Author: "Li Ang" / Publication Type: Electronic Resources - Searchworks@Jio Institute Digital Library Search Results

568 results on '"Li Ang"'

1. The value of multi-functional urban agriculture in creating sustainable cities

Author: Li, Ang
Abstract: China's cities continue to expand rapidly and under severe challenge of sustainable urban development. The Chinese Government has decided to bring agriculture back into the city in a state-controlled way and to re-educate urban residents to enjoy agriculture activities in urban areas. This research explores the Chinese Government's approach to new urban agriculture in China. It seeks to better understand and evaluate the impacts of multifunctional urban agriculture on sustainable urban development. The work is set within the context of China's extremely rapid urbanization and concerns about pollution, poor lifestyles and an over-emphasis on manufacturing as the economic driver of growth. This thesis has presented a first attempt to redefine the term 'urban' in relation to urban agriculture, extending it to the urban core areas, desakota areas and exurban areas. In this way it suggests a new typology of urban agriculture in China, with a potentially broader range of objectives and possibilities that might normally be associated with the subject or practice. Taking Beijing as the case study city, this study selects 3 of its 16 districts: Chaoyang, Changping and Miyun representing core, desakota and exurban areas. The specific projects in these three districts are totally different, and together they represent the three levels in the model of Chinese new urban agriculture. Each level of model is informed and supported by case study of practical projects. These are: Government fully-owned large projects, Government-supported privately run projects and Folk Custom Villages. Data was collected from direct observation, documentation, archive, physical survey, interviews and questionnaires. This thesis found that the "Chinese" urban agriculture model, through three different types of projects, aims to make people rethink the role of agriculture and see it not simply as something undertaken by others in a rural area, nor as something simply to provide food. Rather, it can be something which enhances the urban experience, improves the urban environment, offers leisure facilities, engages people in traditional culture and provides a diverse range of employment and livelihood activities. A well planned modern agricultural production is required to create an agricultural environment with reasonable spatial layout to reduce pollution and to create aesthetically pleasing and sustainable landscapes. It can help urban agriculture ii integrate into the city system in a more sustainable way by reconnecting urban life and rural culture. This model, therefore, sets urban agriculture in a central role within planned urbanization. In summary, this thesis suggests that this model could become an important strategy for land use planning, urbanization and the sustainable development of Chinese cities, indeed, all cities, in the future. This study will be of interest to those scholars who are seeking to explore the Chinese urban agriculture as an effective method for land use in sustainable urban development.
Published: 2021

2. Multiple-antenna systems : from generic to hardware-informed precoding designs

Author: Li, Ang and Masouros, C.
Subjects: 621.3
Abstract: 5G-and-beyond communication systems are expected to be in a heterogeneous form of multiple-antenna cellular base stations (BSs) overlaid with small cells. The fully-digital BS structures can incur significant power consumption and hardware complexity. Moreover, the wireless BSs for small cells usually have strict size constraints, which incur additional hardware effects such as mutual coupling (MC). Consequently, the transmission techniques designed for future wireless communication systems should respect the hardware structures at the BSs. For this reason, in this thesis we extend generic downlink precoding to more advanced hardware-informed transmission techniques for a variety of BS structures. This thesis firstly extends the vector perturbation (VP) precoding to multiple-modulation scenarios, where existing VP-based techniques are sub-optimal. Subsequently, this thesis focuses on the downlink transmission designs for hardware effects in the form of MC, limited number of radio frequency (RF) chains, and low-precision digital-to-analog converters (DACs). For these scenarios, existing precoding techniques are either sub-optimal or not directly applicable due to the specific hardware constraints. In this context, this thesis first proposes analog-digital (AD) precoding methods for MC exploitation in compact single-user multiple-antenna systems with the concept of constructive interference, and further extends the idea of MC exploitation to multi-user scenarios with a joint optimisation on the precoding matrix and the mutual coupling effect. We further consider precoding for wireless BSs with a limited number of RF chains, in the form of compact parasitic antenna array as well as hybrid analog-digital structures designed for large-scale multiple-antenna systems. In addition, with a reformulation of the constructive interference, this thesis also considers the low-complexity precoding design for the use of low-resolution DACs for a massive-antenna array at the BSs. Analytical and numerical results reveal an improved performance of the proposed techniques compared to the state-of-the-art approaches, which validates the effectiveness of the introduced methods.
Published: 2018

3. Three-dimensional seismic analysis and modelling of marine hydrate systems offshore of Mauritania

Author: Li, Ang
Subjects: 551.46
Abstract: Marine hydrates, which lock-up vast quantities of methane, are considered to be a prospective alternative energy source, a slow tipping point in the global carbon cycle and a probable trigger for submarine failures. In this thesis marine hydrate systems offshore of Mauritania and associated structural and sedimentary features are investigated by utilising two surveys of high-quality three-dimensional (3-D) seismic data. Interpreting them provides new insights into marine hydrate systems and how they respond to changes in ambient conditions. In one region of one of the 3-D seismic surveys, a shear zone covering 50 km2 is identified immediately above the hydrate bottom simulating reflector (BSR). It is considered to be the initial stages of a failure that did not result in widescale downslope transport of the succession. Due to this failure not going to completion, some free gas remains trapped at the level of the BSR. At this level the presence of free gases is supported by the continuous high-amplitude reflections. It is proposed that buoyancy built up by the inter-connected gas accumulation increases the pore pressure of the overlying hydrate-bearing to the level such that its base was critically stressed. In this research there is no seismic evidence for failures triggered by hydrate dissociation but the role of free gas in priming submarine failures is examined. Whether marine hydrates can release significant amounts of methane into the atmosphere is inconclusive. In this research a proposed model indicates that methane was re-captured in the hydrate stability zone after being liberated. Ocean warming since the last glacial maximum (LGM) gave rise to the shoaling of the base of the hydrate stability zone (HSZ). Gases released from hydrate accumulating at the base entered the HSZ, driven by buoyancy built up in the gas accumulation. The hydrate seal was breached and this is manifested by 15 gas chimneys in seismic data. Hydrates then re-formed at a specific level within the HSZ. This study implies that not all of methane would enter the ocean after released from hydrates and therefore the contribution of marine hydrates to the atmospheric methane budget may be not that much as it was predicted before. Gas venting is an effective way to transport methane at depth vertically to the ocean and an example of it is found in the feather edge of marine hydrate. This venting was possible due to the presence of faults above a salt diapir and is manifested by a series of pockmarks and mounds at the seabed. The BSR at this site is convex upwards and hence formed a trapping geometry for underlying free gases. Numerical model shows that this up-convex geometry is caused by the salt diapir having a higher thermal conductivity. Permeable migration conduits along the faults and excess pore pressure at the top of the trap allow for the happening of the venting. Compared with the neighbouring area where the BSR can be well observed, the region affected by diapirism has a limited scale of the observable BSR. This absence is proposed to result from the formed trap intercepting methane-rich pore fluid that would migrate landwards along the level of the base of the HSZ.
Published: 2017

4. An Integrated Carbon Dioxide Capture and Methanation Process

Author: Zhang, Xiaochen, Li, Mengzhu, Liu, Xingwu, Li, Ang, Deng, Yuchen, Peng, Mi, Zhang, Yu, Vogt, Charlotte, Monai, Matteo, Gao, Junxian, Qin, Xuetao, Xu, Yao, Yu, Qiaolin, Wang, Meng, Wang, Guofu, Jiang, Zheng, Han, Xiaodong, Brady, Casper, Li, Wei Xue, Zhou, Wu, Liu, Jin Xun, Xu, Bingjun, Weckhuysen, Bert M., Ma, Ding, Zhang, Xiaochen, Li, Mengzhu, Liu, Xingwu, Li, Ang, Deng, Yuchen, Peng, Mi, Zhang, Yu, Vogt, Charlotte, Monai, Matteo, Gao, Junxian, Qin, Xuetao, Xu, Yao, Yu, Qiaolin, Wang, Meng, Wang, Guofu, Jiang, Zheng, Han, Xiaodong, Brady, Casper, Li, Wei Xue, Zhou, Wu, Liu, Jin Xun, Xu, Bingjun, Weckhuysen, Bert M., and Ma, Ding
Abstract: Reducing the ever-growing level of CO2 in the atmosphere is critical for the sustainable development of human society in the context of global warming. Integration of the capture and upgrading of CO2 is, therefore, highly desirable since each process step is costly, both energetically and economically. Here, we report a CO2 direct air capture (DAC) and fixation process that produces methane. Low concentrations of CO2 (∼400 ppm) in the air are captured by an aqueous solution of sodium hydroxide to form carbonate. The carbonate is subsequently hydrogenated to methane, which is easily separated from the reaction system, catalyzed by TiO2-supported Ru in the aqueous phase with a selectivity of 99.9% among gas-phase products. The concurrent regenerated hydroxide, in turn, increases the alkalinity of the aqueous solution for further CO2 capture, thereby enabling this one-of-its-kind continuous CO2 capture and methanation process. Engineering simulations demonstrate the energy feasibility of this CO2 DAC and methanation process, highlighting its promise for potential large-scale applications.
Published: 2024

5. Identification and Validation of Nicotinamide Metabolism-Related Gene Signatures as a Novel Prognostic Model for Hepatocellular Carcinoma

Author: Yang,Sijia, Li,Ang, Lv,Lihong, Duan,Jinxin, Zheng,Zhihua, Zhuo,Wenfeng, Min,Jun, Wei,Jinxing, Yang,Sijia, Li,Ang, Lv,Lihong, Duan,Jinxin, Zheng,Zhihua, Zhuo,Wenfeng, Min,Jun, and Wei,Jinxing
Abstract: Sijia Yang,1,2,&ast; Ang Li,1,2,&ast; Lihong Lv,3,&ast; Jinxin Duan,2 Zhihua Zheng,4 Wenfeng Zhuo,5 Jun Min,2 Jinxing Wei2 1Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, 510120, Peopleâs Republic of China; 2Department of Hepatobiliary Surgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, 510120, Peopleâs Republic of China; 3Clinical Trial Institution of Pharmaceuticals, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, Guangdong, 510120, Peopleâs Republic of China; 4Guangdong Provincial Key Laboratory of New Drug Design and Evaluation, Guangdong Province Engineering Laboratory for Druggability and New Drug Evaluation, School of Pharmaceutical Sciences, Sun Yat-Sen University, Guangzhou, 510006, Peopleâs Republic of China; 5Department of Hepatobiliary Surgery, the Fifth Affiliated Hospital, Sun Yat-sen University 528406 Zhuhai, Guangdong, Peopleâs Republic of China&ast;These authors contributed equally to this workCorrespondence: Jinxing Wei; Jun Min, Email weijx5@mail.sysu.edu.cn; minjun@mail.sysu.edu.cnBackground: Nicotinamide (NAM+) regulates redox and metabolic activities in the mitochondria. The intention of the research was to identify key genes that relate to nicotinamide in hepatocellular carcinoma (HCC).Methods: Relevant clinical information were collected as well as RNA-seq data using the Cancer Genome Atlas (TCGA) database. Differential analysis was used to discover the genes that were differently expressed. On the key genes associated with NAM, functional enrichment analysis was carried out. Next, receiver operating characteristic (ROC) and prognosis Kaplan-Meier (K-M) curve analyses were used to evaluate the importance of important gene expression, respectively. The immune cell signatures were estimated using the CIBERSORT algorithm. Finally, the anticancer impact
Published: 2024

6. Quantum-centric supercomputing for materials science: A perspective on challenges and future directions

Author: Alexeev, Yuri, Alexeev, Yuri, Amsler, Maximilian, Barroca, Marco Antonio, Bassini, Sanzio, Battelle, Torey, Camps, Daan, Casanova, David, Choi, Young Jay, Chong, Frederic T, Chung, Charles, Codella, Christopher, Córcoles, Antonio D, Cruise, James, Di Meglio, Alberto, Duran, Ivan, Eckl, Thomas, Economou, Sophia, Eidenbenz, Stephan, Elmegreen, Bruce, Fare, Clyde, Faro, Ismael, Fernández, Cristina Sanz, Ferreira, Rodrigo Neumann Barros, Fuji, Keisuke, Fuller, Bryce, Gagliardi, Laura, Galli, Giulia, Glick, Jennifer R, Gobbi, Isacco, Gokhale, Pranav, de la Puente Gonzalez, Salvador, Greiner, Johannes, Gropp, Bill, Grossi, Michele, Gull, Emanuel, Healy, Burns, Hermes, Matthew R, Huang, Benchen, Humble, Travis S, Ito, Nobuyasu, Izmaylov, Artur F, Javadi-Abhari, Ali, Jennewein, Douglas, Jha, Shantenu, Jiang, Liang, Jones, Barbara, de Jong, Wibe Albert, Jurcevic, Petar, Kirby, William, Kister, Stefan, Kitagawa, Masahiro, Klassen, Joel, Klymko, Katherine, Koh, Kwangwon, Kondo, Masaaki, Kürkçüog̃lu, Dog̃a Murat, Kurowski, Krzysztof, Laino, Teodoro, Landfield, Ryan, Leininger, Matt, Leyton-Ortega, Vicente, Li, Ang, Lin, Meifeng, Liu, Junyu, Lorente, Nicolas, Luckow, Andre, Martiel, Simon, Martin-Fernandez, Francisco, Martonosi, Margaret, Marvinney, Claire, Medina, Arcesio Castaneda, Merten, Dirk, Mezzacapo, Antonio, Michielsen, Kristel, Mitra, Abhishek, Mittal, Tushar, Moon, Kyungsun, Moore, Joel, Mostame, Sarah, Motta, Mario, Na, Young-Hye, Nam, Yunseong, Narang, Prineha, Ohnishi, Yu-ya, Ottaviani, Daniele, Otten, Matthew, Pakin, Scott, Pascuzzi, Vincent R, Pednault, Edwin, Piontek, Tomasz, Pitera, Jed, Rall, Patrick, Ravi, Gokul Subramanian, Robertson, Niall, Rossi, Matteo AC, Rydlichowski, Piotr, Ryu, Hoon, Samsonidze, Georgy, Sato, Mitsuhisa, Saurabh, Nishant, Alexeev, Yuri, Alexeev, Yuri, Amsler, Maximilian, Barroca, Marco Antonio, Bassini, Sanzio, Battelle, Torey, Camps, Daan, Casanova, David, Choi, Young Jay, Chong, Frederic T, Chung, Charles, Codella, Christopher, Córcoles, Antonio D, Cruise, James, Di Meglio, Alberto, Duran, Ivan, Eckl, Thomas, Economou, Sophia, Eidenbenz, Stephan, Elmegreen, Bruce, Fare, Clyde, Faro, Ismael, Fernández, Cristina Sanz, Ferreira, Rodrigo Neumann Barros, Fuji, Keisuke, Fuller, Bryce, Gagliardi, Laura, Galli, Giulia, Glick, Jennifer R, Gobbi, Isacco, Gokhale, Pranav, de la Puente Gonzalez, Salvador, Greiner, Johannes, Gropp, Bill, Grossi, Michele, Gull, Emanuel, Healy, Burns, Hermes, Matthew R, Huang, Benchen, Humble, Travis S, Ito, Nobuyasu, Izmaylov, Artur F, Javadi-Abhari, Ali, Jennewein, Douglas, Jha, Shantenu, Jiang, Liang, Jones, Barbara, de Jong, Wibe Albert, Jurcevic, Petar, Kirby, William, Kister, Stefan, Kitagawa, Masahiro, Klassen, Joel, Klymko, Katherine, Koh, Kwangwon, Kondo, Masaaki, Kürkçüog̃lu, Dog̃a Murat, Kurowski, Krzysztof, Laino, Teodoro, Landfield, Ryan, Leininger, Matt, Leyton-Ortega, Vicente, Li, Ang, Lin, Meifeng, Liu, Junyu, Lorente, Nicolas, Luckow, Andre, Martiel, Simon, Martin-Fernandez, Francisco, Martonosi, Margaret, Marvinney, Claire, Medina, Arcesio Castaneda, Merten, Dirk, Mezzacapo, Antonio, Michielsen, Kristel, Mitra, Abhishek, Mittal, Tushar, Moon, Kyungsun, Moore, Joel, Mostame, Sarah, Motta, Mario, Na, Young-Hye, Nam, Yunseong, Narang, Prineha, Ohnishi, Yu-ya, Ottaviani, Daniele, Otten, Matthew, Pakin, Scott, Pascuzzi, Vincent R, Pednault, Edwin, Piontek, Tomasz, Pitera, Jed, Rall, Patrick, Ravi, Gokul Subramanian, Robertson, Niall, Rossi, Matteo AC, Rydlichowski, Piotr, Ryu, Hoon, Samsonidze, Georgy, Sato, Mitsuhisa, and Saurabh, Nishant
Abstract: Computational models are an essential tool for the design, characterization, and discovery of novel materials. Computationally hard tasks in materials science stretch the limits of existing high-performance supercomputing centers, consuming much of their resources for simulation, analysis, and data processing. Quantum computing, on the other hand, is an emerging technology with the potential to accelerate many of the computational tasks needed for materials science. In order to do that, the quantum technology must interact with conventional high-performance computing in several ways: approximate results validation, identification of hard problems, and synergies in quantum-centric supercomputing. In this paper, we provide a perspective on how quantum-centric supercomputing can help address critical computational problems in materials science, the challenges to face in order to solve representative use cases, and new suggested directions.
Published: 2024

7. Characteristics and Prognosis of Type 2 Myocardial Infarction Through Worsening Renal Function and NT-proBNP in Older Adults with Pneumonia

