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Owing to the dependence of algorithms on the measurement of ship soundings and geophysical parameters, the accuracy and coverage of topography still need to be improved. Previous studies have mostly predicted topography using gravity or gravity gradient, However, there is a relative lack of integrated research combining or comparing gravity and gravity gradient. In this study, we develop observation equations to predict topography based on vertical gravity anomalies (VG; also called gravity anomalies) and vertical gravity gradient (VGG) anomalies generated by a rectangular prism. The sources of interference are divided into medium- to high-frequency errors and low-frequency errors, and these new methods reduce these errors through regularization and error equations. We also use numerical simulations to test the efficiency of the algorithm and error-reduction method. Statistics show that VGG anomalies are more sensitive to topographic fluctuations; however, the linear correlation between VG anomalies and topography is stronger. Additionally, we use the EIGEN-6C4 model of VG and VGG anomalies to predict topography in shallow and deep-sea areas, with maximum depths of 2 km and 5 km, respectively. In the shallow and deep-sea areas, the root mean square (RMS) errors of VGG anomalies prediction are 93.8 m and 233.8 m, and the corresponding accuracies improved by 7.3% and 2.3% compared with those of VG anomaly prediction, respectively. Furthermore, we use cubic spline interpolation to fuse ship soundings and improve the accuracy of the final topography results. We develop a novel analytical algorithm by constructing an observation equation system applicable to VG and VGG anomalies. This will provide new insights and directions to refine topography prediction based on VG and VGG anomalies.

Jian Yu,1,2,* Huan Xu,1,2,* Jiaojiao Wei,1,2 Liangliang Niu,1,2 Haohao Zhu,1,3 Chunhui Jiang1,2 1Department of Ophthalmology and Visual Science, Eye, Ear, Nose and Throat Hospital, Shanghai Medical College of Fudan University, Shanghai, People’s Republic of China; 2Key Laboratory of Myopia of State Health Ministry and Key Laboratory of Visual Impairment and Restoration of Shanghai, Shanghai, People’s Republic of China; 3Department of Ophthalmology, Shanghai Fifth People’s Hospital, Fudan University, Shanghai, People’s Republic of China*These authors contributed equally to this workCorrespondence: Chunhui Jiang, Department of Ophthalmology and Vision Science, Eye and ENT Hospital, Fudan University, 83 Fenyang Road, Shanghai, 200031, People’s Republic of China, Email chhjiang70@163.com Haohao Zhu, Department of Ophthalmology, Shanghai Fifth People’s Hospital, Fudan University, 128 Ruili Road, Shanghai, 200240, People’s Republic of China, Tel +86-21-64308151, Fax +86-21-64300477, Email haohao700315@163.comBackground: Bacterial endophthalmitis is an acute progressive visual threatening disease and one of the most important causes of blindness worldwide. Current treatments are unsatisfactory due to the emergence of drug-resistant bacteria and the formation of biofilm.Purpose: The aim of our research was to construct a novel nano-delivery system with better antimicrobial and antibiofilm effects.Methods: This study developed a novel antibiotic nanoparticle delivery system (MXF@UiO-UBI-PEGTK), which is composed of (i) moxifloxacin (MXF)-loaded UiO-66 nanoparticle as the core, (ii) bacteria-targeting peptide ubiquicidin (UBI29-41) immobilized on UiO-66, and (iii) ROS-responsive poly (ethylene glycol)-thioketal (PEG-TK) as the surface shell. Then the important properties of the newly developed delivery system, including biocompatibility, toxicity, release percentage, thermal stability, ability of targeting bacteria, and synergistic antiba

Ultrasound robots are increasingly used in medical diagnostics and early disease screening. However, current ultrasound robots lack the intelligence to understand human intentions and instructions, hindering autonomous ultrasound scanning. To solve this problem, we propose a novel Ultrasound Embodied Intelligence system that equips ultrasound robots with the large language model (LLM) and domain knowledge, thereby improving the efficiency of ultrasound robots. Specifically, we first design an ultrasound operation knowledge database to add expertise in ultrasound scanning to the LLM, enabling the LLM to perform precise motion planning. Furthermore, we devise a dynamic ultrasound scanning strategy based on a \textit{think-observe-execute} prompt engineering, allowing LLMs to dynamically adjust motion planning strategies during the scanning procedures. Extensive experiments demonstrate that our system significantly improves ultrasound scan efficiency and quality from verbal commands. This advancement in autonomous medical scanning technology contributes to non-invasive diagnostics and streamlined medical workflows., Comment: ICRA 2024 Full-day Workshop: C4SR+: Continuum, Compliant, Cooperative, Cognitive

Persistent free radicals (PFR) in carbonized particles may play a role in degradation of environmental compounds. The influence of PFR is evaluated in various carbonized particles on their radical scavenging efficiency upon the common radical indicator 2-2-diphenyl-1-picrylhydrazyl (DPPH). Carbonized particles are derived by hydrothermal carbonization of glucose (C-W) or glucose and urea (NC-W) and ionothermal carbonization of glucose and urea ionic liquid (IL) (NC-IL). The carbonized materials contain OH/COOH, C=C, and C-O functionalities. The addition of urea introduces NH/NH2 functionalities. The content of polar surface groups is lower in IL-processed NC-IL. The scavenging ability, measured as DPPH UV–vis absorption decline, increases with concentration and time for all particles, while the efficiency changes are in the order of C-W > NC-W > NC-IL. Electron paramagnetic resonance analysis reveals similar radical concentration in all carbonized materials studied. The difference in efficiency is, thus, not directly related to the PFR concentration but rather to the type of PFR, surface functionalities and/or scavenging mechanism. According to the g-values, radicals in these particles are carbon-centered. The minor variation in g-values suggests interactions between the radicals and their environmental functional groups. This provides insights into the influence of PFR in carbonized materials on their radical scavenging efficiency.

Persistent free radicals (PFR) in carbonized particles may play a role in degradation of environmental compounds. The influence of PFR is evaluated in various carbonized particles on their radical scavenging efficiency upon the common radical indicator 2-2-diphenyl-1-picrylhydrazyl (DPPH). Carbonized particles are derived by hydrothermal carbonization of glucose (C-W) or glucose and urea (NC-W) and ionothermal carbonization of glucose and urea ionic liquid (IL) (NC-IL). The carbonized materials contain OH/COOH, C=C, and C-O functionalities. The addition of urea introduces NH/NH2 functionalities. The content of polar surface groups is lower in IL-processed NC-IL. The scavenging ability, measured as DPPH UV–vis absorption decline, increases with concentration and time for all particles, while the efficiency changes are in the order of C-W > NC-W > NC-IL. Electron paramagnetic resonance analysis reveals similar radical concentration in all carbonized materials studied. The difference in efficiency is, thus, not directly related to the PFR concentration but rather to the type of PFR, surface functionalities and/or scavenging mechanism. According to the g-values, radicals in these particles are carbon-centered. The minor variation in g-values suggests interactions between the radicals and their environmental functional groups. This provides insights into the influence of PFR in carbonized materials on their radical scavenging efficiency.

Persistent free radicals (PFR) in carbonized particles may play a role in degradation of environmental compounds. The influence of PFR is evaluated in various carbonized particles on their radical scavenging efficiency upon the common radical indicator 2-2-diphenyl-1-picrylhydrazyl (DPPH). Carbonized particles are derived by hydrothermal carbonization of glucose (C-W) or glucose and urea (NC-W) and ionothermal carbonization of glucose and urea ionic liquid (IL) (NC-IL). The carbonized materials contain OH/COOH, C=C, and C-O functionalities. The addition of urea introduces NH/NH2 functionalities. The content of polar surface groups is lower in IL-processed NC-IL. The scavenging ability, measured as DPPH UV–vis absorption decline, increases with concentration and time for all particles, while the efficiency changes are in the order of C-W > NC-W > NC-IL. Electron paramagnetic resonance analysis reveals similar radical concentration in all carbonized materials studied. The difference in efficiency is, thus, not directly related to the PFR concentration but rather to the type of PFR, surface functionalities and/or scavenging mechanism. According to the g-values, radicals in these particles are carbon-centered. The minor variation in g-values suggests interactions between the radicals and their environmental functional groups. This provides insights into the influence of PFR in carbonized materials on their radical scavenging efficiency.

