scholarly journals Continuous-Time Infinite Dynamic Topic Models

2014 ◽  
pp. 187-222 ◽  
Author(s):  
Wesam Elshamy ◽  
William H. Hsu
Keyword(s):  
Continuous Time ◽  
Dirichlet Process ◽  
Probabilistic Models ◽  
Topic Model ◽  
Topic Models ◽  
Process Models ◽  
Time Varying ◽  
Time Dynamic ◽  
Probabilistic Topic Model ◽  
Topic Structure

Topic models are probabilistic models for discovering topical themes in collections of documents. These models provide us with the means of organizing what would otherwise be unstructured collections. The first wave of topic models developed was able to discover the prevailing topics in a big collection of documents spanning a period of time. These time-invariant models were not capable of modeling 1) the time varying number of topics they discover and 2) the time changing structure of these topics. Few models were developed to address these two deficiencies. The online-hierarchical Dirichlet process models the documents with a time varying number of topics, and the continuous-time dynamic topic model evolves topic structure in continuous-time. In this chapter, the authors present the continuous-time infinite dynamic topic model that combines the advantages of these two models. It is a probabilistic topic model that changes the number of topics and topic structure over continuous-time.

scholarly journals Detection of Cases of Noncompliance to Drug Treatment in Patient Forum Posts: Topic Model Approach (Preprint)

2017 ◽  
Author(s):  
Redhouane Abdellaoui ◽  
Pierre Foulquié ◽  
Nathalie Texier ◽  
Carole Faviez ◽  
Anita Burgun ◽  
...  
Keyword(s):  
Social Media ◽  
Virtual Communities ◽  
Latent Dirichlet Allocation ◽  
Topic Model ◽  
Antidepressant Drug ◽  
Topic Models ◽  
Web Crawler ◽  
Probabilistic Topic Model ◽  
Manual Review ◽  
Model Approach

BACKGROUND Medication nonadherence is a major impediment to the management of many health conditions. A better understanding of the factors underlying noncompliance to treatment may help health professionals to address it. Patients use peer-to-peer virtual communities and social media to share their experiences regarding their treatments and diseases. Using topic models makes it possible to model themes present in a collection of posts, thus to identify cases of noncompliance. OBJECTIVE The aim of this study was to detect messages describing patients’ noncompliant behaviors associated with a drug of interest. Thus, the objective was the clustering of posts featuring a homogeneous vocabulary related to nonadherent attitudes. METHODS We focused on escitalopram and aripiprazole used to treat depression and psychotic conditions, respectively. We implemented a probabilistic topic model to identify the topics that occurred in a corpus of messages mentioning these drugs, posted from 2004 to 2013 on three of the most popular French forums. Data were collected using a Web crawler designed by Kappa Santé as part of the Detec’t project to analyze social media for drug safety. Several topics were related to noncompliance to treatment. RESULTS Starting from a corpus of 3650 posts related to an antidepressant drug (escitalopram) and 2164 posts related to an antipsychotic drug (aripiprazole), the use of latent Dirichlet allocation allowed us to model several themes, including interruptions of treatment and changes in dosage. The topic model approach detected cases of noncompliance behaviors with a recall of 98.5% (272/276) and a precision of 32.6% (272/844). CONCLUSIONS Topic models enabled us to explore patients’ discussions on community websites and to identify posts related with noncompliant behaviors. After a manual review of the messages in the noncompliance topics, we found that noncompliance to treatment was present in 6.17% (276/4469) of the posts.

scholarly journals Booms and Busts in the Oil Market: Identifying Speculative Bubbles Using a Continuous-Time Dynamic System

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Kaizhi Yu ◽  
Yun Zhang
Keyword(s):  
Continuous Time ◽  
Oil Prices ◽  
Time Varying ◽  
Continuous Time Model ◽  
Time Model ◽  
Time Dynamic ◽  
Speculative Bubbles ◽  
Economic Fundamentals ◽  
Oil Market ◽  
Popular View

The sharp changes in oil prices since 2004 featured a nonlinear data-generating mechanism which displayed bubble-like behavior. A popular view is that such a salient pattern cannot be explained by shifts in economic fundamentals, but was driven by speculative bubbles as a consequence of the increased financialization of oil future markets. Testing this hypothesis, however, is challenging since the fundamental component of the oil price is unobservable. This paper attempts to isolate the contribution of speculative bubbles and fundamentals to the evolution of oil prices by providing a stylized model of commodity pricing. Motivated by our theoretical model, we adopt a continuous-time model with a random and time-varying persistence parameter to empirically investigate the presence of speculative bubbles in daily oil future prices over the period April 1983 to June 2020. We do not find any evidence in favor of speculative bubbles, although we indeed find that oil prices exhibit episodes of unstable behavior after 2004.

scholarly journals Regularization of multilingual topic models

2015 ◽  
pp. 26-38
Author(s):  
М.А. Дударенко
Keyword(s):  
Topic Model ◽  
Information Source ◽  
Topic Models ◽  
Bilingual Dictionary ◽  
Bilingual Dictionaries ◽  
Probabilistic Topic Model ◽  
Word Translation ◽  
Cross Lingual ◽  
Cross Language

