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Connecting theory with practice, this systematic and rigorous introduction covers the fundamental principles, algorithms and applications of key mathematical models for high-dimensional data analysis. Comprehensive in its approach, it provides unified coverage of many different low-dimensional models and analytical techniques, including sparse and low-rank models, and both convex and non-convex formulations. Readers will learn how to develop efficient and scalable algorithms for solving real-world problems, supported by numerous examples and exercises throughout, and how to use the computational tools learnt in several application contexts. Applications presented include scientific imaging, communication, face recognition, 3D vision, and deep networks for classification. With code available online, this is an ideal textbook for senior and graduate students in electrical engineering, computer science and data science, as well as for those taking courses on sparsity, low-dimensional structures, and high-dimensional data. Foreword by Emmanuel Candès.
To determine the impact of an inpatient stewardship intervention targeting fluoroquinolone use on inpatient and postdischarge Clostridioides difficile infection (CDI).
We used an interrupted time series study design to evaluate the rate of hospital-onset CDI (HO-CDI), postdischarge CDI (PD-CDI) within 12 weeks, and inpatient fluoroquinolone use from 2 years prior to 1 year after a stewardship intervention.
An academic healthcare system with 4 hospitals.
All inpatients hospitalized between January 2017 and September 2020, excluding those discharged from locations caring for oncology, bone marrow transplant, or solid-organ transplant patients.
Introduction of electronic order sets designed to reduce inpatient fluoroquinolone prescribing.
Among 163,117 admissions, there were 683 cases of HO-CDI and 1,104 cases of PD-CDI. In the context of a 2% month-to-month decline starting in the preintervention period (P < .01), we observed a reduction in fluoroquinolone days of therapy per 1,000 patient days of 21% after the intervention (level change, P < .05). HO-CDI rates were stable throughout the study period. In contrast, we also detected a change in the trend of PD-CDI rates from a stable monthly rate in the preintervention period to a monthly decrease of 2.5% in the postintervention period (P < .01).
Our systemwide intervention reduced inpatient fluoroquinolone use immediately, but not HO-CDI. However, a downward trend in PD-CDI occurred. Relying on outcome measures limited to the inpatient setting may not reflect the full impact of inpatient stewardship efforts.
Dairy intake was suggested to reduce the risk of gastrointestinal cancers. This study investigated the association between dairy intake and the risk of pancreatic cancer (PAC) using a prospective cohort study and meta-analysis of prospective cohort studies. First, we included 59,774 people aged 40-79 years from the Japan Collaborative Cohort Study (JACC Study). The Cox regression was used to compute the hazard ratios (HRs) and 95% confidence intervals (CIs) of incident PAC for individuals who reported the highest intakes of milk, cheese, and yogurt compared with not consuming the corresponding dairy products. Then, we combined our results with those from other four prospective cohort studies that were eligible after searching several databases, in a meta-analysis, using the fixed-effects model before evaluating publication bias and heterogeneity across studies. In the JACC study, the highest versus no intakes of milk, cheese, and yogurt were not associated with the reduced risk of PAC after a median follow-up of 13.4 years: HRs (95% CIs)= 0.93 (0.64, 1.33), 0.91 (0.51, 1.62), and 0.68 (0.38, 1.21), respectively. The results did not significantly change in the meta-analysis: 0.95 (0.82, 1.11) for milk, 1.16 (0.87, 1.55) for cheese, and 0.91 (0.79, 1.05) for yogurt. The meta-analysis showed no signs of publication bias or heterogeneity across studies. To conclude, consumption of milk, cheese, and yogurt was not associated with the risk of PAC either in the JACC study or the meta-analysis.
The self-focusing condition of a charged particle beam in a resistive plasma has been studied. When plasma heating is weak, the beam focusing is intensified by increasing the beam density or velocity. However, when plasma heating is strong, the beam focusing is only determined by the beam velocity. Especially, in weak heating conditions, the beam trends to be focused into the centre as a whole, and in strong heating conditions, a double-peak structure with a hollow centre is predicted to appear. Furthermore, it is found that the beam radius has a significant effect on focusing distance: a larger the beam radius will result in a longer focusing distance. Simulation results also show that when the beam radius is large enough, filamentation of the beam appears. Our results will serve as a reference for relevant beam–plasma experiments and theoretical analyses, such as heavy ion fusion and ion-beam-driven high energy density physics.
