Transfer Learning across Networks for Collective Classification

This paper addresses the problem of transferring useful knowledge from a source network to predict node labels in a newly formed target network. While existing transfer learning research has primarily focused on vector-based data, in which the instances are assumed to be independent and identically distributed, how to effectively transfer knowledge across different information networks has not been well studied, mainly because networks may have their distinct node features and link relationships between nodes. In this paper, we propose a new transfer learning algorithm that attempts to transfer common latent structure features across the source and target networks. The proposed algorithm discovers these latent features by constructing label propagation matrices in the source and target networks, and mapping them into a shared latent feature space. The latent features capture common structure patterns shared by two networks, and serve as domain-independent features to be transferred between networks. Together with domain-dependent node features, we thereafter propose an iterative classification algorithm that leverages label correlations to predict node labels in the target network. Experiments on real-world networks demonstrate that our proposed algorithm can successfully achieve knowledge transfer between networks to help improve the accuracy of classifying nodes in the target network.Comment: Published in the proceedings of IEEE ICDM 201

Opinion dynamics on directed small-world networks

In this paper, we investigate the self-affirmation effect on formation of public opinion in a directed small-world social network. The system presents a non-equilibrium phase transition from a consensus state to a disordered state with coexistence of opinions. The dynamical behaviors are very sensitive to the density of long-range interactions and the strength of self-affirmation. When the long-range interactions are sparse and individual generally does not insist on his/her opinion, the system will display a continuous phase transition, in the opposite case with high self-affirmation strength and dense long-range interactions, the system does not display a phase transition. Between those two extreme cases, the system undergoes a discontinuous phase transition.Comment: 6 pages, 5 figure

Active class discovery and learning for networked data

With the recent explosion of social network applications, active learning has increasingly become an important paradigm for classifying networked data. While existing research has shown promising results by exploiting network properties to improve the active learning performance, they are all based on a static setting where the number and the type of classes underlying the networked data remain stable and unchanged. For most social network applications, the dynamic change of users and their evolving relationships, along with the emergence of new social events, often result in new classes that need to be immediately discovered and labeled for classification. This paper proposes a novel approach called ADLNET for active class discovery and learning with networked data. Our proposed method uses the Dirichlet process defined over class distributions to enable active discovery of new classes, and explicitly models label correlations in the utility function of active learning. Experimental results on two real-world networked data sets demonstrate that our proposed approach outperforms other state-of-the-art methods

Empirical Determination of the Pion Mass Distribution

Existing pion+nucleus Drell-Yan and electron+pion scattering data are used to develop ensembles of model-independent representations of the pion generalised parton distribution (GPD). Therewith, one arrives at a data-driven prediction for the pion mass distribution form factor, $\theta_2$. Compared with the pion elastic electromagnetic form factor, $\theta_2$ is harder: the ratio of the radii derived from these two form factors is $r_\pi^{\theta_2}/r_\pi = 0.79(3)$. Our data-driven predictions for the pion GPD, related form factors and distributions should serve as valuable constraints on theories of pion structure.Comment: 9 pages, 6 figures, 1 tabl

Effectiveness of laparoscopic sleeve gastrectomy for weight loss and obesity-associated co-morbidities: a 3-year outcome from Mainland Chinese patients

AbstractBackgroundLaparoscopic sleeve gastrectomy (LSG) is becoming a stand-alone bariatric surgery for obesity, but its effectiveness for Mainland Chinese patients remains unclear.ObjectivesTo evaluate the effectiveness and safety of LSG for Mainland Chinese patientsSettingA tertiary hospitalMethodsRetrospective analysis of patients admitted for LSG between January 2011 and February 2012 was performed. Medium-term outcome measures were: total weight loss (%TWL), excess weight loss (%EWL), co-morbidities, improvement, and complications.ResultsSeventy patients (body mass index [BMI] 40.8±5.9 kg/m2) underwent LSG, comprising 40 women and 30 men. The most common co-morbidity was diabetes (n = 29, 41.4%). Lost to follow-up rate for weight loss was 15.7%, 31.4%, and 41% at 1, 2, and 3 years. The %TWL was 34.4±6.1, 34.7±6.2 and 33.7±7.1 at 1, 2, and 3 years. The %EWL increased to 77.1±13.0, 77.9±12.2 and 77.2±13.1 at 1, 2, and 3years. The proportions of patients having successful weight loss were 100% or 85% at 3 years according the definition of %TWL>10% or %EWL>50%. Approximately 79.3%, 51.7%, and 44.8% of patients completed follow-up for glycemic control at each time point, respectively. The proportions of patients with optimal glycemic control (fasting blood glucose [FBG]<5.6 mmol/L; hemoglobin A1C [HbA1C]<6.5%) were 47.9%, 60.0%, and 69.2% at 1, 2, and 3years. The weight loss and glycemic control effect may be greater in the high BMI group (≥40 kg/m2). Early and late complications occurred in 8.6% and 7.1% of patients during follow-up.ConclusionsLSG is effective in weight loss and glycemic control and is safe for Mainland Chinese obese patients, especially for patients with a BMI≥40 kg/m2

On-Line Adaptive Radiation Therapy: Feasibility and Clinical Study

The purpose of this paper is to evaluate the feasibility and clinical dosimetric benefit of an on-line, that is, with the patient in the treatment position, Adaptive Radiation Therapy (ART) system for prostate cancer treatment based on daily cone-beam CT imaging and fast volumetric reoptimization of treatment plans. A fast intensity-modulated radiotherapy (IMRT) plan reoptimization algorithm is implemented and evaluated with clinical cases. The quality of these adapted plans is compared to the corresponding new plans generated by an experienced planner using a commercial treatment planning system and also evaluated by an in-house developed tool estimating achievable dose-volume histograms (DVHs) based on a database of existing treatment plans. In addition, a clinical implementation scheme for ART is designed and evaluated using clinical cases for its dosimetric qualities and efficiency

Low Cost, Robust, Environmentally Friendly Geopolymer–Mesoporous Carbon Composites for Efficient Solar Powered Steam Generation

High-efficiency, environment friendly, renewable energy-based methods of desalination represent attractive and potentially very powerful solutions to the long-standing problem of global water shortage. Many new laboratory-scale materials have been developed for photothermal desalination but the development of low-cost, easy-to-manufacture, and scalable materials and systems that can convert solar irradiation into exploitable thermal energy in this context is still a significant challenge. This paper presents work on a geopolymer–biomass mesoporous carbon composite (GBMCC) device with mesoporous and macroporous structures for harvesting solar energy, which is then used in a device to generate water vapor with high efficiency using negative pressure, wind-driven, steam generation. The GBMCC device gives water evaporation rates of 1.58 and 2.71 kg m−2 h−1 under 1 and 3 suns illumination, with the solar thermal conversion efficiency up to 84.95% and 67.6%, respectively. A remarkable, record high water vapor generation rate of 7.55 kg m−2 h−1 is achieved under 1 sun solar intensity at the wind speed of 3 m s−1. This is a key step forward todays efficient, sustainable and economical production of clean water from seawater or common wastewater with free solar energy