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Irregularly sampled time series are ubiquitous, presenting significant challenges for analysis due to missing values. Despite existing methods address imputation, they predominantly focus on leveraging intra-series information, neglecting the potential benefits that inter-series information could provide, such as reducing uncertainty and memorization effect. To bridge this gap, we propose PRIME, a Prototype Recurrent Imputation ModEl, which integrates both intra-series and inter-series information for imputing missing values in irregularly sampled time series. Our framework comprises a prototype memory module for learning inter-series information, a bidirectional gated recurrent unit utilizing prototype information for imputation, and an attentive prototypical refinement module for adjusting imputations. We conducted extensive experiments on three datasets, and the results underscore PRIME's superiority over the state-of-the-art models by up to 26% relative improvement on mean square error.

Electronic health record (EHR) data has emerged as a valuable resource for analyzing patient health status. However, the prevalence of missing data in EHR poses significant challenges to existing methods, leading to spurious correlations and suboptimal predictions. While various imputation techniques have been developed to address this issue, they often obsess unnecessary details and may introduce additional noise when making clinical predictions. To tackle this problem, we propose SMART, a Self-Supervised Missing-Aware RepresenTation Learning approach for patient health status prediction, which encodes missing information via elaborated attentions and learns to impute missing values through a novel self-supervised pre-training approach that reconstructs missing data representations in the latent space. By adopting missing-aware attentions and focusing on learning higher-order representations, SMART promotes better generalization and robustness to missing data. We validate the effectiveness of SMART through extensive experiments on six EHR tasks, demonstrating its superiority over state-of-the-art methods.

Objective: Peritoneal Dialysis (PD) is one of the most widely used life-supporting therapies for patients with End-Stage Renal Disease (ESRD). Predicting mortality risk and identifying modifiable risk factors based on the Electronic Medical Records (EMR) collected along with the follow-up visits are of great importance for personalized medicine and early intervention. Here, our objective is to develop a deep learning model for a real-time, individualized, and interpretable mortality prediction model - AICare. Method and Materials: Our proposed model consists of a multi-channel feature extraction module and an adaptive feature importance recalibration module. AICare explicitly identifies the key features that strongly indicate the outcome prediction for each patient to build the health status embedding individually. This study has collected 13,091 clinical follow-up visits and demographic data of 656 PD patients. To verify the application universality, this study has also collected 4,789 visits of 1,363 hemodialysis dialysis (HD) as an additional experiment dataset to test the prediction performance, which will be discussed in the Appendix. Results: 1) Experiment results show that AICare achieves 81.6%/74.3% AUROC and 47.2%/32.5% AUPRC for the 1-year mortality prediction task on PD/HD dataset respectively, which outperforms the state-of-the-art comparative deep learning models. 2) This study first provides a comprehensive elucidation of the relationship between the causes of mortality in patients with PD and clinical features based on an end-to-end deep learning model. 3) This study first reveals the pattern of variation in the importance of each feature in the mortality prediction based on built-in interpretability. 4) We develop a practical AI-Doctor interaction system to visualize the trajectory of patients' health status and risk indicators.

Deep learning algorithms, especially Transformer-based models, have achieved significant performance by capturing long-range dependencies and historical information. However, the power of convolution has not been fully investigated. Moreover, most existing works ignore the dynamic interaction among variables and evolutionary noise in series. Addressing these issues, we propose a Hierarchical Memorizing Network (HMNet). In particular, a hierarchical convolution structure is introduced to extract the information from the series at various scales. Besides, we propose a dynamic variable interaction module to learn the varying correlation and an adaptive denoising module to search and exploit similar patterns to alleviate noises. These modules can cooperate with the hierarchical structure from the perspective of fine to coarse grain. Experiments on five benchmarks demonstrate that HMNet significantly outperforms the state-of-the-art models by 10.6% on MSE and 5.7% on MAE. Our code is released at https://github.com/yzhHoward/HMNet.

