Nanyang Technological University

About: Nanyang Technological University is a education organization based out in Singapore, Singapore. It is known for research contribution in the topics: Computer science & Catalysis. The organization has 48003 authors who have published 112815 publications receiving 3294199 citations. The organization is also known as: NTU & Universiti Teknologi Nanyang.

SARS-CoV-2 Titers in Wastewater Are Higher than Expected from Clinically Confirmed Cases

Abstract: Wastewater surveillance represents a complementary approach to clinical surveillance to measure the presence and prevalence of emerging infectious diseases like the novel coronavirus SARS-CoV-2. This innovative data source can improve the precision of epidemiological modeling to understand the penetrance of SARS-CoV-2 in specific vulnerable communities. Here, we tested wastewater collected at a major urban treatment facility in Massachusetts and detected SARS-CoV-2 RNA from the N gene at significant titers (57 to 303 copies per ml of sewage) in the period from 18 to 25 March 2020 using RT-qPCR. We validated detection of SARS-CoV-2 by Sanger sequencing the PCR product from the S gene. Viral titers observed were significantly higher than expected based on clinically confirmed cases in Massachusetts as of 25 March. Our approach is scalable and may be useful in modeling the SARS-CoV-2 pandemic and future outbreaks. IMPORTANCE Wastewater-based surveillance is a promising approach for proactive outbreak monitoring. SARS-CoV-2 is shed in stool early in the clinical course and infects a large asymptomatic population, making it an ideal target for wastewater-based monitoring. In this study, we develop a laboratory protocol to quantify viral titers in raw sewage via qPCR analysis and validate results with sequencing analysis. Our results suggest that the number of positive cases estimated from wastewater viral titers is orders of magnitude greater than the number of confirmed clinical cases and therefore may significantly impact efforts to understand the case fatality rate and progression of disease. These data may help inform decisions surrounding the advancement or scale-back of social distancing and quarantine efforts based on dynamic wastewater catchment-level estimations of prevalence.

Convergence of Edge Computing and Deep Learning: A Comprehensive Survey

Abstract: Ubiquitous sensors and smart devices from factories and communities are generating massive amounts of data, and ever-increasing computing power is driving the core of computation and services from the cloud to the edge of the network. As an important enabler broadly changing people’s lives, from face recognition to ambitious smart factories and cities, developments of artificial intelligence (especially deep learning, DL) based applications and services are thriving. However, due to efficiency and latency issues, the current cloud computing service architecture hinders the vision of “providing artificial intelligence for every person and every organization at everywhere”. Thus, unleashing DL services using resources at the network edge near the data sources has emerged as a desirable solution. Therefore, edge intelligence , aiming to facilitate the deployment of DL services by edge computing, has received significant attention. In addition, DL, as the representative technique of artificial intelligence, can be integrated into edge computing frameworks to build intelligent edge for dynamic, adaptive edge maintenance and management. With regard to mutually beneficial edge intelligence and intelligent edge , this paper introduces and discusses: 1) the application scenarios of both; 2) the practical implementation methods and enabling technologies, namely DL training and inference in the customized edge computing framework; 3) challenges and future trends of more pervasive and fine-grained intelligence. We believe that by consolidating information scattered across the communication, networking, and DL areas, this survey can help readers to understand the connections between enabling technologies while promoting further discussions on the fusion of edge intelligence and intelligent edge , i.e., Edge DL.

How the world’s most improved school systems keep getting better

Abstract: How the World’s Most Improved School Systems Keep Getting Better is a McKinsey & Company report that attempts to answer the question posed in its title through an examination of 20 education systems in action. These 20 school systems, which come from all parts of the world, have achieved significant, sustained, and widespread gains, as measured by national and international standards of assessment. The report builds on the 2007 McKinsey & Company report on the common attributes of excellent school systems titled How the World’s BestPerforming School Systems Come Out on Top. It includes in its appendix an explanation of their system selection criteria, as well as the database structure for the detailed evidence the researchers gathered to map the experiences of nearly 575 reform interventions made across the school systems. In addition to a quantitative analysis, the researchers interviewed more than 200 system leaders and their staff and visited all 20 systems to see them in action. In the current report, the authors sort out various education systems according to their starting point and progression, according to four performance stages: from poor to fair, fair to good, good to great, and great to excellence. In each case study of an improving education system, the authors argue that the system is led by astute leaders who are able to manipulate a small number of critical system parameters to create positive movement. However, the researchers are also able to point out the elements which are specific to the individual system and those which are of broader or universal relevance. The first section of the report is about ‘‘Intervention’’. According to the authors, every school system faces the challenge of deciding the interventions it should make in order to improve its performance. The authors contend that their research identifies common clusters of interventions that all improving systems carry out at each journey stage on the long path from poor to excellent. They argue that a

Influence Maximization: Near-Optimal Time Complexity Meets Practical Efficiency

Abstract: Given a social network G and a constant k, the influence maximization problem asks for k nodes in G that (directly and indirectly) influence the largest number of nodes under a pre-defined diffusion model. This problem finds important applications in viral marketing, and has been extensively studied in the literature. Existing algorithms for influence maximization, however, either trade approximation guarantees for practical efficiency, or vice versa. In particular, among the algorithms that achieve constant factor approximations under the prominent independent cascade (IC) model or linear threshold (LT) model, none can handle a million-node graph without incurring prohibitive overheads. This paper presents TIM, an algorithm that aims to bridge the theory and practice in influence maximization. On the theory side, we show that TIM runs in O((k+\ell) (n+m) \log n / \epsilon^2) expected time and returns a (1-1/e-\epsilon)-approximate solution with at least 1 - n^{-\ell} probability. The time complexity of TIM is near-optimal under the IC model, as it is only a \log n factor larger than the \Omega(m + n) lower-bound established in previous work (for fixed k, \ell, and \epsilon). Moreover, TIM supports the triggering model, which is a general diffusion model that includes both IC and LT as special cases. On the practice side, TIM incorporates novel heuristics that significantly improve its empirical efficiency without compromising its asymptotic performance. We experimentally evaluate TIM with the largest datasets ever tested in the literature, and show that it outperforms the state-of-the-art solutions (with approximation guarantees) by up to four orders of magnitude in terms of running time. In particular, when k = 50, \epsilon = 0.2, and \ell = 1, TIM requires less than one hour on a commodity machine to process a network with 41.6 million nodes and 1.4 billion edges.

Ultrathin MoS₂ Nanosheets Supported on N-doped Carbon Nanoboxes with Enhanced Lithium Storage and Electrocatalytic Properties.

Abstract: Molybdenum disulfide (MoS2) has received considerable interest for electrochemical energy storage and conversion. In this work, we have designed and synthesized a unique hybrid hollow structure by growing ultrathin MoS2 nanosheets on N-doped carbon shells (denoted as C@MoS2 nanoboxes). The N-doped carbon shells can greatly improve the conductivity of the hybrid structure and effectively prevent the aggregation of MoS2 nanosheets. The ultrathin MoS2 nanosheets could provide more active sites for electrochemical reactions. When evaluated as an anode material for lithium-ion batteries, these C@MoS2 nanoboxes show high specific capacity of around 1000 mAh g−1, excellent cycling stability up to 200 cycles, and superior rate performance. Moreover, they also show enhanced electrocatalytic activity for the electrochemical hydrogen evolution.