7 research outputs found
Individualized Dynamic Latent Factor Model for Multi-resolutional Data with Application to Mobile Health
Mobile health has emerged as a major success for tracking individual health
status, due to the popularity and power of smartphones and wearable devices.
This has also brought great challenges in handling heterogeneous,
multi-resolution data which arise ubiquitously in mobile health due to
irregular multivariate measurements collected from individuals. In this paper,
we propose an individualized dynamic latent factor model for irregular
multi-resolution time series data to interpolate unsampled measurements of time
series with low resolution. One major advantage of the proposed method is the
capability to integrate multiple irregular time series and multiple subjects by
mapping the multi-resolution data to the latent space. In addition, the
proposed individualized dynamic latent factor model is applicable to capturing
heterogeneous longitudinal information through individualized dynamic latent
factors. Our theory provides a bound on the integrated interpolation error and
the convergence rate for B-spline approximation methods. Both the simulation
studies and the application to smartwatch data demonstrate the superior
performance of the proposed method compared to existing methods.Comment: 43 pages, 3 figure
Effects of Three Traditional Heat Processing Methods on the Physicochemical Properties and Structure of Cassava
Cassava (Manihot esculenta Crantz) is one of the three major potato crops in the world. Consumption of cassava necessitates thermal processing, and the impact of such processing on the physicochemical and structural characteristics of cassava remained unknown. In this paper, three traditional thermal processing methods (boiling, steaming and frying) were used to heat-treat cassava. The physicochemical properties (chemical composition, pasting properties, thermal properties) and structural changes (microstructure, long-range ordering, and short-range ordering) of the processed cassava and untreated cassava was characterized by scanning electron microscopy, Fourier transform infrared spectrometry, X-ray diffractometer, and differential thermal scanning calorimetry. The findings indicated a significant reduction in the total starch content of cassava following diverse heat treatments, accompanied by a notable increase in amylose content (P<0.05). The relative crystallinity and short-range ordering of starch in cassava decreased, with a 74.35% decrease in crystal structure after frying and a 65.16% decrease in crystal structure after steaming. The effect of heat treatment on the gelatinization characteristics of cassava was significant: after heat treatment, the peak viscosity and disintegration value showed an overall upward trend, while the gelatinization temperature significantly decreased (P<0.05). Boiling and steaming treatments resulted in a 49.67% and 43.98% increase in recovery value, respectively, while frying treatment decreased by 23.25%. There were no significant differences observed in the thermal properties and infrared spectrum groups among cassava samples that underwent different heat treatments. Generally, steaming treatment exhibited minimal damage to the crystal structure of starch in cassava, boiling treatment enhanced its gel formation and susceptibility to aging while frying treatment demonstrated superior thermal stability. The results can serve as a theoretical reference for selecting appropriate heat processing methods for cassava in food applications and designing a variety of cassava products
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Individualized dynamic latent factor model and inference for multi-resolution data in mobile health
Data integration plays a crucial role in many studies such as recommender systems and mobile health, which involve multi-resolution, multi-source or multi-modality data collection. However, this type of data brings several challenges, such as strong dependence among variables, high heterogeneity across subjects, and varied sampling resolution in time-series data. This dissertation focuses on developing innovative statistical methods and machine learning tools to address these challenges.Dependency structure in recommender systems has been widely adopted in recent years to improve prediction accuracy. In the first project, we propose an innovative tensor-based recommender system, namely, the Tensor Factorization with Dependency (TFD). The proposed method utilizes shared factors to characterize the dependency between different modes, in addition to pairwise additive tensor factorization to integrate information among multiple modes. One advantage of the proposed method is that it provides flexibility for different dependency structures by incorporating shared latent factors. In addition, the proposed method unifies both binary and ordinal ratings in recommender systems. We achieve scalable computation for scarce tensors with high missing rates. In theory, we show the asymptotic consistency of estimators with various loss functions for both binary and ordinal data. Our numerical studies demonstrate that the proposed method outperforms the existing methods, especially on prediction accuracy.