Predicting health risks from electronic health records (EHR) is a topic of recent interest. Deep learning models have achieved success by modeling temporal and feature interaction. However, these methods learn insufficient representations and lead to poor performance when it comes to patients with few visits or sparse records. Inspired by the fact that doctors may compare the patient with typical patients and make decisions from similar cases, we propose a Progressive Prototypical Network (PPN) to select typical patients as prototypes and utilize their information to enhance the representation of the given patient. In particular, a progressive prototype memory and two prototype separation losses are proposed to update prototypes. Besides, a novel integration is introduced for better fusing information from patients and prototypes. Experiments on three real-world datasets demonstrate that our model brings improvement on all metrics. To make our results better understood by physicians, we developed an application at http://ppn.ai-care.top. Our code is released at https://github.com/yzhHoward/PPN.

Two-dimensional (2D) semiconducting monolayers such as transition metal dichalcogenides (TMDs) are promising channel materials to extend Moore's Law in advanced electronics. Synthetic TMD layers from chemical vapor deposition (CVD) are scalable for fabrication but notorious for their high defect densities. Therefore, innovative endeavors on growth reaction to enhance their quality are urgently needed. Here, we report that the hydroxide W species, an extremely pure vapor phase metal precursor form, is very efficient for sulfurization, leading to about one order of magnitude lower defect density compared to those from conventional CVD methods. The field-effect transistor (FET) devices based on the proposed growth reach a peak electron mobility similar to 200 cm(2)/Vs (similar to 800 cm(2)/Vs) at room temperature (15 K), comparable to those from exfoliated flakes. The FET device with a channel length of 100 nm displays a high on-state current of similar to 400 mu A/mu m, encouraging the industrialization of 2D materials.

A fundamental limit for the supply voltage of conventional field-effect transistors is the long high-energy tail of the Boltzmann distribution of the carrier population at the source junction, which requires a gate voltage at least 60 mV to change one decade of current. Here 2D semiconductors are adopted as channel materials and hafnium zirconium oxide (HZO) as negative capacitance (NC) gate stack to realize low-power complementary logic inverter. With HZO/Al2O3NC gate stack, the 2D semiconductor field-effect transistor (FET) shows an average subthreshold slope less than Boltzmann limit (as low as 18 mV dec(-1)) at room temperature for both forward and reverse gate voltage sweeps, which allows to reach the same ON-state current at a lowerV(dd)without increasing the OFF-state current. The drain current can be modulated by 5 x 10(4)within 220 mV, still exhibiting average SS below 60 mV dec(-1). By constructing van der Waals contact to improve the charge injection and control the carrier type, unipolar p-type WSe2FET with reduced hole Schottky barrier height is achieved. The complementary inverter with MoS(2)and WSe2NCFETs shows the power consumption of 68 pW.

A fundamental limit for the supply voltage of conventional field-effect transistors is the long high-energy tail of the Boltzmann distribution of the carrier population at the source junction, which requires a gate voltage at least 60 mV to change one decade of current. Here 2D semiconductors are adopted as channel materials and hafnium zirconium oxide (HZO) as negative capacitance (NC) gate stack to realize low-power complementary logic inverter. With HZO/Al2O3NC gate stack, the 2D semiconductor field-effect transistor (FET) shows an average subthreshold slope less than Boltzmann limit (as low as 18 mV dec(-1)) at room temperature for both forward and reverse gate voltage sweeps, which allows to reach the same ON-state current at a lowerV(dd)without increasing the OFF-state current. The drain current can be modulated by 5 x 10(4)within 220 mV, still exhibiting average SS below 60 mV dec(-1). By constructing van der Waals contact to improve the charge injection and control the carrier type, unipolar p-type WSe2FET with reduced hole Schottky barrier height is achieved. The complementary inverter with MoS(2)and WSe2NCFETs shows the power consumption of 68 pW.

