Mining Reaction and Diffusion Dynamics in Social Activities

Large quantifies of online user activity data, such as weekly web search volumes, which co-evolve with the mutual influence of several queries and locations, serve as an important social sensor. It is an important task to accurately forecast the future activity by discovering latent interactions from such data, i.e., the ecosystems between each query and the flow of influences between each area. However, this is a difficult problem in terms of data quantity and complex patterns covering the dynamics. To tackle the problem, we propose FluxCube, which is an effective mining method that forecasts large collections of co-evolving online user activity and provides good interpretability. Our model is the expansion of a combination of two mathematical models: a reaction-diffusion system provides a framework for modeling the flow of influences between local area groups and an ecological system models the latent interactions between each query. Also, by leveraging the concept of physics-informed neural networks, FluxCube achieves high interpretability obtained from the parameters and high forecasting performance, together. Extensive experiments on real datasets showed that FluxCube outperforms comparable models in terms of the forecasting accuracy, and each component in FluxCube contributes to the enhanced performance. We then show some case studies that FluxCube can extract useful latent interactions between queries and area groups.Comment: Accepted by CIKM 202

Personalized Purchase Prediction of Market Baskets with Wasserstein-Based Sequence Matching

Personalization in marketing aims at improving the shopping experience of customers by tailoring services to individuals. In order to achieve this, businesses must be able to make personalized predictions regarding the next purchase. That is, one must forecast the exact list of items that will comprise the next purchase, i.e., the so-called market basket. Despite its relevance to firm operations, this problem has received surprisingly little attention in prior research, largely due to its inherent complexity. In fact, state-of-the-art approaches are limited to intuitive decision rules for pattern extraction. However, the simplicity of the pre-coded rules impedes performance, since decision rules operate in an autoregressive fashion: the rules can only make inferences from past purchases of a single customer without taking into account the knowledge transfer that takes place between customers. In contrast, our research overcomes the limitations of pre-set rules by contributing a novel predictor of market baskets from sequential purchase histories: our predictions are based on similarity matching in order to identify similar purchase habits among the complete shopping histories of all customers. Our contributions are as follows: (1) We propose similarity matching based on subsequential dynamic time warping (SDTW) as a novel predictor of market baskets. Thereby, we can effectively identify cross-customer patterns. (2) We leverage the Wasserstein distance for measuring the similarity among embedded purchase histories. (3) We develop a fast approximation algorithm for computing a lower bound of the Wasserstein distance in our setting. An extensive series of computational experiments demonstrates the effectiveness of our approach. The accuracy of identifying the exact market baskets based on state-of-the-art decision rules from the literature is outperformed by a factor of 4.0.Comment: Accepted for oral presentation at 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2019

DIFFERENCES IN STROKE TECHNIQUE TO EXERT HAND PROPULSION BEWEEN ADVANCED AND INTERMEDIATE SWIMMERS

The aim of this study was to investigate differences in hand propulsion exerted by advanced and intermediate swimmers during insweep and upsweep phases in the front crawl stroke. Swimmers wore pressure sensors on their hands while performing the front crawl stroke in the swimming pool where a motion capture system was set up. The hand propulsive drag (PD) and lift (PL) were estimated during the two phases. The advanced swimmers exerted more PD than PL (70% vs 30%) during the insweep phase and used a similar amount of PD to PL in the upsweep phase. The intermediate swimmers used a similar amount of PD to PL in the insweep phase and exerted more PD than PL in the upsweep phase (65% vs 35%). The advanced swimmers used the different technique to exert hand propulsion in the two phases as compared to the intermediate ones

HOW ELITE SWIMMERS CONTROL THEIR HAND PROPULSIVE FORCE AND ARM COORDINATION WITH INCREASING VELOCITY DURING FRONT CRAWL

The purpose of this study was to investigate the change in the intensity and timing of the hand propulsive force by using pressure sensor and motion capture systems as increasing velocity during front crawl swimming. Twelve elite swimmers participated in this study. The swimmers swam three different velocity; i.e. 70%, 80% and 900h of maximal velocity. The propulsive force of both hands were recorded by multiple pressure sensors, and whole body kinematics was measured by using motion capture system. The average propulsive force during the pull and push phase, and thus, total stroke cycle increased as increasing swimming velocity. The non-propulsive phase decreased as increasing swimming velocity. Swimmers increase their swimming velocity with both increasing their hand propulsive force and decreasing their arm non-propulsive duration during a stroke cycle of front crawl swimming

Online multiscale dynamic topic models

We propose an online topic model for sequentially analyzing the time evolution of topics in document collections. Topics naturally evolve with multiple timescales. For example, some words may be used consistently over one hundred years, while other words emerge and disappear over periods of a few days. Thus, in the proposed model, current topicspecific distributions over words are assumed to be generated based on the multiscale word distributions of the previous epoch. Considering both the long-timescale dependency as well as the short-timescale dependency yields a more robust model. We derive efficient online inference procedures based on a stochastic EM algorithm, in which the model is sequentially updated using newly obtained data; this means that past data are not required to make the inference. We demonstrate the effectiveness of the proposed method in terms of predictive performance and computational efficiency by examining collections of real documents with timestamps

