Energy consumption data of production machines often exhibit quasi-periodicity, and anomalies are observed when deviations from the quasi-periodicity are detected. For such data, it is crucial to quickly estimate the individual cycles at each time point and detect abnormalities. In this study, we propose a system that satisfies these requirements. The proposed system trains a neural network with an attention mechanism and applies the weight vectors in the mechanism to the two tasks. Experimental results demonstrate that the proposed method outperforms benchmark methods for sensor data that mimic power consumption data of production lines.

A demand for occupancy estimates using low time frequency such as 30 minutes interval energy data from smart meters is increasing, since it is beneficial to society in many ways. Knowing whether a household is occupied by its residents in real time contributes to removing absent delivery, route optimization for work activities, and balancing the supply and demand of energy. A Previous work demonstrated machine learning models in conventional methods such as a random forest (RF) and a support vector machine (SVM) can detect occupancy status with over 80 percent accuracy. In this paper, we develop a new model, bidirectional long short term memory (LSTM) combined with an attention mechanism (BiLSTM-Attention). The two major differences from the previous study are learning long-term time dependency which a LSTM enables and effective use of information at other times that are highly similar to a given time by an attention. We demonstrated that our proposing BiLSTM-Attention can lead to detect occupancy status equally or more accurately compared with a previous study in the most large publicly available data.

Real-time estimation of human mobility following a massive disaster will play a crucial role in disaster relief. Because human mobility in massive disasters is quite different from their usual mobility, real-time human location data is necessary for precise estimation. Due to privacy concerns, real-time data is anonymized and a popular form of anonymization is population distribution. In this paper, we aim to estimate human mobility following an unprecedented disaster using such population distribution data. To overcome technical obstacles including high dimensionality, we propose novel particle filter by devising proposal distribution. Our proposal distribution provides states considering both prediction model and acquired observation. Therefore, particles maintain high likelihood. In the experiments, our methods realized more accurate estimation than the baselines, and its estimated mobility was consistent with the survey researches. The computational cost is significantly low enough for real-time operations. The GPS data collected on the day of the Great East Japan Earthquake is used for the evaluation.

Tide gauge observations along the coastline of Japan have recorded the land sinking due to the continuous subduction of the oceanic plates, indicating that stress and strain energies have been accumulating at the plate boundary, which would eventually cause large oceanic trench earthquakes like the 2011 Great East Japan Earthquake. The proposed method extracts such long-term activities of the Earth's crust together with rapid displacements related to earthquakes, even before the establishment of the global positioning system, from monthly mean data of the sea levels. A state space model decomposes the tide gauge time series into trend, seasonal, autoregressive and observation noise components, each of which are estimated using the particle filter algorithm. The spatial and temporal distributions of the extracted trend component clearly indicate high-risk regions, near which giant earthquakes have occurred or are predicted to occur. A multivariate analysis of the observatories located at the northeast coast of Japan successfully determines the past crustal displacement in the case of the 1978 Off-Miyagi Earthquake. The proposed method has the potential application for monitoring crustal activities related to the accumulation of earthquake energy.

MOTIVATION: Biochemical reactions in cells are made of several types of biological circuits. In current systems biology, making differential equation (DE) models simulatable in silico has been an appealing, general approach to uncover a complex world of biochemical reaction dynamics. Despite of a need for simulation-aided studies, our research field has yet provided no clear answers: how to specify kinetic values in models that are difficult to measure from experimental/theoretical analyses on biochemical kinetics. RESULTS: We present a novel non-parametric Bayesian approach to this problem. The key idea lies in the development of a Dirichlet process (DP) prior distribution, called Bayesian experts, which reflects substantive knowledge on reaction mechanisms inherent in given models and experimentally observable kinetic evidences to the subsequent parameter search. The DP prior identifies significant local regions of unknown parameter space before proceeding to the posterior analyses. This article reports that a Bayesian expert-inducing stochastic search can effectively explore unknown parameters of in silico transcription circuits such that solutions of DEs reproduce transcriptomic time course profiles. AVAILABILITY: A sample source code is available at the URL http://daweb.ism.ac.jp/~yoshidar/lisdas/.

In the kernel method, the appropriate selection or design of the kernel function is important for the construction of a high-performance classifier. The present paper describes a dynamic spectrum classification method using kernel classifiers with the divergence-based kernel and its application to the detection of worn-out banknotes. We introduce the divergence-based kernel that was proposed as a measure between two probability distributions into the dynamic spectrum classification. The present method is applied to the detection of worn-out banknotes by using acoustic signals for the facilitation of identifying counterfeit banknotes. As a result, the classification performance using the divergence-based kernel is shown to have better performance than those using common kernels such as the Gaussian kernel or the polynomial kernel.

