10,162 results on '"Qin, Yu"'
Search Results
2. Preferential Occurrence of Fast Radio Bursts in Massive Star-Forming Galaxies
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Sharma, Kritti, Ravi, Vikram, Connor, Liam, Law, Casey, Ocker, Stella Koch, Sherman, Myles, Kosogorov, Nikita, Faber, Jakob, Hallinan, Gregg, Harnach, Charlie, Hellbourg, Greg, Hobbs, Rick, Hodge, David, Hodges, Mark, Lamb, James, Rasmussen, Paul, Somalwar, Jean, Weinreb, Sander, Woody, David, Leja, Joel, Anand, Shreya, Das, Kaustav Kashyap, Qin, Yu-Jing, Rose, Sam, Dong, Dillon Z., Miller, Jessie, and Yao, Yuhan
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Astrophysics - High Energy Astrophysical Phenomena ,Astrophysics - Astrophysics of Galaxies - Abstract
Fast Radio Bursts (FRBs) are millisecond-duration events detected from beyond the Milky Way. FRB emission characteristics favor highly magnetized neutron stars, or magnetars, as the sources, as evidenced by FRB-like bursts from a galactic magnetar, and the star-forming nature of FRB host galaxies. However, the processes that produce FRB sources remain unknown. Although galactic magnetars are often linked to core-collapse supernovae (CCSNe), it's uncertain what determines which supernovae result in magnetars. The galactic environments of FRB sources can be harnessed to probe their progenitors. Here, we present the stellar population properties of 30 FRB host galaxies discovered by the Deep Synoptic Array. Our analysis shows a significant deficit of low-mass FRB hosts compared to the occurrence of star-formation in the universe, implying that FRBs are a biased tracer of star-formation, preferentially selecting massive star-forming galaxies. This bias may be driven by galaxy metallicity, which is positively correlated with stellar mass. Metal-rich environments may favor the formation of magnetar progenitors through stellar mergers, as higher metallicity stars are less compact and more likely to fill their Roche lobes, leading to unstable mass transfer. Although massive stars do not have convective interiors to generate strong magnetic fields by dynamo, merger remnants are thought to have the requisite internal magnetic-field strengths to result in magnetars. The preferential occurrence of FRBs in massive star-forming galaxies suggests that CCSN of merger remnants preferentially forms magnetars., Comment: Accepted for publication in Nature. The final version will be published by the journal
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- 2024
3. The Statistics and Environments of Hostless Supernovae
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Qin, Yu-Jing, Zabludoff, Ann, Arcavi, Iair, Smith, Nathan, Faerman, Yakov, and Maoz, Dan
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Astrophysics - Solar and Stellar Astrophysics ,Astrophysics - Astrophysics of Galaxies ,Astrophysics - High Energy Astrophysical Phenomena - Abstract
Transient surveys routinely detect supernovae (SNe) without obvious host galaxies. To understand the demographics of these "hostless" SNe and to constrain the possible host properties, we identify 161 SNe reported to the Transient Name Server since 2016 that do not have hosts cataloged from pre-explosion wide-field galaxy surveys. Using forced aperture photometry, we detect excess flux around only 56 of these SNe. Both thermonuclear and core-collapse (CC) SNe are present in our sample. Compared to flux-limited SNe samples with known hosts, superluminous supernovae (SLSNe), particularly hydrogen-deficient SLSNe, are over-represented here relative to all other SNe types; among CC SNe, there is also a higher fraction of interacting SNe than non-interacting. On the low-luminosity side, seven SNe have host absolute magnitude upper limits fainter than M_g=-12, about 1 per cent of the Small Magellanic Cloud's luminosity; the faintest limits are close to the luminosity of globular clusters or ultra-faint dwarf galaxies (M_g~-8). Fitting multi-band forced photometry, 11 SNe have host stellar masses <10^6 Msun assuming quiescent hosts, and 13 SNe have host stellar masses <10^5 Msun assuming star-forming hosts. The spatial distribution of hostless SNe indicates that the majority are not associated with known galaxy groups and clusters, ruling out intracluster stellar light as the primary contributor of such SNe. Hostless Type Ia SNe tend to be more luminous and slow-fading than SNe Ia with known host galaxies, implying a hidden population of low-mass and star-forming hosts. We conclude that any undetected host galaxies are likely star-forming dwarfs in the field., Comment: Accepted for publication in MNRAS
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- 2024
4. Linking Transients to their Host Galaxies: II. A Comparison of Host Galaxy Properties and Rate Dependencies across Supernova Types
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Qin, Yu-Jing and Zabludoff, Ann
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Astrophysics - Astrophysics of Galaxies ,Astrophysics - Cosmology and Nongalactic Astrophysics ,Astrophysics - High Energy Astrophysical Phenomena ,Astrophysics - Solar and Stellar Astrophysics - Abstract
We use the latest dataset of supernova (SN) host galaxies to investigate how the host properties -- stellar mass, star formation rate, metallicity, absolute magnitude, and colour -- differ across SN types, with redshift-driven selection effects controlled. SN Ib and Ic host galaxies, on average, are more massive, metal-rich, and redder than SN II hosts. For subtypes, SN Ibn and Ic-BL have bluer hosts than their normal SN Ib and Ic siblings; SN IIb has consistent host properties with SN Ib, while hosts of SN IIn are more metal-rich than those of SN II. Hydrogen-deficient superluminous supernovae feature bluer and lower luminosity hosts than most subtypes of core-collapse supernova (CC SN). Assuming simple proportionality of CC SN rates and host star formation rates (SFRs) does not recover the observed mean host properties; either a population of long-lived progenitors or a metallicity-dependent SN production efficiency better reproduces the observed host properties. Assuming the latter case, the rates of SN II are insensitive to host metallicity, but the rates of SN Ib and Ic are substantially enhanced in metal-rich hosts by a factor of ~10 per dex increase in metallicity. Hosts of SN Ia are diverse in their observed properties; subtypes including SN Ia-91T, Ia-02cx, and Ia-CSM prefer star-forming hosts, while subtypes like SN Ia-91bg and Ca-rich prefer quiescent hosts. The rates of SN Ia-91T, Ia-02cx, and Ia-CSM are closely dependent on, or even proportional to, their host SFRs, indicating relatively short-lived progenitors. Conversely, the rates of SN Ia-91bg and Ca-rich transients are proportional to the total stellar mass, favoring long-lived progenitors., Comment: Accepted for publication in MNRAS
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- 2024
5. ICML Topological Deep Learning Challenge 2024: Beyond the Graph Domain
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Bernárdez, Guillermo, Telyatnikov, Lev, Montagna, Marco, Baccini, Federica, Papillon, Mathilde, Ferriol-Galmés, Miquel, Hajij, Mustafa, Papamarkou, Theodore, Bucarelli, Maria Sofia, Zaghen, Olga, Mathe, Johan, Myers, Audun, Mahan, Scott, Lillemark, Hansen, Vadgama, Sharvaree, Bekkers, Erik, Doster, Tim, Emerson, Tegan, Kvinge, Henry, Agate, Katrina, Ahmed, Nesreen K, Bai, Pengfei, Banf, Michael, Battiloro, Claudio, Beketov, Maxim, Bogdan, Paul, Carrasco, Martin, Cavallo, Andrea, Choi, Yun Young, Dasoulas, George, Elphick, Matouš, Escalona, Giordan, Filipiak, Dominik, Fritze, Halley, Gebhart, Thomas, Gil-Sorribes, Manel, Goomanee, Salvish, Guallar, Victor, Imasheva, Liliya, Irimia, Andrei, Jin, Hongwei, Johnson, Graham, Kanakaris, Nikos, Koloski, Boshko, Kovač, Veljko, Lecha, Manuel, Lee, Minho, Leroy, Pierrick, Long, Theodore, Magai, German, Martinez, Alvaro, Masden, Marissa, Mežnar, Sebastian, Miquel-Oliver, Bertran, Molina, Alexis, Nikitin, Alexander, Nurisso, Marco, Piekenbrock, Matt, Qin, Yu, Rygiel, Patryk, Salatiello, Alessandro, Schattauer, Max, Snopov, Pavel, Suk, Julian, Sánchez, Valentina, Tec, Mauricio, Vaccarino, Francesco, Verhellen, Jonas, Wantiez, Frederic, Weers, Alexander, Zajec, Patrik, Škrlj, Blaž, and Miolane, Nina
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Computer Science - Machine Learning ,Computer Science - Artificial Intelligence - Abstract
