Search

We study incentivized exploration for the multi-armed bandit (MAB) problem with non-stationary reward distributions, where players receive compensation for exploring arms other than the greedy choice and may provide biased feedback on the reward. We consider two different non-stationary environments: abruptly-changing and continuously-changing, and propose respective incentivized exploration algorithms. We show that the proposed algorithms achieve sublinear regret and compensation over time, thus effectively incentivizing exploration despite the nonstationarity and the biased or drifted feedback.

We introduce NeuV-SLAM, a novel dense simultaneous localization and mapping pipeline based on neural multiresolution voxels, characterized by ultra-fast convergence and incremental expansion capabilities. This pipeline utilizes RGBD images as input to construct multiresolution neural voxels, achieving rapid convergence while maintaining robust incremental scene reconstruction and camera tracking. Central to our methodology is to propose a novel implicit representation, termed VDF that combines the implementation of neural signed distance field (SDF) voxels with an SDF activation strategy. This approach entails the direct optimization of color features and SDF values anchored within the voxels, substantially enhancing the rate of scene convergence. To ensure the acquisition of clear edge delineation, SDF activation is designed, which maintains exemplary scene representation fidelity even under constraints of voxel resolution. Furthermore, in pursuit of advancing rapid incremental expansion with low computational overhead, we developed hashMV, a novel hash-based multiresolution voxel management structure. This architecture is complemented by a strategically designed voxel generation technique that synergizes with a two-dimensional scene prior. Our empirical evaluations, conducted on the Replica and ScanNet Datasets, substantiate NeuV-SLAM's exceptional efficacy in terms of convergence speed, tracking accuracy, scene reconstruction, and rendering quality.

Effective multi-agent collaboration is imperative for solving complex, distributed problems. In this context, two key challenges must be addressed: first, autonomously identifying optimal objectives for collective outcomes; second, aligning these objectives among agents. Traditional frameworks, often reliant on centralized learning, struggle with scalability and efficiency in large multi-agent systems. To overcome these issues, we introduce a decentralized state-based value learning algorithm that enables agents to independently discover optimal states. Furthermore, we introduce a novel mechanism for multi-agent interaction, wherein less proficient agents follow and adopt policies from more experienced ones, thereby indirectly guiding their learning process. Our theoretical analysis shows that our approach leads decentralized agents to an optimal collective policy. Empirical experiments further demonstrate that our method outperforms existing decentralized state-based and action-based value learning strategies by effectively identifying and aligning optimal objectives., Comment: 10 pages, 3 figures, extended version of our 2024 American Control Conference publication

The formation mechanism of high-concentration dwarf galaxies is still a mystery. We perform a comparative study of the intrinsic shape of nearby low-mass galaxies with different stellar concentration. The intrinsic shape is parameterized by the intermediate-to-major axis ratios B/A and the minor-to-major axis ratios C/A of triaxial ellipsoidal models. Our galaxies ($10^{7.5} M_\odot$ < $M_\star$ < $10^{10.0} M_\odot$) are selected to have spectroscopic redshift from SDSS or GAMA, and have broadband optical images from the HSC-SSP Wide layer survey. The deep HSC-SSP images allow to measure the apparent axis ratios $q$ at galactic radii beyond the central star-forming area of our galaxies. We infer the intrinsic axis ratios based on the $q$ distributions. We find that 1) our galaxies have typical intrinsic shape similarly close to be oblate ($\mu_{B/A}$ $\sim$ 0.9--1), regardless of the concentration, stellar mass, star formation activity, and local environment (being central or satellite); 2) galaxies with the highest concentration tend to have intrinsic thickness similar to or (in virtually all cases) slightly thinner (i.e. smaller mean $\mu_{C/A}$ or equivalently lower triaxiality) than ordinary galaxies, regardless of other properties explored here. This appears to be in contrast with the expectation of the classic merger scenario for high-concentration galaxies. Given the lack of a complete understanding of dwarf-dwarf merger, we cannot draw a definite conclusion about the relevance of mergers in the formation of high-concentration dwarfs. Other mechanisms such as halo spin may also play important roles in the formation of high-concentration dwarf galaxies., Comment: 12 pages, 8 figures, 2 tables, accepted for publication in ApJ

Low-dimensional halide perovskites with broad emission are a hot topic for their promising application as white light sources. However, the physical origin of this broadband emission in the sub-bandgap region is still controversial. This work investigates the broad Stokes-shifted emission bands in mixed lead-tin 2D perovskite films prepared by mixing precursor solutions of phenethylammonium lead iodide (PEA2PbI4, PEA = phenethylammonium) and phenethylammonium tin iodide (PEA2SnI4). The bandgap can be tuned by the lead-tin ratio, whereas the photoluminescence is broad and significantly Stokes-shifted and appears to be fairly insensitive to the relative amount of Pb and Sn. It is experimentally observed that these low-dimensional systems show substantially less bandgap bowing than their 3D counterpart. Theoretically, this can be attributed to the smaller spin–orbit coupling effect on the 2D perovskites compared to that of 3D ones. The time-resolved photoluminescence shows an ultrafast decay in the high-energy range of the spectra that coincides with the emission range of PEA2SnI4, while the broadband emission decay is slower, up to the microsecond range. Sub-gap photoexcitation experiments exclude exciton self-trapping as the origin of the broadband emission, pointing to defects as the origin of the broadband emission in 2D Sn/Pb perovskite alloys.

In birds, little is known about how individuals choose nest sites based on their personality traits. Here, we investigate whether a female's personality (activity and breathing rate) can affect patterns of nest site selection at different spatial scales in a wild population of chestnut thrush (Turdus rubrocanus) and determine whether nest site characteristics and female personality traits affect clutch size and nest success during incubation. We found that neither activity nor breathing rate were associated with large-scale nesting habitat variables. At the fine-scale level, more active females chose nest sites with greater nest lateral concealment. Females with higher breathing rates laid smaller clutch sizes than individuals with lower breathing rates. Nests of females with lower breathing rate had higher nest success during incubation. This work highlights the relationships between personality and nest site selection in birds, and the important role of female personality traits in reproductive success.