Drought is a major concern among abiotic stresses in wheat (Triticum aestivum L.) production. Breeding resistant cultivars are the most effective means to manage drought stress. F6 recombinant inbred lines (RIL) derived from the cross of Berkut/Worrakatta were used to identify quantitative trait loci (QTL) for drought tolerance at germination stage under treatment of PEG6000 using the wheat 50 K single nucleotide polymorphism (SNP) array. Twenty-eight linkage groups were constructed, covering a length of 2220.26 cM. Eighteen QTL were detected based on the drought tolerance coefficients and D-value, explaining 2.7–6.5% of the phenotypic variances, in which 15 were likely to be novel. Three QTL, QGR.xjau-5AS, QCL.xjau-5AS and QD.xjau-5AS for GR, CL and D-value, respectively, at physical positions of 11.70–20.61 Mb between markers AX-111258240 and AX-94458300 on chromosome 5AS accounted for 3.4–4.8% of the phenotypic variances. Three QTL, QGP.xjau-5DL, QSH.xjau-5DL and QD.xjau-5DL for GP, SH and D-value, respectively, were flanked by markers AX-94524442 and AX-110998507 at 560.42–567.39 Mb on chromosome 5DL, accounting for 4.4–6.5% of the phenotypic variances. In addition, the candidate genes TraesCS5A02G022100, TraesCS5B02G014200 and TraesCS5D02G563900 were predicted. Based on transcriptional expression data, the results showed that the expression level of TaGATAs-5A, TaUbox-5B and TaGSTP-5D changed with the increase of treatment time under drought stress in tolerant and sensitive varieties. These are interesting targets in mining drought tolerance genes and the improvement of drought tolerance in wheat.
The trade war between the USA and China that started around 2018 exposed the vulnerability of the international trade law regime anchored on the WTO. This essay explores the possibility that the escalating conflict between the world’s two most powerful economies may be resolved in emerging global markets defined not by an information revolution but by a knowledge revolution. The conventional wisdom among Western pundits discounts the possibility that China might emerge as victorious in a contest with the West to decide who is best at advancing the frontier of knowledge. America won the last global knowledge economy “land rush” triggered by the commercialization of the Internet. Early evidence suggests the next great global knowledge economy land rush will be fueled by innovations including artificial intelligence, mobile computing, cloud computing, social production and the Internet of Things, with early evidence showing it might well be won by China. If this were to occur, then the international trade law regime might continue to drift away from the WTO framework based on Westphalian notions of public international law and may drift closer to China’s distinctive legal institutions and traditions.
This chapter introduces three cross-cutting themes that illustrate the relationship between artificial intelligence and international economic law (IEL): disruption, regulation, and reconfiguration. We explore the theme of disruption along the trifecta of AI-related technological, economic, and legal change. In this context, the impact of AI triggers political and economic pressures, as evidenced by intensive lobbying and engagement in different governance venues for and against various regulatory choices, including what will be regulated, how to regulate it, and whom should be regulated. Along these lines, we assess the extent to which IEL has already been reconfigured and examine the need for further reconfiguration. We conclude this introduction chapter by bringing the contributions we assembled in this volume into conversation with one another and identify topics that warrant further research. Taken as a whole, this book portrays the interaction between AI and IEL. We have collectively explored and evaluated the impact of AI disruption, the need for AI regulation, and directions for IEL reconfiguration.
This chapter uses the case of CAV standards to explore how this “disruptive innovation” may alter the boundaries of international trade agreements. Amid the transition to a driverless future, the transformative nature of disruptive innovation renders the interpretation and application of trade rules challenging. The chapter focuses on two issues – the goods/services boundaries, and the public/private sector boundaries. Considering the data-driven business models and the integrated technical features, CAV-related safety standards may disrupt the scope of coverage of the TBT Agreement. As CAVs evolve from level 0 to level 5, how should CAV standards be reclassified? In addition, for levels 1–4, in which humans and CAVs are co-drivers, the industry-led voluntary standards provide a baseline for judges in the evaluation of appropriate levels and evidence of CAV safety prior to deployment, which may become a strong incentive for CAV manufacturers to comply with “guidance,” “best practice,” or “codes of conduct.” To what extent should a WTO tribunal assume the responsibility of members with regard to CAV safety standards prepared and published by a private entity? The development of disruptive innovation involves changes in governance frameworks and calls for new governance approaches that break the boundaries of existing trade disciplines.