This paper describes the 2nd edition of the ICML Topological Deep Learning Challenge that was hosted within the ICML 2024 ELLIS Workshop on Geometry-grounded Representation Learning and Generative Modeling (GRaM). The challenge focused on the problem of representing data in different discrete topological domains in order to bridge the gap between Topological Deep Learning (TDL) and other types of structured datasets (e.g. point clouds, graphs). Specifically, participants were asked to design and implement topological liftings, i.e. mappings between different data structures and topological domains --like hypergraphs, or simplicial/cell/combinatorial complexes. The challenge received 52 submissions satisfying all the requirements. This paper introduces the main scope of the challenge, and summarizes the main results and findings., Comment: Proceedings of the Geometry-grounded Representation Learning and Generative Modeling Workshop (GRaM) at ICML 2024
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- 2024
6. ZTF SN Ia DR2: Overview
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Rigault, Mickael, Smith, Mathew, Goobar, Ariel, Maguire, Kate, Dimitriadis, Georgios, Burgaz, Umut, Dhawan, Suhail, Sollerman, Jesper, Regnault, Nicolas, Kowalski, Marek, Amenouche, Melissa, Aubert, Marie, Barjou-Delayre, Chloé, Bautista, Julian, Bloom, Josh S., Carreres, Bastien, Chen, Tracy X., Copin, Yannick, Deckers, Maxime, Fouchez, Dominique, Fremling, Christoffer, Galbany, Lluis, Ginolin, Madeleine, Graham, Matthew, Kasliwal, Mancy M., Kenworthy, W. D'Arcy, Kim, Young-Lo, Kuhn, Dylan, Masci, Frank F., Müller-Bravo, Tomas, Miller, Adam, Johansson, Joel, Nordin, Jakob, Nugent, Peter, Andreoni, Igor, Bellm, Eric, Betoule, Marc, Osman, Mahmoud, Perley, Dan, Popovic, Brodie, Rosnet, Philippe, Rosselli, Damiano, Ruppin, Florian, Senzel, Robert, Rusholme, Ben, Schweyer, Tassilo, Terwel, Jacco H., Townsend, Alice, Tzanidakis, Andy, Wold, Avery, Purdum, Josiah, Qin, Yu-Jing, Racine, Benjamin, Reusch, Simeon, Riddle, Reed, and Yan, Lin
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Astrophysics - Cosmology and Nongalactic Astrophysics ,Astrophysics - High Energy Astrophysical Phenomena - Abstract
We present the first homogeneous release of several thousand Type Ia supernovae (SNe Ia), all having spectroscopic classification, and spectroscopic redshifts for half the sample. This release, named the "DR2", contains 3628 nearby (z < 0.3) SNe Ia discovered, followed and classified by the Zwicky Transient Facility survey between March 2018 and December 2020. Of these, 3000 have good-to-excellent sampling and 2667 pass standard cosmology light-curve quality cuts. This release is thus the largest SN Ia release to date, increasing by an order of magnitude the number of well characterized low-redshift objects. With the "DR2", we also provide a volume-limited (z < 0.06) sample of nearly a thousand SNe Ia. With such a large, homogeneous and well controlled dataset, we are studying key current questions on SN cosmology, such as the linearity SNe Ia standardization, the SN and host dependencies, the diversity of the SN Ia population, and the accuracy of the current light-curve modeling. These, and more, are studied in detail in a series of articles associated with this release. Alongside the SN Ia parameters, we publish our force-photometry gri-band light curves, 5138 spectra, local and global host properties, observing logs, and a python tool to ease use and access of these data. The photometric accuracy of the "DR2" is not yet suited for cosmological parameter inference, which will follow as "DR2.5" release. We nonetheless demonstrate that the multi-thousand SN Ia Hubble Diagram has a typical 0.15 mag scatter., Comment: ZTF SN Ia DR2 release paper. Submitted to A&A (ZTF DR2 Special Issue). Already 1 response to referee
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- 2024
7. A cosmic formation site of silicon and sulphur revealed by a new type of supernova explosion
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Schulze, Steve, Gal-Yam, Avishay, Dessart, Luc, Miller, Adam A., Woosley, Stan E., Yang, Yi, Bulla, Mattia, Yaron, Ofer, Sollerman, Jesper, Filippenko, Alexei V., Hinds, K-Ryan, Perley, Daniel A., Tsuna, Daichi, Lunnan, Ragnhild, Sarin, Nikhil, Brennan, Sean J., Brink, Thomas G., Bruch, Rachel J., Chen, Ping, Das, Kaustav K., Dhawan, Suhail, Fransson, Claes, Fremling, Christoffer, Gangopadhyay, Anjasha, Irani, Ido, Jerkstrand, Anders, Knezevic, Nikola, Kushnir, Doron, Maeda, Keiichi, Maguire, Kate, Ofek, Eran, Omand, Conor M. B., Qin, Yu-Jing, Sharma, Yashvi, Sit, Tawny, Srinivasaragavan, Gokul P., Strothjohann, Nora L., Takei, Yuki, Waxman, Eli, Yan, Lin, Yao, Yuhan, Zheng, WeiKang, Zimmerman, Erez A., Bellm, Eric C., Coughlin, Michael W., Masci, Frank. J., Purdum, Josiah, Rigault, Mickael, Wold, Avery, and Kulkarni, Shrinivas R.
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Astrophysics - High Energy Astrophysical Phenomena - Abstract
The cores of stars are the cosmic furnaces where light elements are fused into heavier nuclei. The fusion of hydrogen to helium initially powers all stars. The ashes of the fusion reactions are then predicted to serve as fuel in a series of stages, eventually transforming massive stars into a structure of concentric shells. These are composed of natal hydrogen on the outside, and consecutively heavier compositions inside, predicted to be dominated by helium, carbon/oxygen, oxygen/neon/magnesium, and oxygen/silicon/sulphur. Silicon and sulphur are fused into inert iron, leading to the collapse of the core and either a supernova explosion or the direct formation of a black hole. Stripped stars, where the outer hydrogen layer has been removed and the internal He-rich layer (in Wolf-Rayet WN stars) or even the C/O layer below it (in Wolf-Rayet WC/WO stars) are exposed, provide evidence for this shell structure, and the cosmic element production mechanism it reflects. The types of supernova explosions that arise from stripped stars embedded in shells of circumstellar material (most notably Type Ibn supernovae from stars with outer He layers, and Type Icn supernovae from stars with outer C/O layers) confirm this scenario. However, direct evidence for the most interior shells, which are responsible for the production of elements heavier than oxygen, is lacking. Here, we report the discovery of the first-of-its-kind supernova arising from a star peculiarly stripped all the way to the silicon and sulphur-rich internal layer. Whereas the concentric shell structure of massive stars is not under debate, it is the first time that such a thick, massive silicon and sulphur-rich shell, expelled by the progenitor shortly before the SN explosion, has been directly revealed., Comment: 48 pages, 12 figures and 10 tables. Submitted to a high-impact journal. The reduced spectra and photometry will be made available via the journal webpage and the WISeREP archive after the acceptance of the paper
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- 2024
8. Decoupled and Interactive Regression Modeling for High-performance One-stage 3D Object Detection
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Xiao, Weiping, Wu, Yiqiang, Liu, Chang, Qin, Yu, Li, Xiaomao, and Xin, Liming
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Computer Science - Computer Vision and Pattern Recognition - Abstract
Inadequate bounding box modeling in regression tasks constrains the performance of one-stage 3D object detection. Our study reveals that the primary reason lies in two aspects: (1) The limited center-offset prediction seriously impairs the bounding box localization since many highest response positions significantly deviate from object centers. (2) The low-quality sample ignored in regression tasks significantly impacts the bounding box prediction since it produces unreliable quality (IoU) rectification. To tackle these problems, we propose Decoupled and Interactive Regression Modeling (DIRM) for one-stage detection. Specifically, Decoupled Attribute Regression (DAR) is implemented to facilitate long regression range modeling for the center attribute through an adaptive multi-sample assignment strategy that deeply decouples bounding box attributes. On the other hand, to enhance the reliability of IoU predictions for low-quality results, Interactive Quality Prediction (IQP) integrates the classification task, proficient in modeling negative samples, with quality prediction for joint optimization. Extensive experiments on Waymo and ONCE datasets demonstrate that DIRM significantly improves the performance of several state-of-the-art methods with minimal additional inference latency. Notably, DIRM achieves state-of-the-art detection performance on both the Waymo and ONCE datasets.
