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This work is concerned with discovering the governing partial differential equation (PDE) of a physical system. Existing methods have demonstrated the PDE identification from finite observations but failed to maintain satisfying results against noisy data, partly owing to suboptimal estimated derivatives and found PDE coefficients. We address the issues by introducing a noise-aware physics-informed machine learning (nPIML) framework to discover the governing PDE from data following arbitrary distributions. We propose training a couple of neural networks, namely solver and preselector, in a multi-task learning paradigm, which yields important scores of basis candidates that constitute the hidden physical constraint. After they are jointly trained, the solver network estimates potential candidates, e.g., partial derivatives, for the sparse regression algorithm to initially unveil the most likely parsimonious PDE, decided according to the information criterion. We also propose the denoising physics-informed neural networks (dPINNs), based on Discrete Fourier Transform (DFT), to deliver a set of the optimal finetuned PDE coefficients respecting the noise-reduced variables. The denoising PINNs are structured into forefront projection networks and a PINN, by which the formerly learned solver initializes. Our extensive experiments on five canonical PDEs affirm that the proposed framework presents a robust and interpretable approach for PDE discovery, applicable to a wide range of systems, possibly complicated by noise., 13 pages, 8 figures, v2, v3: corrected typos and author names, v4, v5: improved notations

We propose a novel mining algorithm called cluster sequence mining (CSM) to extract cluster pairs with occurrence correlation from event sequence data. CSM extracts patterns with a pair of clusters that satisfies space proximity of the individual clusters and temporal proximity between events from different clusters in time intervals. CSM extends a unique co-occurring cluster mining (CCM) algorithm by considering the order of event occurrences and distribution of time intervals. The probability density of time intervals is inferred using Bayesian inference for robustness against uncertainty. To improve inference accuracy of the density function of time intervals, we utilize the idea of dynamic programming (DP) matching to obtain the correspondence between multiple event occurrences. With an experiment using synthetic data, we confirm that CSM is capable of extracting clusters with a high F-measure and low estimation error of the time interval distribution even under uncertainty. In addition, we find that DP matching can improve the inference accuracy of the density function of time intervals. Finally, CSM is applied to a real-world acoustic emission event sequence data set to evaluate damage interactions in a fuel cell.

Automated mental health analysis of stress could lead towards diagnosis tools that can be used in environments such as clinics, schools and corporations. However, attempts at building general models are often limited by the subjectivity of physiological stress responses. This work aims to discover the effects of combining data from physiological signals and psychological context from work activities when building a machine-learned model of mental stress. A software application was built to guide subjects through a monitoring process which allowed pre and post-assessment of psychological context through various stress-related annotation modules including the Cohen Stress Scale and the COPE inventory. Meanwhile, wearable sensors tracked physiological data in the form of heart beats. Tests were performed on this data by building supervised and unsupervised machine-learned models. Results show a general increase in classification performance when psychological context data is integrated into the models. Furthermore, models present similar performance using either questionnaire answers or coping profile scores.

Solid oxide fuel cell (SOFC) is an efficient generator and researched for practical use. However, one of the problems is the durability. In this study, we research the mechanical correlations among components of SOFC by analyzing the co-occurrence of acoustic emission (AE) events which are caused by damage. Then we proposed a novel method for mining patterns from the numerical data such as AE. The conventional method has possible problems when mining patterns from the numerical data. In the clustering, clusters may contain data which does not contribute a certain pattern, or may not contain data which contribute a pattern. On the other hand, the proposed method extracts patterns of two clusters considering co-occurrence between clusters and similarity within each cluster at the same time. In addition, the dendrogram obtained from hierarchical clustering is utilized for the reduction of search space. First, we evaluate the performance of proposed method with artificial data, and demonstrate that we can obtain appropriate clusters corresponding to patterns. Then, we apply the proposed method to AE data, and the damage patterns which represent the major mechanical correlations were extracted. We can acquire novel knowledge about damage mechanism of SOFC from the results.

Traffic jam is one of critical issues in urban life. By which, many social problems, for example, time loss, economical loss, and environmental pollution are caused. There are two typical methods for solving traffic jam, improvement of car navigation system and control of traffic lights. We focus on control of traffic lights. Existing traffic light control system is basically centralized control type and lacks robustness and scalability. If the central computer becomes breakdown, all traffic lights received the damage of it. In this paper, we propose a new traffic light control system based on multi-agent model. The offset value, one of the main traffic light parameters, is controlled by using only local information, and green-wave formation is formed through the coordination of each intersection agent.

