Robust and Efficient Network Reconstruction in Complex System via Adaptive Signal Lasso

Network reconstruction is important to the understanding and control of collective dynamics in complex systems. Most real networks exhibit sparsely connected properties, and the connection parameter is a signal (0 or 1). Well-known shrinkage methods such as lasso or compressed sensing (CS) to recover structures of complex networks cannot suitably reveal such a property; therefore, the signal lasso method was proposed recently to solve the network reconstruction problem and was found to outperform lasso and CS methods. However, signal lasso suffers the problem that the estimated coefficients that fall between 0 and 1 cannot be successfully selected to the correct class. We propose a new method, adaptive signal lasso, to estimate the signal parameter and uncover the topology of complex networks with a small number of observations. The proposed method has three advantages: (1) It can effectively uncover the network topology with high accuracy and is capable of completely shrinking the signal parameter to either 0 or 1, which eliminates the unclassified portion in network reconstruction; (2) The method performs well in scenarios of both sparse and dense signals and is robust to noise contamination; (3) The method only needs to select one tuning parameter versus two in signal lasso, which greatly reduces the computational cost and is easy to apply. The theoretical properties of this method are studied, and numerical simulations from linear regression, evolutionary games, and Kuramoto models are explored. The method is illustrated with real-world examples from a human behavioral experiment and a world trade web.Comment: 15 pages, 8 figures, 4 table

VIGOR: A Versatile, Individualized and Generative ORchestrator to Motivate the Movement of the People with Limited Mobility

Physical inactivity is a major national concern, particularly among individuals with chronic conditions and/or disabilities. There is an urgent need to devise practical and innovative fitness methods, designed and grounded in physical, psychological and social considerations that will effectively promote physical fitness participation among individuals of all age groups with chronic health condition(s) and/or disabilities. This research is dedicated to achieving Versatile, Individualized, and Generative ORchestrator (VIGOR) to motivate the movement of the people with limited mobility. Tai-Chi is a traditional mind–body wellness and healing art, and its clinical benefits have been well documented. This work presents a Tai-Chi based VIGOR under development. Through the use of Helping, Pushing and Coaching (HPC) functions by following Tai-Chi kinematics, the VIGOR system is designed to make engagement in physical activity an affordable, individually engaging, and enjoyable experience for individuals who live with mobility due to disease or injury. VIGOR consists of the following major modules: (1) seamless human-machine interaction based on the acquisition, transmission, and reconstruction of 4D data (XYZ plus somatosensory) using affordable I/O instruments such as Kinect, Sensor and Tactile actuator, and active-orthosis/exoskeleton; (2) processing and normalization of kinetic data; (3) Identification and grading of kinetics in real time; (4) adaptive virtual limb generation and its reconstruction on virtual reality (VR) or active-orthosis/exoskeleton; and (5) individualized physical activity choreography (i.e., creative movement design). Aiming at developing a deep-learning-enabled rehab and fitness modality through infusing the domain knowledge (physical therapy, medical anthropology, psychology, electrical engineering, bio-mechanics, and athletic aesthetics) into deep neural network, this work is transformative in that the technology can be applied to the broad research areas of intelligent systems, human-computer interaction, and cyber-physical human systems. The resulting VIGOR has significant potentials as both rehabilitative and fitness modalities and can be adapted to other movement modalities and chronic medical conditions (e.g., yoga and balance exercise; fibromyalgia, multiple sclerosis, Parkinson disease)

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Using a Layered Ensemble of Physics-Guided Graph Attention Networks to Predict COVID-19 Trends

The COVID-19 pandemic has spread rapidly and significantly impacted most countries in the world. Providing an accurate forecast of COVID-19 at multiple scales would help inform public health decisions, but recent forecasting models are typically used at the state or country level. Furthermore, traditional mathematical models are limited by simplifying assumptions, while machine learning algorithms struggle to generalize to unseen trends. This motivates the need for hybrid machine learning models that integrate domain knowledge for accurate long-term prediction. We propose a three-layer, geographically informed ensemble, an extensive peer-learning framework, for predicting COVID-19 trends at the country, continent, and global levels. As the base layer, we develop a country-level predictor using a hybrid Graph Attention Network that incorporates a modified SIR model, adaptive loss function, and edge weights informed by mobility data. We aggregated 163 country GATs to train the continent and world MLP layers of the ensemble. Our results indicate that incorporating quantitatively accurate equations and real-world data to model inter-community interactions improves the performance of spatio-temporal machine learning algorithms. Additionally, we demonstrate that integrating geographic information (continent composition) improves the performance of the world predictor in our layered architecture

Underwater Geomagnetic Localization Based on Adaptive Fission Particle-Matching Technology

