Automatic Structural Scene Digitalization

In this paper, we present an automatic system for the analysis and labeling of structural scenes, floor plan drawings in Computer-aided Design (CAD) format. The proposed system applies a fusion strategy to detect and recognize various components of CAD floor plans, such as walls, doors, windows and other ambiguous assets. Technically, a general rule-based filter parsing method is fist adopted to extract effective information from the original floor plan. Then, an image-processing based recovery method is employed to correct information extracted in the first step. Our proposed method is fully automatic and real-time. Such analysis system provides high accuracy and is also evaluated on a public website that, on average, archives more than ten thousands effective uses per day and reaches a relatively high satisfaction rate.Comment: paper submitted to PloS On

Comparator Design in Sensors for Environmental Monitoring

This paper presents circuit design considerations of comparator in analog-to-digital converters (ADC) applied for a portable, low-cost and high performance nano-sensor chip which can be applied to detect the airborne magnetite pollution nano particulate matter (PM) for environmental monitoring. High-resolution ADC plays a vital important role in high perfor-mance nano-sensor, while high-resolution comparator is a key component in ADC. In this work, some important design issues related to comparators in analog-to-digital converters (ADCs) are discussed, simulation results show that the resolution of the comparator proposed can achieve 5µV , and it is appropriate for high-resolution application

Flourish the Market of Open Source Enterprise Systems Through Cloud-Based Technology: An Perspective of Cross-Side Network Effects

Open source enterprise systems (OS-ES) have become an appealing option for small and medium-sized enterprises (SMEs) to bring powerful business computing tools into their organizations. However, the impact of OS-ES is still in their nascent stage because of the critical challenges they pose. We conclude that the challenges of OS-ES mainly come from three perspectives: limited economic scale, product-oriented business strategy, and insufficient product support. In this study, we propose that the emergence of cloud-based technology can catalyze the flourishing process of OS-ES market through leveraging both the OS-ES user-side and developer-side economic scale. Because of the two-sided nature of OS-ES market, we then draw on the cross-side network effects (CNEs) theory to explain the positive reinforcement loop between user-side adoption and developer-side engagement of OS-ES projects

Correlation Analysis of Road Freight Transport and Economic Development in Shaanxi Province

Based on the data from 1987-2017 of the Statistical Yearbook of Shaanxi Province, this paper selects Shaanxi Road freight transportation evaluation indicators and economic development evaluation indicators, and uses the method of co-integration test and ADF unit root test to determine whether there is a long-term equilibrium relationship between the indicators. Through the establishment of VAR model and analysis, it demonstrates the impact of road freight transportation on economic development in Shaanxi Province. Based on the impulse impact between the road freight transportation and economic development in Shaanxi Province, the correlation between road freight transportation and economic development in Shaanxi Province is analyzed and studied to provide suggestions for the coordinated development of road freight transportation and economy in Shaanxi Province

Quasi-optimal Learning with Continuous Treatments

Many real-world applications of reinforcement learning (RL) require making decisions in continuous action environments. In particular, determining the optimal dose level plays a vital role in developing medical treatment regimes. One challenge in adapting existing RL algorithms to medical applications, however, is that the popular infinite support stochastic policies, e.g., Gaussian policy, may assign riskily high dosages and harm patients seriously. Hence, it is important to induce a policy class whose support only contains near-optimal actions, and shrink the action-searching area for effectiveness and reliability. To achieve this, we develop a novel \emph{quasi-optimal learning algorithm}, which can be easily optimized in off-policy settings with guaranteed convergence under general function approximations. Theoretically, we analyze the consistency, sample complexity, adaptability, and convergence of the proposed algorithm. We evaluate our algorithm with comprehensive simulated experiments and a dose suggestion real application to Ohio Type 1 diabetes dataset.Comment: The first two authors contributed equally to this wor

Pathway Interaction Network Analysis Identifies Dysregulated Pathways in Human Monocytes Infected by Listeria monocytogenes

In our study, we aimed to extract dysregulated pathways in human monocytes infected by Listeria monocytogenes (LM) based on pathway interaction network (PIN) which presented the functional dependency between pathways. After genes were aligned to the pathways, principal component analysis (PCA) was used to calculate the pathway activity for each pathway, followed by detecting seed pathway. A PIN was constructed based on gene expression profile, protein-protein interactions (PPIs), and cellular pathways. Identifying dysregulated pathways from the PIN was performed relying on seed pathway and classification accuracy. To evaluate whether the PIN method was feasible or not, we compared the introduced method with standard network centrality measures. The pathway of RNA polymerase II pretranscription events was selected as the seed pathway. Taking this seed pathway as start, one pathway set (9 dysregulated pathways) with AUC score of 1.00 was identified. Among the 5 hub pathways obtained using standard network centrality measures, 4 pathways were the common ones between the two methods. RNA polymerase II transcription and DNA replication owned a higher number of pathway genes and DEGs. These dysregulated pathways work together to influence the progression of LM infection, and they will be available as biomarkers to diagnose LM infection

Policy Learning for Individualized Treatment Regimes on Infinite Time Horizon

With the recent advancements of technology in facilitating real-time monitoring and data collection, "just-in-time" interventions can be delivered via mobile devices to achieve both real-time and long-term management and control. Reinforcement learning formalizes such mobile interventions as a sequence of decision rules and assigns treatment arms based on the user's status at each decision point. In practice, real applications concern a large number of decision points beyond the time horizon of the currently collected data. This usually refers to reinforcement learning in the infinite horizon setting, which becomes much more challenging. This article provides a selective overview of some statistical methodologies on this topic. We discuss their modeling framework, generalizability, and interpretability and provide some use case examples. Some future research directions are discussed in the end