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- 2024
9. Cataclysmic Variables and AM CVn Binaries in SRG/eROSITA + Gaia: Volume Limited Samples, X-ray Luminosity Functions, and Space Densities
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Rodriguez, Antonio C., El-Badry, Kareem, Suleimanov, Valery, Pala, Anna F., Kulkarni, Shrinivas R., Gaensicke, Boris, Mori, Kaya, Rich, R. Michael, Sarkar, Arnab, Bao, Tong, de Oliveira, Raimundo Lopes, Ramsay, Gavin, Szkody, Paula, Graham, Matthew, Prince, Thomas A., Caiazzo, Ilaria, Vanderbosch, Zachary P., van Roestel, Jan, Das, Kaustav K., Qin, Yu-Jing, Kasliwal, Mansi M., Wold, Avery, Groom, Steven L., Reiley, Daniel, and Riddle, Reed
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Astrophysics - High Energy Astrophysical Phenomena ,Astrophysics - Astrophysics of Galaxies ,Astrophysics - Solar and Stellar Astrophysics - Abstract
We present volume-limited samples of cataclysmic variables (CVs) and AM CVn binaries jointly selected from SRG/eROSITA eRASS1 and \textit{Gaia} DR3 using an X-ray + optical color-color diagram (the ``X-ray Main Sequence"). This tool identifies all CV subtypes, including magnetic and low-accretion rate systems, in contrast to most previous surveys. We find 23 CVs, 3 of which are AM CVns, out to 150 pc in the Western Galactic Hemisphere. Our 150 pc sample is spectroscopically verified and complete down to $L_X = 1.3\times 10^{29} \;\textrm{erg s}^{-1}$ in the 0.2--2.3 keV band, and we also present CV candidates out to 300 pc and 1000 pc. We discovered two previously unknown systems in our 150 pc sample: the third nearest AM CVn and a magnetic period bouncer. We find the mean $L_X$ of CVs to be $\langle L_X \rangle \approx 4.6\times 10^{30} \;\textrm{erg s}^{-1}$, in contrast to previous surveys which yielded $\langle L_X \rangle \sim 10^{31}-10^{32} \;\textrm{erg s}^{-1}$. We construct X-ray luminosity functions that, for the first time, flatten out at $L_X\sim 10^{30} \; \textrm{erg s}^{-1}$. We find average number, mass, and luminosity densities of $\rho_\textrm{N, CV} = (3.7 \pm 0.7) \times 10^{-6} \textrm{pc}^{-3}$, $\rho_M = (5.0 \pm 1.0) \times 10^{-5} M_\odot^{-1}$, and $\rho_{L_X} = (2.3 \pm 0.4) \times 10^{26} \textrm{erg s}^{-1}M_\odot^{-1}$, respectively, in the solar neighborhood. Our uniform selection method also allows us to place meaningful estimates on the space density of AM CVns, $\rho_\textrm{N, AM CVn} = (5.5 \pm 3.7) \times 10^{-7} \textrm{pc}^{-3}$. Magnetic CVs and period bouncers make up $35\%$ and $25\%$ of our sample, respectively. This work, through a novel discovery technique, shows that the observed number densities of CVs and AM CVns, as well as the fraction of period bouncers, are still in tension with population synthesis estimates., Comment: Submitted to PASP, comments welcome
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- 2024
10. Learning production functions for supply chains with graph neural networks
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Chang, Serina, Lin, Zhiyin, Yan, Benjamin, Bembde, Swapnil, Xiu, Qi, Wong, Chi Heem, Qin, Yu, Kloster, Frank, Luo, Alex, Palleti, Raj, and Leskovec, Jure
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Computer Science - Machine Learning ,Computer Science - Computers and Society ,Computer Science - Social and Information Networks - Abstract
The global economy relies on the flow of goods over supply chain networks, with nodes as firms and edges as transactions between firms. While we may observe these external transactions, they are governed by unseen production functions, which determine how firms internally transform the input products they receive into output products that they sell. In this setting, it can be extremely valuable to infer these production functions, to better understand and improve supply chains, and to forecast future transactions more accurately. However, existing graph neural networks (GNNs) cannot capture these hidden relationships between nodes' inputs and outputs. Here, we introduce a new class of models for this setting, by combining temporal GNNs with a novel inventory module, which learns production functions via attention weights and a special loss function. We evaluate our models extensively on real supply chains data, along with data generated from our new open-source simulator, SupplySim. Our models successfully infer production functions, with a 6-50% improvement over baselines, and forecast future transactions on real and synthetic data, outperforming baselines by 11-62%.
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- 2024
11. Rapid and Precise Topological Comparison with Merge Tree Neural Networks
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Qin, Yu, Fasy, Brittany Terese, Wenk, Carola, and Summa, Brian
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Computer Science - Machine Learning ,Computer Science - Computational Geometry - Abstract
Merge trees are a valuable tool in the scientific visualization of scalar fields; however, current methods for merge tree comparisons are computationally expensive, primarily due to the exhaustive matching between tree nodes. To address this challenge, we introduce the Merge Tree Neural Network (MTNN), a learned neural network model designed for merge tree comparison. The MTNN enables rapid and high-quality similarity computation. We first demonstrate how to train graph neural networks, which emerged as effective encoders for graphs, in order to produce embeddings of merge trees in vector spaces for efficient similarity comparison. Next, we formulate the novel MTNN model that further improves the similarity comparisons by integrating the tree and node embeddings with a new topological attention mechanism. We demonstrate the effectiveness of our model on real-world data in different domains and examine our model's generalizability across various datasets. Our experimental analysis demonstrates our approach's superiority in accuracy and efficiency. In particular, we speed up the prior state-of-the-art by more than $100\times$ on the benchmark datasets while maintaining an error rate below $0.1\%$.