A fundamental limit for the supply voltage of conventional field-effect transistors is the long high-energy tail of the Boltzmann distribution of the carrier population at the source junction, which requires a gate voltage at least 60 mV to change one decade of current. Here 2D semiconductors are adopted as channel materials and hafnium zirconium oxide (HZO) as negative capacitance (NC) gate stack to realize low-power complementary logic inverter. With HZO/Al2O3NC gate stack, the 2D semiconductor field-effect transistor (FET) shows an average subthreshold slope less than Boltzmann limit (as low as 18 mV dec(-1)) at room temperature for both forward and reverse gate voltage sweeps, which allows to reach the same ON-state current at a lowerV(dd)without increasing the OFF-state current. The drain current can be modulated by 5 x 10(4)within 220 mV, still exhibiting average SS below 60 mV dec(-1). By constructing van der Waals contact to improve the charge injection and control the carrier type, unipolar p-type WSe2FET with reduced hole Schottky barrier height is achieved. The complementary inverter with MoS(2)and WSe2NCFETs shows the power consumption of 68 pW.

Due to the characteristics of COVID-19, the epidemic develops rapidly and overwhelms health service systems worldwide. Many patients suffer from systemic life-threatening problems and need to be carefully monitored in ICUs. Thus the intelligent prognosis is in an urgent need to assist physicians to take an early intervention, prevent the adverse outcome, and optimize the medical resource allocation. However, in the early stage of the epidemic outbreak, the data available for analysis is limited due to the lack of effective diagnostic mechanisms, rarity of the cases, and privacy concerns. In this paper, we propose a deep-learning-based approach, CovidCare, which leverages the existing electronic medical records to enhance the prognosis for inpatients with emerging infectious diseases. It learns to embed the COVID-19-related medical features based on massive existing EMR data via transfer learning. The transferred parameters are further trained to imitate the teacher model's representation behavior based on knowledge distillation, which embeds the health status more comprehensively in the source dataset. We conduct the length of stay prediction experiments for patients on a real-world COVID-19 dataset. The experiment results indicate that our proposed model consistently outperforms the comparative baseline methods. CovidCare also reveals that, 1) hs-cTnI, hs-CRP and Platelet Counts are the most fatal biomarkers, whose abnormal values usually indicate emergency adverse outcome. 2) Normal values of gamma-GT, AP and eGFR indicate the overall improvement of health. The medical findings extracted by CovidCare are empirically confirmed by human experts and medical literatures.

With the rise of the concept of financial technology, financial and technology gradually in-depth integration, scientific and technological means to become financial product innovation, improve financial efficiency and reduce financial transaction costs an important driving force. In this context, the new technology platform is from the business philosophy, business model, technical means, sales, internal management, and other dimensions to re-shape the financial industry. In this paper, the existing big data platform architecture technology innovation, adding space-time data elements, combined with the insurance industry for practical analysis, put forward a meaningful product circle and customer circle., Comment: 4 pages, 3 figures, 2018 International Conference on Big Data Engineering and Technology

The observation of single mode propagation in an air-clad single crystal sapphire optical fiber at wavelengths at and above 783 nm is presented for the first time. A high-temperature wet acid etching method was used to reduce the diameter of a 10 cm length of commercially-sourced sapphire fiber from 125 micrometers to 6.5 micrometers, and far-field imaging provided modal information at intervals as the fiber diameter decreased. Modal volume was shown to decrease with decreasing diameter, and single mode behavior was observed at the minimum diameter achieved. While weakly-guiding approximations are generally inaccurate for low modal volume optical fiber with high core-cladding refractive index disparity, consistency between these approximations and experimental results was observed when the effective numerical aperture was measured and substituted for the theoretical numerical aperture in weakly-guiding approximation calculations. With the demonstration of very low modal volume in sapphire at fiber diameters much larger than anticipated by legacy calculations, the resolution of sapphire fiber distributed sensors may be increased and other sensing schemes requiring very low modal volume, such as fiber Bragg gratings, may be realized in extreme environment applications.

The observation of single mode propagation in an air-clad single crystal sapphire optical fiber at wavelengths at and above 783 nm is presented for the first time. A high-temperature wet acid etching method was used to reduce the diameter of a 10 cm length of commercially-sourced sapphire fiber from 125 micrometers to 6.5 micrometers, and far-field imaging provided modal information at intervals as the fiber diameter decreased. Modal volume was shown to decrease with decreasing diameter, and single mode behavior was observed at the minimum diameter achieved. While weakly-guiding approximations are generally inaccurate for low modal volume optical fiber with high core-cladding refractive index disparity, consistency between these approximations and experimental results was observed when the effective numerical aperture was measured and substituted for the theoretical numerical aperture in weakly-guiding approximation calculations. With the demonstration of very low modal volume in sapphire at fiber diameters much larger than anticipated by legacy calculations, the resolution of sapphire fiber distributed sensors may be increased and other sensing schemes requiring very low modal volume, such as fiber Bragg gratings, may be realized in extreme environment applications.