Persistent free radicals (PFR) in carbonized particles may play a role in degradation of environmental compounds. The influence of PFR is evaluated in various carbonized particles on their radical scavenging efficiency upon the common radical indicator 2-2-diphenyl-1-picrylhydrazyl (DPPH). Carbonized particles are derived by hydrothermal carbonization of glucose (C-W) or glucose and urea (NC-W) and ionothermal carbonization of glucose and urea ionic liquid (IL) (NC-IL). The carbonized materials contain OH/COOH, C=C, and C-O functionalities. The addition of urea introduces NH/NH2 functionalities. The content of polar surface groups is lower in IL-processed NC-IL. The scavenging ability, measured as DPPH UV–vis absorption decline, increases with concentration and time for all particles, while the efficiency changes are in the order of C-W > NC-W > NC-IL. Electron paramagnetic resonance analysis reveals similar radical concentration in all carbonized materials studied. The difference in efficiency is, thus, not directly related to the PFR concentration but rather to the type of PFR, surface functionalities and/or scavenging mechanism. According to the g-values, radicals in these particles are carbon-centered. The minor variation in g-values suggests interactions between the radicals and their environmental functional groups. This provides insights into the influence of PFR in carbonized materials on their radical scavenging efficiency., QC 20230626

High-performance air filtration materials are importantfor addressingthe airborne pollutants. Herein, we propose an unprecedented accessto biodegradable poly-(lactic acid) (PLA)-based MOFilters with excellentfiltering performance and antibacterial activity. The fabricationinvolved a stepwise in situ growth of zeolitic imidazolate framework-8(ZIF-8) crystals at the surface of microfibrous PLA membranes, followedby mechanical polarization under high pressure and low temperature(5 MPa, 40 degrees C) to trigger the ordered alignment of dipoles inPLA chains and ZIF-8. The unique structural features allowed thesePLA-based MOFilters to achieve an exceptional combination of excellenttensile properties, high dielectric constant (up to 2.4 F/m), andenhanced surface potential as high as 4 kV. Arising from the remarkablesurface activity and electrostatic adsorption effect, a significantincrease (from over 12% to nearly 20%) in PM0.3 filtrationefficiency was observed for the PLA-based MOFilters compared to thatof pure PLA counterparts, with weak relation to the airflow velocities(10-85 L/min). Moreover, the air resistance was controlledat a considerably low level for all the MOFilters, that is, below183 Pa even at 85 L/min. It is worth noting that distinct antibacterialproperties were achieved for the MOFilters, as illustrated by theinhibitive rates of 87 and 100% against Escherichiacoli and Staphylococcus aureus, respectively. The proposed concept of PLA-based MOFilters offersunprecedented multifunction integration, which may fuel the developmentof biodegradable versatile filters with high capturing and antibacterialperformances yet desirable manufacturing feasibility., QC 20230626

Persistent free radicals (PFR) in carbonized particles may play a role in degradation of environmental compounds. The influence of PFR is evaluated in various carbonized particles on their radical scavenging efficiency upon the common radical indicator 2-2-diphenyl-1-picrylhydrazyl (DPPH). Carbonized particles are derived by hydrothermal carbonization of glucose (C-W) or glucose and urea (NC-W) and ionothermal carbonization of glucose and urea ionic liquid (IL) (NC-IL). The carbonized materials contain OH/COOH, C=C, and C-O functionalities. The addition of urea introduces NH/NH2 functionalities. The content of polar surface groups is lower in IL-processed NC-IL. The scavenging ability, measured as DPPH UV–vis absorption decline, increases with concentration and time for all particles, while the efficiency changes are in the order of C-W > NC-W > NC-IL. Electron paramagnetic resonance analysis reveals similar radical concentration in all carbonized materials studied. The difference in efficiency is, thus, not directly related to the PFR concentration but rather to the type of PFR, surface functionalities and/or scavenging mechanism. According to the g-values, radicals in these particles are carbon-centered. The minor variation in g-values suggests interactions between the radicals and their environmental functional groups. This provides insights into the influence of PFR in carbonized materials on their radical scavenging efficiency.

Longyin Zhu,1 Huan Xu,2 Youmin Pu,1 Chunxiao Fu,1 Qianguang Pan,1,* Hongwen Zhao1,* 1Department of Nephrology, The First Affiliated Hospital of Army Medical University, Chongqing, People’s Republic of China; 2Department of Scientific Affairs, Vision Medicals Center for Infection Diseases, Guangzhou, People’s Republic of China*These authors contributed equally to this workCorrespondence: Hongwen Zhao; Qianguang Pan, Department of Nephrology, The First Affiliated Hospital of Army Medical University, 30 Gaotanyanzheng Street, Shapingba District, Chongqing, 400038, People’s Republic of China, Tel/Fax +8613983360655 ; +86 18584651157, Email zhaohw212@126.com; panqianguang@163.comAbstract: We report a case of pneumocystis jiroveci pneumonia (PJP) in a 46-year-old woman, who previously underwent kidney transplant for chronic renal failure. She did not receive PJP prophylaxis treatment for the history of sulfonamide allergies. Four months after renal transplantation, the patient had cough, chest tightness, and shortness of breath. Procalcitonin (PCT) (0.06 ng/mL) and C-reactive protein (CRP) (5.33 mg/L) were normal, but the level of 1, 3-β-D-glucan test (G test, 193.89 pg/mL) were elevated. Metagenomics next-generation sequencing (mNGS) using bronchoalveolar lavage fluid (BALF) rapidly and accurately identified P. jiroveci. Through sulfonamide desensitization and sulfamethoxazole-trimethoprim (TMP-SMX) combined with caspofungin (CAS) treatment, PJP was controlled. However, the patients’ conditions were worsen for the hospital-acquired secondary pulmonary infection. A second BALF mNGS identified Enterobacter cloacae complex and Pseudomonas aeruginosa carrying carbapenem drug resistance genes, which were confirmed by subsequent culture and antimicrobial susceptibility test within 3 days. Finally, symptoms, such as chest tightness, cough, and shortness of breath, were improved and she was discharged after combined treatment with meropenem (MEM)

The timing of parturition is crucial for neonatal survival and infant health. Yet, its genetic basis remains largely unresolved. We present a maternal genome-wide meta-analysis of gestational duration (n = 195,555), identifying 22 associated loci (24 independent variants) and an enrichment in genes differentially expressed during labor. A meta-analysis of preterm delivery (18,797 cases, 260,246 controls) revealed six associated loci and large genetic similarities with gestational duration. Analysis of the parental transmitted and nontransmitted alleles (n = 136,833) shows that 15 of the gestational duration genetic variants act through the maternal genome, whereas 7 act both through the maternal and fetal genomes and 2 act only via the fetal genome. Finally, the maternal effects on gestational duration show signs of antagonistic pleiotropy with the fetal effects on birth weight: maternal alleles that increase gestational duration have negative fetal effects on birth weight. The present study provides insights into the genetic effects on the timing of parturition and the complex maternal–fetal relationship between gestational duration and birth weight.

