| 1. |
- Kristan, Matej, et al.
(author)
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The first visual object tracking segmentation VOTS2023 challenge results
- 2023
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In: 2023 IEEE/CVF International conference on computer vision workshops (ICCVW). - : Institute of Electrical and Electronics Engineers Inc.. - 9798350307443 - 9798350307450 ; , s. 1788-1810
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Conference paper (peer-reviewed)abstract
- The Visual Object Tracking Segmentation VOTS2023 challenge is the eleventh annual tracker benchmarking activity of the VOT initiative. This challenge is the first to merge short-term and long-term as well as single-target and multiple-target tracking with segmentation masks as the only target location specification. A new dataset was created; the ground truth has been withheld to prevent overfitting. New performance measures and evaluation protocols have been created along with a new toolkit and an evaluation server. Results of the presented 47 trackers indicate that modern tracking frameworks are well-suited to deal with convergence of short-term and long-term tracking and that multiple and single target tracking can be considered a single problem. A leaderboard, with participating trackers details, the source code, the datasets, and the evaluation kit are publicly available at the challenge website1
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| 2. |
- Zhang, Lixiu, et al.
(author)
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Advances in the Application of Perovskite Materials
- 2023
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In: NANO-MICRO LETTERS. - : SHANGHAI JIAO TONG UNIV PRESS. - 2311-6706. ; 15:1
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Research review (peer-reviewed)abstract
- Nowadays, the soar of photovoltaic performance of perovskite solar cells has set off a fever in the study of metal halide perovskite materials. The excellent optoelectronic properties and defect tolerance feature allow metal halide perovskite to be employed in a wide variety of applications. This article provides a holistic review over the current progress and future prospects of metal halide perovskite materials in representative promising applications, including traditional optoelectronic devices (solar cells, light-emitting diodes, photodetectors, lasers), and cutting-edge technologies in terms of neuromorphic devices (artificial synapses and memristors) and pressure-induced emission. This review highlights the fundamentals, the current progress and the remaining challenges for each application, aiming to provide a comprehensive overview of the development status and a navigation of future research for metal halide perovskite materials and devices.
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| 3. |
- Tang, Weidong, et al.
(author)
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The roles of metal oxidation states in perovskite semiconductors
- 2023
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In: Matter. - : CELL PRESS. - 2590-2393 .- 2590-2385. ; 6:11, s. 3782-3802
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Research review (peer-reviewed)abstract
- Metal halide perovskites are an emerging materials platform for optoelectronic, spintronic, and thermoelectric applications. The field of perovskite materials and devices has progressed rapidly over the past decade. For halide perovskite materials, a range of physical and chemical properties such as crystal structure, bandgap, charge carrier density, and stability that govern the device functionalities are critically determined by the oxidation states of the B-site metal ions. However, such an important mechanistic connection unique to halide perovskites is not well established, limiting the pace of development in this area. In this review, we identify the roles of metal oxidation states in perovskite semiconductors. The redox reactions leading to these states, and their effects on the materials properties, are clarified. Finally, we suggest routes to improving device efficiency and stability from the perspective of oxidation state control.
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| 4. |
- Zhang, Liang, et al.
(author)
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Deep Learning for Additive Screening in Perovskite Light-Emitting Diodes
- 2022
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In: Angewandte Chemie International Edition. - : WILEY-V C H VERLAG GMBH. - 1433-7851 .- 1521-3773. ; 61:37
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Journal article (peer-reviewed)abstract
- Additive engineering with organic molecules is of critical importance for achieving high-performance perovskite optoelectronic devices. However, experimentally finding suitable additives is costly and time consuming, while conventional machine learning (ML) is difficult to predict accurately due to the limited experimental data available in this relatively new field. Here, we demonstrate a deep learning method that can predict the effectiveness of additives in perovskite light-emitting diodes (PeLEDs) with a high accuracy up to 96 % by using a small dataset of 132 molecules. This model can maximize the information of the molecules and significantly mitigate the duplicated problem that usually happened with previous models in ML for molecular screening. Very high efficiency PeLEDs with a peak external quantum efficiency up to 22.7 % can be achieved by using the predicated additive. Our work opens a new avenue for further boosting the performance of perovskite optoelectronic devices.
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| 5. |
- Cheng, Anying, et al.