To realize an effective ITS(Intelligent Transport Systems) services, such as a traffic jam prediction system or car navigation system, the traffic information like average traffic speed is indispensable. However, current systems providing traffic information have serious problems about lack of data. Hence, we construct a system which provides traffic information, which complements lack data using incomplete probe and VICS(Vehicle Information and Communication System) data. The system utilizes multi-information such as real time/stored/diffusion/succession information effectively. We verified the performance of the system through experiments using probe/VICS data of Nagoya city, and confirmed beneficial results.

This paper presents a technology for over the air (OTA) updating that allows software updates in consumer devices such as mobile phones and car-navigation systems that are connected to networks, and it adopts NAND flash memories and demand paging technologies. Software updates for these kinds of consumer devices are carried out using the binary difference that minimizes the amount of update data, software construction technologies that minimize the rewriting of flash memories, and fast compression technologies that enables rapid rewriting. This paper focuses on byte pair encoding (BPE), which is one of the most efficient methods of compressing program code in consumer devices that adopts NAND flash memories and demand paging technologies. We describe a fast method of compressing BPE and evaluating it.

This research investigates the use of emotion data derived from analyzing change in activity in the autonomic nervous system (ANS) as revealed by brainwave production to support the creative music compositional intelligence of an adaptive interface. A relational model of the influence of musical events on the listener's affect is first induced using inductive logic programming paradigms with the emotion data and musical score features as inputs of the induction task. The components of composition such as interval and scale, instrumentation, chord progression and melody are automatically combined using genetic algorithm and melodic transformation heuristics that depend on the predictive knowledge and character of the induced model. Out of the four targeted basic emotional states, namely, stress, joy, sadness, and relaxation, the empirical results reported here show that the system is able to successfully compose tunes that convey one of these affective states.

To gain a better understanding of how neural ensembles communicate and process information, neural decoding algorithms are used to extract information encoded in their spiking activity. Bayesian decoding is one of the most used neural population decoding approaches to extract information from the ensemble spiking activity of rat hippocampal neurons. Recently it has been shown how Bayesian decoding can be implemented without the intermediate step of sorting spike waveforms into groups of single units. Here we extend the approach in order to make it suitable for online encoding/decoding scenarios that require real-time decoding such as brain-machine interfaces. We propose an online algorithm for the Bayesian decoding that reduces the time required for decoding neural populations, resulting in a real-time capable decoding framework. More specifically, we improve the speed of the probability density estimation step, which is the most essential and the most expensive computation of the spike-sorting-less decoding process, by developing a kernel density compression algorithm. In contrary to existing online kernel compression techniques, rather than optimizing for the minimum estimation error caused by kernels compression, the proposed method compresses kernels on the basis of the distance between the merging component and its most similar neighbor. Thus, without costly optimization, the proposed method has very low compression latency with a small and manageable estimation error. In addition, the proposed bandwidth matching method for Gaussian kernels merging has an interesting mathematical property whereby optimization in the estimation of the probability density function can be performed efficiently, resulting in a faster decoding speed. We successfully applied the proposed kernel compression algorithm to the Bayesian decoding framework to reconstruct positions of a freely moving rat from hippocampal unsorted spikes, with significant improvements in the decoding speed and acceptable decoding error., National Institute of Mental Health (U.S.) (Grant MH-061976), United States. Office of Naval Research (MURI N00014-10-1-0936 Grant), Japan Society for the Promotion of Science (Strategic Young Researcher Overseas Visits Program for Accelerating Brain Circulation)

To have an instructional plan guide the learning process is significant to various teaching styles and an important task in an ITS. Though various approaches have been used to tackle this task, the compelling need is for an ITS to improve on its own the plans established in a dynamic way. We hypothesize that the use of knowledge derived from student categories can significantly support the improvement of plans on the part of the ITS. This means that category knowledge can become effectors of effective plans. We have conceived a Category-based Self-improving Planning Module (CSPM) for an ITS tutor agent that utilizes the knowledge learned from learner categories to support self-improvement. The learning framework of CSPM employs unsupervised machine learning and knowledge acquisition heuristics for learning from experience. We have experimented on the feasibility of CSPM using recorded teaching scenarios.