Wenyu Zhou,1,* Song Hu,1,2,* Yutong Wu,1,* Huan Xu,1,* Lina Zhu,1 Huimin Deng,1 Sheng Wang,1 Yuanli Chen,1 Huanping Zhou,1 Xin Lv,1 Quanfu Li,1 Hao Yang1 1Department of Anesthesiology, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, 200433, People’s Republic of China; 2Graduate School, Wannan Medical College, Wuhu, AnHui, 241002, People’s Republic of China*These authors contributed equally to this workCorrespondence: Hao Yang, Department of Anesthesiology, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, 507 Zhengmin Road, Shanghai, 200433, People’s Republic of China, Email yanghaozunyi@sina.com Quanfu Li, Department of Anesthesiology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, 507 Zhengmin Road, Shanghai, 200433, People’s Republic of China, Email quanfuli185@163.comBackground: Mesenchymal stem cell-derived exosomes (MSC-exosomes) have been found to effectively improve the systemic inflammatory response caused by acute lung injury and acute respiratory distress syndrome (ALI/ARDS), regulate systemic immune disorders, and help injured cells repair. The purpose of this study was to take a holistic view of the current status and trends of MSC-exosomes research in ALI/ARDS.Methods: Bibliometrix, Citespace and VOSviewer software were used for bibliometric analysis of the data. We analysed the world trends, country distribution, institution contribution, most relevant journals and authors, research hotspots, and research hotspots related to Coronavirus Disease 2019 (COVID-19) based on the data collected.Results: China possessed the largest number of publications, while the USA had the highest H-index and the number of citations. Both China and the USA had a high influence in this research field. The largest number of publications in the field of MSC-exosomes and ALI/ARDS were mainly from the University of California system. Stem Cell Research & Ther

Yang Wang,1,* Zheng Wang,1,* Xiao Wu,1,* Song Zhu,2,* Qiru Guo,1 Zhong Jin,1 Zixian Chen,3 Delai Zhang,2 Wangming Hu,1 Huan Xu,1 Liangqin Shi,1 Lan Yang,1 Yong Wang1 1College of Basic Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, People’s Republic of China; 2Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, People’s Republic of China; 3School of Ethnic Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, People’s Republic of China*These authors contributed equally to this workCorrespondence: Yong Wang, College of Basic Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, People’s Republic of China, Tel +86 18280350393, Email yongwang1008@hotmail.com; wangyong@cdutcm.edu.cnPurpose: Dysfunction of endothelium is associated with multiple pathological vascular diseases. However, how to regulate reendothelialization after vascular injury is not well defined. This study aims to determine whether and how Paeonol controls reendothelialization following artery injury.Methods: The endothelium of murine carotid artery was denuded by catheter guide wires injury. H&E staining and IF staining were performed to determine whether Paeonol is critical for reendothelialization. BRDU Incorporation Assay, Boyden Chamber Migration Assay, Tube Formation Assay, and Spheroid Sprouting Assay were used to investigate whether Paeonol is involved in regulating proliferation and migration of endothelial cells. The underlying mechanism of how Paeonol regulates reendothelialization was determined by Molecular docking simulation and CO-IP Assay.Results: Paeonol treatment significantly inhibits neointima formation in carotid artery ligation model by promoting proliferation and migration of endothelial cells. Mechanistically, Paeonol enhances c-Myc expression, consequently interacts with VEGFR2 results in activating VEGF signaling pathway, and eventually pro

Kang Zou,1,2,* Shu Huang,3,4,* Wensen Ren,1,2,* Huan Xu,1,2 Wei Zhang,1,2 Xiaomin Shi,1,2 Lei Shi,1,2 Xiaolin Zhong,1,2 Yan Peng,1,2 Muhan Lü,1,2 Xiaowei Tang1,2 1Department of Gastroenterology, the Affiliated Hospital of Southwest Medical University, Luzhou, People’s Republic of China; 2Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan Province, Luzhou, People’s Republic of China; 3Department of Gastroenterology, Lianshui County People’ Hospital, Huaian, People’s Republic of China; 4Department of Gastroenterology, Lianshui People’ Hospital of Kangda College Affiliated to Nanjing Medical University, Huaian, People’s Republic of China*These authors contributed equally to this workCorrespondence: Xiaowei Tang; Muhan Lü, Department of Gastroenterology, the Affiliated Hospital of Southwest Medical University, Street Taiping No. 25, Region Jiangyang, Luzhou, Sichuan Province, 646099, People’s Republic of China, Tel +86 8303165200, Fax +86 83061641541, Email solitude5834@hotmail.com; lvmuhan@swmu.edu.cnPurpose: The aim of this study is to develop and validate a predictive model for the prediction of in-hospital mortality in patients with acute pancreatitis (AP) based on the intensive care database.Patients and Methods: We analyzed the data of patients with AP in the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database and Electronic Intensive Care Unit Collaborative Research Database (eICU-CRD). Then, patients from MIMIC-IV were divided into a development group and a validation group according to the ratio of 8:2, and eICU-CRD was assigned as an external validation group. Univariate logistic regression and least absolute shrinkage and selection operator regression were used for screening the best predictors, and multivariate logistic regression was used to establish a dynamic nomogram. We evaluated the discrimination, calibration, and clinical efficacy of the nomogram, and compared the performanc

Persistent free radicals (PFR) in carbonized particles may play a role in degradation of environmental compounds. The influence of PFR is evaluated in various carbonized particles on their radical scavenging efficiency upon the common radical indicator 2-2-diphenyl-1-picrylhydrazyl (DPPH). Carbonized particles are derived by hydrothermal carbonization of glucose (C-W) or glucose and urea (NC-W) and ionothermal carbonization of glucose and urea ionic liquid (IL) (NC-IL). The carbonized materials contain OH/COOH, C=C, and C-O functionalities. The addition of urea introduces NH/NH2 functionalities. The content of polar surface groups is lower in IL-processed NC-IL. The scavenging ability, measured as DPPH UV–vis absorption decline, increases with concentration and time for all particles, while the efficiency changes are in the order of C-W > NC-W > NC-IL. Electron paramagnetic resonance analysis reveals similar radical concentration in all carbonized materials studied. The difference in efficiency is, thus, not directly related to the PFR concentration but rather to the type of PFR, surface functionalities and/or scavenging mechanism. According to the g-values, radicals in these particles are carbon-centered. The minor variation in g-values suggests interactions between the radicals and their environmental functional groups. This provides insights into the influence of PFR in carbonized materials on their radical scavenging efficiency.

Deep models have demonstrated recent success in single-image dehazing. Most prior methods consider fully supervised training and learn from paired clean and hazy images, where a hazy image is synthesized based on a clean image and its estimated depth map. This paradigm, however, can produce low-quality hazy images due to inaccurate depth estimation, resulting in poor generalization of the trained models. In this paper, we explore an alternative approach for generating paired clean-hazy images by leveraging computer graphics. Using a modern game engine, our approach renders crisp clean images and their precise depth maps, based on which high-quality hazy images can be synthesized for training dehazing models. To this end, we present SimHaze: a new synthetic haze dataset. More importantly, we show that training with SimHaze alone allows the latest dehazing models to achieve significantly better performance in comparison to previous dehazing datasets. Our dataset and code will be made publicly available., Comment: Submitted to ICIP 2023

The timing of parturition is crucial for neonatal survival and infant health. Yet, its genetic basis remains largely unresolved. We present a maternal genome-wide meta-analysis of gestational duration (n = 195,555), identifying 22 associated loci (24 independent variants) and an enrichment in genes differentially expressed during labor. A meta-analysis of preterm delivery (18,797 cases, 260,246 controls) revealed six associated loci and large genetic similarities with gestational duration. Analysis of the parental transmitted and nontransmitted alleles (n = 136,833) shows that 15 of the gestational duration genetic variants act through the maternal genome, whereas 7 act both through the maternal and fetal genomes and 2 act only via the fetal genome. Finally, the maternal effects on gestational duration show signs of antagonistic pleiotropy with the fetal effects on birth weight: maternal alleles that increase gestational duration have negative fetal effects on birth weight. The present study provides insights into the genetic effects on the timing of parturition and the complex maternal–fetal relationship between gestational duration and birth weight.