Metal halide perovskites show the capability of performing structural transformation, allowing the formation of functional heterostructures. Unfortunately, the elusive mechanism governing these transformations limits their technological application. Herein, the mechanism of 2D–3D structural transformation is unraveled as catalyzed by solvents. By combining a spatial-temporal cation interdiffusivity simulation with experimental findings, it is validated that, protic solvents foster the dissociation degree of formadinium iodide (FAI) via dynamic hydrogen bond, then the stronger hydrogen bond of phenylethylamine (PEA) cation with selected solvents compared to dissociated FA cation facilitates 2D–3D transformation from (PEA)2PbI4 to FAPbI3. It is discovered that, the energy barrier of PEA out-diffusion and the lateral transition barrier of inorganic slab are diminished. For 2D films the protic solvents catalyze grain centers (GCs) and grain boundaries (GBs) transforme into 3D phases and quasi-2D phases, respectively. While in the solvent-free case, GCs transform into 3D–2D heterostructures along the direction perpendicular to the substrate, and most GBs evolve into 3D phases. Finally, memristor devices fabricated using the transformed films uncover that, GBs composed of 3D phases are more prone to ion migration. This work elucidates the fundamental mechanism of structural transformation in metal halide perovskites, allowing their use to fabricate complex heterostructures.

Purpose: Congenital heart defect (CHD) is the most common birth defect. Thoracic echocardiography (TTE) can provide sufficient cardiac structure information, evaluate hemodynamics and cardiac function, and is an effective method for atrial septal defect (ASD) examination. This paper aims to study a deep learning method based on cardiac ultrasound video to assist in ASD diagnosis. Materials and methods: We select two standard views of the atrial septum (subAS) and low parasternal four-compartment view (LPS4C) as the two views to identify ASD. We enlist data from 300 children patients as part of a double-blind experiment for five-fold cross-validation to verify the performance of our model. In addition, data from 30 children patients (15 positives and 15 negatives) are collected for clinician testing and compared to our model test results (these 30 samples do not participate in model training). We propose an echocardiography video-based atrial septal defect diagnosis system. In our model, we present a block random selection, maximal agreement decision and frame sampling strategy for training and testing respectively, resNet18 and r3D networks are used to extract the frame features and aggregate them to build a rich video-level representation. Results: We validate our model using our private dataset by five-cross validation. For ASD detection, we achieve 89.33 AUC, 84.95 accuracy, 85.70 sensitivity, 81.51 specificity and 81.99 F1 score. Conclusion: The proposed model is multiple instances learning-based deep learning model for video atrial septal defect detection which effectively improves ASD detection accuracy when compared to the performances of previous networks and clinical doctors.

Cross-lingual entity alignment is the task of finding the same semantic entities from different language knowledge graphs. In this paper, we propose a simple and novel unsupervised method for cross-language entity alignment. We utilize the deep learning multi-language encoder combined with a machine translator to encode knowledge graph text, which reduces the reliance on label data. Unlike traditional methods that only emphasize global or local alignment, our method simultaneously considers both alignment strategies. We first view the alignment task as a bipartite matching problem and then adopt the re-exchanging idea to accomplish alignment. Compared with the traditional bipartite matching algorithm that only gives one optimal solution, our algorithm generates ranked matching results which enabled many potentials downstream tasks. Additionally, our method can adapt two different types of optimization (minimal and maximal) in the bipartite matching process, which provides more flexibility. Our evaluation shows, we each scored 0.966, 0.990, and 0.996 Hits@1 rates on the DBP15K dataset in Chinese, Japanese, and French to English alignment tasks. We outperformed the state-of-the-art method in unsupervised and semi-supervised categories. Compared with the state-of-the-art supervised method, our method outperforms 2.6% and 0.4% in Ja-En and Fr-En alignment tasks while marginally lower by 0.2% in the Zh-En alignment task., Comment: 17 pages,5 figures, Accepted by ECML PKDD 2023(Research Track)

Viral infections can alter host transcriptomes by manipulating host splicing machinery. Despite intensive transcriptomic studies on SARS-CoV-2, a systematic analysis of alternative splicing (AS) in severe COVID-19 patients remains largely elusive. Here we integrated proteomic and transcriptomic sequencing data to study AS changes in COVID-19 patients. We discovered that RNA splicing is among the major down-regulated proteomic signatures in COVID-19 patients. The transcriptome analysis showed that SARS-CoV-2 infection induces widespread dysregulation of transcript usage and expression, affecting blood coagulation, neutrophil activation, and cytokine production. Notably, CD74 and LRRFIP1 had increased skipping of an exon in COVID-19 patients that disrupts a functional domain, which correlated with reduced antiviral immunity. Furthermore, the dysregulation of transcripts was strongly correlated with clinical severity of COVID-19, and splice-variants may contribute to unexpected therapeutic activity. In summary, our data highlight that a better understanding of the AS landscape may aid in COVID-19 diagnosis and therapy.

Background: The microbiota plays a key role in early immunity maturation that affects infant health and is associated with the development of non-communicable diseases and allergies in later life. Objective: The MYBIOTA is a prospective mother-infant cohort study in Malaysia aiming to determine the association between gut microbiota with infant health (temperament, gastrointestinal disorders, eczema, asthma, and developmental delays) in Selangor, Malaysia. Methods: Pregnant mothers will be enrolled in their first trimester of pregnancy, and follow-ups will be done for infants during their first year of life. Maternal-infant biological samples (blood, feces, saliva, urine, and breast milk), anthropometric, dietary, and clinical information will be collected at different time points from early pregnancy to 12 months postpartum. Discussion: This study could provide a better understanding of the colonization and development of the gut microbiome during early life and its impact on infant health. Clinical trial registration: https://clinicaltrials.gov/, identifier NCT04919265.

Background: The microbiota plays a key role in early immunity maturation that affects infant health and is associated with the development of non-communicable diseases and allergies in later life. Objective: The MYBIOTA is a prospective mother-infant cohort study in Malaysia aiming to determine the association between gut microbiota with infant health (temperament, gastrointestinal disorders, eczema, asthma, and developmental delays) in Selangor, Malaysia. Methods: Pregnant mothers will be enrolled in their first trimester of pregnancy, and follow-ups will be done for infants during their first year of life. Maternal-infant biological samples (blood, feces, saliva, urine, and breast milk), anthropometric, dietary, and clinical information will be collected at different time points from early pregnancy to 12 months postpartum. Discussion: This study could provide a better understanding of the colonization and development of the gut microbiome during early life and its impact on infant health. Clinical trial registration: https://clinicaltrials.gov/, identifier NCT04919265.