Author: Ma,Jinling, Bian,Suyan, Li,Ang, Chen,Qian, Ma,Jinling, Bian,Suyan, Li,Ang, and Chen,Qian
Abstract: Jinling Ma,&ast; Suyan Bian, Ang Li, Qian Chen&ast; Department of Geriatric Cardiology, the Second Medical Center and National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing, Peopleâs Republic of China&ast;These authors contributed equally to this workCorrespondence: Jinling Ma, Department of Geriatric Cardiology, the Second Medical Center and National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, No. 28, Fuxing Road, Haidian District, Beijing, 100853, Peopleâs Republic of China, Email majinling1@163.comBackground: Type 2 myocardial infarction (MI) is becoming more recognized. This study aimed to assess the factors linked to type 2 MI in older adults with pneumonia and further determine the predictive factors of 90-day adverse events (refractory heart failure, cardiogenic shock, and all-cause mortality).Methods: A single-center retrospective analysis was conducted among older adults with pneumonia. The primary outcome was the prevalence of type 2 MI. The secondary objective was to assess the adverse events in these patients with type 2 MI within 90 days.Results: A total of 2618 patients were included. Of these, 361 patients (13.8%) suffered from type 2 MI. Multivariable predictors of type 2 MI were chronic kidney disease (CKD), age-adjusted Charlson comorbidity index (ACCI) score, and NT-proBNP > 4165pg/mL. Moreover, the independent predictive factors of 90-day adverse events included NT-proBNP > 4165pg/mL, age, ACCI score, and CKD. The KaplanâMeier adverse events curves revealed that the type 2 MI patients with CKD and NT-proBNP > 4165pg/mL had a higher risk than CKD or NT-proBNP > 4165pg/mL alone.Conclusion: Type 2 MI in older pneumonia hospitalization represents a heterogeneous population. Elevated NT-proBNP level and prevalence of CKD are important predictors of type 2 MI and 90-day adverse events in type 2 MI patients.Keywords: type 2 myocardial infarction, pn
Published: 2024

8. X-ray studies of neutron stars and the equation of state

Author: Miao, Zhiqiang, Qi, Liqiang, Zhang, Juan, Li, Ang, Ge, Mingyu, Miao, Zhiqiang, Qi, Liqiang, Zhang, Juan, Li, Ang, and Ge, Mingyu
Abstract: The understanding of neutron star equation of state hinges on a comprehensive analysis of multi-messenger, multi-wavelength data. The recent scrutiny of PSR J0030+0451 data by NICER introduces complexities, unveiling a tension with another X-ray observation of the central compact object in HESS J1731-347, specifically concerning the mass-radius constraint of low-mass neutron stars. This tension persists when integrating NICER's updated data with LIGO/Virgo's gravitational-wave data from the GW170817 binary neutron star merger. Despite attempts to reconcile these disparate observations, the current combined data still can not distinguish different types of neutron stars -- whether they are pure neutron stars or hybrid stars. Bayesian inference indicates only modest changes in the posterior ranges of parameters related to the nuclear matter and deconfinement phase transition. This ongoing exploration underscores the intricate challenges in precisely characterizing neutron stars. It also points out that it is possible to probe the equation of state at different density regimes from future more accurate radii of neutron stars with various masses., Comment: 12 pages, 6 figures, 2 tables
Published: 2024

9. The Application of Generative Artificial Intelligence in Virtual Financial Advisor and Capital Market Analysis

Author: Jiang, Wei, Yang, Tianyi, Li, Ang, Lin, Yiyu, Bai, Xinzhu, Jiang, Wei, Yang, Tianyi, Li, Ang, Lin, Yiyu, and Bai, Xinzhu
Abstract: This paper explores the application of Generating Artificial Intelligence (GAI) to virtual financial advisory and capital market analysis. This paper first introduces the basic principle of GAI and its importance in financial decision-making, and then analyzes the problems and limitations of traditional financial advisory model through literature review and actual data. Then, it discusses the development trend and advantages of intelligent financial advisor, and the difference compared with traditional financial advisor model. Then, it describes the application of generative AI in the financial field, including intelligent investment advisory, risk assessment, investment decision-making and so on. Finally, the realization path of building intelligent financial decision based on generative AI is proposed, including data collection and collation, building large model of enterprise real data, building interactive data analysis and decision system based on natural language, personalized financial analysis and decision support, etc. Through these measures, we can better achieve intelligent, personalized and efficient financial decision support, and provide more intelligent and efficient support for financial decision making.
Published: 2024

10. Applications of Artificial Intelligence Generative Adversarial Techniques in the Financial Sector

Author: Yuan, Jiaqiang, Lin, Yiyu, Shi, Yadong, Yang, Tianyi, Li, Ang, Yuan, Jiaqiang, Lin, Yiyu, Shi, Yadong, Yang, Tianyi, and Li, Ang
Abstract: This paper explores the application of artificial intelligence (AI) techniques in the financial sector, with a particular focus on the role of generative Adversarial networks (GANs) in defending against automated attacks in the financial sector. AI technology is regarded as the fundamental technology of the fourth Industrial revolution, and its applications cover robotics, speech recognition, image recognition, natural language processing and other fields. In the financial sector, AI technology has improved business efficiency and accuracy by developing terminal programs with business operation skills that partially or completely replace manual labor. A generative adversarial network is a system of generators and discriminators that generate realistic fake data to help financial institutions identify fraud. Therefore, it is suggested that websites should not only abandon the traditional verification code, but also find other more secure verification methods, and consider introducing a verification system with the ability to generate counter-network to improve the accuracy and security of authentication.
Published: 2024

11. Lifting-Line Aerodynamics for Airborne Wind Energy on a Prescribed Path

Author: Gaunaa, Mac (author), Li, Ang (author), McWilliam, Michael (author), Kelly, Mark (author), Gaunaa, Mac (author), Li, Ang (author), McWilliam, Michael (author), and Kelly, Mark (author)
Published: 2024

12. HetArch: Heterogeneous Microarchitectures for Superconducting Quantum Systems

Author: Massachusetts Institute of Technology. Department of Physics, Stein, Samuel, Sussman, Sara, Tomesh, Teague, Guinn, Charles, Tureci, Esin, Lin, Sophia Fuhui, Tang, Wei, Ang, James, Chakram, Srivatsan, Li, Ang, Martonosi, Margaret, Chong, Fred, Houck, Andrew A., Chuang, Isaac L., Demarco, Michael, Massachusetts Institute of Technology. Department of Physics, Stein, Samuel, Sussman, Sara, Tomesh, Teague, Guinn, Charles, Tureci, Esin, Lin, Sophia Fuhui, Tang, Wei, Ang, James, Chakram, Srivatsan, Li, Ang, Martonosi, Margaret, Chong, Fred, Houck, Andrew A., Chuang, Isaac L., and Demarco, Michael
Abstract: Noisy Intermediate-Scale Quantum Computing (NISQ) has dominated headlines in recent years, with the longer-term vision of Fault-Tolerant Quantum Computation (FTQC) offering significant potential albeit at currently intractable resource costs and quantum error correction (QEC) overheads. For problems of interest, FTQC will require millions of physical qubits with long coherence times, high-fidelity gates, and compact sizes to surpass classical systems. Just as heterogeneous specialization has offered scaling benefits in classical computing, it is likewise gaining interest in FTQC. However, systematic use of heterogeneity in either hardware or software elements of FTQC systems remains a serious challenge due to the vast design space and variable physical constraints. This paper meets the challenge of making heterogeneous FTQC design practical by introducing HetArch, a toolbox for designing heterogeneous quantum systems, and using it to explore heterogeneous design scenarios. Using a hierarchical approach, we successively break quantum algorithms into smaller operations (akin to classical application kernels), thus greatly simplifying the design space and resulting tradeoffs. Specializing to superconducting systems, we then design optimized heterogeneous hardware composed of varied superconducting devices, abstracting physical constraints into design rules that enable devices to be assembled into standard cells optimized for specific operations. Finally, we provide a heterogeneous design space exploration framework which reduces the simulation burden by a factor of 104 or more and allows us to characterize optimal design points. We use these techniques to design superconducting quantum modules for entanglement distillation, error correction, and code teleportation, reducing error rates by 2.6 ×, 10.7 ×, and 3.0 × compared to homogeneous systems.
Published: 2024

13. A Challenge Dataset and Effective Models for Conversational Stance Detection

Author: Niu, Fuqiang, Yang, Min, Li, Ang, Zhang, Baoquan, Peng, Xiaojiang, Zhang, Bowen, Niu, Fuqiang, Yang, Min, Li, Ang, Zhang, Baoquan, Peng, Xiaojiang, and Zhang, Bowen
Abstract: Previous stance detection studies typically concentrate on evaluating stances within individual instances, thereby exhibiting limitations in effectively modeling multi-party discussions concerning the same specific topic, as naturally transpire in authentic social media interactions. This constraint arises primarily due to the scarcity of datasets that authentically replicate real social media contexts, hindering the research progress of conversational stance detection. In this paper, we introduce a new multi-turn conversation stance detection dataset (called \textbf{MT-CSD}), which encompasses multiple targets for conversational stance detection. To derive stances from this challenging dataset, we propose a global-local attention network (\textbf{GLAN}) to address both long and short-range dependencies inherent in conversational data. Notably, even state-of-the-art stance detection methods, exemplified by GLAN, exhibit an accuracy of only 50.47\%, highlighting the persistent challenges in conversational stance detection. Furthermore, our MT-CSD dataset serves as a valuable resource to catalyze advancements in cross-domain stance detection, where a classifier is adapted from a different yet related target. We believe that MT-CSD will contribute to advancing real-world applications of stance detection research. Our source code, data, and models are available at \url{https://github.com/nfq729/MT-CSD}.
Published: 2024

14. Accurate and Data-Efficient Micro-XRD Phase Identification Using Multi-Task Learning: Application to Hydrothermal Fluids

Author: Li, Yanfei, Liu, Juejing, Zhao, Xiaodong, Liu, Wenjun, Geng, Tong, Li, Ang, Zhang, Xin, Li, Yanfei, Liu, Juejing, Zhao, Xiaodong, Liu, Wenjun, Geng, Tong, Li, Ang, and Zhang, Xin
Abstract: Traditional analysis of highly distorted micro-X-ray diffraction ({\mu}-XRD) patterns from hydrothermal fluid environments is a time-consuming process, often requiring substantial data preprocessing and labeled experimental data. This study demonstrates the potential of deep learning with a multitask learning (MTL) architecture to overcome these limitations. We trained MTL models to identify phase information in {\mu}-XRD patterns, minimizing the need for labeled experimental data and masking preprocessing steps. Notably, MTL models showed superior accuracy compared to binary classification CNNs. Additionally, introducing a tailored cross-entropy loss function improved MTL model performance. Most significantly, MTL models tuned to analyze raw and unmasked XRD patterns achieved close performance to models analyzing preprocessed data, with minimal accuracy differences. This work indicates that advanced deep learning architectures like MTL can automate arduous data handling tasks, streamline the analysis of distorted XRD patterns, and reduce the reliance on labor-intensive experimental datasets.
Published: 2024

15. HRLAIF: Improvements in Helpfulness and Harmlessness in Open-domain Reinforcement Learning From AI Feedback

Author: Li, Ang, Xiao, Qiugen, Cao, Peng, Tang, Jian, Yuan, Yi, Zhao, Zijie, Chen, Xiaoyuan, Zhang, Liang, Li, Xiangyang, Yang, Kaitong, Guo, Weidong, Gan, Yukang, Yu, Xu, Wang, Daniell, Shan, Ying, Li, Ang, Xiao, Qiugen, Cao, Peng, Tang, Jian, Yuan, Yi, Zhao, Zijie, Chen, Xiaoyuan, Zhang, Liang, Li, Xiangyang, Yang, Kaitong, Guo, Weidong, Gan, Yukang, Yu, Xu, Wang, Daniell, and Shan, Ying
Abstract: Reinforcement Learning from AI Feedback (RLAIF) has the advantages of shorter annotation cycles and lower costs over Reinforcement Learning from Human Feedback (RLHF), making it highly efficient during the rapid strategy iteration periods of large language model (LLM) training. Using ChatGPT as a labeler to provide feedback on open-domain prompts in RLAIF training, we observe an increase in human evaluators' preference win ratio for model responses, but a decrease in evaluators' satisfaction rate. Analysis suggests that the decrease in satisfaction rate is mainly due to some responses becoming less helpful, particularly in terms of correctness and truthfulness, highlighting practical limitations of basic RLAIF. In this paper, we propose Hybrid Reinforcement Learning from AI Feedback (HRLAIF). This method enhances the accuracy of AI annotations for responses, making the model's helpfulness more robust in training process. Additionally, it employs AI for Red Teaming, further improving the model's harmlessness. Human evaluation results show that HRLAIF inherits the ability of RLAIF to enhance human preference for outcomes at a low cost while also improving the satisfaction rate of responses. Compared to the policy model before Reinforcement Learning (RL), it achieves an increase of 2.08\% in satisfaction rate, effectively addressing the issue of a decrease of 4.58\% in satisfaction rate after basic RLAIF., Comment: 18 pages, 7 figures
Published: 2024

16. Personalized Audiobook Recommendations at Spotify Through Graph Neural Networks

Author: De Nadai, Marco, Fabbri, Francesco, Gigioli, Paul, Wang, Alice, Li, Ang, Silvestri, Fabrizio, Kim, Laura, Lin, Shawn, Radosavljevic, Vladan, Ghael, Sandeep, Nyhan, David, Bouchard, Hugues, Lalmas-Roelleke, Mounia, Damianou, Andreas, De Nadai, Marco, Fabbri, Francesco, Gigioli, Paul, Wang, Alice, Li, Ang, Silvestri, Fabrizio, Kim, Laura, Lin, Shawn, Radosavljevic, Vladan, Ghael, Sandeep, Nyhan, David, Bouchard, Hugues, Lalmas-Roelleke, Mounia, and Damianou, Andreas
Abstract: In the ever-evolving digital audio landscape, Spotify, well-known for its music and talk content, has recently introduced audiobooks to its vast user base. While promising, this move presents significant challenges for personalized recommendations. Unlike music and podcasts, audiobooks, initially available for a fee, cannot be easily skimmed before purchase, posing higher stakes for the relevance of recommendations. Furthermore, introducing a new content type into an existing platform confronts extreme data sparsity, as most users are unfamiliar with this new content type. Lastly, recommending content to millions of users requires the model to react fast and be scalable. To address these challenges, we leverage podcast and music user preferences and introduce 2T-HGNN, a scalable recommendation system comprising Heterogeneous Graph Neural Networks (HGNNs) and a Two Tower (2T) model. This novel approach uncovers nuanced item relationships while ensuring low latency and complexity. We decouple users from the HGNN graph and propose an innovative multi-link neighbor sampler. These choices, together with the 2T component, significantly reduce the complexity of the HGNN model. Empirical evaluations involving millions of users show significant improvement in the quality of personalized recommendations, resulting in a +46% increase in new audiobooks start rate and a +23% boost in streaming rates. Intriguingly, our model's impact extends beyond audiobooks, benefiting established products like podcasts., Comment: To appear in The Web Conference 2024 proceedings
Published: 2024

17. Enhancing Court View Generation with Knowledge Injection and Guidance

Author: Li, Ang, Wu, Yiquan, Liu, Yifei, Wu, Fei, Cai, Ming, Kuang, Kun, Li, Ang, Wu, Yiquan, Liu, Yifei, Wu, Fei, Cai, Ming, and Kuang, Kun
Abstract: Court View Generation (CVG) is a challenging task in the field of Legal Artificial Intelligence (LegalAI), which aims to generate court views based on the plaintiff claims and the fact descriptions. While Pretrained Language Models (PLMs) have showcased their prowess in natural language generation, their application to the complex, knowledge-intensive domain of CVG often reveals inherent limitations. In this paper, we present a novel approach, named Knowledge Injection and Guidance (KIG), designed to bolster CVG using PLMs. To efficiently incorporate domain knowledge during the training stage, we introduce a knowledge-injected prompt encoder for prompt tuning, thereby reducing computational overhead. Moreover, to further enhance the model's ability to utilize domain knowledge, we employ a generating navigator, which dynamically guides the text generation process in the inference stage without altering the model's architecture, making it readily transferable. Comprehensive experiments on real-world data demonstrate the effectiveness of our approach compared to several established baselines, especially in the responsivity of claims, where it outperforms the best baseline by 11.87%.
Published: 2024

18. From Graph to Word Bag: Introducing Domain Knowledge to Confusing Charge Prediction

Author: Li, Ang, Chen, Qiangchao, Wu, Yiquan, Cai, Ming, Zhou, Xiang, Wu, Fei, Kuang, Kun, Li, Ang, Chen, Qiangchao, Wu, Yiquan, Cai, Ming, Zhou, Xiang, Wu, Fei, and Kuang, Kun
Abstract: Confusing charge prediction is a challenging task in legal AI, which involves predicting confusing charges based on fact descriptions. While existing charge prediction methods have shown impressive performance, they face significant challenges when dealing with confusing charges, such as Snatch and Robbery. In the legal domain, constituent elements play a pivotal role in distinguishing confusing charges. Constituent elements are fundamental behaviors underlying criminal punishment and have subtle distinctions among charges. In this paper, we introduce a novel From Graph to Word Bag (FWGB) approach, which introduces domain knowledge regarding constituent elements to guide the model in making judgments on confusing charges, much like a judge's reasoning process. Specifically, we first construct a legal knowledge graph containing constituent elements to help select keywords for each charge, forming a word bag. Subsequently, to guide the model's attention towards the differentiating information for each charge within the context, we expand the attention mechanism and introduce a new loss function with attention supervision through words in the word bag. We construct the confusing charges dataset from real-world judicial documents. Experiments demonstrate the effectiveness of our method, especially in maintaining exceptional performance in imbalanced label distributions.
Published: 2024