In surgical procedures, correct instrument counting is essential. Instance segmentation is a location method that locates not only an object's bounding box but also each pixel's specific details. However, obtaining mask-level annotations is labor-intensive in instance segmentation. To address this issue, we propose a novel yet effective weakly-supervised surgical instrument instance segmentation approach, named Point-based Weakly-supervised Instance Segmentation (PWISeg). PWISeg adopts an FCN-based architecture with point-to-box and point-to-mask branches to model the relationships between feature points and bounding boxes, as well as feature points and segmentation masks on FPN, accomplishing instrument detection and segmentation jointly in a single model. Since mask level annotations are hard to available in the real world, for point-to-mask training, we introduce an unsupervised projection loss, utilizing the projected relation between predicted masks and bboxes as supervision signal. On the other hand, we annotate a few pixels as the key pixel for each instrument. Based on this, we further propose a key pixel association loss and a key pixel distribution loss, driving the point-to-mask branch to generate more accurate segmentation predictions. To comprehensively evaluate this task, we unveil a novel surgical instrument dataset with manual annotations, setting up a benchmark for further research. Our comprehensive research trial validated the superior performance of our PWISeg. The results show that the accuracy of surgical instrument segmentation is improved, surpassing most methods of instance segmentation via weakly supervised bounding boxes. This improvement is consistently observed in our proposed dataset and when applied to the public HOSPI-Tools dataset., Comment: This work has been submitted to IEEE International Symposium on Biomedical Imaging (ISBI) 2024 for possible publication

PyPose is an open-source library for robot learning. It combines a learning-based approach with physics-based optimization, which enables seamless end-to-end robot learning. It has been used in many tasks due to its meticulously designed application programming interface (API) and efficient implementation. From its initial launch in early 2022, PyPose has experienced significant enhancements, incorporating a wide variety of new features into its platform. To satisfy the growing demand for understanding and utilizing the library and reduce the learning curve of new users, we present the fundamental design principle of the imperative programming interface, and showcase the flexible usage of diverse functionalities and modules using an extremely simple Dubins car example. We also demonstrate that the PyPose can be easily used to navigate a real quadruped robot with a few lines of code.

When doing private domain marketing with cloud services, the merchants usually have to purchase different machine learning models for the multiple marketing purposes, leading to a very high cost. We present a unified user-item matching framework to simultaneously conduct item recommendation and user targeting with just one model. We empirically demonstrate that the above concurrent modeling is viable via modeling the user-item interaction matrix with the multinomial distribution, and propose a bidirectional bias-corrected NCE loss for the implementation. The proposed loss function guides the model to learn the user-item joint probability $p(u,i)$ instead of the conditional probability $p(i|u)$ or $p(u|i)$ through correcting both the users and items' biases caused by the in-batch negative sampling. In addition, our framework is model-agnostic enabling a flexible adaptation of different model architectures. Extensive experiments demonstrate that our framework results in significant performance gains in comparison with the state-of-the-art methods, with greatly reduced cost on computing resources and daily maintenance.

BackgroundPregnant women in Malawi are at risk of selenium deficiency, which can have adverse effects on pregnancy outcomes. Interventions for improving selenium status are needed.ObjectivesTo assess the effect of provision of small-quantity lipid-based nutrient supplements (SQ-LNSs) to Malawian women during pregnancy on their plasma selenium concentrations at 36 wk of gestation.MethodsPregnant women (≤20 wk of gestation) were randomly assigned to receive daily either: 1) iron and folic acid (IFA); 2) multiple micronutrients (MMN; 130 µg selenium per capsule); or 3) SQ-LNS (130 µg selenium/20 g). Plasma selenium concentrations were measured by inductively coupled plasma mass spectrometry at baseline and after ≥16 wk of intervention (at 36 wk of gestation) and compared by intervention group.ResultsAt 36 wk of gestation, median (quartile 1, quartile 3) plasma selenium concentrations (micromoles per liter) were 0.96 (0.73, 1.23), 0.94 (0.78, 1.18), and 1.01 (0.85, 1.28) in the IFA, MMN, and SQ-LNS groups, respectively. Geometric mean (GM) plasma selenium concentration was 5.4% (95% CI: 1.8%, 9.0%) higher in the SQ-LNS group than in the MMN group and tended to be higher than in the IFA group (+4.2%; 95% CI: 1.0%, 7.8%). The prevalence of adjusted plasma selenium concentrations <1 µmol/L was 55.1%, 57.8%, and 47.3% in the IFA, MMN, and SQ-LNS groups, respectively; it was lower in the SQ-LNS group than in the MMN group, OR = 0.44 (95% CI: 0.24, 0.83), and tended to be lower than in the IFA group, OR = 0.54 (95% CI: 0.29, 1.03). There was a significant interaction between baseline plasma selenium concentration and intervention group (P = 0.003). In the lowest tertile of baseline selenium concentrations, GM plasma selenium concentration was higher, and the prevalence of low values was lower in the SQ-LNS group compared with the MMN and IFA groups at 36 wk of gestation (P ≤ 0.007).ConclusionsProvision o

BackgroundPregnant women in Malawi are at risk of selenium deficiency, which can have adverse effects on pregnancy outcomes. Interventions for improving selenium status are needed.ObjectivesTo assess the effect of provision of small-quantity lipid-based nutrient supplements (SQ-LNSs) to Malawian women during pregnancy on their plasma selenium concentrations at 36 wk of gestation.MethodsPregnant women (≤20 wk of gestation) were randomly assigned to receive daily either: 1) iron and folic acid (IFA); 2) multiple micronutrients (MMN; 130 µg selenium per capsule); or 3) SQ-LNS (130 µg selenium/20 g). Plasma selenium concentrations were measured by inductively coupled plasma mass spectrometry at baseline and after ≥16 wk of intervention (at 36 wk of gestation) and compared by intervention group.ResultsAt 36 wk of gestation, median (quartile 1, quartile 3) plasma selenium concentrations (micromoles per liter) were 0.96 (0.73, 1.23), 0.94 (0.78, 1.18), and 1.01 (0.85, 1.28) in the IFA, MMN, and SQ-LNS groups, respectively. Geometric mean (GM) plasma selenium concentration was 5.4% (95% CI: 1.8%, 9.0%) higher in the SQ-LNS group than in the MMN group and tended to be higher than in the IFA group (+4.2%; 95% CI: 1.0%, 7.8%). The prevalence of adjusted plasma selenium concentrations <1 µmol/L was 55.1%, 57.8%, and 47.3% in the IFA, MMN, and SQ-LNS groups, respectively; it was lower in the SQ-LNS group than in the MMN group, OR = 0.44 (95% CI: 0.24, 0.83), and tended to be lower than in the IFA group, OR = 0.54 (95% CI: 0.29, 1.03). There was a significant interaction between baseline plasma selenium concentration and intervention group (P = 0.003). In the lowest tertile of baseline selenium concentrations, GM plasma selenium concentration was higher, and the prevalence of low values was lower in the SQ-LNS group compared with the MMN and IFA groups at 36 wk of gestation (P ≤ 0.007).ConclusionsProvision o

Autonomous systems promise significant improvements in many fields. These systems will be subject to legal governance requirements. The literature has largely focused on “autonomous governance” as a framework that is broadly applicable to autonomous devices regardless of the type of system (e.g., aviation or motor vehicles) at issue. While there are regulatory principles applicable to autonomous systems generally, an “autonomy-focused” approach is an inadequate lens to consider the governance of these systems. Rather, because autonomous systems are improvements of currently regulated complex systems, the regulation of autonomous elements will occur within those systems’ preexisting regulatory framework. Accordingly, the nature of future autonomous regulation will likely depend on the preexisting features of that substantive system, rather than on an optimal approach divorced from that history, an attribute known in the social science literature as path dependency. In order to characterize diverse regulated systems with an eye toward assessing future autonomous developments, we develop a framework of regulatory approaches to identify specific features of the preexisting regulatory scheme for a given system. We then analyze that approach by examining three different regulatory regimes (aviation, motor vehicles, and medical devices), across two different continents, and consider how the same type of requirement, e.g., fail-safe systems, can lead to different types of regulations depending on the differing baseline framework.

In this paper we explore the problem of task allocation when communication is very low, e.g., when the probability of a successful message between agents is ≪0.01 . Such situations may occur when agents choose not to communicate for reasons of stealth or when agent-to-agent communication is actively jammed by an adversary. In such cases, agents may need to divide tasks without knowing the locations of each other. Given the assumption that agents know the total number of agents in the workspace, we investigate solutions that ensure all tasks are eventually completed—even if some of the agents are destroyed. We present two task allocation algorithms that assume communication may not happen, but that benefit whenever communications are successful. (1) The Spatial Division Playbook Algorithm divides task among agents based on an area decomposition. (2) The Traveling Salesman Playbook Algorithm considers mission travel distance by leveraging Christofides’ 3/2 approximation algorithm. These algorithms have task completion runtime complexity of O(mlogm) and O(m3) , respectively, where m refers to the total number of tasks. We compare both algorithms to four state-of-the-art task allocation algorithms — ACBBA, DHBA, PIA and GA — across multiple communication levels and multiple numbers of targets, and using three different communication models. The new algorithms perform favorably, in terms of the time required to ensure all targets are visited, in the special case when communication is very low.