Meiotic cohesins modulate chromosome compaction during meiotic prophase in fission yeast

The meiotic cohesin Rec8 is required for the stepwise segregation of chromosomes during the two rounds of meiotic division. By directly measuring chromosome compaction in living cells of the fission yeast Schizosaccharomyces pombe, we found an additional role for the meiotic cohesin in the compaction of chromosomes during meiotic prophase. In the absence of Rec8, chromosomes were decompacted relative to those of wild-type cells. Conversely, loss of the cohesin-associated protein Pds5 resulted in hypercompaction. Although this hypercompaction requires Rec8, binding of Rec8 to chromatin was reduced in the absence of Pds5, indicating that Pds5 promotes chromosome association of Rec8. To explain these observations, we propose that meiotic prophase chromosomes are organized as chromatin loops emanating from a Rec8-containing axis: the absence of Rec8 disrupts the axis, resulting in disorganized chromosomes, whereas reduced Rec8 loading results in a longitudinally compacted axis with fewer attachment points and longer chromatin loops

Histamine-1 receptor is not required as a downstream effector of orexin-2 receptor in maintenance of basal sleep/wake states

金沢大学医薬保健研究域医学系Aim: The effect of orexin on wakefulness has been suggested to be largely mediated by activation of histaminergic neurones in the tuberomammillary nucleus (TMN) via orexin receptor-2 (OX2R). However, orexin receptors in other regions of the brain might also play important roles in maintenance of wakefulness. To dissect the role of the histaminergic system as a downstream mediator of the orexin system in the regulation of sleep/wake states without compensation by the orexin receptor-1 (OX1R) mediated pathways, we analysed the phenotype of Histamine-1 receptor (H1R) and OX 1R double-deficient (H1R-/-;OX 1R-/-) mice. These mice lack OX1R-mediated pathways in addition to deficiency of H1R, which is thought to be the most important system in downstream of OX2R. Methods: We used H 1R deficient (H1R-/-) mice, H1R -/-;OX1R-/- mice, OX1R and OX 2R double-deficient (OX1R-/-;OX 2R-/-) mice, and wild type controls. Rapid eye movement (REM) sleep, non-REM (NREM) sleep and awake states were determined by polygraphic electroencephalographic/electromyographic recording. Results: No abnormality in sleep/wake states was observed in H1R-/- mice, consistent with previous studies. H1R-/-;OX 1R-/- mice also showed a sleep/wake phenotype comparable to that of wild type mice, while OX1R-/-; OX 2R-/- mice showed severe fragmentation of sleep/wake states. Conclusion: Our observations showed that regulation of the sleep/wake states is completely achieved by OX2R-expressing neurones without involving H1R-mediated pathways. The maintenance of basal physiological sleep/wake states is fully achieved without both H1 and OX1 receptors. Downstream pathways of OX2R other than the histaminergic system might play an important role in the maintenance of sleep/wake states. © 2009 Scandinavian Physiological Society

Real-time Forecasting of Time-evolving Controlled Sequence

本論文では，大規模制御応答時系列データストリームにおける制御量予測手法であるC-Castについて述べる．C-Castは，制御量（Controlled sequence），動作信号，操作量の三要素で構成される制御応答時系列データから，制御量の時系列パターンをとらえることで，パターン間の遷移に基づく高速な制御量予測を実現する．より具体的には，動作信号および操作量を考慮できるように動的システムを拡張し，制御応答時系列データを適応型動的システムとしてモデル化することで，重要なパターンや複雑なパターンの遷移を柔軟に表現する．提案手法は，(a)制御応答時系列データストリームから重要な特徴を発見し，刻々と変化していく潜在的なパターンやパターン遷移を高速かつ自動的に認識し，(b)将来的な制御量予測を実現する．さらに，提案手法は(c)データストリームの長さに依存しない．実データを用いた実験では，提案手法が制御応答時系列データストリームの中から重要な時系列パターンを発見し，制御量予測を高精度に行うことを確認した．さらに，最新の既存手法と比較し大幅な精度向上を達成し，その計算速度はデータサイズに依存せず，高速に動作することを明らかにした．Given a large collection of complex data sequences of control response, which consists of multiple attributes (e.g., Controlled sequence, Operation signal, Manipulated sequence), how can we effectively predict future controlled sequence? In this paper, we present C-Cast, an efficient and effective method for forecasting time-evolving data streams of control response. Our proposed method has the following properties: (a) Adaptive: it captures important time-evolving patterns and discontinuity in time-evolving data streams of control response. (b) Effective: it enables real-time controlled sequence forecasting. (c) Scalable: our algorithm does not depend on data size, and thus is applicable to very large sequences. Extensive experiments on a real dataset demonstrate that C-Castconsistently outperforms the best existing state-of-the-art methods as regards accuracy, and the execution speed is sufficiently fast