19. FTTN: Feature-Targeted Testing for Numerical Properties of NVIDIA & AMD Matrix Accelerators

Author: Li, Xinyi, Li, Ang, Fang, Bo, Swirydowicz, Katarzyna, Laguna, Ignacio, Gopalakrishnan, Ganesh, Li, Xinyi, Li, Ang, Fang, Bo, Swirydowicz, Katarzyna, Laguna, Ignacio, and Gopalakrishnan, Ganesh
Abstract: NVIDIA Tensor Cores and AMD Matrix Cores (together called Matrix Accelerators) are of growing interest in high-performance computing and machine learning owing to their high performance. Unfortunately, their numerical behaviors are not publicly documented, including the number of extra precision bits maintained, the accumulation order of addition, and predictable subnormal number handling during computations. This makes it impossible to reliably port codes across these differing accelerators. This paper contributes a collection of {\em Feature Targeted Tests for Numerical Properties} that that help determine these features across five floating-point formats, four rounding modes and additional that highlight the rounding behaviors and preservation of extra precision bits. To show the practical relevance of FTTN, we design a simple matrix-multiplication test designed with insights gathered from our feature-tests. We executed this very simple test on five platforms, producing different answers: V100, A100, and MI250X produced 0, MI100 produced 255.875, and Hopper H100 produced 191.875. Our matrix multiplication tests employ patterns found in iterative refinement-based algorithms, highlighting the need to check for significant result variability when porting code across GPUs.
Published: 2024

20. A Quantum-Classical Collaborative Training Architecture Based on Quantum State Fidelity

Author: L'Abbate, Ryan, D'Onofrio Jr., Anthony, Stein, Samuel, Chen, Samuel Yen-Chi, Li, Ang, Chen, Pin-Yu, Chen, Juntao, Mao, Ying, L'Abbate, Ryan, D'Onofrio Jr., Anthony, Stein, Samuel, Chen, Samuel Yen-Chi, Li, Ang, Chen, Pin-Yu, Chen, Juntao, and Mao, Ying
Abstract: Recent advancements have highlighted the limitations of current quantum systems, particularly the restricted number of qubits available on near-term quantum devices. This constraint greatly inhibits the range of applications that can leverage quantum computers. Moreover, as the available qubits increase, the computational complexity grows exponentially, posing additional challenges. Consequently, there is an urgent need to use qubits efficiently and mitigate both present limitations and future complexities. To address this, existing quantum applications attempt to integrate classical and quantum systems in a hybrid framework. In this study, we concentrate on quantum deep learning and introduce a collaborative classical-quantum architecture called co-TenQu. The classical component employs a tensor network for compression and feature extraction, enabling higher-dimensional data to be encoded onto logical quantum circuits with limited qubits. On the quantum side, we propose a quantum-state-fidelity-based evaluation function to iteratively train the network through a feedback loop between the two sides. co-TenQu has been implemented and evaluated with both simulators and the IBM-Q platform. Compared to state-of-the-art approaches, co-TenQu enhances a classical deep neural network by up to 41.72% in a fair setting. Additionally, it outperforms other quantum-based methods by up to 1.9 times and achieves similar accuracy while utilizing 70.59% fewer qubits., Comment: IEEE Transactions on Quantum Engineering
Published: 2024
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21. Multi-modal Stance Detection: New Datasets and Model

Author: Liang, Bin, Li, Ang, Zhao, Jingqian, Gui, Lin, Yang, Min, Yu, Yue, Wong, Kam-Fai, Xu, Ruifeng, Liang, Bin, Li, Ang, Zhao, Jingqian, Gui, Lin, Yang, Min, Yu, Yue, Wong, Kam-Fai, and Xu, Ruifeng
Abstract: Stance detection is a challenging task that aims to identify public opinion from social media platforms with respect to specific targets. Previous work on stance detection largely focused on pure texts. In this paper, we study multi-modal stance detection for tweets consisting of texts and images, which are prevalent in today's fast-growing social media platforms where people often post multi-modal messages. To this end, we create five new multi-modal stance detection datasets of different domains based on Twitter, in which each example consists of a text and an image. In addition, we propose a simple yet effective Targeted Multi-modal Prompt Tuning framework (TMPT), where target information is leveraged to learn multi-modal stance features from textual and visual modalities. Experimental results on our five benchmark datasets show that the proposed TMPT achieves state-of-the-art performance in multi-modal stance detection., Comment: ACL'24 Findings
Published: 2024

22. Mitigating Biases of Large Language Models in Stance Detection with Calibration

Author: Li, Ang, Zhao, Jingqian, Liang, Bin, Gui, Lin, Wang, Hui, Zeng, Xi, Wong, Kam-Fai, Xu, Ruifeng, Li, Ang, Zhao, Jingqian, Liang, Bin, Gui, Lin, Wang, Hui, Zeng, Xi, Wong, Kam-Fai, and Xu, Ruifeng
Abstract: Large language models (LLMs) have achieved remarkable progress in many natural language processing tasks. However, our experiment reveals that, in stance detection tasks, LLMs may generate biased stances due to spurious sentiment-stance correlation and preference towards certain individuals and topics, thus harming their performance. Therefore, in this paper, we propose to Mitigate Biases of LLMs in stance detection with Calibration (MB-Cal). In which, a novel gated calibration network is devised to mitigate the biases on the stance reasoning results from LLMs. Further, to make the calibration more accurate and generalizable, we construct counterfactual augmented data to rectify stance biases. Experimental results on in-target and zero-shot stance detection tasks show that the proposed MB-Cal can effectively mitigate biases of LLMs, achieving state-of-the-art results.
Published: 2024

23. Ground-Fusion: A Low-cost Ground SLAM System Robust to Corner Cases

Author: Yin, Jie, Li, Ang, Xi, Wei, Yu, Wenxian, Zou, Danping, Yin, Jie, Li, Ang, Xi, Wei, Yu, Wenxian, and Zou, Danping
Abstract: We introduce Ground-Fusion, a low-cost sensor fusion simultaneous localization and mapping (SLAM) system for ground vehicles. Our system features efficient initialization, effective sensor anomaly detection and handling, real-time dense color mapping, and robust localization in diverse environments. We tightly integrate RGB-D images, inertial measurements, wheel odometer and GNSS signals within a factor graph to achieve accurate and reliable localization both indoors and outdoors. To ensure successful initialization, we propose an efficient strategy that comprises three different methods: stationary, visual, and dynamic, tailored to handle diverse cases. Furthermore, we develop mechanisms to detect sensor anomalies and degradation, handling them adeptly to maintain system accuracy. Our experimental results on both public and self-collected datasets demonstrate that Ground-Fusion outperforms existing low-cost SLAM systems in corner cases. We release the code and datasets at https://github.com/SJTU-ViSYS/Ground-Fusion.
Published: 2024

24. Enhancing One-Shot Federated Learning Through Data and Ensemble Co-Boosting

Author: Dai, Rong, Zhang, Yonggang, Li, Ang, Liu, Tongliang, Yang, Xun, Han, Bo, Dai, Rong, Zhang, Yonggang, Li, Ang, Liu, Tongliang, Yang, Xun, and Han, Bo
Abstract: One-shot Federated Learning (OFL) has become a promising learning paradigm, enabling the training of a global server model via a single communication round. In OFL, the server model is aggregated by distilling knowledge from all client models (the ensemble), which are also responsible for synthesizing samples for distillation. In this regard, advanced works show that the performance of the server model is intrinsically related to the quality of the synthesized data and the ensemble model. To promote OFL, we introduce a novel framework, Co-Boosting, in which synthesized data and the ensemble model mutually enhance each other progressively. Specifically, Co-Boosting leverages the current ensemble model to synthesize higher-quality samples in an adversarial manner. These hard samples are then employed to promote the quality of the ensemble model by adjusting the ensembling weights for each client model. Consequently, Co-Boosting periodically achieves high-quality data and ensemble models. Extensive experiments demonstrate that Co-Boosting can substantially outperform existing baselines under various settings. Moreover, Co-Boosting eliminates the need for adjustments to the client's local training, requires no additional data or model transmission, and allows client models to have heterogeneous architectures., Comment: To be published in ICLR2024
Published: 2024

25. How does the blade element momentum method see swept or prebent blades?

Author: Li, Ang, Gaunaa, Mac, Pirrung, Georg Raimund, Lønbæk, Kenneth, Li, Ang, Gaunaa, Mac, Pirrung, Georg Raimund, and Lønbæk, Kenneth
Abstract: The blade element momentum (BEM) method is among the mostly used engineering aerodynamic models for wind turbine rotors. Even though the BEM method is strictly only applicable to straight blades forming a planar rotor, it is in practice used for load calculation and design optimization of blades with relatively large sweep, prebend or coning. The present work aims to answer the question: How does the BEM method see swept or prebent blades? The blade element part of the method can be adapted for swept or prebent blades by projecting the velocity and loads between the 2-D airfoil section and the 3-D blade geometry under the cross-flow principle. However, momentum theory considers the 3-D wake and the induction of a planar actuator disc with straight blades. There could be significant differences between the results calculated from different implementations of the BEM method, while the details of the implementations are often omitted in the literature. In the present work, a consistent implementation including the definition of airfoil section alignments and the coordinate systems is provided. It is shown that for a given circulation distribution, the BEM method will predict approximately the same local aerodynamic loads for non-straight blades as if the blades were straight and the rotor was planar. This means using the BEM method to perform aerodynamic optimization of modern rotors with curved blades and/or rotors that are not planar can only have little meaningful result. In order to model the impact of the wake geometry on the induction, more advanced models that take into account the wake geometry on the induction are necessary.
Published: 2024

26. A correction model for the effect of spanwise flow on the viscous force contribution in BEM and Lifting Line methods

Author: Gaunaa, Mac, Sørensen, Niels N., Li, Ang, Gaunaa, Mac, Sørensen, Niels N., and Li, Ang
Abstract: Design optimization and aeroelastic load evaluation for wind turbines are still infeasible for CFD due to computational cost. These tasks are carried out using 3D engineering aerodynamic methods, where the local aerodynamic loads are obtained from tabulated 2D aerodynamic polars, which makes the models fast. With recent developments such as highly flexible blades, coned/prebent/swept blades, the relative inflow direction to the “inner” 2D airfoil section systems can have a significant component in the spanwise direction. The Crossflow Principle (CP) is used to treat the effects of this in a physically consistent manner. Cases dominated by pressure forces are described well with the CP method, but it is shown in the paper that CP fails to describe the part of the forces stemming from the friction forces correctly. It is shown in the paper that the power loss due to friction forces will be underestimated with the crossflow principle for rotors with significantly swept blades or other designs with a significant amount of local crossflow on the blades. The present work presents a simple model to correct the baseline crossflow principle method to take into account also the friction force part in a consistent manner. The model is validated with 3D CFD results on a 2D airfoil section. It is shown that the new model successfully corrects for the addition of the viscous forces due to spanwise flow component. The paper includes examples of the effect of using the model on rotor designs with different amounts of blade sweep simulated using a blade element momentum (BEM) and blade element vortex cylinder (BEVC) methods.
Published: 2024

27. Multi-fidelity, steady-state aeroelastic modelling of a 22-megawatt wind turbine

Author: Zahle, Frederik, Li, Ang, Lønbæk, Kenneth, Sørensen, Niels N., Riva, Riccardo, Zahle, Frederik, Li, Ang, Lønbæk, Kenneth, Sørensen, Niels N., and Riva, Riccardo
Abstract: In this work we present multi-fidelity steady-state aeroelastic framework that leverages the state-of-the-art simulation tool HAWC2 for the structural model, and a variety of aerodynamic models, comprising of the low fidelity blade element momentum (BEM) method, the medium fidelity blade element vortex cylinder (BEVC) method and the coupled near wake and vortex cylinder method, and finally the high-fidelity CFD solver EllipSys3D. The aeroelastic framework is part of AESOpt, an aerostructural framework for design of wind turbine blades. The different aerodynamic models are applied to compute the aeroelastic steady state of the newly designed IEA 22 MW Reference Wind Turbine. The results show a very good agreement between the medium- and high-fidelity aerodynamic models with a maximum of 2.7% difference between the high-fidelity aeroelastic response and that of the lower fidelities.
Published: 2024

28. Demystifying the Compression of Mixture-of-Experts Through a Unified Framework

Author: He, Shwai, Dong, Daize, Ding, Liang, Li, Ang, He, Shwai, Dong, Daize, Ding, Liang, and Li, Ang
Abstract: Scaling large language models has revolutionized the performance across diverse domains, yet the continual growth in model size poses significant challenges for real-world deployment. The Mixture of Experts (MoE) approach addresses this by dynamically selecting and activating only a subset of experts, significantly reducing computational costs while maintaining high performance. However, MoE introduces potential redundancy (e.g., parameters) and extra costs (e.g., communication overhead). Despite numerous compression techniques developed for mitigating the redundancy in dense models, the compression of MoE remains under-explored. We first bridge this gap with a cutting-edge unified framework that not only seamlessly integrates mainstream compression methods but also helps systematically understand MoE compression. This framework approaches compression from two perspectives: Expert Slimming which compresses individual experts and Expert Trimming which removes structured modules. Within this framework, we explore the optimization space unexplored by existing methods,and further introduce aggressive Expert Trimming techniques, i.e., Layer Drop and Block Drop, to eliminate redundancy at larger scales. Based on these insights,we present a comprehensive recipe to guide practitioners in compressing MoE effectively. Extensive experimental results demonstrate the effectiveness of the compression methods under our framework and the proposed recipe, achieving a 6.05x speedup and only 20.0GB memory usage while maintaining over 92% of performance on Mixtral-8x7B.
Published: 2024

29. Causality in the Can: Diet Coke's Impact on Fatness

Author: Qi, Yicheng, Li, Ang, Qi, Yicheng, and Li, Ang
Abstract: Artificially sweetened beverages like Diet Coke are often considered healthier alternatives, but the debate over their impact on obesity persists. Previous research has predominantly relied on observational data or randomized controlled trials (RCTs), which may not accurately capture the causal relationship between Diet Coke consumption and obesity. This study uses causal inference methods, employing data from the National Health and Nutrition Examination Survey (NHANES) to examine this relationship across diverse demographics. Instead of relying on RCT data, we constructed a causal graph and applied the back-door criterion with its adjustment formula to estimate the RCT distributions. We then calculated the counterfactual quantity, the Probability of Necessity and Sufficiency (PNS), using both NHANES data and estimated RCT data. We propose that PNS is the essential metric for assessing the impact of Diet Coke on obesity. Our results indicate that between 20% to 50% of individuals, especially those with poor dietary habits, are more likely to gain weight from Diet Coke. Conversely, in groups like young females with healthier diets, only a small proportion experience weight gain due to Diet Coke. These findings highlight the influence of individual lifestyle and potential hormonal factors on the varied effects of Diet Coke, providing a new framework for understanding its nutritional impacts on health., Comment: 9 pages, 4 figures, uses aaai24.sty
Published: 2024

30. Stereo-LiDAR Depth Estimation with Deformable Propagation and Learned Disparity-Depth Conversion

Author: Li, Ang, Hu, Anning, Xi, Wei, Yu, Wenxian, Zou, Danping, Li, Ang, Hu, Anning, Xi, Wei, Yu, Wenxian, and Zou, Danping
Abstract: Accurate and dense depth estimation with stereo cameras and LiDAR is an important task for automatic driving and robotic perception. While sparse hints from LiDAR points have improved cost aggregation in stereo matching, their effectiveness is limited by the low density and non-uniform distribution. To address this issue, we propose a novel stereo-LiDAR depth estimation network with Semi-Dense hint Guidance, named SDG-Depth. Our network includes a deformable propagation module for generating a semi-dense hint map and a confidence map by propagating sparse hints using a learned deformable window. These maps then guide cost aggregation in stereo matching. To reduce the triangulation error in depth recovery from disparity, especially in distant regions, we introduce a disparity-depth conversion module. Our method is both accurate and efficient. The experimental results on benchmark tests show its superior performance. Our code is available at https://github.com/SJTU-ViSYS/SDG-Depth., Comment: Accepted in ICRA 2024. 8 pages, 6 figures
Published: 2024

31. Scalable Circuit Cutting and Scheduling in a Resource-constrained and Distributed Quantum System

Author: Kan, Shuwen, Du, Zefan, Palma, Miguel, Stein, Samuel A, Liu, Chenxu, Wei, Wenqi, Chen, Juntao, Li, Ang, Mao, Ying, Kan, Shuwen, Du, Zefan, Palma, Miguel, Stein, Samuel A, Liu, Chenxu, Wei, Wenqi, Chen, Juntao, Li, Ang, and Mao, Ying
Abstract: Despite quantum computing's rapid development, current systems remain limited in practical applications due to their limited qubit count and quality. Various technologies, such as superconducting, trapped ions, and neutral atom quantum computing technologies are progressing towards a fault tolerant era, however they all face a diverse set of challenges in scalability and control. Recent efforts have focused on multi-node quantum systems that connect multiple smaller quantum devices to execute larger circuits. Future demonstrations hope to use quantum channels to couple systems, however current demonstrations can leverage classical communication with circuit cutting techniques. This involves cutting large circuits into smaller subcircuits and reconstructing them post-execution. However, existing cutting methods are hindered by lengthy search times as the number of qubits and gates increases. Additionally, they often fail to effectively utilize the resources of various worker configurations in a multi-node system. To address these challenges, we introduce FitCut, a novel approach that transforms quantum circuits into weighted graphs and utilizes a community-based, bottom-up approach to cut circuits according to resource constraints, e.g., qubit counts, on each worker. FitCut also includes a scheduling algorithm that optimizes resource utilization across workers. Implemented with Qiskit and evaluated extensively, FitCut significantly outperforms the Qiskit Circuit Knitting Toolbox, reducing time costs by factors ranging from 3 to 2000 and improving resource utilization rates by up to 3.88 times on the worker side, achieving a system-wide improvement of 2.86 times.
Published: 2024