Significant developments in digital technologies can potentially provide managers and engineers with the ability to improve the quality of the construction industry. Acknowledging the current and future use of digital technologies in construction quality management (CQM), we address the following research question: What developments in digital technologies can be used to improve quality in the construction industry? In addressing this research question, a systematic review approach is used to examine the studies that have been used for the management of quality in the construction industry. This review indicates that there is a need for digital technology-based quality management to be: (1) enhance defect management for concealed work, (2) enhance pre-construction defects prevention as well as post-completion product function testing, and (3) research on construction compliance inspection as a direction. We suggest that future research focus on quality culture development, advanced data analytics, and behavioral quality assessment.

Significant developments in digital technologies can potentially provide managers and engineers with the ability to improve the quality of the construction industry. Acknowledging the current and future use of digital technologies in construction quality management (CQM), we address the following research question: What developments in digital technologies can be used to improve quality in the construction industry? In addressing this research question, a systematic review approach is used to examine the studies that have been used for the management of quality in the construction industry. This review indicates that there is a need for digital technology-based quality management to be: (1) enhance defect management for concealed work, (2) enhance pre-construction defects prevention as well as post-completion product function testing, and (3) research on construction compliance inspection as a direction. We suggest that future research focus on quality culture development, advanced data analytics, and behavioral quality assessment.

We report our experiences implementing standards-based grading at scale in an Algorithms course, which serves as the terminal required CS Theory course in our department's undergraduate curriculum. The course had 200-400 students, taught by two instructors, eight graduate teaching assistants, and supported by two additional graders and several undergraduate course assistants. We highlight the role of standards-based grading in supporting our students during the COVID-19 pandemic. We conclude by detailing the successes and adjustments we would make to the course structure., Comment: This is an extended version of our paper, which will appear in ITiCSE 2022

In this paper, we propose an optimal control-estimation architecture for distribution networks, which jointly solves the optimal power flow (OPF) problem and static state estimation (SE) problem through an online gradient-based feedback algorithm. The main objective is to enable a fast and timely interaction between the optimal controllers and state estimators with limited sensor measurements. First, convergence and optimality of the proposed algorithm are analytically established. Then, the proposed gradient-based algorithm is modified by introducing statistical information of the inherent estimation and linearization errors for an improved and robust performance of the online control decisions. Overall, the proposed method eliminates the traditional separation of control and operation, where control and estimation usually operate at distinct layers and different time-scales. Hence, it enables a computationally affordable, efficient and robust online operational framework for distribution networks under time-varying settings.

This article analyzes the current status of indexing investment performance in China and the characteristics of fund managers and fund management companies. It also theoretically analyzes the influence of these factors on indexing investment performance in terms of fund governance structure, characteristics of fund managers and fund companies, stock selection ability and timing ability. Next, it uses multiple regression analysis from three dimensions mentioned above to verify the influencing factors on investment performance. The results show that in terms of passive investment, fund tracking error is significantly negatively correlated with institutional holdings and fund managers’ stock selection ability, and significantly positively correlated with fund managers’ timing ability. Female fund managers’ tracking errors are smaller. Institutional investors play a positive role in promoting the performance of active investment. The fund manager’s stock selection ability can effectively reduce the fund’s sensitivity to target index fluctuations, and the fund manager’s timing ability can significantly improve the performance of fund allocation. Funds allocated by female fund managers will be less sensitive to target index fluctuations. Keywords: indexing investment, performance, fund management, fund managers DOI: 10.7176/RJFA/12-22-01 Publication date: November 30th 2021

Based on the characteristics of investor behavior, this article analyzes the impact of institutional investors and investor sentiment on the liquidity, profitability and stability of the capital market, and analyzes the impact of investor overreaction on the market. Through multiple regression analysis, it is verified that the holding ratio of institutional investors has a significantly negative relationship with the turnover rate of funds, and it has a positive relationship with the annual rate of return of the fund and the annual volatility of the fund. Investor sentiment shows a positive correlation with the turnover rate of funds, the yield of the fund and the volatility of the fund. Through quantile regression, it is found that when the volatility of an index is at a high level, it is more susceptible to the negative impact of the previous trading volume. Keywords: behaviour characteristics, indexing investment, investor sentiment, capital market DOI: 10.7176/EJBM/13-22-01 Publication date: November 30th 2021

Adaptive charging can charge electric vehicles (EVs) at scale cost effectively, despite the uncertainty in EV arrivals. We formulate adaptive EV charging as a feasibility problem that meets all EVs' energy demands before their deadlines while satisfying constraints in charging rate and total charging power. We propose an online algorithm, smoothed least-laxity-first (sLLF), that decides the current charging rates without the knowledge of future arrivals and demands. We characterize the performance of the sLLF algorithm analytically and numerically. Numerical experiments with real-world data show that it has a significantly higher rate of feasible EV charging than several other existing EV charging algorithms. Resource augmentation framework is employed to assess the feasibility condition of the algorithm. The assessment shows that the sLLF algorithm achieves perfect feasibility with only a 0.07 increase in resources.