Chunmei Qin,1– 3 Yiting Wang,1,2 Lijun Zhao,1,2 Junlin Zhang,1,2 Honghong Ren,1,2 Yutong Zou,1,2 Yucheng Wu,1,2 Tingli Wang,1,2 Yuancheng Zhao,1,2 Rui Zhang,1,2 Huan Xu,4 Jie Zhang,5 Mark E Cooper,6 Fang Liu1,2 1Division of Nephrology, West China Hospital of Sichuan University, Chengdu, People’s Republic of China; 2Laboratory of Diabetic Kidney Disease, Centre of Diabetes and Metabolism Research, West China Hospital of Sichuan University, Chengdu, People’s Republic of China; 3Department of Nephrology, Luzhou People’s Hospital, Luzhou, People’s Republic of China; 4Division of Pathology, West China Hospital of Sichuan University, Chengdu, People’s Republic of China; 5Key Laboratory of Transplant Engineering and Immunology, Chengdu, People’s Republic of China; 6Department of Diabetes, Central Clinical School, Monash University, Melbourne, AustraliaCorrespondence: Fang Liu, Division of Nephrology, West China Hospital of Sichuan University, No. 37, Guoxue Alley, Chengdu, Sichuan, 610041, People’s Republic of China, Tel +86-28-18980601214, Fax +86-28-85422335, Email liufangfh@163.comObjective: The rate of kidney function decline in patients with diabetic kidney disease (DKD) is known to differ. This study analyzed the clinicopathologic features and related risk factors affecting long-term renal survival in Chinese type 2 diabetic patients with rapid estimated glomerular filtration rate (eGFR) decline.Methods: In this retrospective descriptive study, 191 DKD patients were first classified as rapid eGFR decliners and slow eGFR decliners on the basis of the median eGFR slope value (− 8.0 mL/min/1.73 m2/year). In total, 96 patients with rapid eGFR decline were included in the analyses and subsequently allocated to end-stage renal disease (ESRD) and non-ESRD groups. Baseline clinicopathological data of rapid eGFR decliners were collected. Cox proportional hazard analysis was performed to calculate the hazard ratios (HRs) for progression to ESRD.Results: Durin

Si Wang,1,&ast; Jin-He Ying,2,&ast; Huan Xu2 1Department of Information Centre, Lishui Hospital, Zhejiang University School of Medicine, Lishui, 323000, Zhejiang, People’s Republic of China; 2Department of Joint Surgery, Lishui Hospital, Zhejiang University School of Medicine, Lishui, 323000, Zhejiang, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Huan Xu, Tel +86 578 2285310, Fax +865782133457, Email detriment701@163.comPurpose: The present study aimed to explore potential diagnostic biomarkers for fatty infiltration (FI) of the rotator cuff muscles after rotator cuff tear (RCT) and investigate the influence of stromal and immune cell infiltration on this pathology.Methods: The GSE130447 and GSE103266 datasets were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were identified, and gene set enrichment analyses were performed by R software. Two machine learning algorithms, random forest and multiple support vector machine recursive feature elimination (mSVM-RFE), were used to screen candidate biomarkers. The diagnostic value of the screened biomarkers was further validated by the area under the ROC curve (AUC) in the GSE103266 dataset. Murine microenvironment cell population counter (mMCP-counter) method was employed to estimate stromal and immune cell infiltration of FI. The correlation between biomarkers and infiltrated immune and stromal cell subsets was further analyzed.Results: A total of 2123 DEGs were identified. The identified DEGs were predominantly linked to immune system process, extracellular matrix organization and PPAR signalling pathway. FABP5 (AUC = 0.958) and MGP (AUC = 1) were screened as diagnostic biomarkers of FI. Stromal and immune cell infiltration analysis showed that monocytes, mast cells, vessels, endothelial cells and fibroblasts may be related to the process of FI. FABP5 and MGP were positively correlated with vessels whereas negati

BackgroundSelenium (Se), an essential trace mineral, has been implicated in preterm birth (PTB). We aimed to determine the association of maternal Se concentrations during pregnancy with PTB risk and gestational duration in a large number of samples collected from diverse populations.MethodsGestational duration data and maternal plasma or serum samples of 9946 singleton live births were obtained from 17 geographically diverse study cohorts. Maternal Se concentrations were determined by inductively coupled plasma mass spectrometry analysis. The associations between maternal Se with PTB and gestational duration were analysed using logistic and linear regressions. The results were then combined using fixed-effect and random-effect meta-analysis.FindingsIn all study samples, the Se concentrations followed a normal distribution with a mean of 93.8 ng/mL (SD: 28.5 ng/mL) but varied substantially across different sites. The fixed-effect meta-analysis across the 17 cohorts showed that Se was significantly associated with PTB and gestational duration with effect size estimates of an OR=0.95 (95% CI: 0.9 to 1.00) for PTB and 0.66 days (95% CI: 0.38 to 0.94) longer gestation per 15 ng/mL increase in Se concentration. However, there was a substantial heterogeneity among study cohorts and the random-effect meta-analysis did not achieve statistical significance. The largest effect sizes were observed in UK (Liverpool) cohort, and most significant associations were observed in samples from Malawi.InterpretationWhile our study observed statistically significant associations between maternal Se concentration and PTB at some sites, this did not generalise across the entire cohort. Whether population-specific factors explain the heterogeneity of our findings warrants further investigation. Further evidence is needed to understand the biologic pathways, clinical efficacy and safety, before changes to antenatal nutritional recommendations for Se supplementation are considered.

BackgroundSelenium (Se), an essential trace mineral, has been implicated in preterm birth (PTB). We aimed to determine the association of maternal Se concentrations during pregnancy with PTB risk and gestational duration in a large number of samples collected from diverse populations.MethodsGestational duration data and maternal plasma or serum samples of 9946 singleton live births were obtained from 17 geographically diverse study cohorts. Maternal Se concentrations were determined by inductively coupled plasma mass spectrometry analysis. The associations between maternal Se with PTB and gestational duration were analysed using logistic and linear regressions. The results were then combined using fixed-effect and random-effect meta-analysis.FindingsIn all study samples, the Se concentrations followed a normal distribution with a mean of 93.8 ng/mL (SD: 28.5 ng/mL) but varied substantially across different sites. The fixed-effect meta-analysis across the 17 cohorts showed that Se was significantly associated with PTB and gestational duration with effect size estimates of an OR=0.95 (95% CI: 0.9 to 1.00) for PTB and 0.66 days (95% CI: 0.38 to 0.94) longer gestation per 15 ng/mL increase in Se concentration. However, there was a substantial heterogeneity among study cohorts and the random-effect meta-analysis did not achieve statistical significance. The largest effect sizes were observed in UK (Liverpool) cohort, and most significant associations were observed in samples from Malawi.InterpretationWhile our study observed statistically significant associations between maternal Se concentration and PTB at some sites, this did not generalise across the entire cohort. Whether population-specific factors explain the heterogeneity of our findings warrants further investigation. Further evidence is needed to understand the biologic pathways, clinical efficacy and safety, before changes to antenatal nutritional recommendations for Se supplementation are considered.

Selenium (Se), an essential trace mineral, has been implicated in preterm birth (PTB). We aimed to determine the association of maternal Se concentrations during pregnancy with PTB risk and gestational duration in a large number of samples collected from diverse populations. Gestational duration data and maternal plasma or serum samples of 9946 singleton live births were obtained from 17 geographically diverse study cohorts. Maternal Se concentrations were determined by inductively coupled plasma mass spectrometry analysis. The associations between maternal Se with PTB and gestational duration were analysed using logistic and linear regressions. The results were then combined using fixed-effect and random-effect meta-analysis. In all study samples, the Se concentrations followed a normal distribution with a mean of 93.8 ng/mL (SD: 28.5 ng/mL) but varied substantially across different sites. The fixed-effect meta-analysis across the 17 cohorts showed that Se was significantly associated with PTB and gestational duration with effect size estimates of an OR=0.95 (95% CI: 0.9 to 1.00) for PTB and 0.66 days (95% CI: 0.38 to 0.94) longer gestation per 15 ng/mL increase in Se concentration. However, there was a substantial heterogeneity among study cohorts and the random-effect meta-analysis did not achieve statistical significance. The largest effect sizes were observed in UK (Liverpool) cohort, and most significant associations were observed in samples from Malawi. While our study observed statistically significant associations between maternal Se concentration and PTB at some sites, this did not generalise across the entire cohort. Whether population-specific factors explain the heterogeneity of our findings warrants further investigation. Further evidence is needed to understand the biologic pathways, clinical efficacy and safety, before changes to antenatal nutritional recommendations for Se supplementation are considered.