32. SHED: Shapley-Based Automated Dataset Refinement for Instruction Fine-Tuning

Author: He, Yexiao, Wang, Ziyao, Shen, Zheyu, Sun, Guoheng, Dai, Yucong, Wu, Yongkai, Wang, Hongyi, Li, Ang, He, Yexiao, Wang, Ziyao, Shen, Zheyu, Sun, Guoheng, Dai, Yucong, Wu, Yongkai, Wang, Hongyi, and Li, Ang
Abstract: The pre-trained Large Language Models (LLMs) can be adapted for many downstream tasks and tailored to align with human preferences through fine-tuning. Recent studies have discovered that LLMs can achieve desirable performance with only a small amount of high-quality data, suggesting that a large amount of the data in these extensive datasets is redundant or even harmful. Identifying high-quality data from vast datasets to curate small yet effective datasets has emerged as a critical challenge. In this paper, we introduce SHED, an automated dataset refinement framework based on Shapley value for instruction fine-tuning. SHED eliminates the need for human intervention or the use of commercial LLMs. Moreover, the datasets curated through SHED exhibit transferability, indicating they can be reused across different LLMs with consistently high performance. We conduct extensive experiments to evaluate the datasets curated by SHED. The results demonstrate SHED's superiority over state-of-the-art methods across various tasks and LLMs; notably, datasets comprising only 10% of the original data selected by SHED achieve performance comparable to or surpassing that of the full datasets.
Published: 2024

33. MP-DPD: Low-Complexity Mixed-Precision Neural Networks for Energy-Efficient Digital Predistortion of Wideband Power Amplifiers

Author: Wu, Yizhuo, Li, Ang, Beikmirza, Mohammadreza, Singh, Gagan Deep, Chen, Qinyu, de Vreede, Leo C. N., Alavi, Morteza, Gao, Chang, Wu, Yizhuo, Li, Ang, Beikmirza, Mohammadreza, Singh, Gagan Deep, Chen, Qinyu, de Vreede, Leo C. N., Alavi, Morteza, and Gao, Chang
Abstract: Digital Pre-Distortion (DPD) enhances signal quality in wideband RF power amplifiers (PAs). As signal bandwidths expand in modern radio systems, DPD's energy consumption increasingly impacts overall system efficiency. Deep Neural Networks (DNNs) offer promising advancements in DPD, yet their high complexity hinders their practical deployment. This paper introduces open-source mixed-precision (MP) neural networks that employ quantized low-precision fixed-point parameters for energy-efficient DPD. This approach reduces computational complexity and memory footprint, thereby lowering power consumption without compromising linearization efficacy. Applied to a 160MHz-BW 1024-QAM OFDM signal from a digital RF PA, MP-DPD gives no performance loss against 32-bit floating-point precision DPDs, while achieving -43.75 (L)/-45.27 (R) dBc in Adjacent Channel Power Ratio (ACPR) and -38.72 dB in Error Vector Magnitude (EVM). A 16-bit fixed-point-precision MP-DPD enables a 2.8X reduction in estimated inference power. The PyTorch learning and testing code is publicly available at \url{https://github.com/lab-emi/OpenDPD}., Comment: Accepted to IEEE Microwave and Wireless Technology Letters (MWTL)
Published: 2024
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34. Mentalizing in an economic games context is associated with enhanced activation and connectivity in the left temporoparietal junction.

Author: Chang, Li-Ang, Chang, Li-Ang, Armaos, Konstantinos, Warns, Lotte, Ma de Sousa, Ava Q, Paauwe, Femke, Scholz, Christin, Engelmann, Jan B, Chang, Li-Ang, Chang, Li-Ang, Armaos, Konstantinos, Warns, Lotte, Ma de Sousa, Ava Q, Paauwe, Femke, Scholz, Christin, and Engelmann, Jan B
Abstract: Prior studies in Social Neuroeconomics have consistently reported activation in social cognition regions during interactive economic games, suggesting mentalizing during economic choice. Such mentalizing occurs during active participation in the game, as well as during passive observation of others' interactions. We designed a novel version of the classic false-belief task (FBT) in which participants read vignettes about interactions between agents in the ultimatum and trust games and were subsequently asked to infer the agents' beliefs. We compared activation patterns during the economic games FBT to those during the classic FBT using conjunction analyses. We find significant overlap in the left temporoparietal junction (TPJ) and dorsal medial prefrontal cortex, as well as the temporal pole (TP) during two task phases: belief formation and belief inference. Moreover, generalized Psychophysiological Interaction (gPPI) analyses show that during belief formation, the right TPJ is a target of both the left TPJ and the right TP seed regions, whereas during belief inferences all seed regions show interconnectivity with each other. These results indicate that across different task types and phases, mentalizing is associated with activation and connectivity across central nodes of the social cognition network. Importantly, this is the case for both the novel economic games and the classic FBTs.
Published: 2023

35. 3D computer vision : passive depth from defocus and manifold learning-based human activity recognition

Author: Li, Ang
Subjects: 620, TA Engineering (General). Civil engineering (General)
Abstract: The rational operator-based approach to depth from defocus (DfD) using pill-box point spread function (PSF) enables texture-invariant 3-dimensional (3D) surface reconstructions. However, pill-box PSF produces errors when the amount of lens diffraction and aberrations varies. This thesis proposes two DfD methods, one using the Gaussian PSF that addresses the situation when diffraction and aberrations are dominant, and the second based on the generalised Gaussian PSF that deals with any levels of the problem. The accuracy of DfD can be severely reduced by elliptical lens distortion. This thesis also presents two correction methods, correction by distortion cancellation and correction by least squares fit. Each method is followed by a smoothing algorithm to address the low-texture problem of DfD. Most existing human activity recognition systems pay little attention to an effective way to obtain training silhouettes. This thesis presents an algorithm to obtain silhouettes from any view using 3D data produced by Vicon Nexus. Existing background subtraction algorithms produce moving shadow that has a significant impact on silhouette-based recognition system. Shadow removal methods based on colour and texture fail when the surrounding background has similar colour or texture. This thesis proposes an algorithm based on known position of the sun to remove shadow in outdoor environment, which is able to remove essential part of the shadow to suffice recognition purpose. Unlike most recognition systems that are either speed-variant, temporal-order-variant, inefficient or computational expensive, this thesis presents a near real-time system based on embedded silhouettes. Silhouettes are first embedded with isometric feature mapping, and the transformation is learned by radial basis function. Complex human activities are then learnt with spatial objects created from the patterns of embedded silhouettes.
Published: 2014

36. Investigating the roles of Fascin and Rac1 in cell migration, invasion and metastasis

Author: Li, Ang
Subjects: 616.994, R Medicine (General), Q Science (General)
Abstract: Cell migration and invasion is a central process in embryonic development, wound healing and immune responses. Errors during this process have serious consequences, including mental retardation, vascular disease, tumor formation and metastasis. An understanding of the mechanism by which cells migrate and invade may lead to the development of novel therapeutic strategies for controlling, for example, cancer metastasis. Fascin is an actin-bundling protein involved in filopodia assembly and cancer invasion and metastasis of multiple epithelial cancer types. In this thesis, I have investigated the role of fascin in invadopodia formation, which are invasive finger-like protrusions that cancer cells use to invade into and degrade extracellular matrix. I demonstrated that fascin and its actin bundling activity are required for the assembly of the actin cytoskeleton at invadopodia as well as for the degradation of matrix. Fascin is a very stable component of invadopodia and its presence enhance the stability of actin structures at invadopodia. Furthermore, fascin is required for invasive migration into collagen I-Matrigel gels particularly in cell types that use an elongated mesenchymal type of motility in 3D. These data provide a potential molecular mechanism for how fascin increases the invasiveness of cancer cells. During embryogenesis, melanoblasts proliferate and migrate ventrally through the developing dermis and epidermis as single cells. In the second part of this thesis, I examined the importance of small Rho GTPase Rac1 on melanoblast migration during development. I demonstrate that targeted deletion of Rac1 in the melanoblasts of developing mice causes defects in migration, cell cycle progression and cytokinesis. Rac1 null cells migrate markedly less efficiently, but surprisingly global steering, crossing the dermal/epidermal junction andhoming to hair follicles are normal. Melanoblasts navigate in the epidermis using two classes of protrusion: short stubs and long pseudopods. Short stubs are driven by actin assembly, but unexpectedly are independent of Rac1, Arp2/3 complex, myosin or microtubules. Rac1 positively regulates the frequency of initiation of long pseudopods, which promote migration speed and directional flexibility. Scar/WAVE and Arp2/3 complex drive actin assembly for long pseudopod extension, which is also microtubule dependent. Myosin contractility balances the extension of long pseudopods by effecting retraction and allowing force generation for movement through the complex 3D epidermal environment. In addition, I demonstrated that expression of activated N-Ras did not affect migration and proliferation of melanoblast during embryogensis. However, Rac1 is required for constitutively active N- Ras induced dermal melanocytes survival in mice.
Published: 2011