Предлагается многоязычная вероятностная тематическая модель, одновременно учитывающая двуязычный словарь и связи между документами параллельной или сравнимой коллекции. Для комбинирования этих двух видов информации применяется аддитивная регуляризация тематических моделей (ARTM). Предлагаются два способа использования двуязычного словаря: первый учитывает только сам факт связи между словами--переводами, во втором настраиваются вероятности переводов в каждой теме. Качество многоязычных моделей измеряется на задаче кросс-язычного поиска, когда запросом является документ на одном языке, а поиск производится среди документов другого языка. Показано, что комбинированный учет слов--переводов из двуязычного словаря и связанных документов улучшает качество кросс-язычного поиска по сравнению с моделями, использующими только один тип информации. Сравнение разных методов включения в модель двуязычных словарей показывает, что оценивание вероятностей переводов не только улучшает качество модели, но и позволяет находить тематический контекст для пар слово--перевод. A multilingual probabilistic topic model based on the additive regularization ARTM allowing to combine both a parallel or comparable corpus and a bilingual translation dictionary is proposed. Two approaches to include information from a bilingual dictionary are discussed: the first one takes into account only the fact of connection between word translations, whereas the second one learns the translation probabilities for each topic. To measure the quality of the proposed multilingual topic model, a cross-language search is performed. For each query document in one language, it is found its translation on another language. It is shown that the combined translation of words from a bilingual dictionary and the corresponding connected documents improves the cross-lingual search compared to the models using only one information source. The use of learning word translation probabilities for bilingual dictionaries improves the quality of the model and allows one to determine a context (a set of topics) for each pair of word translations, where these translations are appropriate.

scholarly journals On-line parameter and delay estimation of continuous-time dynamic systems

2015 ◽  
Vol 25 (2) ◽  
pp. 223-232 ◽  
Author(s):  
Janusz Kozłowski ◽  
Zdzisław Kowalczuk
Keyword(s):  
Continuous Time ◽  
Process Models ◽  
Delay Systems ◽  
Spectral Parameters ◽  
Time Dynamic ◽  
Estimation Algorithms ◽  
Continuous Time Process ◽  
On Line ◽  
Simultaneous Identification ◽  
Stationary Delay

Abstract The problem of on-line identification of non-stationary delay systems is considered. The dynamics of supervised industrial processes are usually modeled by ordinary differential equations. Discrete-time mechanizations of continuous-time process models are implemented with the use of dedicated finite-horizon integrating filters. Least-squares and instrumental variable procedures mechanized in recursive forms are applied for simultaneous identification of input delay and spectral parameters of the system models. The performance of the proposed estimation algorithms is verified in an illustrative numerical simulation study.

A new fuzzy filter design for nonlinear continuous-time dynamic systems with time-varying delays

2009 ◽  
Vol 160 (24) ◽  
pp. 3539-3549 ◽  
Author(s):  
Yakun Su ◽  
Bing Chen ◽  
Chong Lin ◽  
Huaguang Zhang
Keyword(s):  
Dynamic Systems ◽  
Continuous Time ◽  
Filter Design ◽  
Time Varying ◽  
Fuzzy Filter ◽  
Time Dynamic

Hierarchical Bayesian continuous time dynamic modeling.

2018 ◽  
Vol 23 (4) ◽  
pp. 774-799 ◽  
Author(s):  
Charles C. Driver ◽  
Manuel C. Voelkle
Keyword(s):  
Dynamic Modeling ◽  
Continuous Time ◽  
Hierarchical Bayesian ◽  
Time Dynamic

Identification of Continuous-time Nonlinear Systems via Local Gaussian Process Models

2014 ◽  
Vol 134 (11) ◽  
pp. 1708-1715
Author(s):  
Tomohiro Hachino ◽  
Kazuhiro Matsushita ◽  
Hitoshi Takata ◽  
Seiji Fukushima ◽  
Yasutaka Igarashi
Keyword(s):  
Nonlinear Systems ◽  
Gaussian Process ◽  
Continuous Time ◽  
Process Models ◽  
Gaussian Process Models

scholarly journals New Time-Varying Sliding Surface for Switching Type Quasi-Sliding Mode Control

Energies ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 3811
Author(s):  
Katarzyna Adamiak ◽  
Andrzej Bartoszewicz
Keyword(s):  
Sliding Mode Control ◽  
Continuous Time ◽  
Sliding Mode ◽  
Representative Point ◽  
Time Varying ◽  
Sliding Surface ◽  
External Disturbances ◽  
Mode Control ◽  
Switching Type ◽  
Time Systems

This study considers the problem of energetical efficiency in switching type sliding mode control of discrete-time systems. The aim of this work is to reduce the quasi-sliding mode band-width and, as follows, the necessary control input, through an application of a new type of time-varying sliding hyperplane in quasi-sliding mode control of sampled time systems. Although time-varying sliding hyperplanes are well known to provide insensitivity to matched external disturbances and uncertainties of the model in the whole range of motion for continuous-time systems, their application in the discrete-time case has never been studied in detail. Therefore, this paper proposes a sliding surface, which crosses the system’s representative point at the initial step and then shifts in the state space according to the pre-generated demand profile of the sliding variable. Next, a controller for a real perturbed plant is designed so that it drives the system’s representative point to its reference position on the sliding plane in each step. Therefore, the impact of external disturbances on the system’s trajectory is minimized, which leads to a reduction of the necessary control effort. Moreover, thanks to a new reaching law applied in the reference profile generator, the sliding surface shift in each step is strictly limited and a switching type of motion occurs. Finally, under the assumption of boundedness and smoothness of continuous-time disturbance, a compensation scheme is added. It is proved that this control strategy reduces the quasi-sliding mode band-width from O(T) to O(T3) order from the very beginning of the regulation process. Moreover, it is shown that the maximum state variable errors become of O(T3) order as well. These achievements directly reduce the energy consumption in the closed-loop system, which is nowadays one of the crucial factors in control engineering.

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