We develop a North-South model of international trade and transboundary pollution to analyze the relationship between environmental technology transfer and the South's incentive to sign an international environmental agreement (IEA). First, we show that technology transfer could either increase or reduce the South's incentive to sign the IEA. Second, we show that the South's participation in the IEA would reduce the market incentive of technology transfer. Both results have very clear policy implications for (i) the sequence of technology transfer and the South's IEA membership and (ii) the legitimacy of South's subsidies for technology transfer.

We develop a North-South model of international trade and transboundary pollution to analyze the relationship between environmental technology transfer and the South's incentive to sign an international environmental agreement (IEA). First, we show that technology transfer could either increase or reduce the South's incentive to sign the IEA. Second, we show that the South's participation in the IEA would reduce the market incentive of technology transfer. Both results have very clear policy implications for (i) the sequence of technology transfer and the South's IEA membership and (ii) the legitimacy of South's subsidies for technology transfer.

We develop a North-South model of international trade and transboundary pollution to analyze the relationship between environmental technology transfer and the South's incentive to sign an international environmental agreement (IEA). First, we show that technology transfer could either increase or reduce the South's incentive to sign the IEA. Second, we show that the South's participation in the IEA would reduce the market incentive of technology transfer. Both results have very clear policy implications for (i) the sequence of technology transfer and the South's IEA membership and (ii) the legitimacy of South's subsidies for technology transfer.

We develop a North-South model of international trade and transboundary pollution to analyze the relationship between environmental technology transfer and the South's incentive to sign an international environmental agreement (IEA). First, we show that technology transfer could either increase or reduce the South's incentive to sign the IEA. Second, we show that the South's participation in the IEA would reduce the market incentive of technology transfer. Both results have very clear policy implications for (i) the sequence of technology transfer and the South's IEA membership and (ii) the legitimacy of South's subsidies for technology transfer.

We develop a North-South model of international trade and transboundary pollution to analyze the relationship between environmental technology transfer and the South's incentive to sign an international environmental agreement (IEA). First, we show that technology transfer could either increase or reduce the South's incentive to sign the IEA. Second, we show that the South's participation in the IEA would reduce the market incentive of technology transfer. Both results have very clear policy implications for (i) the sequence of technology transfer and the South's IEA membership and (ii) the legitimacy of South's subsidies for technology transfer.

We develop a North-South model of international trade and transboundary pollution to analyze the relationship between environmental technology transfer and the South's incentive to sign an international environmental agreement (IEA). First, we show that technology transfer could either increase or reduce the South's incentive to sign the IEA. Second, we show that the South's participation in the IEA would reduce the market incentive of technology transfer. Both results have very clear policy implications for (i) the sequence of technology transfer and the South's IEA membership and (ii) the legitimacy of South's subsidies for technology transfer.

We develop a North-South model of international trade and transboundary pollution to analyze the relationship between environmental technology transfer and the South's incentive to sign a multilateral environmental agreement (MEA). First, we show that technology transfer could either increase or reduce the South's incentive to sign the MEA. Second, we show that the South's participation in the MEA would reduce the market incentive of technology transfer. Both results have very clear policy implications for (i) the sequence of technology transfer and the South's MEA membership and (ii) the legitimacy of South's subsidies for technology transfer.

We develop a North-South model of international trade and transboundary pollution to analyze the relationship between environmental technology transfer and the South's incentive to sign a multilateral environmental agreement (MEA). First, we show that technology transfer could either increase or reduce the South's incentive to sign the MEA. Second, we show that the South's participation in the MEA would reduce the market incentive of technology transfer. Both results have very clear policy implications for (i) the sequence of technology transfer and the South's MEA membership and (ii) the legitimacy of South's subsidies for technology transfer.

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