Mobile health has emerged as a major success for tracking individual health status, due to the popularity and power of smartphones and wearable devices. This has also brought great challenges in handling heterogeneous, multi-resolution data which arise ubiquitously in mobile health due to irregular multivariate measurements collected from individuals. In the second project, we propose an individualized dynamic latent factor model (IDLFM) for irregular multi-resolution time series data to interpolate unsampled measurements of time series with low resolution. One major advantage of the proposed method is its capability to integrate multiple irregular time series and multiple subjects by mapping the multi-resolution data to the latent space. In addition, the proposed individualized dynamic latent factor model is applicable to capturing heterogeneous longitudinal information through individualized dynamic latent factors. Our theory provides a bound on the integrated interpolation error and the convergence rate for B-spline approximation methods. Both the simulation studies and the application to smartwatch data demonstrate the superior performance of the proposed IDLFM compared to existing methods.
The third project introduces a novel approach under a continuous-time reinforcement learning framework for testing a treatment effect. This project is motivated by complex mobile health intervention research. Specifically, our method provides an effective test on carryover effects of treatment over time utilizing the average treatment effect (ATE). The ATE is defined as difference of value functions over an infinite horizon, which accounts for cumulative treatment effects, both immediate and carryover. The proposed method outperforms existing testing procedures such as discrete time reinforcement learning strategies in multi-resolution observation settings where observation times can be irregular. Another advantage of the proposed method is that it can capture treatment effects of a shorter duration and provide greater accuracy compared to discrete-time approximations, through the use of continuous-time estimation for the value function. We apply the proposed test statistics to OhioT1DM diabetes data to evaluate the cumulative treatment effects of bolus insulin on patients’ glucose levels
THE QINGHAI-TIBET PLATEAU: PILOT SITE FOR INTERNATIONAL COLLABORATION IN GEOSCIENCE DURING CHINA'S EARLY PERIOD OF REFORM AND OPENING-UP
In 1978, China ended a decade-long 'Cultural Revolution' and began its Reform and Opening-up process. At the same time, China's scientific community also ended its longterm closed state and began to seek ways to integrate into the world. In this study, we take the Qinghai-Tibet Plateau as a pilot site to illustrate the international geoscience collaboration during this time. We first introduce the International Symposium on the Qinghai-Tibet Plateau, the delegation from the United States and the collaboration between China and France on the Qinghai-Tibet Plateau. Then we examine the successful cooperation between the Chinese Academy of Sciences and the Royal Society of London by focusing on their interactions in the Qinghai-Tibet Plateau Project in detail, on the basis of the archives of these two organizations. Since national policies and systems lagged behind the pace of international cooperation then, there were contradictions between national policies and the needs of specific research projects. We attempt to understand the flexible manner in which Chinese scholars solved these contradictions. We will also explore some of the reasons and contextual factors that shaped such Sino-foreign scientific exchanges early in the Reform era. This study also reflects the opportunities and challenges faced by China's scientific community during the process of social transformation
Soviet scientists in chinese institutes: A historical study of cooperation between the two academies of sciences in 1950s
In the 1950s, the Chinese Academy of Sciences (CAS) engaged in close cooperation with the Soviet Academy of Sciences. The CAS sent scientists to the Soviet Academy to work as interns, study for advanced degrees, or engage in academic cooperation, and a large number of Soviet scientists were invited by the various institutes of the CAS to come to China to give lectures, direct research, help make scientific plans, and collaborate. The comprehensive cooperation between the two academies was launched at a time when the CAS institutes were in their embryonic stage, which suggests that the better-established Soviet scientists had the opportunity to play a dominate role. But the reality is not so straightforward. The case studies in this paper suggest that besides the influence of compatible political movements in China and the Soviet Union and bilateral ties between these two nations' scientific institutes, disharmony in actual working relationships prevented Soviet scientists from playing the role they might have envisioned within the CAS institutes. The rapid development of the cooperative relationship in a short span of time, combined with lack of experience on both sides, made for a disharmonious collaboration