37. Validation of the OAKS prognostic model for acute kidney injury after gastrointestinal surgery

Author: Ahmed, W, Bhatia, S, Mclean, K, Khaw, R, Baker, D, Kamarajah, S, Bell, S, Nepogodiev, D, Harrison, E, Glasbey, J, Borakati, A, Burke, J, Drake, T, Bath, M, Claireaux, H, Gundogan, B, Mohan, M, Deekonda, P, Kong, C, Joyce, H, Mcnamee, L, Woin, E, Khatri, C, Fitzgerald, J, Bhangu, A, Arulkumaran, N, Duthie, F, Hughes, J, Pinkney, T, Prowle, J, Richards, T, Thomas, M, Blanco-Colino, R, Chapman, S, Pata, F, Pellino, G, Sgro, A, van Elst, T, Van Straten, S, Knowles, C, Dynes, K, Patel, M, Patel, P, Wigley, C, Suresh, R, Shaw, A, Klimach, S, Jull, P, Evans, D, Preece, R, Ibrahim, I, Manikavasagar, V, Smith, R, Brown, F, Teo, R, Sim, D, Logan, A, Barai, I, Amin, H, Suresh, S, Sethi, R, Gul, W, Bolton, W, Corbridge, O, Horne, L, Attalla, M, Morley, R, Robinson, C, Hoskins, T, Mcallister, R, Lee, S, Dennis, Y, Nixon, G, Heywood, E, Wilson, H, Ng, L, Samaraweera, S, Mills, A, Doherty, C, Belchos, J, Phan, V, Lim, M, Miller, F, Chouari, T, Gardner, T, Goergen, N, Hayes, J, Macleod, C, Mccormack, R, Mckinley, A, Mckinstry, S, Milligan, W, Ooi, L, Rafiq, N, Sammut, T, Sinclair, E, Smith, M, Wong, J, Ablett, A, Tie-Gill, T, Ramsay, G, De Paola, L, Mcguckin, S, Alshakhs, A, Ahmeidat, A, Francis, A, Baker, C, Boulton, A, Collins, J, Copley, H, Fearnhead, N, Fox, H, Mah, T, Mckenna, J, Naruka, V, Nigam, N, Nourallah, B, Perera, S, Qureshi, A, Saggar, S, Sun, L, Wang, X, Yang, D, Ali, Z, Mullarkey, L, Walshe, R, Lewis, E, Berry, B, Moneim, J, Mookerjee, S, Al-Hadithi, A, Caroll, P, Doyle, C, Elangovan, S, Falamarzi, A, Gascon Perai, K, Greenan, E, Jain, D, Lang-Orsini, M, Lim, S, O'Byrne, L, Ridgway, P, Van der Laan, S, Arthur, J, Barclay, J, Bradley, P, Edwin, C, Finch, E, Hayashi, E, Hopkins, M, Kelly, D, Kelly, M, Mccartan, N, Ormrod, A, Pakenham, A, Christy, S, Hayward, J, Hitchen, C, Kishore, A, Martins, T, Philomen, J, Rao, R, Rickards, C, Burns, N, Copeland, M, Durand, C, Dyal, A, Ghaffar, A, Gidwani, A, Grant, M, Gribbon, C, Gruhn, A, Leer, M, Ojofeitimi, O, Carroll, L, Hylands, A, Delaugere, L, Reveendran, D, Ahmad, K, Beattie, G, Beatty, M, Campbell, G, Donaldson, G, Graham, S, Holmes, D, Kanabar, S, Liu, H, Mccann, C, Stewart, R, Vara, S, Coulter, C, Ralston, C, Laverty, L, Gallagher, P, Ajibola-Taylor, O, Andah, E, Ani, C, Cabdi, N, Ito, G, Jones, M, Komoriyama, A, Titu, L, Ahmed, A, Yeo, Y, Elliott, D, Bennett, J, Mcnamara, M, Sivarajah, S, Dunmore, C, Basra, M, Gallogly, P, Harinath, G, Leong, S, Pradhan, A, Siddiqui, I, Zaat, S, Ali, A, Galea, M, Looi, W, Ng, J, Farhan-Alanie, M, Aitken, G, Nair, M, Aly, M, Buari, M, Atkin, G, Azizi, A, Cargill, Z, China, Z, Elliot, J, Jebakumar, R, Lam, J, Mudalige, G, Onyerindu, C, Renju, M, Shankar Babu, V, Ahmed, K, Sheikh, Z, Monks, M, Lehmann, J, Rotimi, O, Hussain, M, Joji, N, Lovett, B, Mownah, H, Dwyer-Hemmings, L, Busuttil, A, Powell, J, Hensher, C, Vivian, F, Ali, B, Cresswell, B, Dhillon, A, Dupaguntla, Y, Hungwe, C, Lowe-Zinola, J, Tsang, J, Wcislo, K, Bevan, K, Cardus, C, Duggal, A, Hossain, S, Mchugh, M, Scott, M, Millar, Z, Hirosue, S, Ogunmwonyi, I, Nakakande, D, Chan, F, Evans, R, Gurung, E, Haughey, B, Jacob-Ramsdale, B, Kerr, M, Lee, J, Mccann, E, O'Boyle, K, Reid, N, Kwek, I, Gaze, H, Nirmalanantha, A, Bin Amran, M, Foulkes, A, Jones, N, Lau, I, Hayat, F, Hodgson, S, Johnston, R, Jones, W, Khan, M, Linn, T, Long, S, Seetharam, P, Shaman, S, Smart, B, Pillai, S, Khoury, G, Powell, T, Maleyko, I, Sangheli, A, Ransome, M, Isse, M, Aromolaran, O, Bholah, H, Anilkumar, A, Davies, J, Griffith, J, Hughes, B, Islam, Y, Kidanu, D, Mushaini, N, Qamar, I, Robinson, H, Schramm, M, Yan Tan, C, Murray, V, Theocharidou, L, Malik, T, Janmohamed, I, Carhart, B, Khan, A, Apperley, H, Billyard, C, Blazeby, J, Cannon, S, Carse, S, Gopfert, A, Loizidou, A, Parkin, J, Sanders, E, Sharma, S, Slade, G, Telfer, R, Whybrow Huppatz, I, Worley, E, Asif, A, Hullait, R, Quinn, P, Rylance, A, Butt, S, Leathes, J, Chandramoorthy, L, Friend, C, Harris, L, Jain, P, Karim, M, Killington, K, Mcgillicuddy, J, Rafferty, C, Rahunathan, N, Rayne, T, Varathan, Y, Verma, N, Zanichelli, D, Arneill, M, Campbell, B, Crozier, L, Henry, J, Mccusker, C, Prabakaran, P, Wilson, R, Karelia, S, Clements, J, Rainey, M, Asif, U, Connor, M, Dindyal, S, Math, N, Pagarkar, A, Saleem, H, Seth, I, Standfield, N, Swartbol, T, Adamson, R, Choi, J, El Tokhy, O, Ho, W, Javaid, N, Mehdi, A, Menon, D, Plumptre, I, Sturrock, S, Turner, J, Warren, O, Joshi, N, Rahman, A, Gallagher, M, Crane, E, Ferris, B, Gadsby, C, Smallwood, J, Vipond, M, Wilson, V, Amarnath, T, Doshi, A, Gregory, C, Kandiah, K, Powell, B, Spoor, H, Toh, C, Vizor, R, Rebuffa, N, Abdelgalil, R, Siaw Yen Lai, R, Smith, E, Common, M, Dunleavy, K, Harris, S, Luo, C, Mesbah, Z, Prem Kumar, A, Redmond, A, Skulsky, S, Walsh, T, Daly, D, Deery, L, Epanomeritakis, E, Harty, M, Kane, D, Khan, K, Mackey, R, Mcconville, J, Mcginnity, K, Wong, H, Ang, A, Kee, J, Leung, E, Norman, S, Palaniappan, S, Partha Sarathy, P, Yeoh, T, Frost, J, Hazeldine, P, Jones, L, Karbowiak, M, Macdonald, C, Mutarambirwa, A, Omotade, A, Runkel, M, Ryan, G, Sawers, N, Searle, C, Vig, S, Hlukha, L, Pike, G, Kamel, F, Thakkar, R, Mcauliffe, O, Ahmad, A, Mcgartland, R, Sim, R, Song, A, Wayman, J, Brown, R, Chang, L, Concannon, K, Crilly, C, Arnold, T, Burgin, A, Cadden, F, Choy, C, Coleman, M, Lim, D, Luk, J, Mahankali-Rao, P, Prudence-Taylor, A, Ramakrishnan, D, Russell, J, Fawole, A, Gohil, J, Green, B, Hussain, A, Mcmenamin, L, Tang, M, Azmi, F, Benchetrit, S, Cope, T, Haque, A, Harlinska, A, Holdsworth, R, Ivo, T, Martin, J, Nisar, T, Patel, A, Sasapu, K, Trevett, J, Vernet, G, Aamir, A, Bird, C, Durham-Hall, A, Gibson, W, Hartley, J, May, N, Maynard, V, Goble, M, Downs, E, Wheldon, L, Baggus, E, Mandal, A, Nayeem, A, Ahmed, S, Fradley, W, Johnson, S, McDonald Wood, C, O'Brien, M, Orbell, J, Stringfellow, T, Tenters, F, Tresidder, S, Wilson, C, Gallagher, S, Criswell, T, Ward, J, Mukkavilli, A, Stubbs, B, Fordyce, W, Cheung, W, Grant, A, Tod, N, Yi Tew, Z, Suchdev, N, Wei Lim, S, Bews-Hair, M, Lim, Z, Vella-Baldacchino, M, Auckburally, S, Chopada, A, Easdon, S, Goodson, R, Mccurdie, F, Narouz, M, Radford, A, Rea, E, Taylor, O, Yu, T, Sookramanien, S, Chan, A, Bointas, G, Alfa-Wali, M, Amani, L, Auluck, I, Bruce, P, Emberton, J, Kumar, R, Lagzouli, N, Mehta, A, Murtaza, A, Raja, M, Dennahy, I, Frew, K, Given, A, He, Y, Macdonald, E, Mcvinnie, D, Ng, S, Pettit, A, Berthaume-Hawkins, S, Charnley, R, Fenton, K, Jones, D, Murphy, C, Reehal, R, Seraj, S, Shang, E, Tonks, A, White, P, Yeo, A, Paul, M, Ward, K, Bagnall, M, Chong, P, Gabriel, R, Patel, N, Richardson, E, Symons, L, Aubrey-Jones, D, Dawood, S, Dobrzynska, M, Faulkner, S, Griffiths, H, Mahmood, F, Perry, M, Power, A, Simpson, R, Pherwani, S, Wang, K, Mitchell, L, Heyworth, J, Ayyar, S, Brobbey, P, Burrows, A, Elder, P, Ganyani, R, Horseman, C, Hurst, P, Mann, H, Marimuthu, K, Mcbride, S, Pilsworth, E, Powers, N, Stanier, P, Obukofe, R, Polson, R, Mason, D, Mackenzie, E, Innes, R, Kersey, T, Kopczynska, M, Langasco, N, Rajagopal, R, Russell, C, Doyle, N, Habib, Z, Zardab, M, Sartaj, F, Farooq, H, Tabibi, M, Imam, L, Atkins, B, Beasley, W, Cheng Lim, Z, Gill, A, Li Ang, H, Williams, H, Yogeswara, T, Drury, D, James, S, Barnett, R, Teasdale, A, Thomas, D, Williams, R, Najabat-Lattif, H, Akram, B, Carter, R, Fam, M, Fong, J, Latter, J, Long, M, Mackinnon, S, Mckenzie, C, Osmanska, J, Raghuvir, V, Shafi, A, Tsang, K, Walker, L, Cahya, E, Lou, G, Coyle, M, Homyer, K, Zhu, L, Woods, M, Bountra, K, Coldicutt, O, Fletcher, D, Hudson, S, Iqbal, S, Lopez Bernal, T, Moss-Lawton, F, Suchett-Kay, I, Chang, J, O'Callaghan, H, Suchett-Kaye, I, Mihailidis, T, Cardwell, A, Edgerton, K, Laws, J, Rai, A, Robinson, K, Waite, K, Youssef, H, Alawattegama, H, Seite, E, Barrett, A, Riordan, E, Lam, W, Dowdeswell, M, Mulvenna, C, Knight, C, Koo, P, Lazarou, A, Stanger, S, Thorn, C, Triniman, M, Netke, T, Awokoya, O, Gurowich, L, Dhera, K, Hossaini, S, Botha, A, Boyles, L, Cumming, S, Deepak, S, Ezzat, A, Fowler, A, Gwozdz, A, Hussain, S, Khan, S, Li, H, Lu Morrell, B, Neville, J, Nitiahpapand, R, Pickering, O, Sagoo, H, Sharma, E, Welsh, K, Williams, L, Hayat, S, Tincknell, L, Spazzapan, M, Teeling, F, Sysum, K, Denley, S, Latter, M, Agarwal, M, Al-Saadi, N, Bhambra, R, Gupta, A, Jawad, Z, Jiao, L, Mahir, G, Singagireson, S, Thoms, B, Tseu, B, Wei, R, Yang, N, Britton, N, Leinhardt, D, Mahfooz, M, Palkhi, A, Price, M, Sheikh, S, Barker, M, Bowley, D, Cant, M, Datta, U, Farooqi, M, Lee, A, Morley, G, Naushad Amin, M, Parry, A, Patel, S, Strang, S, Yoganayagam, N, Guruswamy, M, Beedham, W, Brazier, E, Elghobashy, M, Bajaj, M, Etel, E, Adlan, A, Chandramoorthy, S, Choudhary, Y, Das, K, Feldman, M, France, B, Grace, R, Puddy, H, Soor, P, Kisiel, A, Woodmass, M, Hayden, H, Ali, Y, Husain, S, Ali, M, Dhillon, P, Faraj, A, Gerard, L, Glover, M, Imran, H, Kim, S, Patrick, Y, Peto, J, Prabhudesai, A, Tang, A, Vadgama, N, Dhaliwal, R, Ecclestone, T, Harris, A, Ong, D, Patel, D, Philp, C, Stewart, E, Wang, L, Wong, E, Xu, Y, Ashaye, T, Fozard, T, Galloway, F, Kaptanis, S, Mistry, P, Nguyen, T, Olagbaiye, F, Osman, M, Philip, Z, Rembacken, R, Tayeh, S, Theodoropoulou, K, Liu, F, Arnold, A, Pedersen, A, Cunha, P, Ahmed, M, Al Zawawi, S, Kudva, V, Herman, A, Lau, J, Saha, A, Trotter, M, Miscampbell, M, Robinson, A, Johnston, J, Dharni, A, Lamb, S, Westerman, T, Adeleye, O, Cave, D, Gunwa, T, Magalhaes, J, Makwana, S, Mason, R, Parish, M, Regan, H, Renwick, P, Roberts, G, Salekin, D, Sivakumar, C, Tariq, A, Liew, I, Mcdade, A, Stewart, D, Evans, E, Campbell, L, Gillespie, M, Cheong, C, Hague, M, Hudson-Peacock, N, Jackson, C, James, F, Pitt, J, Walker, E, Kulathevanayagam, K, Varghese, A, Ike, S, Aftab, R, Ang, J, Anwar, S, Battle, J, Budd, E, Chui, J, Crook, H, Davies, P, Easby, S, Hackney, E, Ho, B, Imam, S, Rammell, J, Chu, T, Baljer, B, Mogg, J, Rai, P, Andrews, H, Perry, C, Schinle, P, Ahmed, P, Aquilina, T, Balai, E, Church, M, Cumber, E, Curtis, A, Davies, G, Dumann, E, Greenhalgh, S, Kim, P, King, S, Metcalfe, K, Passby, L, Redgrave, N, Soonawalla, Z, Waters, S, Zornoza, A, Williams, M, Smillie, R, Goetz, J, Appleby, E, Fadipe, T, Vaughan-Burleigh, S, Davies, R, Gulzar, I, Hole, J, Hull, K, Ishaq, H, Karaj, J, Kelkar, A, Love, E, Thakrar, D, Vine, M, Waterman, A, James, P, Puri, G, Hussain, P, Flather, R, Cutler, A, Pathak, S, Sheldon, J, Collicott, T, al-Ausi, M, Ko, M, Dib, N, Francis, N, Hanson, M, Ingleton, R, Sadanand, K, Sukirthan, N, Mondal, A, Jovaisaite, A, Shah, S, Khalid, N, Gutmann, D, Davison, S, Alame, Y, Syed, L, Owen, W, Arnell, S, Ball, M, Bassam, N, Beghal, G, Chang, A, Dawe, V, George, A, Huq, T, Ikram, B, Kanapeckaite, L, Ramjas, D, Rushd, A, Sait, S, Serry, M, Yardimci, E, Kalderon, R, Ahsan, S, Anthony-Uzoeto, U, Macleod Hall, C, Zheng, S, Wynter, K, Capella, S, Chenciner, L, Episkopos, C, Karam, E, Mccarthy, C, Moore-Kelly, W, Watson, N, Nadama, H, James, C, Sapre, D, Ghosh, R, Baird, J, Cockburn, L, Blackwood, O, Simpson, W, Ahluwalia, V, Barnfield, J, Ben-Gal, O, Bloom, I, Gharatya, A, Khodatars, K, Merchant, N, Moonan, A, Moore, M, Patel, K, Spiers, H, Sundaram, K, Jeong, S, Bishop, S, Black, J, Chadwick, H, Huisman, L, Ingram, H, Martin, L, Metcalfe, M, Sangal, P, Seehra, J, Thatcher, A, Venturini, S, Whitcroft, I, Bate, R, Hobson, C, Adam, A, Redclift, C, Do, J, Poli, F, Batterham, A, Brown, S, Pandya, A, Afzal, Z, Gani, A, Gomaa, A, Hussein, N, Oh, S, Pazhaniappan, N, Sharkey, E, Sivagnanasithiyar, T, Williams, C, Yeung, J, Parekh, J, Clay, W, Pieri, K, Jackson, A, Saxena, A, Gurung, B, Oyebola, T, O'Brien, F, Reilly, S, Cruddas, L, Gurjar, S, Pau, A, Prakash, R, Randhawa, R, Djeugam, B, Gardezi, S, Ul-Hasan, S, Martin-Hernandez, M, Sisley, M, Modi, S, Antakia, R, Archer, J, Chen, L, Eiben, I, Naylor, M, Osei-Bordom, D, Trenear, R, Bannard-Smith, J, Griffiths, N, Patel, B, Saeed, F, Al-Saraff, Z, Elbayouk, A, Soh, Y, Mather, J, Yusuf, Z, Al-Sarraf, Z, Naja, M, Rassool, S, Convill, J, Auty, C, Abdikadir, H, Bennett, M, Church, R, Clements, S, Court, J, Delvi, A, Hubert, J, Macdonald, B, Mansour, F, Patel, R, Perris, R, Small, S, Tam, J, Nikookam, Y, Warsame, A, Pace, C, Kiandee, M, Ridwan, R, Carey, C, Hirri, F, Betts, A, Brown, N, Chong, A, Croitoru, C, Grey, A, Hickland, P, Ho, C, Hollington, D, Mckie, L, Nelson, A, Stewart, H, Eiben, P, Nedham, M, Ali, I, Brown, T, Hunt, C, Joyner, C, Mcalinden, C, Roberts, J, Rogers, D, Thachettu, A, Tyson, N, Vaughan, R, Yasin, T, Powell-Chandler, A, Mcmillan, M, Ling, J, Pendelbury, L, Kerimzade, K, Howard, E, Humayun, S, Wadsworth, O, Roche, C, Andrew, K, Bhamra, N, Mistry, R, Noble, H, Rashed, F, Walker, N, Watson, L, Worsfold, M, Yarham, E, Wood, F, Arshad, A, Barmayehvar, B, Cato, L, Chan-Lam, N, Do, V, Leong, A, Zheleniakova, T, Tan, K, Abdelhameed, F, Haglund, C, Radnaeva, I, Hu, N, Rambhatla, S, Waldron, D, Madahar, P, Malik, S, Coppel, J, Mahmood, R, Nourzaie, R, Sheik-Ali, S, Thomas, A, Alagappan, A, Ashour, R, Bains, H, Diamond, J, Gordon, J, Ibrahim, B, Khalil, M, Mittapalli, D, Neo, Y, Patil, P, Peck, F, Reza, N, Swan, I, Whyte, M, Campbell, A, Meney, L, Kang, C, Chiu, J, Livie, V, Livie, J, Chaudhry, S, Hernon, J, Khawar, H, O'Brien, J, Pullinger, M, Rothnie, K, Ujjal, S, Gorgievska, R, Pooley, G, Shishkin, B, Docherty, J, Southgate, A, Coomes, A, Chaudhry, F, Bhatte, S, Curtis, J, Green, S, Mayer, A, Watkinson, G, Ragavoodoo, A, Mercer, L, Mercer, C, Chapple, K, Hawthorne, T, Khaliq, M, Majkowski, L, Mclauchlan, K, Ng Wei En, B, O'Connor, T, Parton, S, Robinson, S, Saat, M, Shurovi, B, Varatharasasingam, K, Ward, A, Behranwala, K, Bertelli, M, Cohen, J, Duff, F, Fafemi, O, Gupta, R, Manimaran, M, Mayhew, J, Peprah, D, Wong, M, Morice, O, Burzic, A, Cheung, J, Shashidhara, A, Theodoraki, G, Birk, J, Ong, A, Farmer, N, Houghton, C, Kandhari, N, Ladha, D, Mayes, J, Mclennan, F, Panahi, P, Seehra, H, Agrawal, R, Ahmed, I, Ali, S, Birkinshaw, F, Choudhry, M, Gokani, S, Harrogate, S, Jamal, S, Nawrozzadeh, F, Swaray, A, Szczap, A, Warusavitarne, J, Abdalla, M, Asemota, N, Cullum, R, Hartley, M, Maxwell-Armstrong, C, Phillips, J, Yule, A, Ahmed, L, Clement, K, Craig, N, Elseedawy, E, Gorman, D, Kane, L, Moss, E, Naasan, A, Ravi, F, Shields, P, Zhu, Y, Palmer, C, Melaugh, T, Hall, J, Kouli, O, Hassane, A, Azhar, A, Tan, T, Archer, M, Cobley, H, Dennis, R, Downes, C, Guevel, B, Lamptey, E, Murray, H, Radhakrishnan, A, Saravanabavan, S, Sardar, M, Shaw, C, Tilliridou, V, Wright, R, Ye, W, Davidson, K, Perchard, W, Scurr, T, Campbell, E, Kelk, L, Ghosh, A, Gibbins, A, Mala, D, Alturki, N, Helliwell, R, Jones, E, Lambotharan, S, Scott, K, Sivakumar, R, Victor, L, Hall, O, Mecia, L, Hew, C, Varathan, K, Tong, L, Chandrasekar, B, Boraluwe-Rallage, H, Froggatt, P, Haynes, S, Hung, Y, Keyte, A, Matthews, L, Haray, P, John, I, Mathivanan, A, Morgan, L, Oji, O, Okorocha, C, Rutherford, A, Stageman, N, Tsui, A, Whitham, R, Giacci, L, Buchanan, E, O'Connell, M, 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S., Toma T., Wadood Q., Yip J., Charalambides M., Clave Llavall A., Williamson E., Bharwada Y., Zearmal S., Evans H., Panikkar M., de la Cruz G., Caplan J., Ainger E., Busti S., Cunliffe L., Flamini T., Gaffing S., Moorcroft C., Peter M., Simpson L., Stokes E., Stott G., Wilson J., York J., Yousaf A., Ruparelia A., Tanvir T., Soare C., Pang Y. L., Trotter J., Zaidi A., Thakrar V., Brown M., Goaman A., Hodgson B., Ijeomah A., Iroegbu U., Kaur G., Lowe C., Mahmood S., Sattar Z., Sen P., Szuman A., Abbas N., Anto N., Bhome R., Eccles L., Elliott J., Hughes E. J., Jones A., Karunatilleke A. S., Knight J. S., Manson C. C. F., Mekhail I., Michaels L., Noton T. M., Okenyi E., Reeves T., Yasin I. H., Pulickal P., Ahmed H., Parnell J., Lennock S., Ford V., Banfield D. A., Harris R., Mason-Apps C., Roe T., Sandhu J., Shafiq N., Stickler E., Tam J. P., Williams L. 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L., Porter T., Potluri S., Stechman M., Tate S., Thomas D., Walford B., Auld F., Bleakley A., Johnston S., Jones C., Khaw J., Milne S., O'Neill S., Singh K. K. R., Swan A., Thorley N., Yalamarthi S., Yin Z. D., Balian V., Bana R., Clark K., Livesey C., McLachlan G., Mohammad M., Pranesh N., Richards C., Ross F., Sajid M., Liversedge G., Carrington Z., Windebank J., Izzarina A., Akbani U., Craven J., Aldarragi A., Harding S., Millward A., Brooke M., Francombe J., Gresly J., Hutchinson S., Kerrigan K., Matthews E., Nur S., Parsons L., Sandhu A., Vyas M., White F., Zulkifli A., Zuzarte L., Shortland T. C., Bedford M., Al-Mousawi A., Arya J., Azam S., Azri Yahaya A., Gill K., Hallan R., Hathaway C., Leptidis I., McDonagh L., Mitrasinovic S., Mushtaq N., Pang N., Peiris G. B., Rinkoff S., Gopalan V., Midgen A., Khadka P., Cheng O., Taneja S., Manobharath N., Chan L., Christopher E., Gonzalez-Ciscar A., Graham C. J., Lim H., Paterson H. 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E., Sagnotta A., Di Cintio A., Gemini A., Trastulli S., De Luca M., Desiderio J., Gubbiotti F., Campagnaro T., Pedrazzani C., Turri G., Lazzarini E., Conti C., Ruzzenente A., Petrelli F., Marziali I., Mazzocato S., Vergari R., Piazzai F., Kubolli I., Aggiusti A., Paolucci A., Ortenzi M., Gallo G., Clerico G., De Santi G., Bitonti M. F., Trompetto M., Realis Luc A., Papandrea M., Cerra C., Pirillo M., Cravano S., Libri I., Laquatra N., Isabello A., De Luca E., Testa V., Morino M., Mistrangelo M., Sinibaldi G., Angelucci G. P., Scaramuzzo R., Larcinese A., Fioriti C., Picone E., Nardi M., Di Candido F., Spinelli A., Marco M., Bevilacqua M., Maroli A., Colombo F., Cigognini M., Zaffaroni G., Maffioli A., Colombo S., Bondurri A., Sampietro G., Foschi D., Perrone G., Bonati E., Aresu S., Saba A., Biancafarina A., Angelini M., Andolfi E., Miranda E., Scricciolo M., Provenza G., Cavallina G., Frezza B., Fontani A., Malatesti R., Pellicano G. A., Ceccarelli G., Canonico G., Falsetti E., Pagnanelli M., De Nardi P., Rosati R., Marcocci G., Vignali A., Quattromani R., Curletti G., Galleano R., Malerba M., Percivale A., Ghazouani O., Reggiani L., Spirito C., Bini R., de Manzoni Garberini A., Impellizzeri H., Moretto G., Bacchion M., Casaril A., Inama M., Creciun M., Vitali M., Cillara N., Mosino L., Peddis M., Melis S., De Donno G., Aru A. C., Loche G. A., Tutino R., Licari L., Campanella S., Profita G., Falco N., Rotolo G., Venturelli P., De Marco P., Marciano M., Picciariello A., Chetta N., Aquilino F., Papagni V., Coletta D., Cerasari S., Galiffa G., Lapolla P., Del Basso C., Cirillo B., De Toma G., Fazzi K., Bini S., De Padua C., Savino G., Finotti E., Ferrara F., Stella M., Gobatti D., Tamini N., Brenna R., Giani A., Tirotta F., Famularo S., Angrisani M., Ceresoli M., Talamo G., Sucameli F., Gennai A., Moggia E., Bianchi C., Bonfante P., Macina S., Feleppa C., Imperatore M., Berti S., Parini D., Lanari J., Giamundo P., Lo Conte A., Pagano G., Manigrasso M., Danzi M., Amato R., Anastasio L., Mastella F., Basile R., Peltrini R., Marra E., Luglio G., Giglio M., Anoldo P., Vertaldi S., Grimaldi L., Tammaro N., Ripetti V., D'Alessio R., Benvenuto D., Pesce A., Menduni N., Giudicissi R., Zalla T., Baraghini M., Romoli L., Calussi M., Vellei S., Genzano C., Feroci F., Vita M., Giani I., Goldin E., Ciccioli E., Galgano A., Zuin M., Bettella A., Barina A., Vendramin E., Palano G., Schiavone D., Rega D., La Manna V., Migliore G., Aversano A., Fares Bucci A., Belli A., Andriola V., Lafranceschina S., Gordini L., Erdas E., Podda F., Cappellacci F., Salaris C., Sanna S., Marcialis J., Longheu A., Foppa C., Coratti F., Montanelli P., Grandi S., Nelli T., Ben Khaled N., Cammelli F., Ferrini E., Billo M. E., Piccolo G., Romano S., Gaetano P., Pilotta F., Baldassarre L., Birindelli A., D'Errico U., Bianchini S., Ferrari C., Franceschi A., Caristo G., Ballarini Z., Romelli M., Ruggiero S., Percassi A., Milone M., De Rosa M., Pipitone Federico N. S., Molteni B., Magri Piccinini A., Tilocca P. L., Marrosu A. G., Scognamillo F., Pala C., Attene F., Carboni L., Ruggiu M. W., Gabbas G., Sancini G., Roggiani A., Balsamo F., Gori A., Cuicchi D., Vittori L., Piozzi