Carbonaceous nanostructures featuring unique structural characteristics and high cytocompatibility offer a wealth of possibilities to impart enhancements of mechanical properties and biological activities for osteogenic tissue scaffolds. Here, we unveil the fabrication of osteoconductive and antibacterial porous poly(lactic acid) (PLA) membranes by direct electrospinning of microfibers impregnated with coffee-ground-derived quantum dots (QDs). It enabled a straightforward pathway to regulate the diameter and its distribution for the electrospun PLA microfibers, as well as the improved hydrophilicity. The QDs exhibited high affinity to the PLA matrix, permitting remarkable promotion of tensile strength and elastic modulus for the QD-modified PLA membranes while maintaining comparable extensibility. More importantly, osteoblast adhesion and stretching on the electrospun membranes were significantly enhanced with the existence of QDs, as exemplified by the nearly 1.8-fold increase in cell viability cultured onto the composite membrane loaded with 1.5% QDs compared to that of pure PLA. This was accompanied by rapid biomimetic mineralization and uniform apatite formation in an osteofriendly manner. Unexpectedly, immediate and broad-spectrum antibacterial performance was achieved for the composite membranes, likely arising from the synergistic effects of QD-imparted membrane stress and oxidative stress. The unusual combination of mechanical, biomineralization, biological, and antibacterial performances makes the biodegradable membrane scaffolds promising for guided bone/tissue regeneration therapy., QC 20210917

Despite technological feasibility and industrial scalability in toughening polylactide (PLA) by direct blending, avoiding the sacrifice of mechanical strength or full renewability remains a challenge. Here, low-molecular-weight poly(ethylene 2,5-furandicarboxylate)-block-polylactide (PEF-b-PLA) was synthesized to yield a fully biobased copolymer with expected partial miscibility with PLA. The ability of PEF-b-PLA to toughen PLA at 10 wt % (PLA10) and 20 wt % (PLA20) loadings was then evaluated. To understand the achieved properties, the formation of a nanostructured dispersion phase due to favorable interfacial adhesion arising from the miscibility of the PLA block with the PLA matrix was investigated. The morphological features, together with comparable strength inherited from the PEF blocks, afforded an unusual combination of toughness and strength for the PLA blends. Compared to the poor tensile toughness of a pure PLA film (1.77 MJ/m(3)), a striking increase of 839 and 912% was achieved for PLA10 and PLA20, respectively. More importantly, both pure PLA and the blends were characterized by comparable yield strength (similar to 30 MPa). This was accompanied by excellent UV-shielding imparted by the functional groups on the PEF blocks, which would improve the prospects of realizing PLA-based functional packing materials. The proposed design of copolymers, with good industrial feasibility, should be useful to guide the toughening approaches for PLA and probably other degradable polyester blends by profound morphology control., QC 20220525

User representation is essential for providing high-quality commercial services in industry. Universal user representation has received many interests recently, with which we can be free from the cumbersome work of training a specific model for each downstream application. In this paper, we attempt to improve universal user representation from two points of views. First, a contrastive self-supervised learning paradigm is presented to guide the representation model training. It provides a unified framework that allows for long-term or short-term interest representation learning in a data-driven manner. Moreover, a novel multi-interest extraction module is presented. The module introduces an interest dictionary to capture principal interests of the given user, and then generate his/her interest-oriented representations via behavior aggregation. Experimental results demonstrate the effectiveness and applicability of the learned user representations., Comment: 8 pages, during peer review

In this paper, by leveraging abundant observational transaction data, we propose a novel data-driven and interpretable pricing approach for markdowns, consisting of counterfactual prediction and multi-period price optimization. Firstly, we build a semi-parametric structural model to learn individual price elasticity and predict counterfactual demand. This semi-parametric model takes advantage of both the predictability of nonparametric machine learning model and the interpretability of economic model. Secondly, we propose a multi-period dynamic pricing algorithm to maximize the overall profit of a perishable product over its finite selling horizon. Different with the traditional approaches that use the deterministic demand, we model the uncertainty of counterfactual demand since it inevitably has randomness in the prediction process. Based on the stochastic model, we derive a sequential pricing strategy by Markov decision process, and design a two-stage algorithm to solve it. The proposed algorithm is very efficient. It reduces the time complexity from exponential to polynomial. Experimental results show the advantages of our pricing algorithm, and the proposed framework has been successfully deployed to the well-known e-commerce fresh retail scenario - Freshippo., Comment: 10 pages, 7 figures, accepted to KDD'21

We revisit the concept of "adversary" in online learning, motivated by solving robust optimization and adversarial training using online learning methods. While one of the classical setups in online learning deals with the "adversarial" setup, it appears that this concept is used less rigorously, causing confusion in applying results and insights from online learning. Specifically, there are two fundamentally different types of adversaries, depending on whether the "adversary" is able to anticipate the exogenous randomness of the online learning algorithms. This is particularly relevant to robust optimization and adversarial training because the adversarial sequences are often anticipative, and many online learning algorithms do not achieve diminishing regret in such a case. We then apply this to solving robust optimization problems or (equivalently) adversarial training problems via online learning and establish a general approach for a large variety of problem classes using imaginary play. Here two players play against each other, the primal player playing the decisions and the dual player playing realizations of uncertain data. When the game terminates, the primal player has obtained an approximately robust solution. This meta-game allows for solving a large variety of robust optimization and multi-objective optimization problems and generalizes the approach of arXiv:1402.6361.

The ability to tune magnetic orders, such as magnetic anisotropy and topological spin texture, is desired to achieve high-performance spintronic devices. A recent strategy has been to employ interfacial engineering techniques, such as the introduction of spin-correlated interfacial coupling, to tailor magnetic orders and achieve novel magnetic properties. We chose a unique polar-nonpolar LaMnO3/SrIrO3 superlattice because Mn (3d)/Ir (5d) oxides exhibit rich magnetic behaviors and strong spin-orbit coupling through the entanglement of their 3d and 5d electrons. Through magnetization and magnetotransport measurements, we found that the magnetic order is interface-dominated as the superlattice period is decreased. We were able to then effectively modify the magnetization, tilt of the ferromagnetic easy axis, and symmetry transition of the anisotropic magnetoresistance of the LaMnO3/SrIrO3 superlattice by introducing additional Mn (3d) and Ir (5d) interfaces. Further investigations using in-depth first-principles calculations and numerical simulations revealed that these magnetic behaviors could be understood by the 3d/5d electron correlation and Rashba spin-orbit coupling. The results reported here demonstrate a new route to synchronously engineer magnetic properties through the atomic stacking of different electrons, which would contribute to future applications in high-capacity storage devices and advanced computing., De två första författarna delar förstaförfattarskapet.