(author)
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Diagnostic performance of initial blood urea nitrogen combined with D-dimer levels for predicting in-hospital mortality in COVID-19 patients
- 2020
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In: International Journal of Antimicrobial Agents. - : ELSEVIER. - 0924-8579 .- 1872-7913. ; 56:3
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Journal article (peer-reviewed)abstract
- The crude mortality rate in critical pneumonia cases with coronavirus disease 2019 (COVID-19) reaches 49%. This study aimed to test whether levels of blood urea nitrogen (BUN) in combination with D-dimer were predictors of in-hospital mortality in COVID-19 patients. The clinical characteristics of 305 COVID19 patients were analysed and were compared between the survivor and non-survivor groups. Of the 305 patients, 85 (27.9%) died and 220 (72.1%) were discharged from hospital. Compared with discharged cases, non-survivor cases were older and their BUN and D-dimer levels were significantly higher ( P < 0.0 0 01). Least absolute shrinkage and selection operator (LASSO) and multivariable Cox regression analyses identified BUN and D-dimer levels as independent risk factors for poor prognosis. Kaplan-Meier analysis showed that elevated levels of BUN and D-dimer were associated with increased mortality (logrank, P 0.0 0 01). The area under the curve for BUN combined with D-dimer was 0.94 (95% CI 0.90-0.97), with a sensitivity of 85% and specificity of 91%. Based on BUN and D-dimer levels on admission, a nomogram model was developed that showed good discrimination, with a concordance index of 0.94. Together, initial BUN and D-dimer levels were associated with mortality in COVID-19 patients. The combination of BUN 4.6 mmol/L and D-dimer > 0.845 mu g/mL appears to identify patients at high risk of in-hospital mortality, therefore it may prove to be a powerful risk assessment tool for severe COVID-19 patients. (c) 2020 Elsevier B.V. and International Society of Chemotherapy. All rights reserved.
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| 6. |
- Gong, Dawei, et al.
(author)
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A Computer-Assisted Diagnosis System for the Detection of Chronic Gastritis in Endoscopic Images Using A Novel Convolution and Relative Self-Attention Parallel Network
- 2023
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In: IEEE Access. - : Institute of Electrical and Electronics Engineers (IEEE). - 2169-3536. ; 11, s. 116990-117003
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Journal article (peer-reviewed)abstract
- Chronic gastritis mainly includes chronic non-atrophic gastritis (CNAG), autoimmune gastritis (AIG), and type B gastritis. Early detection of AIG and type B gastritis will help identify high-risk groups for gastric cancer and prevent the development of irreversible peripheral neuropathy. We aim to develop a computer-assisted diagnosis (CADx) system by presenting a novel Convolution and Relative Self-Attention Parallel Network (CRSAPNet). We collected 3576 endoscopic images of chronic gastritis from 205 patients. MBConv and Relative Self-Attention Parallel Block (CRSAPB) was proposed to concatenate local features (such as mucosal folds and mucosal vessels extracted by MBConv) and global features (such as atrophied area extracted by Relative Self-Attention) in parallel in the last two stages of CRSAPNet. The CADx system distinguished AIG from type B gastritis and CNAG. The CRSAPNet achieved the highest overall accuracy of 95.44% (94.65% precision, 93.51% recall, 94.08% F1-score for AIG) with the fewest parameters. We used Grad-CAM to visually analyze the heat maps. We only replaced the original blocks of the third stage of ResNet50 and ConvNeXt-T with CRSAPB, resulting in an overall accuracy improvement of 0.37%, and 4.19%, respectively. Furthermore, the CADx system classified the three types of chronic gastritis for the first time. The CRSAPNet achieved an overall accuracy of 91.62%, and the overall accuracies in the location of the gastric body and gastric fundus were 93.43% and 92.51%, respectively. A new state-of-the-art deep learning network is introduced to distinguish AIG from type B gastritis and CNAG, and a classification for three types of chronic gastritis is reported for the first time.
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| 7. |
- Hagstrom, Hannes, et al.
