Post-Hoc, Example-Based Explanations & Error Rates on Debugging a Deep Learning, Black-Box Classifier

This paper reports two experiments (N=349) on the impact of post hoc explanations by example and error rates on peoples perceptions of a black box classifier. Both experiments show that when people are given case based explanations, from an implemented ANN CBR twin system, they perceive miss classifications to be more correct. They also show that as error rates increase above 4%, people trust the classifier less and view it as being less correct, less reasonable and less trustworthy. The implications of these results for XAI are discussed.Comment: 2 Figures, 1 Table, 8 page

Tracing Slow Winds from T Tauri Stars via Low Velocity Forbidden Line Emission

Using Keck/HIRES spectra {\Delta}v ~ 7 km/s, we analyze forbidden lines of [O I] 6300 {\AA}, [O I] 5577 {\AA} and [S II] 6731 {\AA} from 33 T Tauri stars covering a range of disk evolutionary stages. After removing a high velocity component (HVC) associated with microjets, we study the properties of the low velocity component (LVC). The LVC can be attributed to slow disk winds that could be magnetically (MHD) or thermally (photoevaporative) driven. Both of these winds play an important role in the evolution and dispersal of protoplanetary material. LVC emission is seen in all 30 stars with detected [O I] but only in 2 out of eight with detected [S II] , so our analysis is largely based on the properties of the [O I] LVC. The LVC itself is resolved into broad (BC) and narrow (NC) kinematic components. Both components are found over a wide range of accretion rates and their luminosity is correlated with the accretion luminosity, but the NC is proportionately stronger than the BC in transition disks. The FWHM of both the BC and NC correlates with disk inclination, consistent with Keplerian broadening from radii of 0.05 to 0.5 AU and 0.5 to 5 AU, respectively. The velocity centroids of the BC suggest formation in an MHD disk wind, with the largest blueshifts found in sources with closer to face-on orientations. The velocity centroids of the NC however, show no dependence on disk inclination. The origin of this component is less clear and the evidence for photoevaporation is not conclusive

Features of Explainability: How users understand counterfactual and causal explanations for categorical and continuous features in XAI

Counterfactual explanations are increasingly used to address interpretability, recourse, and bias in AI decisions. However, we do not know how well counterfactual explanations help users to understand a systems decisions, since no large scale user studies have compared their efficacy to other sorts of explanations such as causal explanations (which have a longer track record of use in rule based and decision tree models). It is also unknown whether counterfactual explanations are equally effective for categorical as for continuous features, although current methods assume they do. Hence, in a controlled user study with 127 volunteer participants, we tested the effects of counterfactual and causal explanations on the objective accuracy of users predictions of the decisions made by a simple AI system, and participants subjective judgments of satisfaction and trust in the explanations. We discovered a dissociation between objective and subjective measures: counterfactual explanations elicit higher accuracy of predictions than no-explanation control descriptions but no higher accuracy than causal explanations, yet counterfactual explanations elicit greater satisfaction and trust than causal explanations. We also found that users understand explanations referring to categorical features more readily than those referring to continuous features. We discuss the implications of these findings for current and future counterfactual methods in XAI.Comment: 26 pages, 7 figures, appendi