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- 2024
12. Derivative-free tree optimization for complex systems
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Wei, Ye, Peng, Bo, Xie, Ruiwen, Chen, Yangtao, Qin, Yu, Wen, Peng, Bauer, Stefan, and Tung, Po-Yen
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Computer Science - Machine Learning ,Mathematics - Optimization and Control - Abstract
A tremendous range of design tasks in materials, physics, and biology can be formulated as finding the optimum of an objective function depending on many parameters without knowing its closed-form expression or the derivative. Traditional derivative-free optimization techniques often rely on strong assumptions about objective functions, thereby failing at optimizing non-convex systems beyond 100 dimensions. Here, we present a tree search method for derivative-free optimization that enables accelerated optimal design of high-dimensional complex systems. Specifically, we introduce stochastic tree expansion, dynamic upper confidence bound, and short-range backpropagation mechanism to evade local optimum, iteratively approximating the global optimum using machine learning models. This development effectively confronts the dimensionally challenging problems, achieving convergence to global optima across various benchmark functions up to 2,000 dimensions, surpassing the existing methods by 10- to 20-fold. Our method demonstrates wide applicability to a wide range of real-world complex systems spanning materials, physics, and biology, considerably outperforming state-of-the-art algorithms. This enables efficient autonomous knowledge discovery and facilitates self-driving virtual laboratories. Although we focus on problems within the realm of natural science, the advancements in optimization techniques achieved herein are applicable to a broader spectrum of challenges across all quantitative disciplines., Comment: 39 pages, 3 figures
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- 2024
13. Context-based Fast Recommendation Strategy for Long User Behavior Sequence in Meituan Waimai
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Feng, Zhichao, Xie, Junjiie, Li, Kaiyuan, Qin, Yu, Wang, Pengfei, Li, Qianzhong, Yin, Bin, Li, Xiang, Lin, Wei, and Wang, Shangguang
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Computer Science - Information Retrieval - Abstract
In the recommender system of Meituan Waimai, we are dealing with ever-lengthening user behavior sequences, which pose an increasing challenge to modeling user preference effectively. Existing sequential recommendation models often fail to capture long-term dependencies or are too complex, complicating the fulfillment of Meituan Waimai's unique business needs. To better model user interests, we consider selecting relevant sub-sequences from users' extensive historical behaviors based on their preferences. In this specific scenario, we've noticed that the contexts in which users interact have a significant impact on their preferences. For this purpose, we introduce a novel method called Context-based Fast Recommendation Strategy to tackle the issue of long sequences. We first identify contexts that share similar user preferences with the target context and then locate the corresponding PoIs based on these identified contexts. This approach eliminates the necessity to select a sub-sequence for every candidate PoI, thereby avoiding high time complexity. Specifically, we implement a prototype-based approach to pinpoint contexts that mirror similar user preferences. To amplify accuracy and interpretability, we employ JS divergence of PoI attributes such as categories and prices as a measure of similarity between contexts. A temporal graph integrating both prototype and context nodes helps incorporate temporal information. We then identify appropriate prototypes considering both target contexts and short-term user preferences. Following this, we utilize contexts aligned with these prototypes to generate a sub-sequence, aimed at predicting CTR and CTCVR scores with target attention. Since its inception in 2023, this strategy has been adopted in Meituan Waimai's display recommender system, leading to a 4.6% surge in CTR and a 4.2% boost in GMV., Comment: 9 pages, accepted by WWW 2024 Industry Track
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- 2024
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14. AT2023lli: A Tidal Disruption Event with Prominent Optical Early Bump and Delayed Episodic X-ray Emission
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Huang, Shifeng, Jiang, Ning, Zhu, Jiazheng, Wang, Yibo, Wang, Tinggui, Wang, Shan-Qin, Gan, Wen-Pei, Liang, En-Wei, Qin, Yu-Jing, Lin, Zheyu, Xu, Lin-Na, Cai, Min-Xuan, Jiang, Ji-An, Kong, Xu, Li, Jiaxun, Li, Long, Wang, Jian-Guo, Xu, Ze-Lin, Xue, Yongquan, Yuan, Ye-Fei, Cheng, Jingquan, Fan, Lulu, Gao, Jie, Hu, Lei, Hu, Weida, li, Bin, Li, Feng, Liang, Ming, Liu, Hao, Liu, Wei, Lou, Zheng, Luo, Wentao, Qian, Yuan, Tang, Jinlong, Wan, Zhen, Wang, Hairen, Wang, Jian, Yang, Ji, Yao, Dazhi, Zhang, Hongfei, Zhang, Xiaoling, Zhao, Wen, Zheng, Xianzhong, Zhu, Qingfeng, and Zuo, Yingxi
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Astrophysics - High Energy Astrophysical Phenomena ,Astrophysics - Astrophysics of Galaxies - Abstract
High-cadence, multiwavelength observations have continuously revealed the diversity of tidal disruption events (TDEs), thus greatly advancing our knowledge and understanding of TDEs. In this work, we conducted an intensive optical-UV and X-ray follow-up campaign of TDE AT2023lli, and found a remarkable month-long bump in its UV/optical light curve nearly two months prior to maximum brightness. The bump represents the longest separation time from the main peak among known TDEs to date. The main UV/optical outburst declines as $t^{-4.10}$, making it one of the fastest decaying optically selected TDEs. Furthermore, we detected sporadic X-ray emission 30 days after the UV/optical peak, accompanied by a reduction in the period of inactivity. It is proposed that the UV/optical bump could be caused by the self-intersection of the stream debris, whereas the primary peak is generated by the reprocessed emission of the accretion process. In addition, our results suggest that episodic X-ray radiation during the initial phase of decline may be due to the patched obscurer surrounding the accretion disk, a phenomenon associated with the inhomogeneous reprocessing process. The double TDE scenario, in which two stars are disrupted in sequence, is also a possible explanation for producing the observed early bump and main peak. We anticipate that the multicolor light curves of TDEs, especially in the very early stages, and the underlying physics can be better understood in the near future with the assistance of dedicated surveys such as the deep high-cadence survey of the 2.5-meter Wide Field Survey Telescope (WFST)., Comment: 14 pages, 8 figures,accepted for publication by ApJL
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- 2024
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15. Enhancing the 'Immunity' of Mixture-of-Experts Networks for Adversarial Defense
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Han, Qiao, huang, yong, Guo, xinling, Zhai, Yiteng, Qin, Yu, and Yang, Yao
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Computer Science - Machine Learning ,Computer Science - Cryptography and Security - Abstract
Recent studies have revealed the vulnerability of Deep Neural Networks (DNNs) to adversarial examples, which can easily fool DNNs into making incorrect predictions. To mitigate this deficiency, we propose a novel adversarial defense method called "Immunity" (Innovative MoE with MUtual information \& positioN stabilITY) based on a modified Mixture-of-Experts (MoE) architecture in this work. The key enhancements to the standard MoE are two-fold: 1) integrating of Random Switch Gates (RSGs) to obtain diverse network structures via random permutation of RSG parameters at evaluation time, despite of RSGs being determined after one-time training; 2) devising innovative Mutual Information (MI)-based and Position Stability-based loss functions by capitalizing on Grad-CAM's explanatory power to increase the diversity and the causality of expert networks. Notably, our MI-based loss operates directly on the heatmaps, thereby inducing subtler negative impacts on the classification performance when compared to other losses of the same type, theoretically. Extensive evaluation validates the efficacy of the proposed approach in improving adversarial robustness against a wide range of attacks.