G. N., Tringali D., Bevilacqua E., Panizzo V., Lauretta A., Poillucci G., Mulas S., Simcikas D., Samalavicius N. E., Nutautiene V., Zeromskas P., Jurgaitis J., Aliosin O., Slepavicius A., Eismontas V., Kybransiene M., Portelli L., Cachia C., Cefai C., Youssef Y., Degaetano D., Debono S., Sammut M., Cassar J., Sammut K., Andrejevic P., van Wijnbergen J. W. M., Dinger T. L., ten Doesschate S. F. H., van Dalen A. S. H. M., Looijen R. C., Becker M. A. J., Comert D., Stellingwerf M. E., Abdulrahman N., van den Bos D. D., Gruter A., Vroom Y., Voeten D. M., Feenstra T., de Roy van Zuidewijn D., Hansmann M., Nasimi B., Gerdsen M., Stijns R. C. H., Leow T. Y. S., Penningnieuwland G., Davids J., Medina Feliz J., Kuiper S. Z., Sluijpers N. R. F., van de Pas K. G. H., Pruijssers S. R., Molenaar C. J. L., Thomas G., Farik S., Chen S. Y., Kilpatrick K., Kim D. H., Ong H. I., Elliott B. M., Geneta V. P., Alsadat R., Cherry R., Chapman D. A., Singh N., Clucas A., Gatenby G., Kelly B., Ruppeldt P., McIntosh N. D., Li Y., Wilton S., McLaughlin S. J. P., Kearney J., Sprosen H., Kerckhoffs P., Kandelaki H., Welman D., Coulter J., Morreau M., Li E., Rankin A., Winders J., Skipworth C., Peng S. L., Campbell S., Lim Y. K., Yassaie S. S., Dixon S., Yassaie S., Murray M., Haran C., Koh S., Wilms H., Dalzell F., Tewhaiti-Smith J., Kader T., Yam S. T., Dahya D., Hardie Boys M., Fleischl W., Desmond B., Tan J., Skavysh A., Castro J., Orelhas L., Santos J., Pacheco A., Rocha A. F., Jervis M. J., Pedro V., Paixao V., Guerreiro A., Melo D., Laranjeira A., Machado A., Horta V., Rocha-Melo M., Patricio J., Pereira M., Lima R., Cinza A. M., Oliveira J., Velez C., Senhorinho R., Catarino S., Aveiro D., Couto M., Loureiro A. R., Neves-Marques C., Louro T., Morgado M., Ferreira A. S., Alves M., Oliveira N., Lerias R., Estalagem I., Botelho C., Alves-Vale C., Correia J. G., Jardim J. A. M., Domingos S. P., Baiao J. M., Rocha B. S., Lopes N. M. R. O., Jordao D. M., Angelo M. D., Caroco T., Vieira B. N., Silva A. G., Galvao D., Mora A., Bettencourt R., Quintela A. C., Alves D., Dias B., Costa C., Goncalves J. P., Valente P., Freire L., Saraiva R., Moreira M., Peixoto R., Serra M. L., Santos T. C., Lages R. R., Pimentel A., Martins M., Maciel J., Bartolo J., Fidalgo Antunes C., Cabral F., Brito Silva F., Dias Matos A., Santos M., Cunha R., Duarte M., Sousa M., Cunha M., Neves J., Monica I., Oliveira S., Ribeiro H., Ferreira M., Guimaraes N., Azevedo J., Louro H., Queiros T., Castro B., Fonseca S., Carvalho L., Torre A. P., Amado A., Leite M., Romano M., Gomes J. R., Monteiro R. G., Eiro F., Borges F., Simoes J., Botelho P., Cismasiu B., Romano J., Monteiro C., Silva M., Claro M., Almeida P., Cruz A., Vaz Pinto G., Sousa D., Costa Santos D., Silva A., Silva F., Almeida M. R., Parreira R., Faustino A., Medeiros N., Castro R., Soares B., Resendes M., Peyroteo M., Canotilho R., Correia A. M., Martins P. C., Machado N. D., Sa T. C., Bernardes F. M., Saraiva P., Almeida J., Mendes M., Fernandes V., Ribeiro A., Soares D., Capote H., Fialho G., Reia M., Pratas N., Costa J., Morais S., Miranda P., Cunha C., Sousa X., Amado F., Batista A., Seabra J., Praxedes V., Monteiro N., Martins I., Lamas M., Devesa H., Miranda J., Martins S. F., Cavadas D., Silva R., Pina S., Paixao I., Frade S., Guerreiro I., Tome M., Patrocinio S., Costa M., Rosete M., Senaratne R., Kakodkar P., Balasubramanian I., Hogan A., Al-Salihi A., Vyas V., Vedadi A., English C., Mohammed N., Dervan L., Kang S. J., Morrissey E., Compton M., Litvin A., Romanova E., Ponomarev N., Savchenko A., Meszarosova K., Zubowicz M., Suchan P. K., Gerstmeier J., Brazitikou E., Fytrou E., Celik M., Dubovsky M., Schmalenbeck J., Sautter S. C., Ceyran H., Panyko A., Jaich R., Fitchat N., De Lima H., Withey K., Piperidis A., Toyi Y., Nicolaides A., Brooks S., Jaffer T., Sparke A., Van Staden N., McDuling C. R., Claassen L., Van Vuuren S., Marx M., Kruger C., Weyers J., Lecler J., Nshalati Salvation M., Mathebula S., Chauke J., Mculu W., Ngwenya Z., Garcia Cardo J., Mora-Guzman I., Sancho Muriel J., de Andres Olabarria U., Casaval Cornejo L., Vicente Rodriguez I., Jimenez Escovar F., Calvo Fernandez M., Portugal Martinez A., Badiola Bergara I., Landaluce Olavarria A., del Pozo Andres E., Muriel P., Mestres N., Cuello E., Merichal M., Sierra J. E., Escoll J., Rufas M., Pinillos A. I., Ortega J., Cerdan C., Jimenez Vinas C., Alconchel F., Martinez Diaz J. M., Ruiz Manzanera J. J., Navarro Barrios A., Rodriguez Pedreno L., Garcia Moreno P., Garcia Gomez M., Rodrigues K., Abrisqueta J., Esteban Sinovas O., Fairen Oro C., Lopez Otero M., Samartin Toimil C., Primo Alvarez J. C., Rodriguez Fernandez L., Paniagua Garcia Senorans M., Figueroa Jimenez S., Livianos Arias-Camison P., Nieto Arranz J. M., Mateos Sexmero M. J., Diego Alonso E., Gonzalez Amor L., Claramonte Bellmunt O., Martinez Caballero J., Rubio-Perez I., Ramos Martin P., Mate Mate P., Marcano Chavez C., Ayllon H., Aguilar-Martinez M. M., Arroyo A., Segura-Sampedro J. J., Jeri-McFarlane S., Soldevila-Verdeguer C., Pineno-Flores C., Ambrona-Zafra D., Fernandez-Vega L., Gil-Catalan A., Craus-Miguel A., Pujol-Cano N., Sena-Ruiz F., Olmedo Moreno C., Pastor Idoate C., Negre Parra D., Soriano Liebana M. M., Campo Betancourth C. F., Sevila Mico S., Estevez Diz A. M., Martin-Balbuena R., Garcia-Vico A., Roman-Rando A., Sanchez-Galvez M. A., Bersabe-Alonso I. M., Vadillo-Bohorquez A., Bustamante Recuenco C., Calzada M. G., Recuenco C. B., Martin M. C., Licardie Bolanos R. E., Garcia Orozco J. J., Ferrusola Diaz D. A., Garcia Jimenez M. L., Castro Diez L., Mosquera Fernandez C., Fernandez P., Platero Diago J., Veras Lista M., Rifa Terricabras S., Butler Sanz L., Alberch Camprubi E., Rico Ferrandez M., Puig Lao E., De Pablo Miro M., Diaz Padillo A., Padilla Zegarra E. D., Franco M., Martinez A., Forero-Torres A., Alonso-Poza A., Dieguez B., Losada M., Gilsanz C., Alberdi San Roman I., Ramos Jimenez F. A., Perez Ortega A., Trujillo Barbadillo A., Romero Pujana E., Fernandez Illera M. V., Salvador Roses H., Delisau Puig O., Castro A., Codony Bassols C., Fernandes N., Garcia Adamez J., Caula Freixa C., Gomez Romeu N., Merayo Alvarez M., Alonso Batanero E., Cifrian Canales I., Villarejo Campos P., Alberca-Paramo A., Alia Verdejo T., Canete-Manzanares J., Molina-Florez M., Garcia Hernandez L. C., Orozco Nunez S., Suarez-Sanchez M., Martin-Fernandez J., Estaire Gomez M., Lopes Moreira C. C., Gutierrez Rios R. D., Eizaguirre Ubegun M., Novo Sukia I., Uribe Isado M., Ibanez Alda M., Enriquez-Navascues J. M., Uriol Peralta P., Lima Pinto F., Nunez O'Sullivan S., Serrano Navidad M., Vivas Angeles P., Vives Figueras R., Baena Sanfeliu E., Campillo Alonso B., Alberich Prats M., Duran Arrocha J., Cornella Garceso N., Uribe Galeano C., Collado-Roura F., Ripolles-Melchor J., Garcea A., Gomez Facundo H., Troncoso Pereira P., Garcia J. M., Gil A., Giordano H. E., Perez Guarinos C. V., Blaser B., Imadalou L., Piazza G., Teixeira H., Pittet O., Gaspar S., Gagliardi B., Haller M. L., Huot A., Yurdaor S. S., Alim Y. E., Binbuga M., Kurklu O., Ceylan C., Yilmaz S., Calar S. N., Yildirim N., Yavuz G., Biyiklioglu O., Kopac O., Baykan B., Abbasguliyev H., Mutlu V., Mahmoudi Y., Dossa S. A. A., Mizan S. R., Demirtas B., Cavus T., Yuksek A., Karabulut K., Uyanik A., Kargici K., Banaz T., Abdulrahman S. M. F., Yesilsancak M., Yanikoglu E., Karabulut A., Kuyumcu O. F., Kutluk F., Tunc E., Ozmen B. B., Kurtoglu G. K., Sel E. K., Isler V. C., Yilmaz B. S., Adiyaman C., Kara M., Demirkaya H. C., Korkmaz H. K., Pektas A. M., Mutlu D., Tiftik E., Aksoy A. B., Karagoz E., Sabanci R., Tumer S. S., Sarigul L., Aksoy B., Emiroglu B., Mollavelioglu B., Gereklioglu D., Guler M., Toprak S., Afsar H. B., Guler S. E., Alizade E., Aydin T., Yalcinkaya A., Erdem M., Kayacan S., Sahin C., Uysal M., Karakus K., Altiner S., Demirci Z. S., Eren E., Yavas M., Dogan H., Ozoglu F., Mazlum S. S., Yelkenci C., Baran T., Satilmisoglu M., Sahin A. Z., Kilinc A., Isik K., Kocer M. D., Guner B., Islek E. C., Tombul H. B., Boztug C. Y., Tinaz A., Yilmaz M., Karaman Y., Akin F. A., Dansuk S., Bascavus M., Kaya M., Kutlu B., Akyol C., Bilicen G., Yarenci E., Demiroglu Y., Sunal A., Cinar E. N., Tipi O. U., Benli G. E., Tasdelen I., Tura A., Ucar A., Ekci B., Bilgili A. C., Kiraz I. N., Aksoy H., Basak A., Karip B., Uslu O., Seker M. C., Erten V., Memis U., Kocak Z. B., Karapinar Y. E., Fakirullahoglu M., Koseoglu E., Yeni M., Uygur F. A., Kaya Y., Ozen D., Olmez M., Altintas E., Toklu R., Saricicek T., Isik A. E., Demirci K., Suresh G., Anbarasan J., Rehman S., Hu E., Timms S., Reynolds W., Hotchkies A., Misra V., Finch B. J., Philip Rajathasan T., Jeddy H., Kyaw H. A., Wong N., Bhahirathan Y., Donaldson C., Sequeira Campos M., Armitage M. N., Hussain A. S., Hardy B., Hassan Serry M. Y., Herron J., Thakrar C., Rojoa D., Yeap B., Iqbal Z., Rojoa D. M., Cabaleiro Barciela C., Ruddy C. M., Lindwe S., Qamar Y., Chuita S., James A., Bugelli M., Chechelnitskaya Y., Bylinski T., Findlay J. M., Skan O., Kind C., Tupper P., Van Rhee C., Honeyman S. I., Menon G., Sloper W., Burford C., Coey J., Akbari K., Whitehead T., Dimitriadis S., Marshall K., Flint E. J., Can B., Cesmeli E., Kus D., Kavar R. C., Cevlik A. D., Cengiz U., Turk E. G., Yilmaz I., Yumurtaci O., Emin A., Girit C., Yildirak M. K., Senyigit E., Guldag A., Soyleyici B., Aytin Y. E., Akay F. E., Iskan N. G., Mutlu B., Sunay A. O., Bell T., Limnatitou D., Gormley S., Taleongpong P., Patel V., Macgregor L., Amini S., Turner C., To N., Laurence N., Green C., Frostick R., Khera R., Povey M., Mahapatra S., Bradbury M., Osunronbi T., Tam L. Y. C., Khanan Kaabneh A., Lawther J., Fisher P., Tribedi T., Moosa A., Ramdin A., McGee F., Flanagan S., Tan Q. J., Anwar H., Clough R., Chrisp B., Cassels J., Cross G. W. V., Refalo A., Hadley R., McTighe A., Farrow F., Brodie A., Davis G., Shah D. R., Bowers C., Ng M. P. E., Wong R. T. W., Maese S., Tandon R., Gray M., Kumar A., Ciurleo C., Nyamali I., Hiremath S., Sinha S., Chowdhary M., Kirupananthan P., Boh Z. Y., Dass S., Soma A., Newton A., Hill M., Shafiq Y., Brkljac M., Martin H., McClarty C., Sanyal S., Alsaif A., Bhopal S., Muneeb F., Page O., Zeb E., Al-Azzawi A., McIntosh J., Vucicevic A., Hughes M., Brooks L., Fanibi B., Dixon M., Njoku P., Morris D., Jobson J., Trussell S., Chowdhury H., Alawode D. O. T., Udayachandran V., Alsoof D., Ekert J., Joseph N., Zulkefley N., Hunt G., Christodoulou T., Wright O., Soman S., Jamal M., Beqiri S., Borgas P., Pereira F., Browne S., Yiu J., Dworkin A., Mak J., Patrawala S., Milinkovic N., Schofield R., Chauhan M., Hartley L., Hind J., Heybourne A., Morgan H., Wickstone C., Panagiotou D., O'Connell E., Dean K., Iqbal R., Walsh L., Yu N., Rana N., Massie E., Foong J., Lee Y. J., Mijalkov D., Wells A., Bull N., Ajmera A., Warburton T., Morgan S., Mahmoud A., Schachtel M., Mikhail B., Fomin I., Mekaeil B., Jun H. J. S., Hengpoonthana R., Childs S., Ridgway S., Blefari N. D. A., Tam H., Puchalski N., Ngai C., Mackenzie J., Johnson N., Holmes M., Zuzek R., Saluja T., Gould T., Goh Y. K., Selvaraj T., Horne D., Borrow J. L., Campbell C., Cousins G., Jackson L., Maheepala K., Zhao S., Holden E., Tutt L., Thompson B., Collins H., Louie F., Buckland B., Smith D., Chong C., Chua T. H., Nayak C., Redmond J., Tan R. R., Gramlick M., Teh J. S., Ng S. Y., Britten-Jones P., Mohd Rosli R., Pham H. D. V., Harris M., Salibasic M., Pavlov V., Paycheva T., Lyulenina E., Kolev N., Boev B., Nguen D., Mitkov Y., Mitkov E., Vladova P., Dimitrov V., Gabarski A., Feradova H., Mladenov T., Pos M., Adel A., Sotona O., Bartos M., Amjad T., Maly O., Berec S., Hanusova M., Hurny M., Orhalmi J., Sorensen J. S., Chanut F., Magnin J., Schoning L., Simon S. C. S., Galata C., Karampinis I., Thawel T., Seckler A. M., Kerem C., Durdevic S., Herrle F., Artioli E., Giaccari S., Nevoso V., Schimera A., Marino S., Resta G., Ongaro D., Baronio G., Raimondo S., Casiraghi S., Salvadori R., Armellini A., Vulcano I., Bertilone E., Pintabona G., Viscosi F., Mazzeo C., Olivieri M., Belluco C., Antona A. D., Basso S., Frattalone M., Marino F., Carbone F., Incollingo P., Romano F. M., Anania M., Pulighe F., Cruccu A., Murru M. L., Massaiu C., Balestra F., Pazzona M., De Nisco C., Gusai G. 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L., Jelovskis I., Deksnis D., Bauermeister Potts O., Slimbajevs T., Ancans G., Dulskas A., Kuliavas J., Kavaliauskas P., Kavaliauskaite R., te Molder L., Tromp J., Matthee E., Tissen Y. M., Brinkhuis E., de Vries H. S., t Hart E., Beckers K., Bekker Y., Kakar S., van Smaalen T. C., Veen O. C., Dingemans S. A., van den Brink L. C., Vijgen G. H. E. J., van de Voort E. M. F., van der Pool A., van Rest K. L. C., Haak T., Gordinou de Gouberville M. C., Saleh S., Mens M. A., Hoeks E. M., Nieuwenhuizen S., Brandsma A. M., van Praag E. M., Westerduin E., Smit M. P. C. M., van der Lely S. J., de Ruiter A., Ribbers T., Aarts C. A. M., Tulek M. S., de Jonge J., Kocak S., Alqethami H. J., Constansia R., van Elst P. C., Tissink M. W., Azzahhafi J., Bofarid S., Kip M., Saleh W., Franssen S., Boon C. L., Franssen R. J. M., Romaen I., Jense M. T. F., D'Souza J., Pascoe R., Scott A., Stark E., Mulholland K., Lau W. K., Smith B., Adams S. I. B., Shah N., Ling E., Young J., Hawkins R., Jacobson A., Macfater H., Stanfield B., Henderson N., Chuang A., Maskill L., Ker H., Mouldey K., Hu R., Matthews C., Aitken E., Walker M., Winstanley J., Miguel I., Martins J., Cunha M. F., Melo J., Veiga D. N., Rachadell J., Amorim E., Vareda R., Roseira J., Americano M., Correia D., Capella V., Moinhos T., Costa F., Gil I., Morgado J., Varghese S., Boyle E., Aljohmani L., Alexander J., Graziadei V., McCaughey C., Jain A., O'Connor D. B., Ramanayake H., Sabnani R., Colon L. F., Bansal N., Stephens I., Tan R., Hoo M., Doherty G., Fenn S., Mulhare E., Walsh M., Leavey C., Costigan O., Griffin S., Dockry E., Khogali E., Aly A. K., McPhedran R., O'Gorman D., Vermeulen D., Gopaul A., Pentony A. R., Piong C. L., Nossier R., Hanna J., Sabir K., Eow S. Y., Ladak N., Boersma D., Kamath P. J., Soh T. B. W., Dyer A. H., Chee S. Y., Tan I. X. H., Kelliher A., Scanlon K., Adeusi L., Howarth N., McCawley N., O'Neill A., Jones M. R., Saleh R., Singh A., O'Dwyer N., Mana O. C., Gomez H., Riba L., Perez J., Centeno A., Ortega C., Ossola M. E., Camarero E., Martinez Illan M., Aranda Garcia L., Lazaro Cardona M., Montalvo Olmedo C., Izquierdo Moreno A., Cuenca Ramirez A., Margarida A., Pando B., Liquete Marin L., Perez Calle M., Ramos Cordero M. J., Sagredo Cuartango A., Sanchez E., Marcos Rodrigo A., Fernandez A., Curtis C., Bosch M., Triguero D., Bosch Ramirez M., De La Llave Serralvo A., Sanchez M. J., Martin-Borregon P., Quevedo A., Martinez J., Marin J. M., Rodriguez P., Esteban Avendano I., Jimenez Relimpio C., Lancho Lavilla P., Blanco Bartolome I., Gomez Cano I., Blasco Andres C., Abad-Motos A., Marinez-Hurtado E., Salvachua-Fernandez R., Muniesa C., Avelino L., Cholewa H., Sancho-Muriel J., Navasquillo M., Navio A., Abello D., De Jesus M., Alvarez E., Kefleyesus A., Bugmann A., Piotet L. M., Meijers L., Tonchev P., Tamosiunas A., Rottier S. J., Negoi I., Bernado I. 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S., Toma T., Wadood Q., Yip J., Charalambides M., Clave Llavall A., Williamson E., Bharwada Y., Zearmal S., Evans H., Panikkar M., de la Cruz G., Caplan J., Ainger E., Busti S., Cunliffe L., Flamini T., Gaffing S., Moorcroft C., Peter M., Simpson L., Stokes E., Stott G., Wilson J., York J., Yousaf A., Ruparelia A., Tanvir T., Soare C., Pang Y. L., Trotter J., Zaidi A., Thakrar V., Brown M., Goaman A., Hodgson B., Ijeomah A., Iroegbu U., Kaur G., Lowe C., Mahmood S., Sattar Z., Sen P., Szuman A., Abbas N., Anto N., Bhome R., Eccles L., Elliott J., Hughes E. J., Jones A., Karunatilleke A. S., Knight J. S., Manson C. C. F., Mekhail I., Michaels L., Noton T. M., Okenyi E., Reeves T., Yasin I. H., Pulickal P., Ahmed H., Parnell J., Lennock S., Ford V., Banfield D. A., Harris R., Mason-Apps C., Roe T., Sandhu J., Shafiq N., Stickler E., Tam J. P., Williams L. 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L., Porter T., Potluri S., Stechman M., Tate S., Thomas D., Walford B., Auld F., Bleakley A., Johnston S., Jones C., Khaw J., Milne S., O'Neill S., Singh K. K. R., Swan A., Thorley N., Yalamarthi S., Yin Z. D., Balian V., Bana R., Clark K., Livesey C., McLachlan G., Mohammad M., Pranesh N., Richards C., Ross F., Sajid M., Liversedge G., Carrington Z., Windebank J., Izzarina A., Akbani U., Craven J., Aldarragi A., Harding S., Millward A., Brooke M., Francombe J., Gresly J., Hutchinson S., Kerrigan K., Matthews E., Nur S., Parsons L., Sandhu A., Vyas M., White F., Zulkifli A., Zuzarte L., Shortland T. C., Bedford M., Al-Mousawi A., Arya J., Azam S., Azri Yahaya A., Gill K., Hallan R., Hathaway C., Leptidis I., McDonagh L., Mitrasinovic S., Mushtaq N., Pang N., Peiris G. B., Rinkoff S., Gopalan V., Midgen A., Khadka P., Cheng O., Taneja S., Manobharath N., Chan L., Christopher E., Gonzalez-Ciscar A., Graham C. J., Lim H., Paterson H. 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E., Sagnotta A., Di Cintio A., Gemini A., Trastulli S., De Luca M., Desiderio J., Gubbiotti F., Campagnaro T., Pedrazzani C., Turri G., Lazzarini E., Conti C., Ruzzenente A., Petrelli F., Marziali I., Mazzocato S., Vergari R., Piazzai F., Kubolli I., Aggiusti A., Paolucci A., Ortenzi M., Gallo G., Clerico G., De Santi G., Bitonti M. F., Trompetto M., Realis Luc A., Papandrea M., Cerra C., Pirillo M., Cravano S., Libri I., Laquatra N., Isabello A., De Luca E., Testa V., Morino M., Mistrangelo M., Sinibaldi G., Angelucci G. P., Scaramuzzo R., Larcinese A., Fioriti C., Picone E., Nardi M., Di Candido F., Spinelli A., Marco M., Bevilacqua M., Maroli A., Colombo F., Cigognini M., Zaffaroni G., Maffioli A., Colombo S., Bondurri A., Sampietro G., Foschi D., Perrone G., Bonati E., Aresu S., Saba A., Biancafarina A., Angelini M., Andolfi E., Miranda E., Scricciolo M., Provenza G., Cavallina G., Frezza B., Fontani A., Malatesti R., Pellicano G. 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Abstract: Background: Postoperative acute kidney injury (AKI) is a common complication of major gastrointestinal surgery with an impact on short- and long-term survival. No validated system for risk stratification exists for this patient group. This study aimed to validate externally a prognostic model for AKI after major gastrointestinal surgery in two multicentre cohort studies. Methods: The Outcomes After Kidney injury in Surgery (OAKS) prognostic model was developed to predict risk of AKI in the 7 days after surgery using six routine datapoints (age, sex, ASA grade, preoperative estimated glomerular filtration rate, planned open surgery and preoperative use of either an angiotensin-converting enzyme inhibitor or an angiotensin receptor blocker). Validation was performed within two independent cohorts: a prospective multicentre, international study (‘IMAGINE’) of patients undergoing elective colorectal surgery (2018); and a retrospective regional cohort study (‘Tayside’) in major abdominal surgery (2011–2015). Multivariable logistic regression was used to predict risk of AKI, with multiple imputation used to account for data missing at random. Prognostic accuracy was assessed for patients at high risk (greater than 20 per cent) of postoperative AKI. Results: In the validation cohorts, 12.9 per cent of patients (661 of 5106) in IMAGINE and 14.7 per cent (106 of 719 patients) in Tayside developed 7-day postoperative AKI. Using the OAKS model, 558 patients (9.6 per cent) were classified as high risk. Less than 10 per cent of patients classified as low-risk developed AKI in either cohort (negative predictive value greater than 0.9). Upon external validation, the OAKS model retained an area under the receiver operating characteristic (AUC) curve of range 0.655–0.681 (Tayside 95 per cent c.i. 0.596 to 0.714; IMAGINE 95 per cent c.i. 0.659 to 0.703), sensitivity values range 0.323–0.352 (IMAGINE 95 per cent c.i. 0.281 to 0.368; Tayside 95 per cent c.i. 0.253 to 0.461), and specif
Published: 2022