The geomagnetic field constitutes a massive fingerprint database, and its unique structure provides potential position correction information. In recent years, particle filter technology has received more attention in the context of robot navigation. However, particle degradation and impoverishment have constrained navigation systems’ performance. This paper transforms particle filtering into a particle-matching positioning problem and proposes a geomagnetic localization method based on an adaptive fission particle filter. This method employs particle-filtering technology to construct a geomagnetic matching positioning model. Through adaptive particle fission and sampling, the problem of particle degradation and impoverishment in traditional particle filtering is solved, resulting in improved geomagnetic matching positioning accuracy. Finally, the proposed method was tested in a sea trial, and the results show that the proposed method has a lower positioning error than traditional particle-filtering and intelligent particle-filtering algorithms. Under geomagnetic map conditions, an average positioning accuracy of about 546.44 m is achieved

Big data-enabled multiscale serviceability analysis for aging bridges☆

This work is dedicated to constructing a multi-scale structural health monitoring system to monitor and evaluate the serviceability of bridges based on the Hadoop Ecosystem (MS-SHM-Hadoop). By taking the advantages of the fault-tolerant distributed file system called the Hadoop Distributed File System (HDFS) and high-performance parallel data processing engine called MapReduce programming paradigm, MS-SHM-Hadoop features include high scalability and robustness in data ingestion, fusion, processing, retrieval, and analytics. MS-SHM-Hadoop is a multi-scale reliability analysis framework, which ranges from nationwide bridge-surveys, global structural integrity analysis, and structural component reliability analysis. This Nationwide bridge survey uses deep-learning techniques to evaluate the bridge serviceability according to real-time sensory data or archived bridge-related data such as traffic status, weather conditions and bridge structural configuration. The global structural integrity analysis of a targeted bridge is made by processing and analyzing the measured vibration signals incurred by external loads such as wind and traffic flow. Component-wise reliability analysis is also enabled by the deep learning technique, where the input data is derived from the measured structural load effects, hyper-spectral images, and moisture measurement of the structural components. As one of its major contributions, this work employs a Bayesian network to formulate the integral serviceability of a bridge according to its components serviceability and inter-component correlations. Here the inter-component correlations are jointly specified using a statistics-oriented machine learning method (e.g., association rule learning) or structural mechanics modeling and simulation

<i>In Situ</i> Thermal Ablation Repair of Delamination in Carbon Fiber-Reinforced Thermosetting Composites

Repairing delamination damage is critical to guarantee the structural safety of carbon fiber-reinforced thermosetting composites. The popular repair approaches, scarf repair and injection repair, can significantly restore the in-plane mechanical performance. However, the out-of-plane properties become worse due to the sacrifice of fiber continuity in these repairing processes, leading to the materials being susceptible under service loads. Here, we propose a novel in situ delamination repair approach of controllable thermal ablation in damage removal, achieving a high repair efficiency without impairing the fiber continuity in carbon fiber/epoxy panels. The epoxy resin in the delaminated region was eliminated under the carbonization temperature in a few minutes, allowing the carbon fiber frame to retain its structural integrity. The healing agent, refilled in the damaged region, was cured by the Joule heating of designed electrodes for 30 min at 80 °C, yielding the whole repair process to be accomplished within one hour. For the delaminated carbon fiber/epoxy panels with thicknesses from 2.5 to 6.8 mm, the in-plane compression-after-impact strength after repair could recover to 90.5% of the pristine one, and still retain 74.9% after three successive repair cycles of the 6.8 mm-thick sample. The simplicity and cost-saving advantages of this repair method offer great potential for practical applications of prolonging the service life of carbon fiber-reinforced thermosetting composites

Down-Regulation of Neuropathy Target Esterase in Preeclampsia Placenta Inhibits Human Trophoblast Cell Invasion via Modulating MMP-9 Levels

Background/Aims: Neuropathy target esterase (NTE, also known as neurotoxic esterase) is proven to deacylate phosphatidylcholine (PC) to glycerophosphocholine as a phospholipase B. Recently; studies showed that artificial phosphatidylserine/PC microvesicles can induce preeclampsia (PE)-like changes in pregnant mice. However, it is unclear whether NTE plays a key role in the pathology of PE, a pregnancy-related disease, which was characterized by deficient trophoblast invasion and reduced trophoblast-mediated remodeling of spiral arteries. The aim of this study was to investigate the expression pattern of NTE in the placenta from women with PE and normal pregnancy, and the molecular mechanism of NTE involved in the development of PE. Methods: NTE expression levels in placentas from 20 pregnant women with PE and 20 healthy pregnant women were detected using quantitative PCR and immunohistochemistry staining. The effect of NTE on trophoblast migration and invasion and the underlying mechanisms were examined in HTR-8/SVneo cell lines by transfection method. Results: NTE mRNA and protein expression levels were significantly decreased in preeclamptic placentas than normal control. Over-expression of NTE in HTR-8/SVneo cells significantly promoted trophoblast cells migration and invasion and was associated with increased MMP-9 levels. Conversely, shRNA-mediated down-regulation of NTE markedly inhibited the cell migration and invasion. In addition, silencing NTE reduced the MMP-9 activity and phosphorylated Erk1/2 and AKT levels. Conclusions: Our results suggest that the decreased NTE may contribute to the development of PE through impairing trophoblast invasion by down-regulating MMP-9 via the Erk1/2 and AKT signaling pathway