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- 2024
16. Dramatic rebrightening of the type-changing stripped-envelope supernova SN 2023aew
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Sharma, Yashvi, Sollerman, Jesper, Kulkarni, Shrinivas R., Moriya, Takashi J., Schulze, Steve, Barmentloo, Stan, Fausnaugh, Michael, Gal-Yam, Avishay, Jerkstrand, Anders, Ahumada, Tomás, Bellm, Eric C., Das, Kaustav K., Drake, Andrew, Fremling, Christoffer, Hall, Saarah, Hinds, K. R., Laz, Theophile Jegou du, Karambelkar, Viraj, Kasliwal, Mansi M., Masci, Frank J., Miller, Adam A., Nir, Guy, Perley, Daniel A., Purdum, Josiah N., Qin, Yu-Jing, Rehemtulla, Nabeel, Rich, R. Michael, Riddle, Reed L., Rodriguez, Antonio C., Rose, Sam, Somalwar, Jean, Wise, Jacob L., Wold, Avery, Yan, Lin, and Yao, Yuhan
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Astrophysics - High Energy Astrophysical Phenomena - Abstract
Multi-peaked supernovae with precursors, dramatic light-curve rebrightenings, and spectral transformation are rare, but are being discovered in increasing numbers by modern night-sky transient surveys like the Zwicky Transient Facility (ZTF). Here, we present the observations and analysis of SN 2023aew, which showed a dramatic increase in brightness following an initial luminous (-17.4 mag) and long (~100 days) unusual first peak (possibly precursor). SN 2023aew was classified as a Type IIb supernova during the first peak but changed its type to resemble a stripped-envelope supernova (SESN) after the marked rebrightening. We present comparisons of SN 2023aew's spectral evolution with SESN subtypes and argue that it is similar to SNe Ibc during its main peak. P-Cygni Balmer lines are present during the first peak, but vanish during the second peak's photospheric phase, before H$\alpha$ resurfaces again during the nebular phase. The nebular lines ([O I], [Ca II], Mg I], H$\alpha$) exhibit a double-peaked structure which hints towards a clumpy or non-spherical ejecta. We analyze the second peak in the light curve of SN 2023aew and find it to be broader than normal SESNe as well as requiring a very high $^{56}$Ni mass to power the peak luminosity. We discuss the possible origins of SN 2023aew including an eruption scenario where a part of the envelope is ejected during the first peak which also powers the second peak of the light curve through SN-CSM interaction., Comment: 22 pages, 11 figures, 5 tables
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- 2024
17. The Zwicky Transient Facility Bright Transient Survey. III. $\texttt{BTSbot}$: Automated Identification and Follow-up of Bright Transients with Deep Learning
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Rehemtulla, Nabeel, Miller, Adam A., Laz, Theophile Jegou Du, Coughlin, Michael W., Fremling, Christoffer, Perley, Daniel A., Qin, Yu-Jing, Sollerman, Jesper, Mahabal, Ashish A., Laher, Russ R., Riddle, Reed, Rusholme, Ben, and Kulkarni, Shrinivas R.
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Astrophysics - Instrumentation and Methods for Astrophysics - Abstract
The Bright Transient Survey (BTS) aims to obtain a classification spectrum for all bright ($m_\mathrm{peak}\,\leq\,18.5\,$mag) extragalactic transients found in the Zwicky Transient Facility (ZTF) public survey. BTS critically relies on visual inspection ("scanning") to select targets for spectroscopic follow-up, which, while effective, has required a significant time investment over the past $\sim5$ yr of ZTF operations. We present $\texttt{BTSbot}$, a multi-modal convolutional neural network, which provides a bright transient score to individual ZTF detections using their image data and 25 extracted features. $\texttt{BTSbot}$ is able to eliminate the need for daily human scanning by automatically identifying and requesting spectroscopic follow-up observations of new bright transient candidates. $\texttt{BTSbot}$ recovers all bright transients in our test split and performs on par with scanners in terms of identification speed (on average, $\sim$1 hour quicker than scanners). We also find that $\texttt{BTSbot}$ is not significantly impacted by any data shift by comparing performance across a concealed test split and a sample of very recent BTS candidates. $\texttt{BTSbot}$ has been integrated into Fritz and $\texttt{Kowalski}$, ZTF's first-party marshal and alert broker, and now sends automatic spectroscopic follow-up requests for the new transients it identifies. During the month of October 2023, $\texttt{BTSbot}$ selected 296 sources in real-time, 93% of which were real extragalactic transients. With $\texttt{BTSbot}$ and other automation tools, the BTS workflow has produced the first fully automatic end-to-end discovery and classification of a transient, representing a significant reduction in the human-time needed to scan. Future development has tremendous potential for creating similar models to identify and request follow-up observations for specific types of transients., Comment: 26 pages, 12 figures; to be submitted to ApJ; comments welcome
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- 2024
18. Tumor microenvironment-responsive drug self-delivery systems to treat cancer and overcome MDR
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Li, Ling-Mei, Xie, Yi-Pin, Qin, Yu-Rong, Chu, Hai-Ping, Xie, Hui, Zang, De-Jin, and Liu, Teng
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- 2024
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19. Remission of iron overload in adipose tissue of obese mice by fatty acid-modified polyoxovanadates
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Chen, Kun, Qin, Yu-Rong, Liu, Sheng-Qiu, and Chen, Rou-Ling
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- 2024
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20. Strategic design for enhancing performance in additively manufactured multi-material structures of high-strength steel and Ti6Al4V
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Wei, Chao, Zhao, Zhuang, Wang, Chao, Shen, Xianfeng, Yang, Jialin, Wang, Guowei, Qin, Yu, Sun, Mingyan, Tang, Jingang, Yang, Yang, and Le, Guomin
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- 2024
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21. Comparative pharmacokinetic investigation on crocetin in hyperlipidemia and normal rats after oral administration
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She, Cheng-Ye, Deng, Yuan-Xiong, Wu, Qin-Yu, and Li, Jing
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- 2024
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22. Clinical application potential of large language model: a study based on thyroid nodules
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Xia, Shujun, Hua, Qing, Mei, Zihan, Xu, Wenwen, Lai, Limei, Wei, Minyan, Qin, Yu, Luo, Lin, Wang, Changhua, Huo, ShengNan, Fu, Lijun, Zhou, Feidu, Wu, Jiang, Zhang, Li, Lv, De, Li, Jianxin, Wang, Xin, Li, Ning, Song, Yanyan, and Zhou, Jianqiao
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- 2024
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23. A Non-Uniform Low-Light Image Enhancement Method with Multi-Scale Attention Transformer and Luminance Consistency Loss
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Fang, Xiao, Gao, Xin, Li, Baofeng, Zhai, Feng, Qin, Yu, Meng, Zhihang, Lu, Jiansheng, and Xiao, Chun
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Computer Science - Computer Vision and Pattern Recognition - Abstract
Low-light image enhancement aims to improve the perception of images collected in dim environments and provide high-quality data support for image recognition tasks. When dealing with photos captured under non-uniform illumination, existing methods cannot adaptively extract the differentiated luminance information, which will easily cause over-exposure and under-exposure. From the perspective of unsupervised learning, we propose a multi-scale attention Transformer named MSATr, which sufficiently extracts local and global features for light balance to improve the visual quality. Specifically, we present a multi-scale window division scheme, which uses exponential sequences to adjust the window size of each layer. Within different-sized windows, the self-attention computation can be refined, ensuring the pixel-level feature processing capability of the model. For feature interaction across windows, a global transformer branch is constructed to provide comprehensive brightness perception and alleviate exposure problems. Furthermore, we propose a loop training strategy, using the diverse images generated by weighted mixing and a luminance consistency loss to improve the model's generalization ability effectively. Extensive experiments on several benchmark datasets quantitatively and qualitatively prove that our MSATr is superior to state-of-the-art low-light image enhancement methods, and the enhanced images have more natural brightness and outstanding details. The code is released at https://github.com/fang001021/MSATr.