MOTIVATION: Statistical inference of gene networks by using time-course microarray gene expression profiles is an essential step towards understanding the temporal structure of gene regulatory mechanisms. Unfortunately, most of the current studies have been limited to analysing a small number of genes because the length of time-course gene expression profiles is fairly short. One promising approach to overcome such a limitation is to infer gene networks by exploring the potential transcriptional modules which are sets of genes sharing a common function or involved in the same pathway. RESULTS: In this article, we present a novel approach based on the state space model to identify the transcriptional modules and module-based gene networks simultaneously. The state space model has the potential to infer large-scale gene networks, e.g. of order 10(3), from time-course gene expression profiles. Particularly, we succeeded in the identification of a cell cycle system by using the gene expression profiles of Saccharomyces cerevisiae in which the length of the time-course and number of genes were 24 and 4382, respectively. However, when analysing shorter time-course data, e.g. of length 10 or less, the parameter estimations of the state space model often fail due to overfitting. To extend the applicability of the state space model, we provide an approach to use the technical replicates of gene expression profiles, which are often measured in duplicate or triplicate. The use of technical replicates is important for achieving highly-efficient inferences of gene networks with short time-course data. The potential of the proposed method has been demonstrated through the time-course analysis of the gene expression profiles of human umbilical vein endothelial cells (HUVECs) undergoing growth factor deprivation-induced apoptosis. AVAILABILITY: Supplementary Information and the software (TRANS-MNET) are available at http://daweb.ism.ac.jp/~yoshidar/software/ssm/.

MOTIVATION: Mathematical modeling and simulation, based on biochemical rate equations, provide us a rigorous tool for unraveling complex mechanisms of biological pathways. To proceed to simulation experiments, it is an essential first step to find effective values of model parameters, which are difficult to measure from in vivo and in vitro experiments. Furthermore, once a set of hypothetical models has been created, any statistical criterion is needed to test the ability of the constructed models and to proceed to model revision. RESULTS: The aim of our research is to present a new statistical technology towards data-driven construction of in silico biological pathways. The method starts with a knowledge-based modeling with hybrid functional Petri net. It then proceeds to the Bayesian learning of model parameters for which experimental data are available. This process exploits quantitative measurements of evolving biochemical reactions, e.g. gene expression data. Another important issue that we consider is statistical evaluation and comparison of the constructed hypothetical pathways. For this purpose, we have developed a new Bayesian information-theoretic measure that assesses the predictability and the biological robustness of in silico pathways. AVAILABILITY: The FORTRAN source codes are available at the URL http://daweb.ism.ac.jpyoshidar/GDA/ SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

This article presents a static type system for the Java virtual machine (JVM) code that enforces an access control mechanism similar to that found in a Java implementation. In addition to verifying type consistency of a given JVM code, the type system statically verifies whether the code accesses only those resources that are granted by the prescribed access policy. The type system is proved to be sound with respect to an operational semantics that enforces access control dynamically, similar to Java stack inspection. This result ensures that “well-typed code cannot violate access policy.” The authors then develop a type inference algorithm and show that it is sound with respect to the type system. These results allow us to develop a static system for JVM access control, without resorting to costly runtime stack inspection.

Conventional sensors have been designed to minimize noise effects. Any sensor that is designed to detect a certain physical variable is influenced to a certain degree by other physical variables. This suggests that any sensor is potentially tap able of detecting multiple physical variables. In the present study, we consider sensing devices that are easily influenced by several physical variables and make full use of their multi-sensing characteristics through statistical signal processing and machine learning techniques with a wide variety of prior information. The proposed sensor design approach is completely different from the conventional approach with respect to system design and has advantages in terms of cost and system simplification compared to existing approaches. This new idea can be realized by developing a novel multiple-input/single-output sensor that can detect various variables such as pressure, acceleration, temperature and light emission by a single device. The sensor is applied to monitor the symptoms of tire, earthquake and break-in for the purpose of home security. The proposed security system consists of the following three steps: (1) detection of disaster by a probabilistic outlier detection procedure using an auto-regressive model, (2) disaster feature extraction by Kalman filter on a state space model, and (3) disaster classification by multiclass support vector machine.

SUMMARY: One of the significant challenges in gene expression analysis is to find unknown subtypes of several diseases at the molecular levels. This task can be addressed by grouping gene expression patterns of the collected samples on the basis of a large number of genes. Application of commonly used clustering methods to such a dataset however are likely to fail owing to over-learning, because the number of samples to be grouped is much smaller than the data dimension which is equal to the number of genes involved in the dataset. To overcome such difficulty, we developed a novel model-based clustering method, referred to as the mixed factors analysis. The ArrayCluster is a freely available software to perform the mixed factors analysis. It provides us some analytic tools for clustering DNA microarray experiments, data visualization and an automatic detector for module transcriptional of genes that are relevant to the calibrated molecular subtypes and so on.

This paper presents a static type system for JAVA Virtual Machine (JVM) code that enforces an access control mechanism similar to the one found, for example, in a JAVA implementation. In addition to verifying type consistency of a given JVM code, the type system statically verifies that the code accesses only those resources that are granted by the prescribed access policy. The type system is proved to be sound with respect to an operational semantics that enforces access control dynamically, similarly to JAVA stack inspection. This result ensures that "well typed code cannot violate access policy." The paper then develops a type inference algorithm and shows that it is sound with respect to the type system and that it always infers a minimal set of access privileges. These results allows us to develop a static system for JVM access control without resorting to costly runtime stack inspection.

We propose a type system for the Java bytecode language, prove the type soundness, and develop a type inference algorithm. In contrast to the existing proposals, our type system yields a typed term calculus similar to type systems of lambda calculi. This enables us to transfer existing techniques and results of type theory to a JVM-style bytecode language. We show that ML-style let polymorphism and recursive types can be used to type JVM subroutines, and that there is an ML-style type inference algorithm.The type inference algorithm has been implemented. The ability to verify type soundness is a simple corollary of the existence of type inference algorithm. Moreover, our type theoretical approach opens up various type safe extensions including higher-order methods, flexible polymorphic typing through polymorphic type inference, and type-preserving compilation.