38. Quantum flux operators for Carrollian diffeomorphism in general dimensions

Author: Li, Ang, Liu, Wen-Bin, Long, Jiang, Yu, Run-Ze, Li, Ang, Liu, Wen-Bin, Long, Jiang, and Yu, Run-Ze
Abstract: We construct Carrollian scalar field theories in general dimensions, mainly focusing on the boundaries of Minkowski and Rindler spacetime, whose quantum flux operators form a faithful representation of Carrollian diffeomorphism up to a central charge, respectively. At future/past null infinity, the fluxes are physically observable and encode rich information of the radiation. The central charge may be regularized to be finite by the spectral zeta function or heat kernel method on the unit sphere. For the theory at the Rindler horizon, the effective central charge is proportional to the area of the bifurcation surface after regularization. Moreover, the zero mode of supertranslation is identified as the modular Hamiltonian, linking Carrollian diffeomorphism to quantum information theory. Our results may hold for general null hypersurfaces and provide new insight in the study of the Carrollian field theory, asymptotic symmetry group and entanglement entropy., Comment: 65 pages, 3 figures, typos are fixed
Published: 2023

39. Pulmonary Infections and Outcomes in AIDS Patients with Respiratory Failure: A 10-Year Retrospective Review

Author: Hao,Jingjing, Liu,Jingyuan, Pu,Lin, Li,Chuansheng, Yin,Ningning, Li,Ang, Hao,Jingjing, Liu,Jingyuan, Pu,Lin, Li,Chuansheng, Yin,Ningning, and Li,Ang
Abstract: Jingjing Hao, Jingyuan Liu, Lin Pu, Chuansheng Li, Ningning Yin, Ang Li Department of Critical Care Medicine, Beijing Ditan Hospital, Capital Medical University, Beijing, Peopleâs Republic of ChinaCorrespondence: Ang Li, Email liang@ccmu.edu.cnBackground: Respiratory failure in acquired immunodeficiency syndrome (AIDS) patients was the leading cause of intensive care unit (ICU) admission in our center. We aimed to describe the pulmonary infections and outcomes for respiratory failure in AIDS patients.Methods: A retrospective study was conducted on AIDS adult patients with respiratory failure who were admitted to the ICU in Beijing Ditan hospital, China, from January 2012 to December 2021. We investigated pulmonary infections complicated by respiratory failure in AIDS patients. The primary outcome was ICU mortality, and a comparison between survivors and nonsurvivors was performed. Multiple logistic regression analysis was used to identify predictors of ICU mortality. The Kaplan-Meier curve and Log rank test were used for survival analysis.Results: A total of 231 AIDS patients were admitted to ICU with respiratory failure over a 10-year period with a male predominance (95.7%). Pneumocystis jirovecii pneumonia was the main etiology of pulmonary infections (80.1%). The ICU mortality was 32.9%. In multivariate analysis, ICU mortality was independently associated with invasive mechanical ventilation (IMV) [odds ratio (OR), 27.910; 95% confidence interval (CI, 8.392â 92.818; p = 0.000) and the time before ICU admission (OR, 0.959; 95% CI, 0.920â 0.999; p = 0.046). In the survival analysis, patients with IMV and later admission to ICU had a higher probability of mortality.Conclusion: Pneumocystis jirovecii pneumonia was the primary etiology for respiratory failure in AIDS patients admitted to the ICU. Respiratory failure remains a severe illness with high mortality, and ICU mortality was negatively associated with IMV and later admission to ICU.Keywords: acquired imm
Published: 2023

40. Predictive Risk Factors of Pancreatic Exocrine Insufficiency Developed After Acute Pancreatitis: A Retrospective Cohort Study

Author: Guo,Yulin, Wang,Xiaohui, Wang,Shuo, Li,Ang, Cao,Feng, Li,Fei, Guo,Yulin, Wang,Xiaohui, Wang,Shuo, Li,Ang, Cao,Feng, and Li,Fei
Abstract: Yulin Guo,1,2,&ast; Xiaohui Wang,1,2,&ast; Shuo Wang,1,2,&ast; Ang Li,1,2 Feng Cao,1,2 Fei Li1,2 1Department of General Surgery, Xuanwu Hospital of Capital Medical University, Beijing, Peopleâs Republic of China; 2Acute Pancreatitis Clinical Center of Capital Medical University, Beijing, Peopleâs Republic of China&ast;These authors contributed equally to this workCorrespondence: Fei Li; Feng Cao, Department of General Surgery, Xuanwu Hospital, Capital Medical University, No. 45 Changchun Street, Beijing, 100053, Peopleâs Republic of China, Tel/Fax +86-10-83198835, Email feili36@ccmu.edu.cn; caofeng1221@163.comPurpose: The aim of this study was to compare the clinical characteristics of acute pancreatitis (AP) patients between those who developed pancreatic exocrine insufficiency (PEI) and those who did not, and to investigate the predictive factors of PEI.Patients and Methods: From October 1st 2019 to July 30th 2021, AP patients admitted at our center were included. The fecal elastase-1 assay was adopted for PEI diagnosis. The clinical characteristics, treatments, and outcomes between the patients with and without PEI were analyzed.Results: In total, 63 males and 42 females were included. There were 27 patients with mild AP, 54 with moderately severe AP, and 24 with severe AP. The median modified computed tomography severity index (MCTSI) was 6.000(4.000, 8.000). During the follow-up, 38 patients developed PEI after AP. The univariate analysis showed that higher ASA grade (P = 0.006), more severe AP (P = 0.000), the presence of multiple organ dysfunction syndrome (P = 0.030), higher MCTSI (P = 0.000), the development of infected pancreatic necrosis (P = 0.002) and local complications (P = 0.000), higher levels of triacylglycerol (P = 0.022), video-assisted retroperitoneal debridement intervention (P = 0.015), and longer intensive care unit stay (P = 0.044) were correlated with PEI development. Furthermore, the logistic regression analyses showe
Published: 2023

