Tree species explain only half of explained spatial variability in plant water sensitivity
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Alexandra G. Konings↗
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Results for “explainability”
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Heavy fermion behavior explained by bosons
Conventional heavy fermion (HF) theories require existence of massive fermions. We show that heavy fermion phenomena can also be simply explained by existence of bosons with moderate mass but temperature dependent concentration below the formation temperature T(sub B), which in turn is close to room temperature. The bosons B(++) are proposed to be in chemical equilibrium with a system of holes h(+): B(++) = h(+) + h(+). This equilibrium is governed by a boson breaking function f(T), which determines the decreasing boson density and the increasing fermion density with increasing temperature. Since HF-compounds are hybridized from minimum two elements, we assume in addition existence of another fermion component h(sub s)(+) with temperature independent density. This spectator component is thought to be the main agent in binding the bosons in analogy with electronic or muonic molecules. Using a linear boson breaking function we can explain temperature dependence of the giant linear specific heat coefficient gamma(T) coming essentially from bosons. The maxima in resistivity, Hall coefficient, and susceptibility are explained by boson localization effects due to the Wigner crystallization. The antiferromagnetic transitions in turn are explained by similar localization of the pairing fermion system when their density n(sub h)(T(sub FL)) becomes lower than n(sub WC), the critical density of Wigner crystallization. The model applies irrespective whether a compound is superconducting or not. The same model explains the occurrence of low temperature antiferromagnetism also in high-T(sub c) superconductors. The double transition in UPt3 is proposed to be due to the transition of the pairing fermion liquid from spin polarized to unpolarized state.
Kallio, A.↗
Enhancing Neural Network Explainability with Variational Autoencoders
Machine intelligence has been used to tackle increasingly complex problems and deep learning solutions are at the forefront of tackling these problems. In general, these architectures have a great number of parameters that are methodically updated in training. The vast number and complexity of deep neural networks makes it very difficult to decipher the inner workings of the neurons and layers that make up the network. This paper posits that trustworthiness and trust in autonomous systems are increased through eXplainable Artificial Intelligence (XAI) and presents a method that enhances the explainability and understanding of a neural network decision. We leverage variational autoencoders to produce human interpretable features from complex data sets. We show that the explainable features can then be used for machine learning applications. Explainability inspires trust in autonomous systems that use deep learning, which is necessary for safety critical systems.
Loc Tran↗
Explainable expert systems: A research program in information processing
Our work in Explainable Expert Systems (EES) had two goals: to extend and enhance the range of explanations that expert systems can offer, and to ease their maintenance and evolution. As suggested in our proposal, these goals are complementary because they place similar demands on the underlying architecture of the expert system: they both require the knowledge contained in a system to be explicitly represented, in a high-level declarative language and in a modular fashion. With these two goals in mind, the Explainable Expert Systems (EES) framework was designed to remedy limitations to explainability and evolvability that stem from related fundamental flaws in the underlying architecture of current expert systems.
Paris, Cecile L.↗
An Electrochemical Framework to Explain Intergranular Stress Corrosion Cracking in an Al-5.4%Cu-0.5%Mg-0.5%Ag Alloy
A modified version of the Cu-depletion electrochemical framework was used to explain the metallurgical factor creating intergranular stress corrosion cracking susceptibility in an aged Al-Cu-Mg-Ag alloy, C416. This framework was also used to explain the increased resistance to intergranular stress corrosion cracking in the overaged temper. Susceptibility in the under aged and T8 condition is consistent with the grain boundary Cu-depletion mechanism. Improvements in resistance of the T8+ thermal exposure of 5000 h at 225 F (T8+) compared to the T8 condition can be explained by depletion of Cu from solid solution.
Little, D. A.↗
Towards Explainability of UAV-Based Convolutional Neural Networks for Object Classification
f autonomous systems using trust and trustworthiness is the focus of Autonomy Teaming and TRAjectories for Complex Trusted Operational Reliability (ATTRACTOR), a new NASA Convergent Aeronautical Solutions (CAS) Project. One critical research element of ATTRACTOR is explainability of the decision-making across relevant subsystems of an autonomous system. The ability to explain why an autonomous system makes a decision is needed to establish a basis of trustworthiness to safely complete a mission. Convolutional Neural Networks (CNNs) are popular visual object classifiers that have achieved high levels of classification performances without clear insight into the mechanisms of the internal layers and features. To explore the explainability of the internal components of CNNs, we reviewed three feature visualization methods in a layer-by-layer approach using aviation related images as inputs. Our approach to this is to analyze the key components of a classification event in order to generate component labels for features of the classified image at different layers of depths. For example, an airplane has wings, engines, and landing gear. These could possibly be identified somewhere in the hidden layers from the classification and these descriptive labels could be provided to a human or machine teammate while conducting a shared mission and to engender trust. Each descriptive feature may also be decomposed to a combination of primitives such as shapes and lines. We expect that knowing the combination of shapes and parts that create a classification will enable trust in the system and insight into creating better structures for the CNN.