Knowledge acquisition with machine learning techniques is a fundamental requirement for knowledge discovery from databases and data mining systems. Two techniques in particular -- inductive learning and theory revision -- have been used toward this end. A method that combines both approaches to effectively acquire theories (regularity) from a set of training examples is presented. Inductive learning is used to acquire new regularity from the training examples; and theory revision is used to improve an initial theory. In addition, a theory preference criterion that is a combination of the MDL-based heuristic and the Laplace estimate has been successfully employed in the selection of the promising theory. The resulting algorithm developed by integrating inductive learning and theory revision and using the criterion has the ability to deal with complex problems, obtaining useful theories in terms of its predictive accuracy.

With the development of computer techniques, active mining which is a combination of active information gathering, user-centered mining and active user reaction has played an important role in the success of mining novel knowledge from data. Active information gathering is the technique which aims at effectively searching relevant information and conducting preprocess required before the user-centered mining process. Even though there are a large number of researches concerning the problem of mining biomedical literature databases, the importance of active information gathering in such kinds of researches has not been mentioned so far. In this paper, we consider the problem of selecting the articles of experts' interest from a literature database by making use of existing databases and machine learning techniques. This problem could be considered as an active information gathering problem and is useful from the viewpoint of active mining prospect. The results show the effectiveness of making use of existing databases in terms of reducing the number of training data required for the learning system while maintaining the quality of the obtained documents.

Detecting and diagnosing errors in novice behavior is an important student modeling task. In this paper, we describe MEDD, an unsupervised incremental multistrategy system for the discovery of classes of errors from, and their detection in, novice programs. Experimental results show that MEDD can effectively detect and discover misconceptions and other knowledge-level errors that underlie novice Prolog programs, even when multiple errors are enmeshed together in a single program, and when the programs are presented to MEDD in a different order.

Inductive logic programming (ILP) is concerned with the induction of logic programs from examples and background knowledge. In ILP, the shift of attention from program synthesis to knowledge discovery resulted in advanced techniques that are practically applicable for discovering knowledge in relational databases. This paper gives a brief introduction to ILP, presents selected ILP techniques for relational knowledge discovery and reviews selected ILP applications.

In this paper, we examine the issue of learning multiple predicates from given training examples. A proposed MPL-CORE algorithm efficiently induces Horn clauses from examples and background knowledge by employing a single predicate learning module CORE. A fast failure mechanism is also proposed which contributes learning effiency and learnability to the algorithm. MPL-CORE employs background knowledge that can be represented in intensional (Horn clauses) or extensional (ground atoms) forms during its learning process. With the fast failure mechanism, MPL-CORE outperforms previous multiple predicate learning systems in both the computational complexity and learnability.

Self-regulation is an important skill for students to possess. It allows them to learn more effectively and it has been shown to cause better learning gains. Self-regulation is not an easy task especially for poor learners. This is the motivation behind researches that use computer-based learning environments to promote self-regulation through embedded tools that help students keep track of their self-regulation processes. Although these researches have shown promising results, they focus on self-regulation processes inside controlled learning environments. Not much research has been done on learning in unsupervised learning environments where students learn on their own, introducing additional challenges. In this research, we developed software to help students perform self-regulation in this setting. Results showed that the software was able to help students set goals, monitor their activities and evaluate their learning behavior. Students who used the software reported that it made them more aware of the activities they did when they were learning and it also helped them identify what to do in order to improve their learning behavior in succeeding learning sessions.

This paper presents an over-the-air method for updating the software of consumer devices such as mobile phones and car navigation systems. Software update is carried out using binary difference technology, software construction technology, and fast compression technology. In this paper, we focus on a fast BPE (byte pair encoding) method of program codes on devices and obtain good results using it.

We propose a visual computation model called theBox and Plane Model (BPM), which visually clarifies the semantics of backtracking, the cut operator, and side-effects, thus allowing the procedural features of Prolog to be grasped. On the bases of the BPM, we developed a visual debugger for Prolog, PROEDIT2, which has proved that this kind of pragmatic computation model for Prolog increases the efficiency of the debugging work.

The authors aim at constructing an agent that learns appropriate actions in a Multi-Agent environment with and without social dilemmas. The agent ought to voluntarily give up its profit in a dilemma situation and it should keep its profit in another situation. We divide the environment into three situations and introduce reward-handling manners for learning actions, which are effective in each situation. Since the agent must select an effective manner for the situation, the authors contrive criteria for recognizing the situation. This paper shows that the agent having the manners and the criteria acts well in two of the three Multi-Agent situations composed of homogeneous agents.