41. Nomogram Prediction Model of Hypernatremia on Mortality in Critically Ill Patients

Author: Qi,Zhili, Lu,Jiaqi, Liu,Pei, Li,Tian, Li,Ang, Duan,Meili, Qi,Zhili, Lu,Jiaqi, Liu,Pei, Li,Tian, Li,Ang, and Duan,Meili
Abstract: Zhili Qi,1,* Jiaqi Lu,2,* Pei Liu,1 Tian Li,1 Ang Li,3 Meili Duan1 1Department of Critical Care Medicine, Capital Medical University, Beijing, People’s Republic of China; 2Department of Critical Care Medicine, Beijing Ditan Hospital, Capital Medical University, Beijing, People’s Republic of China; 3Beijing Ditan Hospital, Capital Medical University, Beijing, People’s Republic of China*These authors contributed equally to the articleCorrespondence: Ang Li, Beijing Ditan Hospital, Capital Medical University, Beijing Ditan Hospital 8 Jing Shun East Street, Beijing, People’s Republic of China, Email liang@ccmu.edu.cn Meili Duan, Department of Critical Care Medicine, Beijing Friendship Hospital, Capital Medical University, No. 95 Yong’an Road, Xicheng District, Beijing, 10005, People’s Republic of China, Email dmeili@ccmu.edu.cnObjective: To investigate the value of hypernatremia in the intensive care unit (ICU) for the risk prediction of mortality in severe patients.Methods: Clinical data of critically ill patients admitted to the ICU of Beijing Friendship Hospital, were collected for retrospective analysis. Univariate and multivariate logistic regression analyses were employed to analyze the influencing factors. Nomograms predicting the mortality were constructed with R software and validated with repeated sampling.Results: A total of 442 cases were eligible for this study. Hypernatremia within 48 hours of ICU admission, change in sodium concentration (CNa+) within 48 hours, septic shock, APACHE II score, hyperlactatemia within 48 hours, use of continuous renal replacement therapy (CRRT) within 48 hours, and the use of mechanical ventilation (MV) within 48 hours of ICU admission were all identified as independent risk factors for death within 28 days of ICU admission. These predictors were included in a nomogram of 28-day mortality in severe patients, which was constructed using R software.Conclusion: The nomo
Published: 2023

42. Optimising Machine Learning Models for Imbalanced Swedish Text Financial Datasets: A Study on Receipt Classification : Exploring Balancing Methods, Naive Bayes Algorithms, and Performance Tradeoffs

Author: Hu, Li Ang, Ma, Long, Hu, Li Ang, and Ma, Long
Abstract: This thesis investigates imbalanced Swedish text financial datasets, specifically receipt classification using machine learning models. The study explores the effectiveness of under-sampling and over-sampling methods for Naive Bayes algorithms, collaborating with Fortnox for a controlled experiment. Evaluation metrics compare balancing methods regarding the accuracy, Matthews's correlation coefficient (MCC) , F1 score, precision, and recall. Findings contribute to Swedish text classification, providing insights into balancing methods. The thesis report examines balancing methods and parameter tuning on machine learning models for imbalanced datasets. Multinomial Naive Bayes (MultiNB) algorithms in Natural language processing (NLP) are studied, with potential application in image classification for assessing industrial thin component deformation. Experiments show balancing methods significantly affect MCC and recall, with a recall-MCC-accuracy tradeoff. Smaller alpha values generally improve accuracy. Synthetic Minority Oversampling Technique (SMOTE) and Tomek's algorithm for removing links developed in 1976 by Ivan Tomek. First Tomek, then SMOTE (TomekSMOTE) yield promising accuracy improvements. Due to time constraints, Over-sampling using SMOTE and cleaning using Tomek links. First SMOTE, then Tomek (SMOTETomek) training is incomplete. This thesis report finds the best MCC is achieved when $\alpha$ is 0.01 on imbalanced datasets.
Published: 2023

43. Equation of state of nuclear matter and neutron stars: Quark mean-field model versus relativistic mean-field model

Author: Zhu, Zhenyu, Li, Ang, Hu, Jinniu, Shen, Hong, Zhu, Zhenyu, Li, Ang, Hu, Jinniu, and Shen, Hong
Abstract: The equation of state of neutron-rich nuclear matter is of interest to both nuclear physics and astrophysics. We have demonstrated the consistency between laboratory and astrophysical nuclear matter in neutron stars by considering low-density nuclear physics constraints (from $^{208}$Pb neutron-skin thickness) and high-density astrophysical constraints (from neutron star global properties). We have used both quark-level and hadron-level models, taking the quark mean-field (QMF) model and the relativistic mean-field (RMF) model as examples, respectively. We have constrained the equation of states of neutron stars and some key nuclear matter parameters within the Bayesian statistical approach, using the first multi-messenger event GW170817/AT 2017gfo, as well as the mass-radius simultaneous measurements of PSR J0030+0451 and PSR J0740+6620 from NICER, and the neutron-skin thickness of $^{208}$Pb from both PREX-II measurement and ab initio calculations. Our results show that, compared with the RMF model, QMF model's direct coupling of quarks with mesons and gluons leads to the evolution of the in-medium nucleon mass with the quark mass correction. This feature enables QMF model a wider range of model applicability, as shown by a slow drop of the nucleon mass with density and a large value at saturation that is jointly constrained by nuclear physics and astronomy., Comment: 14 pages, 4 figures, 3 tables, Phys. Rev. C (2023) accepted
Published: 2023

44. Resolving phase transition properties of dense matter through tidal-excited g-mode from inspiralling neutron stars

Author: Miao, Zhiqiang, Zhou, Enping, Li, Ang, Miao, Zhiqiang, Zhou, Enping, and Li, Ang
Abstract: The investigation of the phase state of dense matter is hindered by complications of first-principle nonperturbative quantum chromodynamics. By performing the first consistent general-relativistic calculations of tidal-excited g-mode of neutron stars with a first-order strong interaction phase transition in the high-density core, we demonstrate that gravitational wave signal during binary neutron star inspiral probes their innermost hadron-quark transition and provides potent constraints from present and future gravitational-wave detectors., Comment: 5 pages, 3 figures, 7 pages supplemental material
Published: 2023

45. Diquark and chiral condensate in a self-consistent NJL-type model

Author: Yuan, Wen-Li, Chao, Jingyi, Li, Ang, Yuan, Wen-Li, Chao, Jingyi, and Li, Ang
Abstract: In this work, a modified two-flavor NJL-type model is utilized, in which the contact current-current interaction is Fierz-transformed into quark-antiquark interactions and quark-quark interactions, which are directly related to the chiral condensate and diquark condensate, respectively. Under mean-field approximation, the chiral condensate and the diquark condensate are studied on the same footing. We discuss in detail the competition between the chiral condensate and the diquark condensate, which are exclusively paired with u and d quarks, while also investigating the order of the chiral phase transition through an analysis of the resulting chiral susceptibility., Comment: 9 pages,5 figures, Phys. Rev. D (2023) accepted
Published: 2023

46. A beta cell subset with enhanced insulin secretion and glucose metabolism is reduced in type 2 diabetes.

Author: Rubio-Navarro, Alfonso, Rubio-Navarro, Alfonso, Gómez-Banoy, Nicolás, Stoll, Lisa, Dündar, Friederike, Mawla, Alex, Ma, Lunkun, Cortada, Eric, Zumbo, Paul, Li, Ang, Reiterer, Moritz, Montoya-Oviedo, Nathalia, Homan, Edwin, Imai, Norihiro, Gilani, Ankit, Liu, Chengyang, Naji, Ali, Yang, Boris, Chong, Angie, Cohen, David, Chen, Shuibing, Cao, Jingli, Pitt, Geoffrey, Betel, Doron, Lo, James, Huising, Mark, Rubio-Navarro, Alfonso, Rubio-Navarro, Alfonso, Gómez-Banoy, Nicolás, Stoll, Lisa, Dündar, Friederike, Mawla, Alex, Ma, Lunkun, Cortada, Eric, Zumbo, Paul, Li, Ang, Reiterer, Moritz, Montoya-Oviedo, Nathalia, Homan, Edwin, Imai, Norihiro, Gilani, Ankit, Liu, Chengyang, Naji, Ali, Yang, Boris, Chong, Angie, Cohen, David, Chen, Shuibing, Cao, Jingli, Pitt, Geoffrey, Betel, Doron, Lo, James, and Huising, Mark
Abstract: The pancreatic islets are composed of discrete hormone-producing cells that orchestrate systemic glucose homeostasis. Here we identify subsets of beta cells using a single-cell transcriptomic approach. One subset of beta cells marked by high CD63 expression is enriched for the expression of mitochondrial metabolism genes and exhibits higher mitochondrial respiration compared with CD63lo beta cells. Human and murine pseudo-islets derived from CD63hi beta cells demonstrate enhanced glucose-stimulated insulin secretion compared with pseudo-islets from CD63lo beta cells. We show that CD63hi beta cells are diminished in mouse models of and in humans with type 2 diabetes. Finally, transplantation of pseudo-islets generated from CD63hi but not CD63lo beta cells into diabetic mice restores glucose homeostasis. These findings suggest that loss of a specific subset of beta cells may lead to diabetes. Strategies to reconstitute or maintain CD63hi beta cells may represent a potential anti-diabetic therapy.
Published: 2023

47. Study on Respiratory Function and Hemodynamics of AIDS Patients with Respiratory Failure

Author: Qiu,Kai, Lu,Jiaqi, Guo,Hebing, Du,Chunjing, Liu,Jingyuan, Li,Ang, Qiu,Kai, Lu,Jiaqi, Guo,Hebing, Du,Chunjing, Liu,Jingyuan, and Li,Ang
Abstract: Kai Qiu,1,2 Jiaqi Lu,1 Hebing Guo,1 Chunjing Du,1 Jingyuan Liu,1 Ang Li3 1Department of Critical Care Medicine, Beijing Ditan Hospital, Capital Medical University, Beijing, Peopleâs Republic of China; 2Department of Intensive Care Medicine, Liangxiang Hospital of Beijing Fangshan District, Beijing, Peopleâs Republic of China; 3Beijing Ditan Hospital, Capital Medical University, Beijing, Peopleâs Republic of ChinaCorrespondence: Jingyuan Liu; Ang Li, Beijing Ditan Hospital, Capital Medical University, &num;8 Jing Shun East St Chaoyang, Beijing, 100015, Peopleâs Republic of China, Email dtyyicu@ccmu.edu.cn; liang@ccmu.edu.cnObjective: We performed a comparative analysis of respiratory function and hemodynamics among patients with Acquired Immunodeficiency Syndrome (AIDS)-associated respiratory failure and those with non-AIDS-associated respiratory failure.Methods: Data were collected from critically ill patients diagnosed with Acquired Immunodeficiency Syndrome who were admitted to the Intensive Care Unit (ICU) of Beijing Ditan Hospital, affiliated with Capital Medical University, between January 1, 2019, and December 31, 2019. We simultaneously gathered data from non-AIDS patients admitted to the ICU of Beijing Liangxiang Hospital within the same timeframe. A comparative study was performed to analyze clinical data from these two patient groups, encompassing parameters related to respiratory mechanics and hemodynamic indicators.Results: A total of 12 patients diagnosed with Acquired Immunodeficiency Syndrome (AIDS) and experiencing respiratory failure, along with 23 patients with respiratory failure independent of AIDS, were included in our study. Subsequently, a comparative analysis of clinical information was conducted between the two patient cohorts. Our findings demonstrate non-statistically significant differences between the two patient groups when assessing various indicators, encompassing peak airway pressure, plateau pressure, mean pressure, comp
Published: 2023

48. SRSF2 plays an unexpected role as reader of m5C on mRNA, linking epitranscriptomics to cancer.

Author: Ma, Haili, Bizet, Martin, Soares Da Costa, Christelle, Murisier, Frédéric, de Bony, Eric James, Wang, Meng-Ke, Yoshimi, Akihide, Lin, Kuan-Ting, Riching, Kristin M., Wang, Xing, Beckman, John I., Arya, Shailee, Droin, Nathalie, Calonne, Emilie, Hassabi, Bouchra, Zhang, Qing-Yang, Li, Ang, Putmans, Pascale, Malbec, Lionel, Hubert, Céline, Lan, Jie, Mies, Frédérique, Bula Ibula Yanga, Yanga, Solary, Eric, Daniels, Danette, Gupta, Yogesh K., Deplus, Rachel, Abdel-Wahab, Omar, Yang, Yun-Gui, Fuks, François, Ma, Haili, Bizet, Martin, Soares Da Costa, Christelle, Murisier, Frédéric, de Bony, Eric James, Wang, Meng-Ke, Yoshimi, Akihide, Lin, Kuan-Ting, Riching, Kristin M., Wang, Xing, Beckman, John I., Arya, Shailee, Droin, Nathalie, Calonne, Emilie, Hassabi, Bouchra, Zhang, Qing-Yang, Li, Ang, Putmans, Pascale, Malbec, Lionel, Hubert, Céline, Lan, Jie, Mies, Frédérique, Bula Ibula Yanga, Yanga, Solary, Eric, Daniels, Danette, Gupta, Yogesh K., Deplus, Rachel, Abdel-Wahab, Omar, Yang, Yun-Gui, and Fuks, François
Abstract: A common mRNA modification is 5-methylcytosine (m5C), whose role in gene-transcript processing and cancer remains unclear. Here, we identify serine/arginine-rich splicing factor 2 (SRSF2) as a reader of m5C and impaired SRSF2 m5C binding as a potential contributor to leukemogenesis. Structurally, we identify residues involved in m5C recognition and the impact of the prevalent leukemia-associated mutation SRSF2P95H. We show that SRSF2 binding and m5C colocalize within transcripts. Furthermore, knocking down the m5C writer NSUN2 decreases mRNA m5C, reduces SRSF2 binding, and alters RNA splicing. We also show that the SRSF2P95H mutation impairs the ability of the protein to read m5C-marked mRNA, notably reducing its binding to key leukemia-related transcripts in leukemic cells. In leukemia patients, low NSUN2 expression leads to mRNA m5C hypomethylation and, combined with SRSF2P95H, predicts poor outcomes. Altogether, we highlight an unrecognized mechanistic link between epitranscriptomics and a key oncogenesis driver., SCOPUS: ar.j, info:eu-repo/semantics/published
Published: 2023

49. The Influence of Authoritarian-Benevolent Leadership on Subordinates’ Work Engagement: A Social Information Processing Perspective

Author: Zhou,Chengxu, Yu,Guilan, Meng,Ying, Li,Ang, Zhou,Chengxu, Yu,Guilan, Meng,Ying, and Li,Ang
Abstract: Chengxu Zhou,1 Guilan Yu,1 Ying Meng,2 Ang Li3 1School of Business and Management, Jilin University, Changchun, Peopleâs Republic of China; 2Yatai School of Business Administration, Jilin University of Finance and Economics, Changchun, Peopleâs Republic of China; 3Dalian Base, China Southern Technic Branch, Dalian, Peopleâs Republic of ChinaCorrespondence: Chengxu Zhou, School of Business and Management, Jilin University, Changchun, Jilin, 130015, Peopleâs Republic of China, Tel +86 155 0001 9909, Email zhoucx17@mails.jlu.edu.cnPurpose: The dynamic and complex external environment calls for leaders to be both benevolent and authoritarian to cope with the mutually exclusive demands in the management process. But few research paid attention to the dark side of leadersâ inconsistent behaviors on employeesâ work outcomes. Based on social information processing theory, this study investigated the impact of authoritarian-benevolent leadership on subordinatesâ work engagement and explored the mediating role of leader-member exchange ambivalence and the moderating role of trait mindfulness.Methods: A two-wave time-lagged survey approach was employed and data from 309 employees from three Chinese companies were collected. R 4.2.1 and SPSS 26.0 were used to test our hypotheses.Results: The findings demonstrated how subordinates were influenced in their work engagement by authoritarian-benevolent leadership and leader-member exchange ambivalence and emphasized the value of maintaining a high level of trait mindfulness. This study indicated that (1) authoritarian-benevolent leadership was negatively associated with work engagement, which was mediated by leader-member exchange ambivalence; (2) subordinatesâ trait mindfulness moderated the relationship between leader-member exchange ambivalence and work engagement; (3) the indirect effect of authoritarian-benevolent leadership on subordinatesâ work engagement through leader-member exchange ambivalence was modera
Published: 2023

50. FastMapSVM for Predicting CSP Satisfiability

Author: Kexin Zheng and Ang Li and Han Zhang and T. K. Satish Kumar, Zheng, Kexin, Li, Ang, Zhang, Han, Kumar, T. K. Satish, Kexin Zheng and Ang Li and Han Zhang and T. K. Satish Kumar, Zheng, Kexin, Li, Ang, Zhang, Han, and Kumar, T. K. Satish
Abstract: Recognizing the satisfiability of Constraint Satisfaction Problems (CSPs) is NP-hard. Although several Machine Learning (ML) approaches have attempted this task by casting it as a binary classification problem, they have had only limited success for a variety of challenging reasons. First, the NP-hardness of the task does not make it amenable to straightforward approaches. Second, CSPs come in various forms and sizes while many ML algorithms impose the same form and size on their training and test instances. Third, the representation of a CSP instance is not unique since the variables and their domain values are unordered. In this paper, we propose FastMapSVM, a recently developed ML framework that leverages a distance function between pairs of objects. We define a novel distance function between two CSP instances using maxflow computations. This distance function is well defined for CSPs of different sizes. It is also invariant to the ordering on the variables and their domain values. Therefore, our framework has broader applicability compared to other approaches. We discuss various representational and combinatorial advantages of FastMapSVM. Through experiments, we also show that it outperforms other state-of-the-art ML approaches.
Published: 2023
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