Sedimentary successions provide direct evidence of climate and tectonics, and these give clues about the causes of the mass extinction around the Permian–Triassic boundary. Terrestrial Permian–Triassic boundary strata in the eastern Ordos Basin, North China, include the Late Permian Sunjiagou, Early Triassic Liujiagou and late Early Triassic Heshanggou formations in ascending order. The Sunjiagou Formation comprises cross‐bedded sandstones overlaid by mudstones, indicating meandering rivers with channel, point bar and floodplain deposits. The Liujiagou Formation was formed in braided rivers of arid sand bars interacting with some aeolian dune deposits, distinguished by abundant sandstones where diverse trough and planar cross‐bedding and aeolian structures (for example, inverse climbing‐ripple, translatent‐ripple lamination, grainfall and grainflow laminations) interchange vertically and laterally. The Heshanggou Formation is a rhythmic succession of mudstones interbedded with thin medium‐grained sandstones mainly deposited in a shallow lacustrine environment. Overall, the sharp meandering to braided to shallow lake sedimentary transition documents palaeoenvironmental changes from semi‐arid to arid and then to semi‐humid conditions across the Permian–Triassic boundary. The die‐off of tetrapods and plants, decreased bioturbation levels in the uppermost Sunjiagou Formation, and the bloom of microbially‐induced sedimentary structures in the Liujiagou Formation marks the mass extinction around the Permian–Triassic boundary. The disappearance of microbially‐induced sedimentary structures, increasingly intense bioturbation from bottom to top and the reoccurrence of reptile footprints in the Heshanggou Formation reveal gradual recovery of the ecosystem after the Permian–Triassic boundary extinction. This study is the first to identify the intensification of aeolian activity following the end‐Permian mass extinction in North China. Moreover, while northern North China conti

The ability to tune magnetic orders, such as magnetic anisotropy and topological spin texture, is desired to achieve high-performance spintronic devices. A recent strategy has been to employ interfacial engineering techniques, such as the introduction of spin-correlated interfacial coupling, to tailor magnetic orders and achieve novel magnetic properties. We chose a unique polar-nonpolar LaMnO3/SrIrO3 superlattice because Mn (3d)/Ir (5d) oxides exhibit rich magnetic behaviors and strong spin-orbit coupling through the entanglement of their 3d and 5d electrons. Through magnetization and magnetotransport measurements, we found that the magnetic order is interface-dominated as the superlattice period is decreased. We were able to then effectively modify the magnetization, tilt of the ferromagnetic easy axis, and symmetry transition of the anisotropic magnetoresistance of the LaMnO3/SrIrO3 superlattice by introducing additional Mn (3d) and Ir (5d) interfaces. Further investigations using in-depth first-principles calculations and numerical simulations revealed that these magnetic behaviors could be understood by the 3d/5d electron correlation and Rashba spin-orbit coupling. The results reported here demonstrate a new route to synchronously engineer magnetic properties through the atomic stacking of different electrons, which would contribute to future applications in high-capacity storage devices and advanced computing.

Sedimentary successions provide direct evidence of climate and tectonics, and these give clues about the causes of the mass extinction around the Permian–Triassic boundary. Terrestrial Permian–Triassic boundary strata in the eastern Ordos Basin, North China, include the Late Permian Sunjiagou, Early Triassic Liujiagou and late Early Triassic Heshanggou formations in ascending order. The Sunjiagou Formation comprises cross‐bedded sandstones overlaid by mudstones, indicating meandering rivers with channel, point bar and floodplain deposits. The Liujiagou Formation was formed in braided rivers of arid sand bars interacting with some aeolian dune deposits, distinguished by abundant sandstones where diverse trough and planar cross‐bedding and aeolian structures (for example, inverse climbing‐ripple, translatent‐ripple lamination, grainfall and grainflow laminations) interchange vertically and laterally. The Heshanggou Formation is a rhythmic succession of mudstones interbedded with thin medium‐grained sandstones mainly deposited in a shallow lacustrine environment. Overall, the sharp meandering to braided to shallow lake sedimentary transition documents palaeoenvironmental changes from semi‐arid to arid and then to semi‐humid conditions across the Permian–Triassic boundary. The die‐off of tetrapods and plants, decreased bioturbation levels in the uppermost Sunjiagou Formation, and the bloom of microbially‐induced sedimentary structures in the Liujiagou Formation marks the mass extinction around the Permian–Triassic boundary. The disappearance of microbially‐induced sedimentary structures, increasingly intense bioturbation from bottom to top and the reoccurrence of reptile footprints in the Heshanggou Formation reveal gradual recovery of the ecosystem after the Permian–Triassic boundary extinction. This study is the first to identify the intensification of aeolian activity following the end‐Permian mass extinction in North China. Moreover, while northern North China conti

A Graph Convolutional Network (GCN) stacks several layers and in each layer performs a PROPagation operation (PROP) and a TRANsformation operation (TRAN) for learning node representations over graph-structured data. Though powerful, GCNs tend to suffer performance drop when the model gets deep. Previous works focus on PROPs to study and mitigate this issue, but the role of TRANs is barely investigated. In this work, we study performance degradation of GCNs by experimentally examining how stacking only TRANs or PROPs works. We find that TRANs contribute significantly, or even more than PROPs, to declining performance, and moreover that they tend to amplify node-wise feature variance in GCNs, causing variance inflammation that we identify as a key factor for causing performance drop. Motivated by such observations, we propose a variance-controlling technique termed Node Normalization (NodeNorm), which scales each node's features using its own standard deviation. Experimental results validate the effectiveness of NodeNorm on addressing performance degradation of GCNs. Specifically, it enables deep GCNs to outperform shallow ones in cases where deep models are needed, and to achieve comparable results with shallow ones on 6 benchmark datasets. NodeNorm is a generic plug-in and can well generalize to other GNN architectures. Code is publicly available at https://github.com/miafei/NodeNorm., Comment: CIKM 2021

To maximize cumulative user engagement (e.g. cumulative clicks) in sequential recommendation, it is often needed to tradeoff two potentially conflicting objectives, that is, pursuing higher immediate user engagement (e.g., click-through rate) and encouraging user browsing (i.e., more items exposured). Existing works often study these two tasks separately, thus tend to result in sub-optimal results. In this paper, we study this problem from an online optimization perspective, and propose a flexible and practical framework to explicitly tradeoff longer user browsing length and high immediate user engagement. Specifically, by considering items as actions, user's requests as states and user leaving as an absorbing state, we formulate each user's behavior as a personalized Markov decision process (MDP), and the problem of maximizing cumulative user engagement is reduced to a stochastic shortest path (SSP) problem. Meanwhile, with immediate user engagement and quit probability estimation, it is shown that the SSP problem can be efficiently solved via dynamic programming. Experiments on real-world datasets demonstrate the effectiveness of the proposed approach. Moreover, this approach is deployed at a large E-commerce platform, achieved over 7% improvement of cumulative clicks.

Deep learning performs remarkably well on many time series analysis tasks recently. The superior performance of deep neural networks relies heavily on a large number of training data to avoid overfitting. However, the labeled data of many real-world time series applications may be limited such as classification in medical time series and anomaly detection in AIOps. As an effective way to enhance the size and quality of the training data, data augmentation is crucial to the successful application of deep learning models on time series data. In this paper, we systematically review different data augmentation methods for time series. We propose a taxonomy for the reviewed methods, and then provide a structured review for these methods by highlighting their strengths and limitations. We also empirically compare different data augmentation methods for different tasks including time series classification, anomaly detection, and forecasting. Finally, we discuss and highlight five future directions to provide useful research guidance., Comment: Accepted by the 30th International Joint Conference on Artificial Intelligence (IJCAI 2021); Selected by Paper Digest into Most Influential IJCAI Papers (Version: 2022-02), Rank 1st (Link: https://www.paperdigest.org/2022/02/most-influential-ijcai-papers-2022-02/)

The monitoring and management of numerous and diverse time series data at Alibaba Group calls for an effective and scalable time series anomaly detection service. In this paper, we propose RobustTAD, a Robust Time series Anomaly Detection framework by integrating robust seasonal-trend decomposition and convolutional neural network for time series data. The seasonal-trend decomposition can effectively handle complicated patterns in time series, and meanwhile significantly simplifies the architecture of the neural network, which is an encoder-decoder architecture with skip connections. This architecture can effectively capture the multi-scale information from time series, which is very useful in anomaly detection. Due to the limited labeled data in time series anomaly detection, we systematically investigate data augmentation methods in both time and frequency domains. We also introduce label-based weight and value-based weight in the loss function by utilizing the unbalanced nature of the time series anomaly detection problem. Compared with the widely used forecasting-based anomaly detection algorithms, decomposition-based algorithms, traditional statistical algorithms, as well as recent neural network based algorithms, RobustTAD performs significantly better on public benchmark datasets. It is deployed as a public online service and widely adopted in different business scenarios at Alibaba Group., Comment: Extended version of the paper at ACM SIGKDD Workshop on Mining and Learning from Time Series (KDD-MiLeTS 2020); 9 pages, 5 figures, and 2 tables