30-m Global Land Cover (GLC) data products permit the detection of land cover changes at the scale of most human land activities, and are therefore used as fundamental information for sustainable development, environmental change studies, and many other societal benefit areas. In the past few years, increasing efforts have been devoted to the accuracy assessment of GlobeLand30 and other finer-resolution GLC data products. However, most of them were conducted either within a limited percentage of map sheets selected from a global scale or in some individual countries (areas), and there are still many areas where the uncertainty of 30-m resolution GLC data products remains to be validated and documented. In order to promote a comprehensive and collaborative validation of 30-m GLC data products, the GEO Global Land Cover Community Activity had organized a project from 2015 to 2017, to examine and explore its major problems, including the lack of international agreed validation guidelines and on-line tools for facilitating collaborative validation activities. With the joint effort of experts and users from 30 GEO member countries or participating organizations, a technical specification for 30-m GLC validation was developed based on the findings and experiences. An on-line validation tool, GLCVal, was developed by integrating land cover validation procedures with the service computing technologies. About 20 countries (regions) have completed the accuracy assessment of GlobeLand30 for their territories with the guidance of the technical specification and the support of GLCVal., QC 20210408

In this paper, we study the problem of jointly estimating the optical flow and scene flow from synchronized 2D and 3D data. Previous methods either employ a complex pipeline that splits the joint task into independent stages, or fuse 2D and 3D information in an "early-fusion" or "late-fusion" manner. Such one-size-fits-all approaches suffer from a dilemma of failing to fully utilize the characteristic of each modality or to maximize the inter-modality complementarity. To address the problem, we propose a novel end-to-end framework, called CamLiFlow. It consists of 2D and 3D branches with multiple bidirectional connections between them in specific layers. Different from previous work, we apply a point-based 3D branch to better extract the geometric features and design a symmetric learnable operator to fuse dense image features and sparse point features. Experiments show that CamLiFlow achieves better performance with fewer parameters. Our method ranks 1st on the KITTI Scene Flow benchmark, outperforming the previous art with 1/7 parameters. Code is available at https://github.com/MCG-NJU/CamLiFlow., Comment: Accepted to CVPR 2022 (Oral)

Based on the characteristics of investor behavior, this article analyzes the impact of institutional investors and investor sentiment on the liquidity, profitability and stability of the capital market, and analyzes the impact of investor overreaction on the market. Through multiple regression analysis, it is verified that the holding ratio of institutional investors has a significantly negative relationship with the turnover rate of funds, and it has a positive relationship with the annual rate of return of the fund and the annual volatility of the fund. Investor sentiment shows a positive correlation with the turnover rate of funds, the yield of the fund and the volatility of the fund. Through quantile regression, it is found that when the volatility of an index is at a high level, it is more susceptible to the negative impact of the previous trading volume. Keywords: behaviour characteristics, indexing investment, investor sentiment, capital market DOI: 10.7176/EJBM/13-22-01 Publication date: November 30th 2021

This article analyzes the current status of indexing investment performance in China and the characteristics of fund managers and fund management companies. It also theoretically analyzes the influence of these factors on indexing investment performance in terms of fund governance structure, characteristics of fund managers and fund companies, stock selection ability and timing ability. Next, it uses multiple regression analysis from three dimensions mentioned above to verify the influencing factors on investment performance. The results show that in terms of passive investment, fund tracking error is significantly negatively correlated with institutional holdings and fund managers’ stock selection ability, and significantly positively correlated with fund managers’ timing ability. Female fund managers’ tracking errors are smaller. Institutional investors play a positive role in promoting the performance of active investment. The fund manager’s stock selection ability can effectively reduce the fund’s sensitivity to target index fluctuations, and the fund manager’s timing ability can significantly improve the performance of fund allocation. Funds allocated by female fund managers will be less sensitive to target index fluctuations. Keywords: indexing investment, performance, fund management, fund managers DOI: 10.7176/RJFA/12-22-01 Publication date: November 30th 2021

This article analyzes the current status of indexing investment performance in China and the characteristics of fund managers and fund management companies. It also theoretically analyzes the influence of these factors on indexing investment performance in terms of fund governance structure, characteristics of fund managers and fund companies, stock selection ability and timing ability. Next, it uses multiple regression analysis from three dimensions mentioned above to verify the influencing factors on investment performance. The results show that in terms of passive investment, fund tracking error is significantly negatively correlated with institutional holdings and fund managers’ stock selection ability, and significantly positively correlated with fund managers’ timing ability. Female fund managers’ tracking errors are smaller. Institutional investors play a positive role in promoting the performance of active investment. The fund manager’s stock selection ability can effectively reduce the fund’s sensitivity to target index fluctuations, and the fund manager’s timing ability can significantly improve the performance of fund allocation. Funds allocated by female fund managers will be less sensitive to target index fluctuations. Keywords: indexing investment, performance, fund management, fund managers DOI: 10.7176/RJFA/12-22-01 Publication date: November 30th 2021

Adaptive charging can charge electric vehicles (EVs) at scale cost effectively, despite the uncertainty in EV arrivals. We formulate adaptive EV charging as a feasibility problem that meets all EVs' energy demands before their deadlines while satisfying constraints in charging rate and total charging power. We propose an online algorithm, smoothed least-laxity-first (sLLF), that decides the current charging rates without the knowledge of future arrivals and demands. We characterize the performance of the sLLF algorithm analytically and numerically. Numerical experiments with real-world data show that it has a significantly higher rate of feasible EV charging than several other existing EV charging algorithms. Resource augmentation framework is employed to assess the feasibility condition of the algorithm. The assessment shows that the sLLF algorithm achieves perfect feasibility with only a 0.07 increase in resources., Comment: 14 pages, 4 figures

A key challenge in wide adoption of sophisticated context-aware applications is the requirement of continuous sensing and context computing. This paper presents Panorama, a middleware that identifies collaboration opportunities to offload context computing tasks to nearby mobile devices as well as cloudlets/cloud. At the heart of Panorama is a multi-objective optimizer that takes into account different constraints such as access cost, computation capability, access latency, energy consumption and data privacy, and efficiently computes a collaboration plan optimized simultaneously for different objectives such as minimizing cost, energy and/or execution time. Panorama provides support for discovering nearby devices and cloudlets/cloud, computing an optimal collaboration plan, distributing computation to participating devices, and getting the results back. The paper provides an extensive evaluation of Panorama via two representative context monitoring applications over a set of Android devices and a cloudlet/cloud under different constraints., Comment: Published in MobiCase 2015

