Vessel Tree Reconstruction with Divergence Prior

Accurate structure analysis of high-resolution 3D biomedical images of vessels is a challenging issue and in demand for medical diagnosis nowadays. Previous curvature regularization based methods [10, 31] give promising results. However, their mathematical models are not designed for bifurcations and generate significant artifacts in such areas. To address the issue, we propose a new geometric regularization principle for reconstructing vector fields based on prior knowledge about their divergence. In our work, we focus on vector fields modeling blood flow pattern that should be divergent in arteries and convergent in veins. We show that this previously ignored regularization constraint can significantly improve the quality of vessel tree reconstruction particularly around bifurcations where non-zero divergence is concentrated. Our divergence prior is critical for resolving (binary) sign ambiguity in flow orientations produced by standard vessel filters, e:g: Frangi. Our vessel tree centerline reconstruction combines divergence constraints with robust curvature regularization. Our unsupervised method can reconstruct complete vessel trees with near-capillary details on both synthetic and real 3D volumes. Also, our method reduces angular reconstruction errors at bifurcations by a factor of two

The Catalyzing Factors of Official Documents Exchange via Microblogging in Public Sectors: A Case Study based on the T-O-E Framework

Since Government transparency and information open has got more attention by both academia and practitioners with the development of information technologies (IT) and Internet, The popularity of Web 2.0 application provides the government some new opportunities and challenges. Official documents exchange via microblogging (ODEM) in the Bureau of Justice, Haining is a practice case of government information open in the new media. For analyzing the determinants from academic perspective and exploring the managerial value of the case, the paper report an exploratory case study based on the technology-organization-environment (TOE) framework. After several field interviews and rigorous data coding by following the case study methodology, we find that top manager’s support, personnel’s IT accomplishment, and regional economic and social environment are the key determinants of the emergence of ODEM, as well as the organizational structure and operational flow is not change immediately in this case. The limitation and future goals of the study are also discussed in the paper

UNDERSTANDING THE IMPACT OF INTERNET MEDIA ON PATIENT-CLINICIAN TRUST: MODEL DEVELOPMENT AND RESEARCH DESIGN

Patient-clinician trust is one of the major concerns regarding the relationship between the public and the healthcare industry. The development of Internet technology, especially Web 2.0 applications, provides us with a greater ability to exchange information and provide feedback. To describe and measure the impact of ICT-based new media on patient-clinician trust, this paper develops a theoretical model that builds on previous literature in both the healthcare and IS research areas. The paper also aims to explain the differing impacts of online reports (Web 1.0) and online comments (Web 2.0), along with the differing impacts of positive and negative comments. Expected contributions and an agenda for future empirical experiment are also discussed in the paper

Absence of remote earthquake triggering within the Coso and Salton Sea geothermal production fields

Geothermal areas are long recognized to be susceptible to remote earthquake triggering, probably due to the high seismicity rates and presence of geothermal fluids. However, anthropogenic injection and extraction activity may alter the stress state and fluid flow within the geothermal fields. Here we examine the remote triggering phenomena in the Coso geothermal field and its surrounding areas to assess possible anthropogenic effects. We find that triggered earthquakes are absent within the geothermal field but occur in the surrounding areas. Similar observation is also found in the Salton Sea geothermal field. We hypothesize that continuous geothermal operation has eliminated any significant differential pore pressure between fractures inside the geothermal field through flushing geothermal precipitations and sediments out of clogged fractures. To test this hypothesis, we analyze the pore-pressure-driven earthquake swarms, and they are found to occur outside or on the periphery of the geothermal production field. Therefore, our results suggest that the geothermal operation has changed the subsurface fracture network, and differential pore pressure is the primary controlling factor of remote triggering in geothermal fields

Superfast Liquid Transfer Strategy Through Sliding on a Liquid Membrane Inspired from Scorpion Setae

Although diversified biological structures have evolved fog collection abilities, the typical speeds of the condensed water droplets on these surfaces are too slow to have practical utility. The main challenge focuses on the elimination of the interfacial hydrodynamic resistance without external energy support. Here, an unusual strategy for superfast self‐support transfer condensed droplets is supported by sliding on seta of desert scorpion. It can be rapidly wetted by the fog droplets owing to its conical shape with linear gradient channels. A loss of interfacial resistance by this hydrodynamically lubricating water membrane could significantly accelerate the movement of the droplets, thus making its velocity increasing by one order of magnitude, or even more. Inspired by this novel strategy, the novel bioinspired materials are fabricated with the similar gradient channel structures and droplet transportation mode, which can make the condensed droplets spontaneously slide on the low‐friction liquid membrane. The fundamental understanding of superfast fog capture and the sliding dynamics of condensed droplets in this system could inspire to develop novel materials or various systems to transfer liquid fast and efficiently without external energy support.An unusual strategy for superfast transferring condensed droplets by sliding on liquid membrane of desert scorpion seta is reported. A loss of interfacial resistance could significantly accelerate the droplets by this hydrodynamically lubricating liquid membrane. Then, the bioinspired materials with similar droplet transportation mode are fabricated, which will inspire to develop novel materials to transport liquid without external energy.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146306/1/admi201800802-sup-0001-S1.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/146306/2/admi201800802.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/146306/3/admi201800802_am.pd

Exploiting Counter-Examples for Active Learning with Partial labels

This paper studies a new problem, \emph{active learning with partial labels} (ALPL). In this setting, an oracle annotates the query samples with partial labels, relaxing the oracle from the demanding accurate labeling process. To address ALPL, we first build an intuitive baseline that can be seamlessly incorporated into existing AL frameworks. Though effective, this baseline is still susceptible to the \emph{overfitting}, and falls short of the representative partial-label-based samples during the query process. Drawing inspiration from human inference in cognitive science, where accurate inferences can be explicitly derived from \emph{counter-examples} (CEs), our objective is to leverage this human-like learning pattern to tackle the \emph{overfitting} while enhancing the process of selecting representative samples in ALPL. Specifically, we construct CEs by reversing the partial labels for each instance, and then we propose a simple but effective WorseNet to directly learn from this complementary pattern. By leveraging the distribution gap between WorseNet and the predictor, this adversarial evaluation manner could enhance both the performance of the predictor itself and the sample selection process, allowing the predictor to capture more accurate patterns in the data. Experimental results on five real-world datasets and four benchmark datasets show that our proposed method achieves comprehensive improvements over ten representative AL frameworks, highlighting the superiority of WorseNet. The source code will be available at \url{https://github.com/Ferenas/APLL}.Comment: 29 pages, Under revie