Online Estimation of Network Point Processes for Event Streams

A common goal in network modeling is to uncover the latent community structure present among nodes. For many real-world networks, the true connections consist of events arriving as streams, which are then aggregated to form edges, ignoring the dynamic temporal component. A natural way to take account of these temporal dynamics of interactions is to use point processes as the foundation of network models for community detection. Computational complexity hampers the scalability of such approaches to large sparse networks. To circumvent this challenge, we propose a fast online variational inference algorithm for estimating the latent structure underlying dynamic event arrivals on a network, using continuous-time point process latent network models. We describe this procedure for networks models capturing community structure. This structure can be learned as new events are observed on the network, updating the inferred community assignments. We investigate the theoretical properties of such an inference scheme, and provide regret bounds on the loss function of this procedure. The proposed inference procedure is then thoroughly compared, using both simulation studies and real data, to non-online variants. We demonstrate that online inference can obtain comparable performance, in terms of community recovery, to non-online variants, while realising computational gains. Our proposed inference framework can also be readily modified to incorporate other popular network structures.Comment: 45 page

Free Space Optical Communication for Inter-Satellite Link: Architecture, Potentials and Trends

The sixth-generation (6G) network is expected to achieve global coverage based on the space-air-ground integrated network, and the latest satellite network will play an important role in it. The introduction of inter-satellite links (ISLs) can significantly improve the throughput of the satellite network, and recently gets lots of attention from both academia and industry. In this paper, we illustrate the advantages of using the laser for ISLs due to its longer communication distance, higher data speed, and stronger security. Specifically, space-borne laser terminals with the acquisition, pointing and tracking mechanism which realize long-distance communication are illustrated, advanced modulation and multiplexing modes that make high communication rates possible are introduced, and the security of ISLs ensured by the characteristics of both laser and the optical channel is also analyzed. Moreover, some open issues such as advanced optical beam steering, routing and scheduling algorithm, and integrated sensing and communication are discussed to direct future research

Group Network Hawkes Process

In this work, we study the event occurrences of individuals interacting in a network. To characterize the dynamic interactions among the individuals, we propose a group network Hawkes process (GNHP) model whose network structure is observed and fixed. In particular, we introduce a latent group structure among individuals to account for the heterogeneous user-specific characteristics. A maximum likelihood approach is proposed to simultaneously cluster individuals in the network and estimate model parameters. A fast EM algorithm is subsequently developed by utilizing the branching representation of the proposed GNHP model. Theoretical properties of the resulting estimators of group memberships and model parameters are investigated under both settings when the number of latent groups $G$ is over-specified or correctly specified. A data-driven criterion that can consistently identify the true $G$ under mild conditions is derived. Extensive simulation studies and an application to a data set collected from Sina Weibo are used to illustrate the effectiveness of the proposed methodology.Comment: 35 page

Application of a warfarin dosing calculator to guide individualized dosing versus empirical adjustment after fixed dosing: a pilot study

Background: Warfarin has a narrow therapeutic window and individual variation, and patients require regular follow-up and monitoring of the International Normalized Ratio (INR) for dose adjustment. The calculation method of Warfarin Dosing Calculator (WDC) software is based on the European and American populations, and its accuracy in the Chinese population is yet to be verified.Objective: This study was to evaluate the feasibility of applying Warfarin Dosing Calculator software intervention in a real-world clinical research setting in China.Methods: The pilot study divided the included patients after valve replacement into an experimental group and a control group, with 38 cases in each group. In the control group, the initial dose was fixed at 2.5 mg/d and the dose was adjusted empirically during the study period; in the experimental group, the Warfarin Dosing Calculator software was applied to guide the dosing, and patients in both groups were followed up for 3 months. Analysis of the incidence anticoagulation outcomes and excessive anticoagulation events in both groups. Kaplan-Meier survival curves were used to analyze the correlation between different dosing regimens and first International Normalized Ratio attainment, and Logrank tests were performed.Results: The mean time required for first International Normalized Ratio compliance in the experimental group was 4.38 days less than in the control group, and the mean number of tests was 1.43 less (p < 05). Time in therapeutic range (TTR) was significantly higher in the experimental group than in the control group (p < 05). Kaplan-Meier survival curve analysis showed that the first International Normalized Ratio attainment rate was significantly higher in the experimental group than in the control group (p = 01). No major bleeding events occurred in either group, but other excessive anticoagulation events (INR>3.5 and minor bleeding) were significantly reduced in the experimental group compared with the control group (p < 05).Conclusion: Application of Warfarin Dosing Calculator software to guide individualized warfarin dosing may be better than a fixed dose of 2.5 mg/d. It may be shorten the time to first International Normalized Ratio attainment, and the attainment rate in the same time, and can better improve the mean Time in therapeutic range level value and reduce excessive anticoagulation events, which improves the safety of warfarin anticoagulation therapy in clinical practice.Clinical Trial Registration:https://www.chictr.org.cn/showproj.html?proj=52793, ChiCTR2000032393