Dolph, Chester V.↗
Using Explainable Scheduling for the Mars 2020 Rover Mission
Understanding the reasoning behind the behavior of an auto- mated scheduling system is essential to ensure that it will be trusted and consequently used to its full capabilities in critical applications. In cases where a scheduler schedules activities in an invalid location, it is usually easy for the user to infer the missing constraint by inspecting the schedule with the in- valid activity to determine the missing constraint. If a sched- uler fails to schedule activities because constraints could not be satisfied, determining the cause can be more challenging. In such cases it is important to understand which constraints caused the activities to fail to be scheduled and how to al- ter constraints to achieve the desired schedule. In this pa- per, we describe such a scheduling system for NASA’s Mars 2020 Perseverance Rover, as well as Crosscheck, an explain- able scheduling tool that explains the scheduler behavior. The scheduling system and Crosscheck are the baseline for oper- ational use to schedule activities for the Mars 2020 rover. As we describe, the scheduler generates a schedule given a set of activities and their constraints and Crosscheck: (1) provides a visual representation of the generated schedule; (2) analyzes and explains why activities failed to schedule given the con- straints provided; and (3) provides guidance on potential con- straint relaxations to enable the activities to schedule in future scheduler runs.
Chien, Steve↗
Machine Learning Explainability and Transferability for Path Navigation
Deep neural networks are powerful tools for machine perception. Unfortunately their decisions are difficult to explain due to the complexity and size of the networks. Previously we have alleviated this issue by using the representational portion of a deep neural network and combining it with a k-nearest neighbor (KNN) classifier. Through inspection of the decisions made by the KNN, we can directly see the training data responsible for the decisions, allowing us to determine the quality of the overall decision and the quality of the representational layer of the deep NN. While the technique worked well, it requires tens of thousands of latent vectors to be stored for classification. In addition, it lacks the ability to show how parts of an image influence the classification decision. Here we address these issues by 1) Using a radial basis function network (RBFN) in place of the KNN allowing far fewer images to be used in deployment and 2) Using an auto encoder network for explainability. In addition to these techniques, we examine the effects of transfer learning to determine that results are robust. All results are tested on a domain where an unmanned aerial vehicle (UAV) navigates a forest trail through a single camera.
explainable machine learning↗
Explaining Soft-Goal Conflicts through Constraint Relaxations
Recent work suggests to explain trade-offs between soft goals in terms of their conflicts, i. e., minimal unsolvable soft-goal subsets. But this does not explain the conflicts themselves: Why can a given set of soft-goals not be jointly achieved? Here we approach that question in terms of the underlying constraints on plans in the task at hand, namely resource availability and time windows. In this context, a natural form of explanation for a soft-goal conflict is a minimal constraint relaxation under which the conflict disappears (“if the deadline was 1 hour later, it would work”). We explore algorithms for computing such explanations. A baseline is to simply loop over all relaxed tasks and compute the conflicts for each separately. We improve over this by two algorithms that leverage information – conflicts, reachable states – across relaxed tasks. We show that these algorithms can exponentially outperform the baseline in theory, and we run experiments confirming that advantage in practice.
Planning↗
Explaining patterns in the ratification of global environmental treaties
A study was made of the ratification behavior of 160 countries with respect to 38 global environmental treaties. The study identifies and explains patterns in the ratification of treaties, providing two means of assessing the likelihood that any given country will support global environmental treaties. National ratification totals reveal a pattern of high ratification by countries in Western Europe, North America, Japan, Australia, and New Zealand. A country's standing within the range of high to low ratification rates can be explained by the statistical model developed in the study. This research allows one to identify countries likely to support global environmental treaties.
Cook, David W.↗
Laboratory Synthesized Calcium Oxide and Calcium Hydroxide Grains: A Candidate to Explain the 6.8 Micron Band
We will demonstrate that CaO and Ca(OH)2 are excellent candidates to explain the 6.8 microns feature, which is one of the most obscure features in young stellar objects. We discuss the condensation of CaO grains and the potential formation of a Ca(OH)2 surface layer. The infrared spectra of these grains are compared with the spectra of fifteen young stellar objects. We note that CaO-rich grains are seen in all meteoritic CAIs (calcium-aluminum-rich inclusions) and the 6.8 micron feature has only been observed in young stellar objects. Therefore, we consider CaO grains to be a plausible candidate to explain the 6.8 microns feature and hypothesize that they are produced in the hot interiors of young stellar environments.
