Duration and Interval Hidden Markov Model for Sequential Data Analysis

Analysis of sequential event data has been recognized as one of the essential tools in data modeling and analysis field. In this paper, after the examination of its technical requirements and issues to model complex but practical situation, we propose a new sequential data model, dubbed Duration and Interval Hidden Markov Model (DI-HMM), that efficiently represents "state duration" and "state interval" of data events. This has significant implications to play an important role in representing practical time-series sequential data. This eventually provides an efficient and flexible sequential data retrieval. Numerical experiments on synthetic and real data demonstrate the efficiency and accuracy of the proposed DI-HMM

拡張隠れセミマルコフモデルによる複数系列データモデリングとデータ収集・管理手法

In recent years, with the development of devices and the development of data aggregation methods, data to be analyzed and aggregating methods have been changed. Regarding the environment of Internet of Things (IoT), sensors or devices are connected to the communication terminal as access point or mobile phone and the terminal aggregate the sensing data and upload them to the cloud server. From the viewpoint of analysis, the aggregated data are sequential data and the grouped sequence is a meaningful set of sequences because the group represents the owner\u27s information. However, most of the researches for sequential data analysis are specialized for the target data, and not focusing on the "grouped" sequences. In addition from the viewpoint of aggregation, it needs to prepare the special terminals as an access point. The preparation of the equipment takes labor and cost. To analyze the "grouped" sequence and aggregate them without any preparation, this paper aims to realize the analysis method for grouped sequences and to realize the aggregation environment virtually. For analysis of grouped sequential data, we firstly analyze the grouped sequential data focusing on the event sequences and extract the requirements for their modeling. The requirements are (1) the order of events, (2) the duration of the event, (3) the interval between two events, and (4) the overlap of the event. To satisfy all requirements, this paper focuses on the Hidden Semi Markov Model (HSMM) as a base model because it can model the order of events and the duration of event. Then, we consider how to model these sequences with HSMM and propose its extensions. For the former consideration, we propose two models; duration and interval hidden semi-Markov model and interval state hidden-semi Markov model to satisfy both the duration of event and the interval between events simultaneously. For the latter consideration, we consider how to satisfy all requirements including the overlap of the events and propose a new modeling methodology, over-lapped state hidden semi-Markov model. The performance of each method are shown compared with HSMM from the view point of the training and recognition time, the decoding performance, and the recognition performance in the simulation experiment. In the evaluation, practical application data are also used in the simulation and it shows the effectiveness. For the data aggregation, most of conventional approaches for aggregating the grouped data are limited using pre-allocated access points or terminals. It can obtain the grouped data stably, but it needs to additional cost to allocate such terminals in order to aggregate a new group of sequences. Therefore, this paper focus on "area based information" as a target of the grouped sequences, and propose an extraordinary method to store such information using the storage of the terminals that exist in the area. It realize the temporary area based storage virtually by relaying the information with existing terminals in the area. In this approach, it is necessary to restrict the labor of terminals and also store the information as long as possible. To control optimally while the trade-off, we propose methods to control the relay timing and the size of the target storage area in ad hoc dynamically. Simulators are established as practical environment to evaluate the performance of both controlling method. The results show the effectiveness of our method compared with flooding based relay control. As a result of above proposal and evaluation, methods for the grouped sequential data modeling and its aggregation are appeared. Finally, we summarize the research with applicable examples.電気通信大学201