Lan Chen,1 Han Zheng,2 Lijun Chen,2 Sunying Wu,2 Saibin Wang3 1Nursing Education Department, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua Municipal Central Hospital, Jinhua, Zhejiang Province, People’s Republic of China; 2Emergency Department, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua Municipal Central Hospital, Jinhua, Zhejiang Province, People’s Republic of China; 3Department of Respiratory Medicine, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua Municipal Central Hospital, Jinhua, 321000, Zhejiang Province, People’s Republic of ChinaCorrespondence: Saibin WangDepartment of Respiratory Medicine, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua Municipal Central Hospital, No. 365, East Renmin Road, Jinhua, 321000, Zhejiang Province, People’s Republic of ChinaTel +86 579 82552278Fax +86 579 82325006Email saibinwang@hotmail.comBackground: For emergency triage, it is very important to identify patient severity according to their vital signs and chief complaint. Several studies have examined the predictive value of the National Early Warning Score (NEWS) for specific emergency patients and have shown it to be effective. However, few have studied the utility of NEWS in emergency triage for general emergency medicine patients. The aim of this research was to investigate the performance of NEWS in emergency triage with regard to predicting adverse outcomes.Methods: This was a retrospective cohort study carried out at a tertiary care center hospital in Jinhua, China. A total of 62,403 patients attending the emergency department (ED) from January to December 2018 were included. The NEWS, Modified Early Warning Score (MEWS), and quick Sepsis Related Organ Failure Assessment (qSOFA) score were obtained from emergency triage. Multivariate logistic regression analysis was performed to evaluate the associations between the NEWS, MEWS, and qSOFA, as well as

Soil microbial respiration is an important source of uncertainty in projecting future climate and carbon (C) cycle feedbacks. However, its feedbacks to climate warming and underlying microbial mechanisms are still poorly understood. Here we show that the temperature sensitivity of soil microbial respiration (Q10) in a temperate grassland ecosystem persistently decreases by 12.0 ± 3.7% across 7 years of warming. Also, the shifts of microbial communities play critical roles in regulating thermal adaptation of soil respiration. Incorporating microbial functional gene abundance data into a microbially-enabled ecosystem model significantly improves the modeling performance of soil microbial respiration by 5-19%, and reduces model parametric uncertainty by 55-71%. In addition, modeling analyses show that the microbial thermal adaptation can lead to considerably less heterotrophic respiration (11.6 ± 7.5%), and hence less soil C loss. If such microbially mediated dampening effects occur generally across different spatial and temporal scales, the potential positive feedback of soil microbial respiration in response to climate warming may be less than previously predicted.

Soil microbial respiration is an important source of uncertainty in projecting future climate and carbon (C) cycle feedbacks. However, its feedbacks to climate warming and underlying microbial mechanisms are still poorly understood. Here we show that the temperature sensitivity of soil microbial respiration (Q10) in a temperate grassland ecosystem persistently decreases by 12.0 ± 3.7% across 7 years of warming. Also, the shifts of microbial communities play critical roles in regulating thermal adaptation of soil respiration. Incorporating microbial functional gene abundance data into a microbially-enabled ecosystem model significantly improves the modeling performance of soil microbial respiration by 5-19%, and reduces model parametric uncertainty by 55-71%. In addition, modeling analyses show that the microbial thermal adaptation can lead to considerably less heterotrophic respiration (11.6 ± 7.5%), and hence less soil C loss. If such microbially mediated dampening effects occur generally across different spatial and temporal scales, the potential positive feedback of soil microbial respiration in response to climate warming may be less than previously predicted.

Multi-robot cooperation requires agents to make decisions that are consistent with the shared goal without disregarding action-specific preferences that might arise from asymmetry in capabilities and individual objectives. To accomplish this goal, we propose a method named SLiCC: Stackelberg Learning in Cooperative Control. SLiCC models the problem as a partially observable stochastic game composed of Stackelberg bimatrix games, and uses deep reinforcement learning to obtain the payoff matrices associated with these games. Appropriate cooperative actions are then selected with the derived Stackelberg equilibria. Using a bi-robot cooperative object transportation problem, we validate the performance of SLiCC against centralized multi-agent Q-learning and demonstrate that SLiCC achieves better combined utility., Comment: 8 pages, 7 figures

We investigate the sparse linear contextual bandit problem where the parameter $\theta$ is sparse. To relieve the sampling inefficiency, we utilize the "perturbed adversary" where the context is generated adversarilly but with small random non-adaptive perturbations. We prove that the simple online Lasso supports sparse linear contextual bandit with regret bound $\mathcal{O}(\sqrt{kT\log d})$ even when $d \gg T$ where $k$ and $d$ are the number of effective and ambient dimension, respectively. Compared to the recent work from Sivakumar et al. (2020), our analysis does not rely on the precondition processing, adaptive perturbation (the adaptive perturbation violates the i.i.d perturbation setting) or truncation on the error set. Moreover, the special structures in our results explicitly characterize how the perturbation affects exploration length, guide the design of perturbation together with the fundamental performance limit of perturbation method. Numerical experiments are provided to complement the theoretical analysis., Comment: 16 pages, 2 figures

We propose a framework for integrating optimal power flow (OPF) with state estimation (SE) in the loop for distribution networks. Our approach combines a primal-dual gradient-based OPF solver with a SE feedback loop based on a limited set of sensors for system monitoring, instead of assuming exact knowledge of all states. The estimation algorithm reduces uncertainty on unmeasured grid states based on a few appropriate online state measurements and noisy "pseudo-measurements". We analyze the convergence of the proposed algorithm and quantify the statistical estimation errors based on a weighted least squares (WLS) estimator. The numerical results on a 4521-node network demonstrate that this approach can scale to extremely large networks and provide robustness to both large pseudo measurement variability and inherent sensor measurement noise., Comment: arXiv admin note: text overlap with arXiv:1909.12763

We consider solving a cooperative multi-robot object manipulation task using reinforcement learning (RL). We propose two distributed multi-agent RL approaches: distributed approximate RL (DA-RL), where each agent applies Q-learning with individual reward functions; and game-theoretic RL (GT-RL), where the agents update their Q-values based on the Nash equilibrium of a bimatrix Q-value game. We validate the proposed approaches in the setting of cooperative object manipulation with two simulated robot arms. Although we focus on a small system of two agents in this paper, both DA-RL and GT-RL apply to general multi-agent systems, and are expected to scale well to large systems., Comment: 3 pages, 3 figures

