Testing for jumps in the stochastic volatility models

This paper proposes the Lagrange multiplier (LM) test, or the score test, for jumps in the stochastic volatility (SV) model in the cases where the innovation term follows the normal and Student t-distributions. The tested null hypothesis is that the jump density has zero variance, which is expressed by Dirac’s delta function. It is shown that the unknown jump probability, which is an unidentified parameter under the null hypothesis, is cancelled out in the LM test statistic, and hence this test is free from the estimation problem of unidentified parameters, which is known as the Davies problem [R.B. Davies, Hypothesis testing when a nuisance parameter is present only under the alternative, Biometrika 64 (1977) 247–254]. Monte Carlo experiments show that the null distribution of the LM test statistic can be approximated by the normal distribution with sufficient accuracy.     

Hairpin-based state machine and conformational addressing: Design and experiment

In this paper, we propose a new architecture for a multi-state DNA machine whose conformation of repeated hairpin structures changes sequentially in response to input oligomers. As an application of the machine, we also propose molecular memory in which the machine is used as a memory unit. Addressing in the memory is realized through state transitions of the machine. We then describe a method for designing DNA sequences of the machine, which exhaustively checks conformational changes of the machine by dividing its secondary structure into hairpin units. The method is based on the minimum free energy of the structure, the structure transition paths, and the total frequency of optimal and suboptimal structures. DNA sequences designed by the method were tested in a chemical experiment in which a machine consisting of two hairpins was actually constructed. As a result, we verified that the multi-state DNA machine realized the expected changes in its secondary structure.     

Chemical durability of a superconducting oxide YBa2Cu3Ox in aqueous solutions of varying pH values

The chemical durability of a superconducting oxide YBa₂Cu₃O _x in the aqueous solution was investigated as a function of pH value by measuring the amount of leaching of the cations in the solution by inductively coupled plasma (ICP) analysis. The chemical durability of YBa₂Cu₃O _x was very low in highly acidic solutions of pH ca, 1, where the cations were dissolved in the same cation ratio as that of the compound. In an ion-exchanged water and NaOH solution, barium ions were leached out preferentially and almost no yttrium and copper ions were leached out. When NH₄OH was used as a basic reagent, the amounts of barium ions leached out were higher than in the NaOH solution of the same pH value and the dissolution of copper ions was observed, unlike in the NaOH solution of the same pH value. These observations were explained in terms of the chemical stability of individual oxide components.     

A Face Identification System Based on the Mahalanobis–Taguchi System

In this paper, we propose a face identification system based on the Mahalanobis–Taguchi System (MTS). The MTS is one of the pattern recognition methods frequently used in quality engineering, and can perform robust pattern recognition by using training data, including noise. It is likely that this advantage will allow the effective implementation of a robust face identification system against lighting and face position fluctuations. Moreover, the MTS can optimize the number of attributes required for identification by using the orthogonal array and the signal/noise (SN) ratio. The face identification system has to deal with many users and the amount of data in a facial image is large. Therefore, the time required for identification can be decreased and the amount of data in the facial image database can be reduced by performing the optimization. We confirmed the effectiveness of the proposed system through practical experiments. The experimental results revealed that the MTS was an effective method for robust face identification, and could effectively reduce the number of attributes required for identification.     

Axioms for Recursion in Call-by-Value

We propose an axiomatization of fixpoint operators in typed call-by-value programming languages, and give its justifications in two ways. First, it is shown to be sound and complete for the notion of uniform T-fixpoint operators of Simpson and Plotkin. Second, the axioms precisely account for Filinski's fixpoint operator derived from an iterator (infinite loop constructor) in the presence of first-class continuations, provided that we define the uniformity principle on such an iterator via a notion of effect-freeness (centrality). We then explain how these two results are related in terms of the underlying categorical structures.     

Navigation behavior based on self-organized spatial representation in hierarchical recurrent neural network

ABSTRACT

A cognitive map is an internal model of the external world and contains the spatial representation of the surrounding environment. The existence of the cognitive map was first identified in rats; rats can navigate to their desired destination using cognitive maps while dealing with environmental uncertainty. We performed a mobile robot navigation experiment where obstacles were randomly placed using hierarchical recurrent neural network (HRNN) with multiple timescales. The HRNN was trained to navigate the mobile robot to the destination indicated by a snapshot image. After the training, the HRNN was able to successfully avoid the obstacles and navigate to the destination from any location in the environment. Analysis of the internal states of the HRNN showed that the module with fast timescale handles obstacle avoidance and the one with slow timescale has spatial representation corresponding to the spatial position of the destination. Moreover, in the experiment wherein the novel path appeared, the trained HRNN performed shortcut behavior. The shortcut behavior shows that the HRNN performed navigation using the self-organized spatial representation in the slow recurrent neural network. This indicates that training of goal-oriented navigation, i.e. the navigation motivated by a snapshot image of the destination results in the self-organization of cognitive map-like representation.     

Unravel four hairpins!

DNA machines consisting of consecutive hairpins, which we have previously described, have various potential applications in DNA computation. In the present study, a 288-base DNA machine containing four consecutive hairpins was successfully constructed by ligation and PCR. PAGE and fluorescence spectroscopy experiments verified that all four hairpins were successfully opened by four opener oligomers, and that hairpin opening was dependent on the proper openers added in the correct order. Quantitative analysis of the final results by fluorescence spectroscopy indicated that all four hairpins were open in about 1/4 to 1/3 of the DNA machines.     

An Extension of the Receiver Operating Characteristic Curve and AUC-Optimal Classification

While most proposed methods for solving classification problems focus on minimization of the classification error rate, we are interested in the receiver operating characteristic (ROC) curve, which provides more information about classification performance than the error rate does. The area under the ROC curve (AUC) is a natural measure for overall assessment of a classifier based on the ROC curve. We discuss a class of concave functions for AUC maximization in which a boosting-type algorithm including RankBoost is considered, and the Bayesian risk consistency and the lower bound of the optimum function are discussed. A procedure derived by maximizing a specific optimum function has high robustness, based on gross error sensitivity. Additionally, we focus on the partial AUC, which is the partial area under the ROC curve. For example, in medical screening, a high true-positive rate to the fixed lower false-positive rate is preferable and thus the partial AUC corresponding to lower false-positive rates is much more important than the remaining AUC. We extend the class of concave optimum functions for partial AUC optimality with the boosting algorithm. We investigated the validity of the proposed method through several experiments with data sets in the UCI repository.     

Optimization of facial skin temperature-based anomaly detection model considering diurnal variation

Artificial Life and Robotics - The amount of blood under the surface of skin is controlled by the autonomic nervous system and directly influences the facial skin temperature. Classification models...