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- 2023
24. A 12.4 day periodicity in a close binary system after a supernova
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Chen, Ping, Gal-Yam, Avishay, Sollerman, Jesper, Schulze, Steve, Post, Richard S., Liu, Chang, Ofek, Eran O., Das, Kaustav K., Fremling, Christoffer, Horesh, Assaf, Katz, Boaz, Kushnir, Doron, Kasliwal, Mansi M., Kulkarni, Shri R., Liu, Dezi, Liu, Xiangkun, Miller, Adam A., Rose, Kovi, Waxman, Eli, Yang, Sheng, Yao, Yuhan, Zackay, Barak, Bellm, Eric C., Dekany, Richard, Drake, Andrew J., Fang, Yuan, Fynbo, Johan P. U., Groom, Steven L., Helou, George, Irani, Ido, Laz, Theophile Jegou du, Liu, Xiaowei, Mazzali, Paolo A., Neill, James D., Qin, Yu-Jing, Riddle, Reed L., Sharon, Amir, Strotjohann, Nora L., Wold, Avery, and Yan, Lin
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Astrophysics - High Energy Astrophysical Phenomena ,Astrophysics - Solar and Stellar Astrophysics - Abstract
Neutron stars and stellar-mass black holes are the remnants of massive star explosions. Most massive stars reside in close binary systems, and the interplay between the companion star and the newly formed compact object has been theoretically explored, but signatures for binarity or evidence for the formation of a compact object during a supernova explosion are still lacking. Here we report a stripped-envelope supernova, SN 2022jli, which shows 12.4-day periodic undulations during the declining light curve. Narrow H$\alpha$ emission is detected in late-time spectra with concordant periodic velocity shifts, likely arising from hydrogen gas stripped from a companion and accreted onto the compact remnant. A new Fermi/LAT $\gamma$-ray source is temporally and positionally consistent with SN 2022jli. The observed properties of SN 2022jli, including periodic undulations in the optical light curve, coherent H$\alpha$ emission shifting, and evidence for association with a $\gamma$-ray source, point to the explosion of a massive star in a binary system leaving behind a bound compact remnant. Mass accretion from the companion star onto the compact object powers the light curve of the supernova and generates the $\gamma$-ray emission., Comment: Accepted for publication in Nature
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- 2023
25. Corneal stress‒strain index in relation to retinal nerve fibre layer thickness among healthy young adults
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Liu, Min-Xin, Li, Dan-Lin, Yin, Zhi-Jian, Li, Yue-Zu, Zheng, Ya-Jie, Qin, Yu, Ma, Rong, Liang, Gang, and Pan, Chen-Wei
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- 2024
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26. Dual stress factors adaptive evolution for high EPA production in Schizochytrium sp. and metabolomics mechanism analysis
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Ou, Ying, Qin, Yu, Feng, Shoushuai, and Yang, Hailin
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- 2024
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27. A novel adaptive filter with a heart-rate-based reference signal for esophageal pressure signal denoising
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Qin, Yu, Huang, Zhiwen, Zhou, Xiaoyong, Gui, Shuiqing, Xiong, Lihong, Liu, Ling, and Liu, Jinglei
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- 2024
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28. Selective depolymerization of lignin into phenolic products over NixZn1 − x/ZrO2-MgO
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Qin, Yu, Wang, Dandan, Chen, Jiajia, Xiu, Pengcheng, Lu, Xinyu, and Gu, Xiaoli
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- 2024
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29. A non-uniform low-light image enhancement method with multi-scale attention transformer and luminance consistency loss
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Fang, Xiao, Gao, Xin, Li, Baofeng, Zhai, Feng, Qin, Yu, Meng, Zhihang, Lu, Jiansheng, and Xiao, Chun
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- 2024
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30. Design and fabrication of a high-efficiency defect inspection prototype for diary plastic cutlery based on machine-vision with improved deep leaning algorithm
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Yang, Jian, Qin, Yu, Zhu, Zhida, Xu, Xiaobin, and Guan, Dong
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- 2024
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31. The chloroplast genome of Camellia sinensis var. assamica cv. Duntsa (Theaceae) and comparative genome analysis: mutational hotspots and phylogenetic relationships
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Li, Jin, Qiu, Xiao-Yan, Qin, Yu, Tang, Han, Tang, Jun, Liu, Tian-Tian, Xiao, Li-Zheng, and Luo, Hua
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- 2024
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32. Metagenomic analysis of individual mosquito viromes reveals the geographical patterns and drivers of viral diversity
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Pan, Yuan-Fei, Zhao, Hailong, Gou, Qin-Yu, Shi, Pei-Bo, Tian, Jun-Hua, Feng, Yun, Li, Kun, Yang, Wei-Hong, Wu, De, Tang, Guangpeng, Zhang, Bing, Ren, Zirui, Peng, Shiqin, Luo, Geng-Yan, Le, Shi-Jia, Xin, Gen-Yang, Wang, Jing, Hou, Xin, Peng, Min-Wu, Kong, Jian-Bin, Chen, Xin-Xin, Yang, Chun-Hui, Mei, Shi-Qiang, Liao, Yu-Qi, Cheng, Jing-Xia, Wang, Juan, Chaolemen, Wu, Yu-Hui, Wang, Jian-Bo, An, Tongqing, Huang, Xinyi, Eden, John-Sebastian, Li, Jun, Guo, Deyin, Liang, Guodong, Jin, Xin, Holmes, Edward C., Li, Bo, Wang, Daxi, Li, Junhua, Wu, Wei-Chen, and Shi, Mang
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- 2024
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33. The chromosomal characteristics of spontaneous abortion and its potential associated copy number variants and genes
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Qin, Yu, Touch, Koksear, Sha, Menghan, Sun, Yanan, Zhang, Shunran, Wu, Jianli, Wu, Yuanyuan, Feng, Ling, Chen, Suhua, and Xiao, Juan
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- 2024
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34. Visualizing Topological Importance: A Class-Driven Approach
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Qin, Yu, Fasy, Brittany Terese, Wenk, Carola, and Summa, Brian
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Computer Science - Machine Learning - Abstract
This paper presents the first approach to visualize the importance of topological features that define classes of data. Topological features, with their ability to abstract the fundamental structure of complex data, are an integral component of visualization and analysis pipelines. Although not all topological features present in data are of equal importance. To date, the default definition of feature importance is often assumed and fixed. This work shows how proven explainable deep learning approaches can be adapted for use in topological classification. In doing so, it provides the first technique that illuminates what topological structures are important in each dataset in regards to their class label. In particular, the approach uses a learned metric classifier with a density estimator of the points of a persistence diagram as input. This metric learns how to reweigh this density such that classification accuracy is high. By extracting this weight, an importance field on persistent point density can be created. This provides an intuitive representation of persistence point importance that can be used to drive new visualizations. This work provides two examples: Visualization on each diagram directly and, in the case of sublevel set filtrations on images, directly on the images themselves. This work highlights real-world examples of this approach visualizing the important topological features in graph, 3D shape, and medical image data., Comment: 11 pages, 11 figures
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- 2023
35. The Progenitor Star of SN 2023ixf: A Massive Red Supergiant with Enhanced, Episodic Pre-Supernova Mass Loss
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Qin, Yu-Jing, Zhang, Keming, Bloom, Joshua, Sollerman, Jesper, Zimmerman, Erez A., Irani, Ido, Schulze, Steve, Gal-Yam, Avishay, Kasliwal, Mansi, Coughlin, Michael W., Perley, Daniel A., Fremling, Christoffer, and Kulkarni, Shrinivas
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Astrophysics - Solar and Stellar Astrophysics ,Astrophysics - Astrophysics of Galaxies - Abstract
We identify the progenitor star of SN 2023ixf in the nearby galaxy Messier 101 using Keck/NIRC2 adaptive optics imaging and pre-explosion HST/ACS images. The supernova position, localized with diffraction-spike pattern and high precision relative astrometry, unambiguously coincides with a single progenitor candidate of m_F814W=24.96(-0.04)(+0.05). Forced photometry further recovers 2-sigma detections in the F673N and F675W bands and imposes robust flux limits on the bluer bands. Given the reported infrared excess and semi-regular variability of the progenitor, we fit a time-dependent spectral energy distribution (SED) model of a dusty red supergiant (RSG) to a combined dataset of HST photometry, as well as ground-based near-infrared and Spitzer/IRAC [3.6], [4.5] photometry from the literature. The progenitor closely resembles a RSG of T_eff=3343+/-27 K and logL=5.10+/-0.02, with a 0.11+/-0.01 dex (25.2+/-1.7 per cent) variation over the mean luminosity at a period of P=1128.3+/-6.5 days, heavily obscured by a dust envelope with an optical depth of tau=2.83+/-0.03 at 1 micron (or A_V=10.28+/-0.11 mag). Such observed signatures match a post-main sequence star of 18.1(-1.2)(+0.7) Msun, close to the most massive SN II progenitor, with a pulsation-enhanced mass-loss rate of M_dot=(3.58+/-0.15) x 10^(-4) Msun/yr. The dense and confined circumstellar material is likely ejected during the last episode of radial pulsation before the explosion. Notably, we find strong evidence for periodic variation of tau (or both T_eff and tau) along with luminosity, a necessary assumption to reproduce the wavelength dependence of the variability, which implies dust sublimation and condensation during radial pulsations. Given the observed SED, partial dust obscuration remains a possible scenario, but any unobstructed binary companion over 7.1 Msun can be ruled out., Comment: submitted