An efficient error reconciliation scheme is important for post-processing of quantum key distribution (QKD). Recently, a multi-matrix low-density parity-check codes based reconciliation algorithm which can provide remarkable perspectives for high efficiency information reconciliation was proposed. This paper concerns the improvement of reconciliation performance. Multi-matrix algorithm is implemented and optimized on the graphics processing unit (GPU) to obtain high reconciliation throughput. Experimental results indicate that GPU-based algorithm can highly improve reconciliation throughput to an average 85.67 Mbps and a maximum 102.084 Mbps with typical code rate and efficiency. This is the best performance of reconciliation on GPU platform to our knowledge., Comment: 8pages, 3figures, 4tables

Key reconciliation of quantum key distribution (QKD) is the process of correcting errors caused by channel noise and eavesdropper to identify the keys of two legitimate users. Reconciliation efficiency is the most important figure for judging the quality of a reconciliation scheme. To improve reconciliation efficiency, rate-compatible technologies was proposed for key reconciliation, which is denoted as the single-matrix ratecompatible reconciliation (SRCR). In this paper, a recently suggested technique called multi-matrix reconciliation is introduced into SRCR, which is referred to as the multi-matrix rate-compatible reconciliation (MRCR), to further improve reconciliation efficiency and promote the throughput of SRCR. Simulation results show that MRCR we proposed outperforms SRCR in reconciliation efficiency and throughput., Comment: 8 pages, 4 figures

In the free online game industry, churn prediction is an important research topic. Reducing the churn rate of a game significantly helps with the success of the game. Churn prediction helps a game operator identify possible churning players and keep them engaged in the game via appropriate operational strategies, marketing strategies, and/or incentives. Playtime related features are some of the widely used universal features for most churn prediction models. In this paper, we consider developing new universal features for churn predictions for long-term players based on players' playtime.

We study incentivized exploration for the multi-armed bandit (MAB) problem where the players receive compensation for exploring arms other than the greedy choice and may provide biased feedback on reward. We seek to understand the impact of this drifted reward feedback by analyzing the performance of three instantiations of the incentivized MAB algorithm: UCB, $\varepsilon$-Greedy, and Thompson Sampling. Our results show that they all achieve $\mathcal{O}(\log T)$ regret and compensation under the drifted reward, and are therefore effective in incentivizing exploration. Numerical examples are provided to complement the theoretical analysis., Comment: 10 pages, 2 figures, AAAI 2020

We propose an alternating optimization algorithm to the nonconvex Koopman operator learning problem for nonlinear dynamic systems. We show that the proposed algorithm will converge to a critical point with rate $O(1/T)$ and $O(\frac{1}{\log T})$ for the constant and diminishing learning rates, respectively, under some mild conditions. To cope with the high dimensional nonlinear dynamical systems, we present the first-ever distributed Koopman operator learning algorithm. We show that the distributed Koopman operator learning has the same convergence properties as the centralized Koopman operator learning, in the absence of optimal tracker, so long as the basis functions satisfy a set of state-based decomposition conditions. Numerical experiments are provided to complement our theoretical results., Comment: 8 pages, 2 figures

Conventional optimal power flow (OPF) solvers assume full observability of the involved system states. However, in practice, there is a lack of reliable system monitoring devices in the distribution networks. To close the gap between the theoretic algorithm design and practical implementation, this work proposes to solve the OPF problems based on the state estimation (SE) feedback for the distribution networks where only a part of the involved system states are physically measured. The SE feedback increases the observability of the under-measured system and provides more accurate system states monitoring when the measurements are noisy. We analytically investigate the convergence of the proposed algorithm. The numerical results demonstrate that the proposed approach is more robust to large pseudo measurement variability and inherent sensor noise in comparison to the other frameworks without SE feedback.

As the inevitable development trend of quantum key distribution, quantum networks have attracted extensive attention, and many prototypes have been deployed over recent years. Existing quantum networks based on optical fibers or quantum satellites can realize the metro and even global communications. However, for some application scenarios that require emergency or temporary communications, such networks cannot meet the requirements of rapid deployment, low cost, and high mobility. To solve this problem, we introduce an important concept of classical networks, i.e., self-organization, to quantum networks, and give two simple network prototypes based on acquisition, tracking, and pointing systems. In these networks, the users only need to deploy the network nodes, which will rapidly, automatically, and adaptively organize and mange a quantum network. Our method expands the application scope of quantum networks, and gives a new approach for the design and implementation of quantum networks. It also provides users with a low-cost access network solution, and can be used to fundamentally solve the security problem of classical self-organizing networks., Comment: 5 pages, 5 figures

Dandan Gao,1 Zhimin Guo,1 Jiabin Wang,2 Gaofeng Hu,1 Yuqiao Su,3 Lijun Chen,1 Qianwen Lv,1 Huimei Yu,2 Jianchun Qin,3 Wei Xu1 1Department of Clinical Laboratory, The First Hospital of Jilin University, Changchun 130021, China; 2Department of Pathology and Pathophysiology, School of Basic Medical Sciences, Jilin University, Changchun 130021, China; 3Department of Biotechnology, College of Plant Sciences, Jilin University, Changchun, Jilin 130062, China Background: Dicerandrol B is a natural antitumor agent that can be isolated from the endophytic fungus, Phomopsis sp. The present study investigated the effects of dicerandrol B on human cervical cancer HeLa cells.Materials and methods: In this study, dicerandrol B was identified by electrospray ionization mass spectrometry and nuclear magnetic resonance spectroscopy. We used MTT to detect the cell viability. Flow cytometry was used to analyze the apoptosis and cell cycle. Western blot was used to examine the expression of related proteins.Results: Dicerandrol B was isolated from the endophytic fungus Phomopsis sp. The MTT assay and flow cytometry showed that dicerandrol B significantly inhibited HeLa cell viability and induced G2/M cell cycle arrest. Western blot analysis demonstrated that dicerandrol B increased the levels of GRP78, ubiquitin, cleaved PARP, and Bax protein, decreased the levels of PARP and Bcl-2 protein, and caused an increase in the Bax/Bcl-2 ratio in HeLa cells. Dicerandrol B increased the production of ROS in HeLa cells, which was attenuated by the antioxidant N-acetyl-l-cysteine.Conclusion: These findings suggest that dicerandrol B induces apoptosis in human HeLa cells, possibly through the endoplasmic reticulum stress and mitochondrial apoptotic pathways. This suggests that dicerandrol B possesses strong anticancer activity in cervical cancer and provides insight into the underlying mechanisms. Keywords: apoptosis, cervical cancer, endoplasmic reticulum stress, mitochondrial damage

