Conformalized Survival Analysis

Existing survival analysis techniques heavily rely on strong modelling assumptions and are, therefore, prone to model misspecification errors. In this paper, we develop an inferential method based on ideas from conformal prediction, which can wrap around any survival prediction algorithm to produce calibrated, covariate-dependent lower predictive bounds on survival times. In the Type I right-censoring setting, when the censoring times are completely exogenous, the lower predictive bounds have guaranteed coverage in finite samples without any assumptions other than that of operating on independent and identically distributed data points. Under a more general conditionally independent censoring assumption, the bounds satisfy a doubly robust property which states the following: marginal coverage is approximately guaranteed if either the censoring mechanism or the conditional survival function is estimated well. Further, we demonstrate that the lower predictive bounds remain valid and informative for other types of censoring. The validity and efficiency of our procedure are demonstrated on synthetic data and real COVID-19 data from the UK Biobank.Comment: 33 pages, 7 figure

Distributed Consensus of Linear Multi-Agent Systems with Adaptive Dynamic Protocols

This paper considers the distributed consensus problem of multi-agent systems with general continuous-time linear dynamics. Two distributed adaptive dynamic consensus protocols are proposed, based on the relative output information of neighboring agents. One protocol assigns an adaptive coupling weight to each edge in the communication graph while the other uses an adaptive coupling weight for each node. These two adaptive protocols are designed to ensure that consensus is reached in a fully distributed fashion for any undirected connected communication graphs without using any global information. A sufficient condition for the existence of these adaptive protocols is that each agent is stabilizable and detectable. The cases with leader-follower and switching communication graphs are also studied.Comment: 17 pages, 5 figue

Big-Data Based Analysis for Communication Effect of Science-Technology Public Accounts On Social Media

Public accounts on social media have become important channels for information dissemination. Well-designed public social media accounts are vital to better communicate science and technology (S-T) achievements. This article defines the S-T communication concept and proposes the analyzing dimensions. In order to measure the communication effect, this research collected 7,246 articles from S-T public accounts on WeChat. We analysis these massive data incorporating neural network (NN) and multivariate linear regression (MLR) model. The evaluation indicator system of communication effect includes three levels indicators. The research found the following factors affecting the S-T communication effect in different degrees: the number of active fans on Science Technology Public Accounts on Social Media (STPA-SM), locations where the articles are published, the authentication status of STPA-SM, and so on. Finally, the article proposes some strategic suggestions for improving the communication effects of S-T achievements through STPA-SM

VNE solution for network differentiated QoS and security requirements: from the perspective of deep reinforcement learning

The rapid development and deployment of network services has brought a series of challenges to researchers. On the one hand, the needs of Internet end users/applications reflect the characteristics of travel alienation, and they pursue different perspectives of service quality. On the other hand, with the explosive growth of information in the era of big data, a lot of private information is stored in the network. End users/applications naturally start to pay attention to network security. In order to solve the requirements of differentiated quality of service (QoS) and security, this paper proposes a virtual network embedding (VNE) algorithm based on deep reinforcement learning (DRL), aiming at the CPU, bandwidth, delay and security attributes of substrate network. DRL agent is trained in the network environment constructed by the above attributes. The purpose is to deduce the mapping probability of each substrate node and map the virtual node according to this probability. Finally, the breadth first strategy (BFS) is used to map the virtual links. In the experimental stage, the algorithm based on DRL is compared with other representative algorithms in three aspects: long term average revenue, long term revenue consumption ratio and acceptance rate. The results show that the algorithm proposed in this paper has achieved good experimental results, which proves that the algorithm can be effectively applied to solve the end user/application differentiated QoS and security requirements

Continuous Femoral Nerve Block versus Intravenous Patient Controlled Analgesia for Knee Mobility and Long-Term Pain in Patients Receiving Total Knee Replacement: A Randomized Controlled Trial

Objectives. To evaluate the comparative analgesia effectiveness and safety of postoperative continuous femoral nerve block (CFNB) with patient controlled intravenous analgesia (PCIA) and their impact on knee function and chronic postoperative pain. Methods. Participants were randomly allocated to receive postoperative continuous femoral nerve block (group CFNB) or intravenous patient controlled analgesia (group PCIA). Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) scores for knee and incidence of chronic postoperative pain at 3, 6, and 12 months postoperatively were compared. postoperative pain and salvage medication at rest or during mobilization 24 hours, 48 hours, and 7 days postoperatively were also recorded. Results. After discharge from the hospital and rehabilitation of joint function, patients in group CFNB reported significantly improved knee flexion and less incidence of chronic postoperative pain at 3 months and 6 months postoperatively (P<0.05). Analgesic rescue medications were significantly reduced in patients receiving CFNB (P<0.001 and P=0.031, resp.). Conclusion. With standardized rehabilitation therapy, continuous femoral nerve block analgesia reduced the incidence of chronic postoperative pain, improved motility of replaced joints, and reduced the dosages of rescue analgesic medications, suggesting a recovery-enhancing effect of peripheral nerve block analgesia

Getting ready for carbon capture and storage in the iron and steel sector in China: Assessing the value of capture readiness