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- 2023
36. Heterogeneous Knowledge Fusion: A Novel Approach for Personalized Recommendation via LLM
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Yin, Bin, Xie, Junjie, Qin, Yu, Ding, Zixiang, Feng, Zhichao, Li, Xiang, and Lin, Wei
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Computer Science - Information Retrieval ,Computer Science - Artificial Intelligence - Abstract
The analysis and mining of user heterogeneous behavior are of paramount importance in recommendation systems. However, the conventional approach of incorporating various types of heterogeneous behavior into recommendation models leads to feature sparsity and knowledge fragmentation issues. To address this challenge, we propose a novel approach for personalized recommendation via Large Language Model (LLM), by extracting and fusing heterogeneous knowledge from user heterogeneous behavior information. In addition, by combining heterogeneous knowledge and recommendation tasks, instruction tuning is performed on LLM for personalized recommendations. The experimental results demonstrate that our method can effectively integrate user heterogeneous behavior and significantly improve recommendation performance., Comment: Accepted at RecSys 2023
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- 2023
37. Dynamic Grouping for Climate Change Negotiation: Facilitating Cooperation and Balancing Interests through Effective Strategies
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Qin, Yu, Zhang, Duo, and Pang, Yuren
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Computer Science - Computers and Society ,Computer Science - Artificial Intelligence - Abstract
In this paper, we propose a dynamic grouping negotiation model for climate mitigation based on real-world business and political negotiation protocols. Within the AI4GCC competition framework, we develop a three-stage process: group formation and updates, intra-group negotiation, and inter-group negotiation. Our model promotes efficient and effective cooperation between various stakeholders to achieve global climate change objectives. By implementing a group-forming method and group updating strategy, we address the complexities and imbalances in multi-region climate negotiations. Intra-group negotiations ensure that all members contribute to mitigation efforts, while inter-group negotiations use the proposal-evaluation framework to set mitigation and savings rates. We demonstrate our negotiation model within the RICE-N framework, illustrating a promising approach for facilitating international cooperation on climate change mitigation., Comment: Presented at AI For Global Climate Cooperation Competition, 2023 (arXiv:cs/2307.06951)
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- 2023
38. Dynamic Grouping for Climate Change Negotiation: Facilitating Cooperation and Balancing Interests through Effective Strategies
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Zhang, Duo, Pang, Yuren, and Qin, Yu
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Computer Science - Computers and Society ,Computer Science - Artificial Intelligence - Abstract
The current framework for climate change negotiation models presents several limitations that warrant further research and development. In this track, we discuss mainly two key areas for improvement, focusing on the geographical impacts and utility framework. In the aspects of geographical impacts, We explore five critical aspects: (1) the shift from local to global impact, (2) variability in climate change effects across regions, (3) heterogeneity in geographical location and political structures, and (4) collaborations between adjacent nations, (5) the importance of including historical and cultural factors influencing climate negotiations. Furthermore, we emphasize the need to refine the utility and rewards framework to reduce the homogeneity and the level of overestimating the climate mitigation by integrating the positive effects of saving rates into the reward function and heterogeneity among all regions. By addressing these limitations, we hope to enhance the accuracy and effectiveness of climate change negotiation models, enabling policymakers and stakeholders to devise targeted and appropriate strategies to tackle climate change at both regional and global levels., Comment: Presented at AI For Global Climate Cooperation Competition, 2023 (arXiv:cs/2307.06951)
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- 2023
39. An hp-version error estimate of spectral collocation methods for weakly singular Volterra integro-differential equations with vanishing delays
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Qin, Yu and Huang, Chengming
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- 2024
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40. Impact of cancer diagnosis on life expectancy by area-level socioeconomic groups in New South Wales, Australia: a population-based study
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Md Mijanur Rahman, Michael David, David Goldsbury, Karen Canfell, Kou Kou, Paramita Dasgupta, Peter Baade, and Xue Qin Yu
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cancer diagnosis ,life expectancy ,loss of life expectancy ,area-level socioeconomic status ,flexible parametric model ,Neoplasms. Tumors. Oncology. Including cancer and carcinogens ,RC254-282 - Abstract
Objective: Improvement in cancer survival over recent decades has not been accompanied by a narrowing of socioeconomic disparities. This study aimed to quantify the loss of life expectancy (LOLE) resulting from a cancer diagnosis and examine disparities in LOLE based on area-level socioeconomic status (SES). Methods: Data were collected for all people between 50 and 89 years of age who were diagnosed with cancer, registered in the NSW Cancer Registry between 2001 and 2019, and underwent mortality follow-up evaluations until December 2020. Flexible parametric survival models were fitted to estimate the LOLE by gender and area-level SES for 12 common cancers. Results: Of 422,680 people with cancer, 24% and 18% lived in the most and least disadvantaged areas, respectively. Patients from the most disadvantaged areas had a significantly greater average LOLE than patients from the least disadvantaged areas for cancers with high survival rates, including prostate [2.9 years (95% CI: 2.5–3.2 years) vs. 1.6 years (95% CI: 1.3–1.9 years)] and breast cancer [1.6 years (95% CI: 1.4–1.8 years) vs. 1.2 years (95% CI: 1.0–1.4 years)]. The highest average LOLE occurred in males residing in the most disadvantaged areas with pancreatic [16.5 years (95% CI: 16.1–16.8 years) vs. 16.2 years (95% CI: 15.7–16.7 years)] and liver cancer [15.5 years (95% CI: 15.0–16.0 years) vs. 14.7 years (95% CI: 14.0–15.5 years)]. Females residing in the least disadvantaged areas with thyroid cancer [0.9 years (95% CI: 0.4–1.4 years) vs. 0.6 years (95% CI: 0.2–1.0 years)] or melanoma [0.9 years (95% CI: 0.8–1.1 years) vs. 0.7 years (95% CI: 0.5–0.8 years)] had the lowest average LOLE. Conclusions: Patients from the most disadvantaged areas had the highest LOLE with SES-based differences greatest for patients diagnosed with cancer at an early stage or cancers with higher survival rates, suggesting the need to prioritise early detection and reduce treatment-related barriers and survivorship challenges to improve life expectancy.
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- 2024
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41. Multiple myeloma survival in New South Wales, Australia, by treatment era to 2020
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Eleonora Feletto, Qingwei Luo, Anna Kelly, Marianne Weber, David Goldsbury, Katherine Barron, Karen Canfell, and Xue Qin Yu
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multiple myeloma ,cancer epidemiology ,survival analysis ,competing risk analysis ,australia ,Neoplasms. Tumors. Oncology. Including cancer and carcinogens ,RC254-282 - Abstract
Objective: Australia has relatively high multiple myeloma (MM) incidence and mortality rates. Advancements in MM treatment over recent decades have driven improvements in MM survival in high-income countries; however, reporting in Australia is limited. We investigated temporal trends in population-wide MM survival across 3 periods of treatment advancements in New South Wales (NSW), Australia. Methods: Individuals with an MM diagnosis in the NSW Cancer Registry between 1985 and 2015 with vital follow-up to 2020, were categorized into 3 previously defined treatment eras according to their diagnosis date (1985–1995, chemotherapy only; 1996–2007, autologous stem cell transplantation; and 2008–2015, novel agents including proteasome inhibitors and immunomodulatory drugs). Both relative survival and cause-specific survival according to Fine and Gray’s competing risks cumulative incidence function were calculated by treatment era and age at diagnosis. Results: Overall, 11,591 individuals were included in the study, with a median age of 70 years at diagnosis. Five-year relative survival improved over the 36-year (1985–2020) study period (31.0% in 1985–1995; 41.9% in 1996–2007; and 56.1% in 2008–2015). For individuals diagnosed before 70 years of age, the 5-year relative survival nearly doubled, from 36.5% in 1985–1995 to 68.5% in 2008–2015. Improvements for those > 70 years of age were less pronounced between 1985–1995 and 1996–2007; however, significant improvements were observed for those diagnosed in 2008–2015. Similar overall and age-specific patterns were observed for cause-specific survival. After adjustment for gender and age at diagnosis, treatment era was strongly associated with both relative and cause-specific survival (P < 0.0001). Conclusions: Survival of individuals with MM is improving in Australia with treatment advances. However, older age groups continue to experience poor survival outcomes with only modest improvements over time. Given the increasing prevalence of MM in Australia, the effects of MM treatment on quality of life, particularly in older age, warrant further attention.