Glycans play critical roles in a number of biological activities. Two common types of glycans, N-linked and O-linked, have been extensively analyzed in the last decades. N-glycans are typically released from glycoproteins by enzymes, while O-glycans are released from glycoproteins by chemical methods. It is important to identify and quantify both N- and O-linked glycans of glycoproteins to determine the changes of glycans. The effort has been dedicated to study glycans from ovarian cancer cells treated with O-linked glycosylation inhibitor qualitatively and quantitatively. We used a solid-phase chemoenzymatic approach to systematically identify and quantify N-glycans and O-glycans in the ovarian cancer cells. It consists of three steps: (1) immobilization of proteins from cells and derivatization of glycans to protect sialic acids; (2) release of N-glycans by PNGase F and quantification of N-glycans by isobaric tags; (3) release and quantification of O-glycans by β-elimination in the presence of 1-phenyl-3-methyl-5-pyrazolone (PMP). We used ovarian cancer cell lines to study effect of O-linked glycosylation inhibitor on protein glycosylation. Results suggested that the inhibition of O-linked glycosylation reduced the levels of O-glycans. Interestingly, it appeared to increase N-glycan level in a lower dose of the O-linked glycosylation inhibitor. The sequential release and analyses of N-linked and O-linked glycans using chemoenzymatic approach are a platform for studying N-glycans and O-glycans in complex biological samples. The solid-phase chemoenzymatic method was used to analyze both N-linked and O-linked glycans sequentially released from the ovarian cancer cells. The biological studies on O-linked glycosylation inhibition indicate the effects of O-glycosylation inhibition to glycan changes in both O-linked and N-linked glycan expression.

We formulate EV charging as a feasibility problem that meets all EVs' energy demands before departure under charging rate constraints and total power constraint. We propose an online algorithm, the smoothed least-laxity-first (sLLF) algorithm, that decides on the current charging rates based on only the information up to the current time. We characterize the performance of the sLLF algorithm analytically and numerically. Numerical experiments with real-world data show that it has significantly higher rate of generating feasible EV charging than several other common EV charging algorithms.

Demand shaping is a promising way to mitigate the wireless cellular capacity shortfall in the presence of ever-increasing wireless data demand. In this paper, we formulate demand shaping as an optimization problem that minimizes the variation in aggregate traffic. We design a distributed and randomized offline demand shaping algorithm under complete traffic information and prove its almost surely convergence. We further consider a more realistic setting where the traffic information is incomplete but the future traffic can be predicted to a certain degree of accuracy. We design an online demand shaping algorithm that updates the schedules of deferrable applications (DAs) each time when new information is available, based on solving at each timeslot an optimization problem over a shrinking horizon from the current time to the end of the day. We compare the performance of the online algorithm against the optimal offline algorithm, and provide numerical examples to complement the theoretical analysis.

This paper formulates a time-varying social-welfare maximization problem for distribution grids with distributed energy resources (DERs) and develops online distributed algorithms to identify (and track) its solutions. In the considered setting, network operator and DER-owners pursue given operational and economic objectives, while concurrently ensuring that voltages are within prescribed limits. The proposed algorithm affords an online implementation to enable tracking of the solutions in the presence of time-varying operational conditions and changing optimization objectives. It involves a strategy where the network operator collects voltage measurements throughout the feeder to build incentive signals for the DER-owners in real time; DERs then adjust the generated/consumed powers in order to avoid the violation of the voltage constraints while maximizing given objectives. The stability of the proposed schemes is analytically established and numerically corroborated.

In this paper we propose a new Koopman operator approach to the decomposition of nonlinear dynamical systems using Koopman Gramians. We introduce the notion of an input-Koopman operator, and show how input-Koopman operators can be used to cast a nonlinear system into the classical state-space form, and identify conditions under which input and state observable functions are well separated. We then extend an existing method of dynamic mode decomposition for learning Koopman operators from data known as deep dynamic mode decomposition to systems with controls or disturbances. We illustrate the accuracy of the method in learning an input-state separable Koopman operator for an example system, even when the underlying system exhibits mixed state-input terms. We next introduce a nonlinear decomposition algorithm, based on Koopman Gramians, that maximizes internal subsystem observability and disturbance rejection from unwanted noise from other subsystems. We derive a relaxation based on Koopman Gramians and multi-way partitioning for the resulting NP-hard decomposition problem. We lastly illustrate the proposed algorithm with the swing dynamics for an IEEE 39-bus system., Comment: 8 pages, submitted to IEEE 2018 ACC

