Revisit Behavior in Social Media: The Phoenix-R Model and Discoveries

How many listens will an artist receive on a online radio? How about plays on a YouTube video? How many of these visits are new or returning users? Modeling and mining popularity dynamics of social activity has important implications for researchers, content creators and providers. We here investigate the effect of revisits (successive visits from a single user) on content popularity. Using four datasets of social activity, with up to tens of millions media objects (e.g., YouTube videos, Twitter hashtags or LastFM artists), we show the effect of revisits in the popularity evolution of such objects. Secondly, we propose the Phoenix-R model which captures the popularity dynamics of individual objects. Phoenix-R has the desired properties of being: (1) parsimonious, being based on the minimum description length principle, and achieving lower root mean squared error than state-of-the-art baselines; (2) applicable, the model is effective for predicting future popularity values of objects.Comment: To appear on European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 201

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

0n climatogy in Misasa spa (twentieth report)

我々は1956年以降,三朝温泉地の気候について観察を行っている｡今回は1997年1月1日から1997年12月末日までの1年間の気象観察資料を報告する｡機器の故障により,記録の一部に欠落部分のあることをお断りしておく｡Climatological data of the last 12monnths (1997.1.1-1997.12.31) obtained by the climate autorecording system at the Misasa Medical Branch in Misasa SPa, Tottori-ken, Japan are presented

Reverse pharmacological effect of loop diuretics and altered rBSC1 expression in rats with lithium nephropathy

Reverse pharmacological effect of loop diuretics and altered rBSC1 expression in rats with lithium nephropathy.BackgroundRenal urinary concentration is associated with enhanced expression of rBSC1, a rat sodium cotransporter, in the thick ascending limb of Henle. Increased expression of rBSC1 was reported recently in nephrogenic diabetes insipidus induced by lithium chloride (Li nephropathy). However, the pathophysiological implication of altered rBSC1 expression has not yet been investigated.MethodsLi nephropathy was induced in rats by an oral administration of 40 mmol lithium/kg dry food. In rats with reduced urinary osmolality to less than 300 mOsm/kg H2O, we examined the expression of rBSC1 mRNA and protein, plasma arginine vasopressin (AVP) and RNA expression of kidney-specific water channel, aquaporin-2 (AQP2), of collecting ducts. Rats with Li nephropathy were treated with furosemide (3 mg/kg body weight), which blocks the activity of rBSC1, and changes in urine concentration, plasma AVP, medullary accumulation of Li ions, and apical AQP2 expression were determined.ResultsRats with Li nephropathy showed increased rBSC1 RNA and protein expression and reduced AQP2 RNA. In these rats, furosemide, which induces dilution of urine and polyuria in normal rats, resulted in a progressive and significant rise in urine osmolality from 167 ± 11 (mean ± SD) at baseline to 450 ± 45 mOsm/kg H2O at three hours after administration, and significant oliguria. In the same rats, plasma AVP decreased significantly from 5.7 to 3.0 pg/mL. In addition, recovery of apical AQP2 expression was noted in a proportion of epithelial cells of the collecting ducts. Although Li+ in the renal medulla was slightly lower in rats with Li nephropathy treated with furosemide, statistical significance was not achieved.ConclusionsOur results suggest that dehydration or high plasma AVP results in an enhanced rBSC1 expression in Li nephropathy, and that rBSC1 expression is closely associated with the adverse effects of Li ions on collecting duct function

Transplantation of Bone Marrow-Derived Mononuclear Cells Improves Mechanical Hyperalgesia, Cold Allodynia and Nerve Function in Diabetic Neuropathy

Relief from painful diabetic neuropathy is an important clinical issue. We have previously shown that the transplantation of cultured endothelial progenitor cells or mesenchymal stem cells ameliorated diabetic neuropathy in rats. In this study, we investigated whether transplantation of freshly isolated bone marrow-derived mononuclear cells (BM-MNCs) alleviates neuropathic pain in the early stage of streptozotocin-induced diabetic rats. Two weeks after STZ injection, BM-MNCs or vehicle saline were injected into the unilateral hind limb muscles. Mechanical hyperalgesia and cold allodynia in SD rats were measured as the number of foot withdrawals to von Frey hair stimulation and acetone application, respectively. Two weeks after the BM-MNC transplantation, sciatic motor nerve conduction velocity (MNCV), sensory nerve conduction velocity (SNCV), sciatic nerve blood flow (SNBF), mRNA expressions and histology were assessed. The BM-MNC transplantation significantly ameliorated mechanical hyperalgesia and cold allodynia in the BM-MNC-injected side. Furthermore, the slowed MNCV/SNCV and decreased SNBF in diabetic rats were improved in the BM-MNC-injected side. BM-MNC transplantation improved the decreased mRNA expression of NT-3 and number of microvessels in the hind limb muscles. There was no distinct effect of BM-MNC transplantation on the intraepidermal nerve fiber density. These results suggest that autologous transplantation of BM-MNCs could be a novel strategy for the treatment of painful diabetic neuropathy