Periodicity detection is a crucial step in time series tasks, including monitoring and forecasting of metrics in many areas, such as IoT applications and self-driving database management system. In many of these applications, multiple periodic components exist and are often interlaced with each other. Such dynamic and complicated periodic patterns make the accurate periodicity detection difficult. In addition, other components in the time series, such as trend, outliers and noises, also pose additional challenges for accurate periodicity detection. In this paper, we propose a robust and general framework for multiple periodicity detection. Our algorithm applies maximal overlap discrete wavelet transform to transform the time series into multiple temporal-frequency scales such that different periodic components can be isolated. We rank them by wavelet variance, and then at each scale detect single periodicity by our proposed Huber-periodogram and Huber-ACF robustly. We rigorously prove the theoretical properties of Huber-periodogram and justify the use of Fisher's test on Huber-periodogram for periodicity detection. To further refine the detected periods, we compute unbiased autocorrelation function based on Wiener-Khinchin theorem from Huber-periodogram for improved robustness and efficiency. Experiments on synthetic and real-world datasets show that our algorithm outperforms other popular ones for both single and multiple periodicity detection., Comment: Accepted by SIGMOD 2021; 10 pages, 6 figures, 8 tables, and 70 referred papers

We study multi-agent coverage algorithms for autonomous monitoring and patrol in urban environments. We consider scenarios in which a team of flying agents uses downward facing cameras (or similar sensors) to observe the environment outside of buildings at street-level. Buildings are considered obstacles that impede movement, and cameras are assumed to be ineffective above a maximum altitude. We study multi-agent urban coverage problems related to this scenario, including: (1) static multi-agent urban coverage, in which agents are expected to observe the environment from static locations, and (2) dynamic multi-agent urban coverage where agents move continuously through the environment. We experimentally evaluate six different multi-agent coverage methods, including: three types of ergodic coverage (that avoid buildings in different ways), lawn-mower sweep, voronoi region based control, and a naive grid method. We evaluate all algorithms with respect to four performance metrics (percent coverage, revist count, revist time, and the integral of area viewed over time), across four types of urban environments [low density, high density] x [short buildings, tall buildings], and for team sizes ranging from 2 to 25 agents. We believe this is the first extensive comparison of these methods in an urban setting. Our results highlight how the relative performance of static and dynamic methods changes based on the ratio of team size to search area, as well the relative effects that different characteristics of urban environments (tall, short, dense, sparse, mixed) have on each algorithm.

The causes of the severest crisis in the history of life around the Permian-Triassic boundary (PTB) remain controversial. Here we report that the latest Permian alluvial plains in Shanxi, North China, went through a rapid transition from meandering rivers to braided rivers and aeolian systems. Soil carbonate carbon isotope (δ13C), oxygen isotope (δ18O), and geochemical signatures of weathering intensity reveal a consistent pattern of deteriorating environments (cool, arid, and anoxic conditions) and climate fluctuations across the PTB. The synchronous ecological collapse is confirmed by a dramatic reduction or disappearance of dominant plants, tetrapods and invertebrates and a bloom of microbially-induced sedimentary structures. A similar rapid switch in fluvial style is seen worldwide (e.g. Karoo Basin, Russia, Australia) in terrestrial boundary sequences, all of which may be considered against a background of global marine regression. The synchronous global expansion of alluvial fans and high-energy braided streams is a response to abrupt climate change associated with aridity, hypoxia, acid rain, and mass wasting. Where neighbouring uplands were not uplifting or basins subsiding, alluvial fans are absent, but in these areas the climate change is evidenced by the disruption of pedogenesis.

While benefiting greatly from electronics, our society also faces a major problem of electronic waste, which has already caused environmental pollution and adverse human health effects. Therefore, recyclability becomes a must-have feature in future electronics. Here, we demonstrate an erasable and recreatable two-dimensional electron gas (2DEG), which can be easily created and patterned by depositing a water-dissolvable overlayer of amorphous Sr3Al2O6 (a-SAO) on SrTiO3 (STO) at room temperature. The 2DEG can be repeatedly erased or recreated by depositing the a-SAO or dissolving in water, respectively. Photoluminescence results show that the 2DEG arises from the a-SAO–induced oxygen vacancy. Furthermore, by gradually depleting the 2DEG, a transition of nonlinear to linear Hall effect is observed, demonstrating an unexpected interfacial band structure. The convenience and repeatability in the creation of the water-dissolvable 2DEG with rich physics could potentially contribute to the exploration of next generation electronics, such as environment-friendly or water-soluble electronics.

Huan Xu, Hui-Ju Bu, Jing-Zhao HeIntensive Care Unit, Dongguan Donghua Hospital, Dongguan, Guangdong, People’s Republic of China The correlation between the extent of coronary artery disease (CAD) and clinical outcomes among patients with diabetes mellitus (DM) was unclear. Recently, we read with great interest the study by Gyldenkerne et al.1 The authors conducted a cohort study of patients with DM who underwent coronary angiography and were stratified according to the extent of coronary artery disease: 0-, 1-, 2- or 3-vessel disease or diffuse vessel disease, with myocardial infarction, all-cause death, and major adverse cardiovascular events (MACE) as endpoints.   View the original paper by Gyldenkerne and colleagues

We study the problem of repeated play in a zero-sum game in which the payoff matrix may change, in a possibly adversarial fashion, on each round; we call these Online Matrix Games. Finding the Nash Equilibrium (NE) of a two player zero-sum game is core to many problems in statistics, optimization, and economics, and for a fixed game matrix this can be easily reduced to solving a linear program. But when the payoff matrix evolves over time our goal is to find a sequential algorithm that can compete with, in a certain sense, the NE of the long-term-averaged payoff matrix. We design an algorithm with small NE regret--that is, we ensure that the long-term payoff of both players is close to minimax optimum in hindsight. Our algorithm achieves near-optimal dependence with respect to the number of rounds and depends poly-logarithmically on the number of available actions of the players. Additionally, we show that the naive reduction, where each player simply minimizes its own regret, fails to achieve the stated objective regardless of which algorithm is used. We also consider the so-called bandit setting, where the feedback is significantly limited, and we provide an algorithm with small NE regret using one-point estimates of each payoff matrix., Comment: arXiv admin note: text overlap with arXiv:1806.08301

Adversarial training is a principled approach for training robust neural networks. Despite of tremendous successes in practice, its theoretical properties still remain largely unexplored. In this paper, we provide new theoretical insights of gradient descent based adversarial training by studying its computational properties, specifically on its inductive bias. We take the binary classification task on linearly separable data as an illustrative example, where the loss asymptotically attains its infimum as the parameter diverges to infinity along certain directions. Specifically, we show that when the adversarial perturbation during training has bounded $\ell_2$-norm, the classifier learned by gradient descent based adversarial training converges in direction to the maximum $\ell_2$-norm margin classifier at the rate of $\tilde{\mathcal{O}}(1/\sqrt{T})$, significantly faster than the rate $\mathcal{O}(1/\log T)$ of training with clean data. In addition, when the adversarial perturbation during training has bounded $\ell_q$-norm for some $q\ge 1$, the resulting classifier converges in direction to a maximum mixed-norm margin classifier, which has a natural interpretation of robustness, as being the maximum $\ell_2$-norm margin classifier under worst-case $\ell_q$-norm perturbation to the data. Our findings provide theoretical backups for adversarial training that it indeed promotes robustness against adversarial perturbation.

We study pool-based active learning with abstention feedbacks where a labeler can abstain from labeling a queried example with some unknown abstention rate. This is an important problem with many useful applications. We take a Bayesian approach to the problem and develop two new greedy algorithms that learn both the classification problem and the unknown abstention rate at the same time. These are achieved by simply incorporating the estimated average abstention rate into the greedy criteria. We prove that both algorithms have near-optimality guarantees: they respectively achieve a ${(1-\frac{1}{e})}$ constant factor approximation of the optimal expected or worst-case value of a useful utility function. Our experiments show the algorithms perform well in various practical scenarios., Comment: Poster presented at 2019 ICML Workshop on Human in the Loop Learning 2019 (non-archival). arXiv admin note: substantial text overlap with arXiv:1705.08481