Bakgrund: Oro drabbar de flesta patienter inför operation. Det finns många faktorer som påverkar patientens upplevelser av oro både inom vården och det liv patienten befinner sig i. Det är av stor vikt att få en ökad förståelse av hur den preoperativa omvårdnaden kan minska patientens oro och lidande utifrån patientens perspektiv. Syfte: Att beskriva patientens upplevelse av oro i samband med den preoperativa omvårdnaden. Metod: En deskriptiv litteraturstudie baserad på kvalitativa artiklar valdes. Tjugo artiklar analyserades med en beskrivande syntes enligt Evans (2002). Resultat: Det uppkom tre huvud teman och sex subtema; Tema ”Oro som är relaterad till operation” med subteman ”Faktorer som orsakar oro hos patienten innan operation” och ”Väntetidenspåverkan”, tema ”Behov av stöd för att vara delaktig” med subteman ”Individuellt anpassad information” och ”Informationsstöd från vårdpersonal och närstående”, tema ”Behov av trygghet och tillit” med subteman ”Bemötande” och ”Vårdmiljön”. Slutsats: Upplevelsen av att känna oro innan operation var huvudsakligen ohanterbart och frustrerande på alla aspekter i patientens livsvärld. Med hjälp av ökad förståelse av patientens upplevelser av oro i förhållande till den preoperativa omvårdnaden kan operationssjuksköterskan minska detta lidande som är relaterad till vården. Det är viktigt för operationssjuksköterskan att reflektera över det stöd som patienter är i behov av med en helhetssyn. Nyckelord: preoperativ omvårdnad, upplevelse, lidande, oro, patient, kvalitativ, Background: Anxiety afflicts the majority of patients before an operation. There are many factors which affect the patient's experience of anxiety both within care and the stage of life in which the patient finds himself. It is of great importance to acquire an increased understanding of how pre-operative care can reduce the patient's anxiety and suffering from the patient's perspective. Aim: To describe the patient's experience of anxiety in relation to pre- operative care. Method: A descriptive literature study based on qualitative articles was chosen. Twenty articles were analyzed with a descriptive synthesis as according to Evans (2002). Results: Three main themes and six sub-themes emerged. Theme “Anxiety related to operation” with the sub-theme “Factors which cause anxiety before an operation” and “The effect of waiting”, theme “The need of support to be involved” with the sub-theme “Personalized information” and “Information support from nursing staff and family”, theme “The need of security and trust” with the sub-theme “Personal treatment” and “Care environment”. Conclusion: The experiencing of feelings of anxiety before an operation could not be addressed and was frustrating in all aspects of the patient's lifeworld. With the help of increased understanding of the patients’ experience of anxiety in relation to the pre-operative care, the operation nurse can reduce this suffering that is related to health care. It is important that the operation nurse reflects over the support the patient needs with a holistic approach. Keywords: preoperative care, experience, suffering, anxiety, patient, qualitative

To provide a detailed analysis of the molecular components and underlying mechanisms associated with ovarian cancer, we performed a comprehensive mass-spectrometry-based proteomic characterization of 174 ovarian tumors previously analyzed by The Cancer Genome Atlas (TCGA), of which 169 were high-grade serous carcinomas (HGSCs). Integrating our proteomic measurements with the genomic data yielded a number of insights into disease, such as how different copy-number alternations influence the proteome, the proteins associated with chromosomal instability, the sets of signaling pathways that diverse genome rearrangements converge on, and the ones most associated with short overall survival. Specific protein acetylations associated with homologous recombination deficiency suggest a potential means for stratifying patients for therapy. In addition to providing a valuable resource, these findings provide a view of how the somatic genome drives the cancer proteome and associations between protein and post-translational modification levels and clinical outcomes in HGSC. VIDEO ABSTRACT.

Bakgrund: Oro drabbar de flesta patienter inför operation. Det finns många faktorer som påverkar patientens upplevelser av oro både inom vården och det liv patienten befinner sig i. Det är av stor vikt att få en ökad förståelse av hur den preoperativa omvårdnaden kan minska patientens oro och lidande utifrån patientens perspektiv. Syfte: Att beskriva patientens upplevelse av oro i samband med den preoperativa omvårdnaden. Metod: En deskriptiv litteraturstudie baserad på kvalitativa artiklar valdes. Tjugo artiklar analyserades med en beskrivande syntes enligt Evans (2002). Resultat: Det uppkom tre huvud teman och sex subtema; Tema ”Oro som är relaterad till operation” med subteman ”Faktorer som orsakar oro hos patienten innan operation” och ”Väntetidenspåverkan”, tema ”Behov av stöd för att vara delaktig” med subteman ”Individuellt anpassad information” och ”Informationsstöd från vårdpersonal och närstående”, tema ”Behov av trygghet och tillit” med subteman ”Bemötande” och ”Vårdmiljön”. Slutsats: Upplevelsen av att känna oro innan operation var huvudsakligen ohanterbart och frustrerande på alla aspekter i patientens livsvärld. Med hjälp av ökad förståelse av patientens upplevelser av oro i förhållande till den preoperativa omvårdnaden kan operationssjuksköterskan minska detta lidande som är relaterad till vården. Det är viktigt för operationssjuksköterskan att reflektera över det stöd som patienter är i behov av med en helhetssyn. Nyckelord: preoperativ omvårdnad, upplevelse, lidande, oro, patient, kvalitativ, Background: Anxiety afflicts the majority of patients before an operation. There are many factors which affect the patient's experience of anxiety both within care and the stage of life in which the patient finds himself. It is of great importance to acquire an increased understanding of how pre-operative care can reduce the patient's anxiety and suffering from the patient's perspective. Aim: To describe the patient's experience of anxiety in relation to pre- operative care. Method: A descriptive literature study based on qualitative articles was chosen. Twenty articles were analyzed with a descriptive synthesis as according to Evans (2002). Results: Three main themes and six sub-themes emerged. Theme “Anxiety related to operation” with the sub-theme “Factors which cause anxiety before an operation” and “The effect of waiting”, theme “The need of support to be involved” with the sub-theme “Personalized information” and “Information support from nursing staff and family”, theme “The need of security and trust” with the sub-theme “Personal treatment” and “Care environment”. Conclusion: The experiencing of feelings of anxiety before an operation could not be addressed and was frustrating in all aspects of the patient's lifeworld. With the help of increased understanding of the patients’ experience of anxiety in relation to the pre-operative care, the operation nurse can reduce this suffering that is related to health care. It is important that the operation nurse reflects over the support the patient needs with a holistic approach. Keywords: preoperative care, experience, suffering, anxiety, patient, qualitative

The new generation of ternary Pb(In1/2Nb1/2)O-3-Pb(Mg1/3Nb2/3)O-3-PbTiO3 ferroelectric single crystals have potential applications in high power devices due to their surperior operational stability relative to the binary system. In this work, a reversible, large electric field induced strain of over 0.9% at room temperature, and in particular over 0.6% above 380 K was obtained. The polarization rotation path and the phase transition sequence of different compositions in these ternary systems have been determined with increasing electric field applied along [001] direction based on x-ray diffraction data. Thereafter, composition dependence of field-temperature phase diagrams were constructed, which provide compositional and thermal prospectus for the electromechanical properties. It was found the structural origin of the large stain, especially at higher temperature is the lattice parameters modulated by dual independent variables in composition of these ternary solid solution crystals.