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

Real-time Forecasting of Co-evolving Epidemics

本論文では，大規模疫病データのための高速予測手法であるEpiCastについて述べる．EpiCastは，様々な地域の大規模疫病データストリームが与えられたときに，その中から疫病の特徴を表現，要約，共有し，長期的かつ継続的に将来の感染者数予測を行う．提案手法は(a)疫病の複雑な拡散過程を非線形モデルで表現し，(b)それらの中に含まれる重要な特徴を各地域で共有し，適切なモデルを選択することで，感染拡大予測を実現する．ここで，提案手法は(c)データストリームの長さに依存せず，一定の計算時間で感染者数を推定する．COVID-19の実データを用いた実験では，EpiCastが大規模疫病データストリームの中から疫病の重要な特徴を発見，共有することで感染者数を長期的に予測し，さらに，既存手法と比較し大幅な精度，性能向上を達成していることを確認した．Given a large collection of co-evolving epidemics, how can we forecast their future characteristics? In this paper, we propose a streaming algorithm, EpiCast, which is able to model, understand and forecast future epidemic outbreaks as well as pandemics. Our method has the following features for the effective and efficient modeling of the dynamics of spreading viruses. (a) Non-linear: we incorporate a non-linear equation that is suitable for complex epidemic modeling. (b) Dynamic: it maintains multiple such non-linear models to share important patterns among locations, and chooses the non-linear model for the forecast while monitoring a co-evolving epidemic data stream. (c) Scalable: it can quickly forecast future phenomena at any time in a practically constant time. In extensive experiments using real COVID-19 datasets over major countries, we demonstrate that our proposed method outperforms existing methods for time series in terms of forecasting accuracy, and significantly reduces the required computational time

Non-linear Mining of Social Activities in Tensor Streams

Web検索履歴等に代表される大規模時系列データは，時刻や地域，キーワードといった様々な情報とともに収集され，テンソルストリームとして扱うことができる．Web上におけるユーザアクティビティの解析では，より高精度な将来予測を実現することが重要な課題の1つであるが，複雑な構造を持つテンソルストリームから将来予測に有用なパターンを発見することが問題となる．本論文では，時間，国，キーワードの3つ組に対するWeb検索数で構成されるテンソルストリームを効果的に解析するためのストリームアルゴリズムであるCUBECASTを提案する．CUBECASTは与えられたテンソルストリームに含まれる潜在的な長期トレンドと季節パターンを発見し，それらを基に類似した特徴を持つ地域グループへと分解する．このとき，提案手法は次の特長を持つ．(a)長期トレンドと季節パターンの非線形特性を単一のモデルで表現する．(b)パラメータチューニングや事前知識を必要とせず，時系列モデルやパターン変化を自動的に推定する．(c)逐次的かつ適応的にパターン変化をとらえ，テンソルストリームを効率的に処理する．実データを用いた実験では，提案手法が将来予測に有用なパターンを効果的かつ効率的に発見できることを示し，既存の時系列予測手法と比較して，予測精度，計算時間の改善を確認した．Given a large time-evolving event series such as Google web-search logs, which are collected according to various aspects, i.e., timestamps, locations and keywords, how accurately can we forecast their future activities? How can we reveal significant patterns that allow us to long-term forecast from such complex tensor streams? In this paper, we propose a streaming method, namely, CUBECAST, that is designed to capture basic trends and seasonality in tensor streams and extract temporal and multi-dimensional relationships between such dynamics. Our proposed method has the following properties: (a) it is effective: it finds both trends and seasonality and summarizes their dynamics into simultaneous non-linear latent space. (b) it is automatic: it automatically recognizes and models such structural patterns without any parameter tuning or prior information. (c) it is scalable: it incrementally and adaptively detects shifting points of patterns for a semi-infinite collection of tensor streams. Extensive experiments that we conducted on real datasets demonstrate that our algorithm can effectively and efficiently find meaningful patterns for generating future values, and outperforms the state-of-the-art algorithms for time series forecasting in terms of forecasting accuracy and computational time

Automatic Network Structure-based Clustering of Multivariate Time Series

本論文では，ネットワーク構造を持つ多次元時系列データのためのパターン検出手法であるNGLについて述べる．NGLは，時間変化するネットワーク構造を持つ多次元時系列データが与えられたときに，その時系列データの中から重要なネットワーク構造を発見し，それらの情報を要約，表現する．具体的に，提案手法は，(a)多次元時系列データからネットワーク構造に基づいた解釈性の高いクラスタを発見する．(b)その際に最適な分割点とクラスタ数を自動的に決定する．すなわち，事前情報の付与が必要ない．そして，(c)自動決定アルゴリズムにより高精度なクラスタリングを実現する．人工データを用いた精度評価実験では最新の既存手法と比較して提案手法が大幅な精度向上を達成していることを明らかにした．また，実データを用いた実験ではNGLが解釈性の高いクラスタを発見していることを確認した．In this paper we present NGL, pattern mining algorithm for multiple time series data with underlying network structures. Our method has the following properties: (a) Interpretable: it provides interpretable network structures for the data; (b) Automatic: it determines the optimal cut points and the number of clusters automatically; (c) Accurate: it provides reliable clustering performance thanks to the automated algorithm. We evaluate our NGL algorithm on synthetic datasets, outperforming state-of-the-art baselines in terms of accuracy. And extensive experiments on real datasets demonstrate that NGL does indeed obtain interpretable network structure clusters