191 research outputs found

    Chaplaincy and the Provision of Spiritual Care

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    Electric Conductance of Rh Atomic Contacts under Electrochemical Potential Control

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    The electric conductance of Rh atomic contacts was investigated under the electrochemical potential control. The conductance histogram of Rh atomic contacts varied with the electrochemical potential. When the electrochemical potential of the contact was kept at Φ0\Phi_{0}= 0.1 V vs. Ag/AgCl (Rh potential), the conductance histogram did not show any features. At Φ0\Phi_{0}= -0.1 V (under potential deposited hydrogen potential), the conductance histogram showed a feature around 2.3 G0G_{0} (G0G_{0} =2e2/he^{2}/h), which agreed with the conductance value of a clean Rh atomic contact, which was observed in ultrahigh vacuum at low temperature. At Φ0\Phi_{0}= -0.25 V (over potential deposited hydrogen potential), the conductance histogram showed features around 0.3 and 1.0 G0G_{0}. The conductance behavior of the Rh atomic contact was discussed by comparing previously reported results of other metals, Au, Ag, Cu, Pt, Pd, Ni, Co, and Fe. The conductance behavior of the metal atomic contacts related with the strength of the interaction between hydrogen and metal surface.Comment: 5 pages, 4 figures, Phys. Rev. B, in press

    Extension of Decision Tree Algorithm for Stream Data Mining Using Real Data

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    Recently, because of increasing amount of data in the society, data stream mining targeting large scale data has attracted attention. The data mining is a technology of discovery new knowledge and patterns from the massive amounts of data, and what the data correspond to data stream is data stream mining. In this paper, we propose the feature selection with online decision tree. At first, we construct online type decision tree to regard credit card transaction data as data stream on data stream mining. At second, we select attributes thought to be important for detection of illegal use. We apply VFDT (Very Fast Decision Tree learner) algorithm to online type decision tree construction

    Hydrogen-assisted stabilization of Ni nanowires in solution

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    We have studied conductance characteristics of mechanically fabricated Ni nanoconstrictions under controlling electrochemical potential and pH of the electrolyte. Conductance histogram showed clear feature peaked at 1-1.5 G0G_{0} (=2e2/h2e^{2}/h) when the potential of the constriction was kept at more negative potential than -900 mV vs. Ag/AgCl in pH=3.7. Comparable feature also appeared at more positive potential when lower pH solution was used. We have revealed that Ni mono atomic contact or mono atomic wire can be stabilized in solution at room temperature under the hydrogen evolution.Comment: 4 pages, 3 figures; to appear in Appl. Phys. Let

    Three reversible states controlled on a gold monoatomic contact by the electrochemical potential

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    Conductance of an Au mono atomic contact was investigated under the electrochemical potential control. The Au contact showed three different behaviors depending on the potential: 1 G0G_{0} (G0G_{0} = 2e2/h2e^{2}/h), 0.5 G0G_{0} and not-well defined values below 1 G0G_{0} were shown when the potential of the contact was kept at -0.6 V (double layer potential), -1.0 V (hydrogen evolution potential), and 0.8 V (oxide formation potential) versus Ag/AgCl in 0.1 M Na2_{2}SO4_{4} solution, respectively. These three reversible states and their respective conductances could be fully controlled by the electrochemical potential. These changes in the conductance values are discussed based on the proposed structure models of hydrogen adsorbed and oxygen incorporated on an Au mono atomic contact.Comment: 8 pages, 4 figures, to be appeared in Physical Review

    Learnability and Algorithm for Continual Learning

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    This paper studies the challenging continual learning (CL) setting of Class Incremental Learning (CIL). CIL learns a sequence of tasks consisting of disjoint sets of concepts or classes. At any time, a single model is built that can be applied to predict/classify test instances of any classes learned thus far without providing any task related information for each test instance. Although many techniques have been proposed for CIL, they are mostly empirical. It has been shown recently that a strong CIL system needs a strong within-task prediction (WP) and a strong out-of-distribution (OOD) detection for each task. However, it is still not known whether CIL is actually learnable. This paper shows that CIL is learnable. Based on the theory, a new CIL algorithm is also proposed. Experimental results demonstrate its effectiveness.Comment: ICML 202

    A Theoretical Study on Solving Continual Learning

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    Continual learning (CL) learns a sequence of tasks incrementally. There are two popular CL settings, class incremental learning (CIL) and task incremental learning (TIL). A major challenge of CL is catastrophic forgetting (CF). While a number of techniques are already available to effectively overcome CF for TIL, CIL remains to be highly challenging. So far, little theoretical study has been done to provide a principled guidance on how to solve the CIL problem. This paper performs such a study. It first shows that probabilistically, the CIL problem can be decomposed into two sub-problems: Within-task Prediction (WP) and Task-id Prediction (TP). It further proves that TP is correlated with out-of-distribution (OOD) detection, which connects CIL and OOD detection. The key conclusion of this study is that regardless of whether WP and TP or OOD detection are defined explicitly or implicitly by a CIL algorithm, good WP and good TP or OOD detection are necessary and sufficient for good CIL performances. Additionally, TIL is simply WP. Based on the theoretical result, new CIL methods are also designed, which outperform strong baselines in both CIL and TIL settings by a large margin.Comment: NeurIPS 202
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