拡張隠れセミマルコフモデルによる複数系列データモデリングとデータ収集・管理手法

電気通信大学201

Chat agents respond more empathetically by using hearsay experience

As the responses of chat dialogue systems have become more natural, the empathy skill of dialogue systems has become an important new issue. In text-based chat dialogue systems, the definition of empathy is not precise, and how to design the kind of utterance that improves the user’s impression of receiving empathy is not clear since the main method used is to imitate utterances and dialogues that humans consider empathetic. In this study, we focus on the necessity of grasping an agent as an experienceable Other, which is considered the most important factor when empathy is performed by an agent, and propose an utterance design that directly conveys the fact that the agent can experience and feel empathy through text. Our system has an experience database including the system’s pseudo-experience and feelings to show empathetic feelings. Then, the system understands the user’s experiences and empathizes with the user on the basis of the system’s experience database, in line with the dialogue content. As a result of developing and evaluating several systems with different ways of conveying the aforementioned rationale, we found that conveying the rationale as a hearsay experience improved the user’s impression of receiving empathy more than conveying it as the system’s own experience. Moreover, an exhaustive evaluation shows that our empathetic utterance design using hearsay experience is effective to improve the user’s impression about the system’s cognitive empathy

Lectin blot analysis of IgA1 purified from mIgA-MIDD serum under reducing and non-reducing conditions.

<p>IgA1 purified from mIgA-MIDD serum was pretreated in SDS buffer with or without 2-ME, separated by SDS-PAGE under non-reducing (lane 1) or reducing (lane 2) conditions, and then subjected to western blotting with an anti-IgA1 mAb (A), ConA (B), and WFA (C). Cross-reacting bands were detected using Konica immunostaining kit (Konica, Tokyo, Japan) for anti-IgA1 mAb and ConA and Western Lightning Chemiluminescence Plus (Perkin-Elmer, Boston, MA) for WFA. A Gal deficient IgA (lane 3), enzymatically deglycosylated with neuraminidase and galactosidase, was used as a positive control for WFA lectin. mIgA-MIDD, monoclonal immunoglobulin deposition disease associated with monoclonal IgA; 2-ME, 2-mercaptoethanol; ConA, jack bean lectin concanavalin A; WFA, Wisteria floribunda agglutinin.</p

Differential glycan profiles of PNGase F-treated IgA1 of mIgA-MIDD.

<p>IgA1 purified from mIgA-MIDD serum was incubated with (left) or without (right) PNGase F. After digestion, the reaction mixture was labeled with Cy3-SE and subjected to the lectin microarray. The relative intensity of each lectin was normalized to the maximum fluorescence intensity. mIgA-MIDD, monoclonal immunoglobulin deposition disease associated with monoclonal IgA; PNGase F, peptide N-glycosidase F.</p

IgA Nephropathy Caused by Unusual Polymerization of IgA1 with Aberrant N-Glycosylation in a Patient with Monoclonal Immunoglobulin Deposition Disease

<div><p>Immunoglobulin A nephropathy (IgAN) is a form of chronic glomerulonephritis characterized by the deposition of IgA immune complexes in the glomerular region. The cause of IgAN is unknown, but multiple mechanisms have been suggested. We previously reported a rare case of mesangioproliferative glomerulonephritis in a patient with monoclonal immunoglobulin deposition disease associated with monoclonal IgA1. In this study, we performed the detailed analyses of serum IgA1 from this patient in comparison with those from patients with mIgA plasma cell disorder without renal involvement and healthy volunteers. We found unusual polymerization of IgA1 with additional <i>N</i>-glycosylation distinctive in this patient, which was different from known etiologies. Glycan profiling of IgA1 by the lectin microarray revealed an intense signal for <i>Wisteria floribunda</i> agglutinin (WFA). This signal was reduced by disrupting the native conformation of IgA1, suggesting that the distinct glycan profile was reflecting the conformational alteration of IgA1, including the glycan conformation detected as additional <i>N</i>-glycans on both the heavy and light chains. This unusually polymerized state of IgA1 would cause an increase of the binding avidity for lectins. WFA specifically recognized highly polymerized and glycosylated IgA1. Our results of analysis in the rare case of a patient with monoclonal immunoglobulin deposition disease suggest that the formation of unusually polymerized IgA1 is caused by divergent mechanisms including multiple structural alterations of glycans, which contributes to IgA1 deposition and mesangium proliferation.</p></div