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- 2024
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42. Nacre-like surface nanolaminates enhance fatigue resistance of pure titanium
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Yong Zhang, Chenyun He, Qin Yu, Xiao Li, Xiaogang Wang, Yin Zhang, Ji Wang, Chao Jiang, Yunfei Jia, Xian-Cheng Zhang, Binhan Sun, Robert O. Ritchie, and Shan-Tung Tu
- Subjects
Science - Abstract
Abstract Fatigue failure is invariably the most crucial failure mode for metallic structural components. Most microstructural strategies for enhancing fatigue resistance are effective in suppressing either crack initiation or propagation, but often do not work for both synergistically. Here, we demonstrate that this challenge can be overcome by architecting a gradient structure featuring a surface layer of nacre-like nanolaminates followed by multi-variant twinned structure in pure titanium. The polarized accommodation of highly regulated grain boundaries in the nanolaminated layer to cyclic loading enhances the structural stability against lamellar thickening and microstructure softening, thereby delaying surface roughening and thus crack nucleation. The decohesion of the nanolaminated grains along horizonal high-angle grain boundaries gives rise to an extraordinarily high frequency (≈1.7 × 103 times per mm) of fatigue crack deflection, effectively reducing fatigue crack propagation rate (by 2 orders of magnitude lower than the homogeneous coarse-grained counterpart). These intriguing features of the surface nanolaminates, along with the various toughening mechanisms activated in the subsurface twinned structure, result in a fatigue resistance that significantly exceeds those of the homogeneous and gradient structures with equiaxed grains. Our work on architecting the surface nanolaminates in gradient structure provides a scalable and sustainable strategy for designing more fatigue-resistant alloys.
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- 2024
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43. The impact of diabetes mellitus on cardiac function assessed by magnetic resonance imaging in patients with hypertrophic cardiomyopathy
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Shi-Qin Yu, Ke Shi, Yuan Li, Jin Wang, Yue Gao, Rui Shi, Wei-Feng Yan, Hua-Yan Xu, Ying-Kun Guo, and Zhi-Gang Yang
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Diabetes mellitus ,Hypertrophic cardiomyopathy ,Cardiac magnetic resonance ,Strain ,Diseases of the circulatory (Cardiovascular) system ,RC666-701 - Abstract
Abstract Background The adverse prognostic impact of diabetes on hypertrophic cardiomyopathy (HCM) is poorly understood. We sought to explore the underlying mechanisms in terms of structural and functional remodelling in HCM patients with coexisting diabetes (HCM-DM). Methods A total of 45 HCM-DM patients were retrospectively included. Isolated HCM controls (HCM patients without diabetes) were matched to HCM-DM patients in terms of maximal wall thickness, age, and gender distribution. Left ventricular (LV) and atrial (LA) performance were evaluated using cardiac magnetic resonance feature tracking strain analyses. The associations between diabetes and LV/LA impairment were investigated by univariable and multivariable linear regression. Results Compared with the isolated HCM controls, the HCM-DM patients had smaller end-diastolic volume and stroke volume, lower ejection fraction, larger mass/volume ratio and impaired strains in all three directions (all P
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- 2024
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44. Crystal structure of poly[octakis(μ-oxido)-tris(μ-1,1′-[[1,1′-biphenyl]-4,4′-diylbis(methylene)]bis(1H-imidazole))-tetrakis(oxido)-tetra-vanadium-dimanganese(II)dihydrate], C30H29MnN6O7V2
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Dai Ling-Ling, Wang Xiao-Jie, and Qin Yu-Cai
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2351581 ,Physics ,QC1-999 ,Crystallography ,QD901-999 - Abstract
C30H29MnN6 O7V2, triclinic, P 1‾ $\overline{1}$ (no. 2), a = 9.0806(3) Å, b = 11.1937(3) Å, c = 16.3585(5) Å, α = 102.2430(10)°, β = 100.090(1)°, γ = 97.7410(10)°, V = 1574.09(8) Å3, Z = 2, Rgt (F) = 0.0335, wRref (F 2) = 0.0886, T = 100 K.
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- 2024
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45. Analysis of Gas Well Production Capacity Control Factors and Evaluation of Favorable Development Areas in the Maokou Formation in Central Sichuan
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Guo, Zhen-hua, Xu, Yan-mei, Qu, Li-cai, Li, Tao, Zhu, Mao, Wu, Yun-long, Xia, Qin-yu, Li, Wen, Wang, Hong-qiu, Hui, Dong, Wu, Wei, Series Editor, and Lin, Jia'en, editor
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- 2024
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46. STK40 inhibits trophoblast fusion by mediating COP1 ubiquitination to degrade P57Kip2
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Li, Xia, Shao, Li-Zhen, Li, Zhuo-Hang, Wang, Yong-Heng, Cai, Qin-Yu, Wang, Shun, Chen, Hong, Sheng, Jie, Luo, Xin, Chen, Xue-Mei, Wang, Ying-Xiong, Ding, Yu-Bin, and Liu, Tai-Hang
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- 2024
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47. Machine learning-based biomarker screening for acute myeloid leukemia prognosis and therapy from diverse cell-death patterns
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Qin, Yu, Pu, Xuexue, Hu, Dingtao, and Yang, Mingzhen
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- 2024
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48. Safety of embryo cryopreservation: insights from mid-term placental transcriptional changes
- Author
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Luo, Qin-Yu, Zhang, Si-Wei, Wu, Hai-Yan, Mo, Jia-Ying, Yu, Jia-En, He, Ren-Ke, Jiang, Zhao-Ying, Zhu, Ke-Jing, Liu, Xue-Ying, Lin, Zhong-Liang, Sheng, Jian-Zhong, Zhang, Yu, Wu, Yan-Ting, and Huang, He-Feng
- Published
- 2024
- Full Text
- View/download PDF
49. Author Correction: Ammonium-derived nitrous oxide is a global source in streams
- Author
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Wang, Shanyun, Lan, Bangrui, Yu, Longbin, Xiao, Manyi, Jiang, Liping, Qin, Yu, Jin, Yucheng, Zhou, Yuting, Armanbek, Gawhar, Ma, Jingchen, Wang, Manting, Jetten, Mike S. M., Tian, Hanqin, Zhu, Guibing, and Zhu, Yong-Guan
- Published
- 2024
- Full Text
- View/download PDF
50. Ammonium-derived nitrous oxide is a global source in streams
- Author
-
Wang, Shanyun, Lan, Bangrui, Yu, Longbin, Xiao, Manyi, Jiang, Liping, Qin, Yu, Jin, Yucheng, Zhou, Yuting, Armanbek, Gawhar, Ma, Jingchen, Wang, Manting, Jetten, Mike S. M., Tian, Hanqin, Zhu, Guibing, and Zhu, Yong-Guan
- Published
- 2024
- Full Text
- View/download PDF
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