Latent Variable Models in Measurement: Theory and Application

Latent variable models play an important role in educational and psychological measurement, where items are presented to individuals, resulting in item response data. Such data entail important information about the individual latent traits, population structure and item design, which are key components to be understood in educational and psychological assessments. This thesis focuses on the development of statistical learning methods based on latent variable models with identifiability theories. The thesis consists of three parts, with three kinds of applications in mind. The first part is on the identifiability of diagnostic classification models (DCMs), which is a special subfamily of latent class models. It aims to examine the test takers' ability based on his/her mastery of set of required skills. A key issue common to DCMs and more generally to latent class model is the identifiability which is a property whether the unknown model or related parameters can be estimated consistently under a suitably defined asymptotic regime. Most existing works focus on the identifiability of DCMs with binary responses and attributes. In this thesis, we provide general identifiability results for DCMs with polytoumous responses and attributes and less parameter restrictions. The second part considers the identifiability of testlet factor models, which is a subfamily of latent variable models with underlying continuous latent traits assumed to follow normal distribution. Similar to DCMs, factor models also suffer from identifiability issues, where the parameters can only be identified up to a rotation in general. However, in most applications, testlet models or bifactor models are popular in educational assessment. They are constrained factor models assuming that the response test items can be accounted for by one primary factor and multiple secondary group-specific factors. By aid of this special structure, we can show that the model can be strictly identifiable and we provide checkable necessary and sufficient conditions accordingly. The third part focuses on the statistical learning in studying the complex problem-solving (CPS) items. With advanced computer technology, there is a new trend of developing CPS test items through online platform, where the examinees are asked to solve challenging tasks in a simulated environment. During the test, all actions performed by examinees will be recored into a log file. Therefore, we can not only observe their final responses, but also have access to their entire solving process. Such data type is known as process data in the measurement literature. The traditional item response model cannot be applicable, at least directly. The analysis of the process data is still in its infancy. In the thesis, we propose a new model-based approach and show its usefulness through an interesting real data application

Best Subset Selection with Efficient Primal-Dual Algorithm

Best subset selection is considered the `gold standard' for many sparse learning problems. A variety of optimization techniques have been proposed to attack this non-convex and NP-hard problem. In this paper, we investigate the dual forms of a family of $\ell_0$-regularized problems. An efficient primal-dual method has been developed based on the primal and dual problem structures. By leveraging the dual range estimation along with the incremental strategy, our algorithm potentially reduces redundant computation and improves the solutions of best subset selection. Theoretical analysis and experiments on synthetic and real-world datasets validate the efficiency and statistical properties of the proposed solutions.Comment: arXiv admin note: text overlap with arXiv:1703.00119 by other author

Hydration Performance of Magnesium Potassium Phosphate Cement Using Sodium Alginate as a Candidate Retarder

Retarders are important factors controlling the hydration and properties of magnesium potassium phosphate cements (MKPCs). Boric acid and borax are the most commonly used retarders for MKPC which could control the setting time in a wide range upon changing their content. However, with the increase in borax content, the early strength of MKPC can be reduced, and boron compounds are now included in the EU candidate list of substances of very high concern for authorization, due to their reproductive toxicity. Exploring alternative set retarders to boron compounds is, thus, of significance. This work investigated the effects of a candidate retarder, namely, sodium alginate, on the setting time, mechanical properties, hydration products, and microstructures of MKPC. Sodium alginate presented dramatically retarding effects on MKPCs in the range of 0% to 2% (by mass of water). One percent of sodium alginate by mass of water could extend the setting time of MKPCs from 15 min to 35 min, which presented a better retarding effect than borax (a typical retarder for MKPCs) and produced higher early strength of MKPCs. Adding no more than 1% of sodium alginate did not have a notably adverse effect on the formation of hydration product over the long term, but an unfavorable effect could be found regardless of the sodium alginate content, which could reduce the compressive strength of MKPCs