Influence of convection on microstructure

The influence was studied of convection during directional solidification on the resulting microstructure of eutectics, specifically lead/tin and manganese/bismuth. A theory was developed for the influence of convection on the microstructure of lamellar and fibrous eutectics, through the effect of convection on the concentration field in the melt in front of the growing eutectic. While the theory agrees with the experimental spin-up spin-down results, it predicts that the weak convection expected due to buoyancy will not produce a measurable change in eutectic microstructure. Thus, this theory does not explain the two fold decrease in MnBi fiber size and spacing observed when MnBi-Bi is solidified in space or on Earth with a magnetic field applied. Attention was turned to the morphology of the MnBi-Bi interface and to the generation of freezing rate fluctuations by convection. Decanting the melt during solidification of MnBi-Bi eutectic showed that the MnBi phase projects into the melt ahead of the Bi matrix. Temperature measurements in a Bi melt in the vertical Bridgman-Stockbarger configuration showed temperature variations of up to 25 C. Conclusions are drawn and discussed

Dynamics of semifluxons in Nb long Josephson 0-pi junctions

We propose, implement and test experimentally long Josephson 0-pi junctions fabricated using conventional Nb-AlOx-Nb technology. We show that using a pair of current injectors, one can create an arbitrary discontinuity of the Josephson phase and in particular a pi-discontinuity, just like in d-wave/s-wave or in d-wave/d-wave junctions, and study fractional Josephson vortices which spontaneously appear. Moreover, using such junctions, we can investigate the \emph{dynamics} of the fractional vortices -- a domain which is not yet available for natural 0-pi-junctions due to their inherently high damping. We observe half-integer zero-field steps which appear on the current-voltage characteristics due to hopping of semifluxons.Comment: Fractional vortices in conventional superconductors ;-

Attitudes Toward Monsters

The concept of monsters is ubiquitous across cultures, but there has been little research on monsters themselves and what factors shape people’s attitudes toward them. Kennesaw State University undergraduate psychology students (N = 450) read unbiased, positively biased, or negatively biased reports of one of 15 fictional monsters before all participants read identical stories about an encounter with the monster. Questionnaire responses indicated that reading a negatively biased report results in significantly more negative attitudes toward a monster than reading an unbiased report, that attitudes toward animals positively correlate with attitudes toward monsters, and that attitudes toward monsters differ depending on what real-life animals they most resemble. The results provide a greater understanding of how humans perceive and react to unfamiliar nonhumans, specifically those with characteristics of various animals, and suggest that research on animal-like monsters can elucidate human perceptions of real-life animals. Applications include identifying the best methods to counteract negative media images of animals, discovering a culture’s views on animals through the monsters in its folklore, and identifying in advance which unfamiliar endangered animals likely need the most publicity in order to engender public support

The Sicily Channel Regional Model forecasting system: initial boundary conditions sensitivity and case study evaluation

The Sicily Channel Regional Model forecasting system was tested using an optimization package for the initial and lateral boundary conditions. Spurious high frequency oscillations during the spin-up time were successfully reduced both in duration and magnitude by optimizing the time tendency of the free surface elevation using the Variational Initialization and Forcing Platform method developed in the framework of the Mediterranean Forecasting System Towards the Environmental Prediction project. The effect of optimization was most profound for the free surface elevation, where all oscillations with periods shorter than 4 h were suppressed

Ada-WHIPS: explaining AdaBoost classification with applications in the health sciences

Background Computer Aided Diagnostics (CAD) can support medical practitioners to make critical decisions about their patients’ disease conditions. Practitioners require access to the chain of reasoning behind CAD to build trust in the CAD advice and to supplement their own expertise. Yet, CAD systems might be based on black box machine learning models and high dimensional data sources such as electronic health records, magnetic resonance imaging scans, cardiotocograms, etc. These foundations make interpretation and explanation of the CAD advice very challenging. This challenge is recognised throughout the machine learning research community. eXplainable Artificial Intelligence (XAI) is emerging as one of the most important research areas of recent years because it addresses the interpretability and trust concerns of critical decision makers, including those in clinical and medical practice. Methods In this work, we focus on AdaBoost, a black box model that has been widely adopted in the CAD literature. We address the challenge – to explain AdaBoost classification – with a novel algorithm that extracts simple, logical rules from AdaBoost models. Our algorithm, Adaptive-Weighted High Importance Path Snippets (Ada-WHIPS), makes use of AdaBoost’s adaptive classifier weights. Using a novel formulation, Ada-WHIPS uniquely redistributes the weights among individual decision nodes of the internal decision trees of the AdaBoost model. Then, a simple heuristic search of the weighted nodes finds a single rule that dominated the model’s decision. We compare the explanations generated by our novel approach with the state of the art in an experimental study. We evaluate the derived explanations with simple statistical tests of well-known quality measures, precision and coverage, and a novel measure stability that is better suited to the XAI setting. Results Experiments on 9 CAD-related data sets showed that Ada-WHIPS explanations consistently generalise better (mean coverage 15%-68%) than the state of the art while remaining competitive for specificity (mean precision 80%-99%). A very small trade-off in specificity is shown to guard against over-fitting which is a known problem in the state of the art methods. Conclusions The experimental results demonstrate the benefits of using our novel algorithm for explaining CAD AdaBoost classifiers widely found in the literature. Our tightly coupled, AdaBoost-specific approach outperforms model-agnostic explanation methods and should be considered by practitioners looking for an XAI solution for this class of models

CHIRPS: Explaining random forest classification

Modern machine learning methods typically produce “black box” models that are opaque to interpretation. Yet, their demand has been increasing in the Human-in-the-Loop pro-cesses, that is, those processes that require a human agent to verify, approve or reason about the automated decisions before they can be applied. To facilitate this interpretation, we propose Collection of High Importance Random Path Snippets (CHIRPS); a novel algorithm for explaining random forest classification per data instance. CHIRPS extracts a decision path from each tree in the forest that contributes to the majority classification, and then uses frequent pattern mining to identify the most commonly occurring split conditions. Then a simple, conjunctive form rule is constructed where the antecedent terms are derived from the attributes that had the most influence on the classification. This rule is returned alongside estimates of the rule’s precision and coverage on the training data along with counter-factual details. An experimental study involving nine data sets shows that classification rules returned by CHIRPS have a precision at least as high as the state of the art when evaluated on unseen data (0.91–0.99) and offer a much greater coverage (0.04–0.54). Furthermore, CHIRPS uniquely controls against under- and over-fitting solutions by maximising novel objective functions that are better suited to the local (per instance) explanation setting

Non-ideal artificial phase discontinuity in long Josephson 0-kappa-junctions

We investigate the creation of an arbitrary $\kappa$-discontinuity of the Josephson phase in a long Nb-AlO_x-Nb Josephson junction (LJJ) using a pair of tiny current injectors, and study the formation of fractional vortices formed at this discontinuity. The current I_inj, flowing from one injector to the other, creates a phase discontinuity kappa ~ I_inj. The calibration of injectors is discussed in detail. The small but finite size of injectors leads to some deviations of the properties of such a 0-kappa-LJJ from the properties of a LJJ with an ideal kappa-discontinuity. These experimentally observed deviations in the dependence of the critical current on I_inj$ and magnetic field can be well reproduced by numerical simulation assuming a finite injector size. The physical origin of these deviations is discussed.Comment: Submitted to Phys. Rev. B (12 figures). v 2: refs updated, long eqs fixed v 3: major changes, fractional vortex dynamics exclude