China’s steel sector, contributing 40% of world steel production, are moving the plants out of highly-populated areas in China. Carbon capture and storage (CCS) is an important technology to achieve a deep reduction of emissions in steel plants. Given by high cost and lack of policy incentive in deploying the CCS process, there has been a lack of progress in CCS within the steel sector in China. Capture readiness is a design concept to ease future CCS retrofit and avoid the carbon lock-in effect in steel plants. Capture Readiness design requires moderate upfront investment, i.e. less than 0.5% additional capital expenditure, but could easily enable the plant to be retrofitted with CCS technologies in their lifetime. The paper develops a novel linear programming model to assess the economic cost of Capture Readiness design in a generic steel plant in China. The Baowu Steel Zhanjiang project was used as a reference plant to develop the generic steel plant for the model. Through a Monte Carlo simulation, the results show that the economic cost of making new steel plants in capture readiness for 0.5 million tonnes capture is CNY 65 million (USD 9.5 million) in a conservative 5% carbon price growth rate scenario. The paper found the value of flexibility brought by capture readiness design is significant and is equal to approximately 15% of initial capital investment. The economically viable chance of retrofitting steel plants with CCS technologies in the lifetime is 49%. In an uncertainty analysis, for a 6% growth rate of carbon price, the option value could be increased to CNY 145 million while the probability of retrofit increases to 79%. China’s CCS policy should consider a requirement for newly built steel plants to adopt capture readiness design to capture the significant economic value and ease emissions reduction in the iron and steel sector in the long term

Measuring the Utility of Mobile Phone Video Service Based on A Service Value Network Model

Service value networks (SVNs) are a new form of industrial cooperation. Within SVNs, service providers engage in loosely coupled relationships to jointly offer dynamically configured complex services to customers. Value is co-created through these joint complex services. By specialization, service providers leverage the knowledge and capital assets of their partners, and share the risk of operating in a changing and uncertain environment. However, as a new approach, SVNs lack theoretical foundations and empirical studies to validate its applicability. This paper seeks to address these problems by making three contributions. First, based on an existing theoretical SVN model, the paper presents a design for a mobile phone video service value network (MPVSVN) in China. Second, we describe a systematic approach to calculate the value and utility of services on the SVN. Third, we demonstrate our approach in detailed steps through real world service examples identified from the MPVSVN

Anomalous impact of thermal fluctuations on spintransfer torque induced ferrimagnetic switching

The dynamics of a spin torque driven ferrimagnetic (FiM) system is investigated using the two-sublattice macrospin model. We demonstrate an ultrafast switching in the picosecond range. However, we find that the excessive current leads to the magnetic oscillation. Therefore, faster switching cannot be achieved by unlimitedly increasing the current. By systematically studying the impact of thermal fluctuations, we find the dynamics of FiMs can also be distinguished into the precessional region, the thermally activated region, and the cross-over region. However, in the precessional region, there is a significant deviation between FiM and ferromagnet (FM), i.e., the FM is insensitive to thermal fluctuations since its switching is only determined by the amount of net charge. In contrast, we find that the thermal effect is pronounced even a very short current pulse is applied to the FiM. We attribute this anomalous effect to the complex relation between the anisotropy and overdrive current. By controlling the magnetic anisotropy, we demonstrate that the FiM can also be configured to be insensitive to thermal fluctuations. This controllable thermal property makes the FiM promising in many emerging applications such as the implementation of tunable activation functions in the neuromorphic computing.Comment: 27 pages, 8 figure

Effects of Wettability and Minerals on Residual Oil Distributions Based on Digital Rock and Machine Learning

AbstractThe wettability of mineral surfaces has significant impacts on transport mechanisms of two-phase flow, distribution characteristics of fluids, and the formation mechanisms of residual oil during water flooding. However, few studies have investigated such effects of mineral type and its surface wettability on rock properties in the literature. To unravel the dependence of hydrodynamics on wettability and minerals distribution, we designed a new experimental procedure that combined the multiphase flow experiments with a CT scan and QEMSCAN to obtain 3D digital models with multiple minerals and fluids. With the aid of QEMSCAN, six mineral components and two fluids in sandstones were segmented from the CT data based on the histogram threshold and watershed methods. Then, a mineral surface analysis algorithm was proposed to extract the mineral surface and classify its mineral categories. The in situ contact angle and pore occupancy were calculated to reveal the wettability variation of mineral surface and distribution characteristics of fluids. According to the shape features of the oil phase, the self-organizing map (SOM) method, one of the machine learning methods, was used to classify the residual oil into five types, namely, network, cluster, film, isolated, and droplet oil. The results indicate that each mineral’s contribution to the mineral surface is not proportional to its relative content. Feldspar, quartz, and clay are the main minerals in the studied sandstones and play a controlling role in the wettability variation. Different wettability samples show various characteristics of pore occupancy. The water flooding front of the weakly water-wet to intermediate-wet sample is uniform, and oil is effectively displaced in all pores with a long oil production period. The water-wet sample demonstrates severe fingering, with a high pore occupancy change rate in large pores and a short oil production period. The residual oil patterns gradually evolve from networks to clusters, isolated, and films due to the effects of snap-off and wettability inversion. This paper reveals the effects of wettability of mineral surface on the distribution characteristics and formation mechanisms of residual oil, which offers us an in-deep understanding of the impacts of wettability and minerals on multiphase flow and helps us make good schemes to improve oil recovery