Kimura, Yuki↗
Explaining Polarization Reversals in STEREO Wave Data
Recently Breneman et al. reported observations of large amplitude lightning and transmitter whistler mode waves from two STEREO passes through the inner radiation belt (L<2). Hodograms of the electric field in the plane transverse to the magnetic field showed that the transmitter waves underwent periodic polarization reversals. Specifically, their polarization would cycle through a pattern of right-hand to linear to left-hand polarization at a rate of roughly 200 Hz. The lightning whistlers were observed to be left-hand polarized at frequencies greater than the lower hybrid frequency and less than the transmitter frequency (21.4 kHz) and right-hand polarized otherwise. Only righthand polarized waves in the inner radiation belt should exist in the frequency range of the whistler mode and these reversals were not explained in the previous paper. We show, with a combination of observations and simulated wave superposition, that these polarization reversals are due to the beating of an incident electromagnetic whistler mode wave at 21.4 kHz and linearly polarized, symmetric lower hybrid sidebands Doppler-shifted from the incident wave by +/-200 Hz. The existence of the lower hybrid waves is consistent with the parametric decay mechanism of Lee and Kuo whereby an incident whistler mode wave decays into symmetric, short wavelength lower hybrid waves and a purely growing (zero-frequency) mode. Like the lower hybrid waves, the purely growing mode is Doppler-shifted by 200 Hz as observed on STEREO. This decay mechanism in the upper ionosphere has been previously reported at equatorial latitudes and is thought to have a direct connection with explosive spread F enhancements. As such it may represent another dissipation mechanism of VLF wave energy in the ionosphere and may help to explain a deficit of observed lightning and transmitter energy in the inner radiation belts as reported by Starks et al.
Breneman, A.↗
Can a Double Component Outflow Explain the X-Ray and Optical Lightcurves of Swift Gamma-Ray Bursts?
An increasing sample of Gamma-Ray Bursts (GRBs) observed by Swift show evidence of 'chromatic breaks', i.e. breaks that are present in the X-ray but not in the optical. We find that in a significant fraction of these GRB afterglows the X-ray and the optical emission cannot be produced by the same component. We propose that these afterglow lightcurves are the result of a two-component jet, in which both components undergo energy injection for the whole observation and the X-ray break is due to a jet break in the narrow outflow. Bursts with chromatic breaks also explain another surprising finding, the paucity of late achromatic breaks. We propose a model that may explain the behaviour of GRB emission in both X-ray and optical bands. This model can be a radical and noteworthy alternative to the current interpretation for the 'canonical' XRT and UVOT lightcurves, and it bears fundamental implications for GRB physics.
De Pasquale, Massimiliano↗
Explainable deep learning for insights in El Niño and river flows
The El Niño Southern Oscillation (ENSO) is a semi-periodic fluctuation in sea surface temperature (SST) over the tropical central and eastern Pacific Ocean that influences interannual variability in regional hydrology across the world through long-range dependence or teleconnections. Recent research has demonstrated the value of Deep Learning (DL) methods for improving ENSO prediction as well as Complex Networks (CN) for understanding teleconnections. However, gaps in predictive understanding of ENSO-driven river flows include the black box nature of DL, the use of simple ENSO indices to describe a complex phenomenon and translating DL-based ENSO predictions to river flow predictions. Here we show that eXplainable DL (XDL) methods, based on saliency maps, can extract interpretable predictive information contained in global SST and discover SST information regions and dependence structures relevant for river flows which, in tandem with climate network constructions, enable improved predictive understanding. Our results reveal additional information content in global SST beyond ENSO indices, develop understanding of how SSTs influence river flows, and generate improved river flow prediction, including uncertainty estimation. Observations, reanalysis data, and earth system model simulations are used to demonstrate the value of the XDL-CN based methods for future interannual and decadal scale climate projections.
SST↗
The pressure dependency in the oxidation of platinum explained by a boundary-layer diffusion mechanism.
Pressure dependency in oxidation of platinum above 800 degrees C explained by boundary layer diffusion mechanism
GASEOUS DIFFUSION↗
Theory helps explain cancer growth
Theory explaining source of uncontrolled malignant growth, and suggestions for developing chemical measures against cancer
Source record↗
On explaining the F-region seasonal anomaly in terms of composition changes in the lower atmosphere
Seasonal anomaly of F-region explained in terms of composition changes in lower atmosphere
Chandra, S.↗
On explaining magnetic storm phenomena in the upper atmosphere and ionosphere.
Changes in ion and neutral compositions and in neutral, ion, and electron temperatures during the main phase of a magnetic storm are studied by solving a system of basic ionospheric and atmospheric differential equations. It is shown that a decrease in the atomic-to-molecular concentration ratio in the lower thermosphere may help explain several phenomena observed during a magnetic storm. These phenomena include decreases in the columnar electron content and increases in neutral temperature.
Stubbe, P.↗