(author)
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Serum levels of endotrophin are associated with nonalcoholic steatohepatitis
- 2021
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In: Scandinavian Journal of Gastroenterology. - : Taylor & Francis. - 0036-5521 .- 1502-7708. ; 56:4, s. 437-442
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Journal article (peer-reviewed)abstract
- Background and aimsThere are no currently available biomarkers that can accurately indicate the presence of non-alcoholic steatohepatitis (NASH). We investigated the association between endotrophin, a cleavage product of collagen type 6α3, and disease severity in patients with non-alcoholic fatty liver disease (NAFLD).MethodsWe measured serum endotrophin levels in 211 patients with NAFLD and nine healthy controls. Liver biopsy data was available for 141 (67%) of the patients. Associations between endotrophin and the presence of NASH and advanced fibrosis were investigated alone and in combination with standard clinical parameters using logistic regression.ResultsA total of 211 patients were enrolled in this study, consisting of 108 (51%) men and 103 (49%) women with a mean age of 55.6 years. 58 (27%) of the patients had advanced fibrosis. Of those with biopsy data, 87 (62%) had NASH. Serum levels of endotrophin were significantly higher in patients with NAFLD than those in healthy controls (37[±12] vs. 17[±7] ng/mL, p<.001). Serum levels of endotrophin were also significantly higher in patients with NASH than in those without NASH (40[±12] vs. 32[±13] ng/mL, p<.001). A model using age, sex, body mass index and levels of alanine aminotransferase (ALT), glucose and endotrophin effectively predicted the presence of NASH in a derivation (AUROC 0.83, 95%CI = 0.74–0.92) and validation cohort (AUROC 0.71, 95%CI = 0.54–0.88). There was no significant association between serum levels of endotrophin and advanced fibrosis.ConclusionsThese data suggest that serum endotrophin could be a valuable biomarker for diagnosing NASH, but not for detecting advanced fibrosis in NAFLD.
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| 8. |
- Huang, Wencheng, et al.
(author)
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Enhanced orbital anisotropy through the proximity to a SrTi O3 layer in the perovskite iridate superlattices
- 2021
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In: Physical Review B. - 2469-9950. ; 104:7
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Journal article (peer-reviewed)abstract
- We have used angle-dependent soft x-ray absorption spectroscopy (XAS) at the O K edge and first-principles calculations to investigate the electronic structures of iridate-based superlattices (SrIrO3)m/(SrTiO3) (m=1, 2, 3, and ∞). We focus on the pre-edge Ir 5d t2g-O 2p orbital hybridization feature in the XAS spectra. By varying the measurement geometry relative to the incident photon polarization, we are able to extract the dichroic contrast and observe the systematic increase in the anisotropy of Ir 5d orbitals as m decreases. First-principles calculations elucidate the orbital anisotropy coming mainly from the enhanced out-of-plane compression of IrO6 octahedra in the SrIrO3 layers that are adjacent to the inserted SrTiO3 layers. As m decreases, the increased volume fraction of these interfacial SrIrO3 layers and their contact with the SrTiO3 layers within the (SrIrO3)m/(SrTiO3) supercell lead to enhanced orbital anisotropy. Furthermore, the tilt and rotation of IrO6 octahedra are shown to be essential to understand the subtle orbital anisotropy in these superlattices, and constraining these degrees of freedom will give an incorrect trend. Our results demonstrate that the structural constraint from the inserted SrTiO3 layers, in addition to other electronic means such as polar interface and charge transfer, can serve as a knob to control the orbital degree of freedom in these iridate-based superlattices.
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| 9. |
- Liu, Dongjie, et al.
(author)
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Enhancing choice-set generation and route choice modeling with data- and knowledge-driven approach
- 2024
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In: Transportation Research, Part C: Emerging Technologies. - 0968-090X. ; 162
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Journal article (peer-reviewed)abstract
- Two central and interconnected problems arise in the specification of a ‘‘complete’’ path-based route choice model: choice-set generation and choice from a choice set. Choice-set generation poses a significant challenge in personalization and the enumeration of the full choice set with large size. Despite the continued prevalence of classic econometric models for modeling choices within a given set, this requirement of knowledge-driven modeling necessitates explicit model structures and intricate domain knowledge, which may result in practical biases. In this study, a Conditional Variational AutoEncoder (CVAE)-based choice set generation model is developed, which approximates the probability distribution of the underlying choice set generation process conditional on individual and OD characteristics without relying on expert knowledge. In order to facilitate a friendly integration between knowledge-driven econometric and machine learning approaches, a neural-embedded route choice model (IAP-NERCM) with implicit availability/perception (IAP) of choice alternatives is proposed to automatically capture the heterogeneity of taste parameters without assuming any a priori relationship. Results based on synthetic data show that the proposed models are capable of reproducing the pre-defined coefficients. Field data of GPS data collected in Toyota City is used to future test the proposed models compared to classical statistical models. Results indicate that IAP-NERCM exhibits the ability to recover underlying taste function and achieves the best performance in terms of goodness-of-fit, predictability, and estimation time.
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| 10. |
- Song, Yuchen, et al.
(author)
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A state-based inverse reinforcement learning approach to model activity-travel choices behavior with reward function recovery
- 2024
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In: Transportation Research Part C. - : Elsevier BV. - 0968-090X .- 1879-2359. ; 158
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Journal article (peer-reviewed)abstract
- Behaviorally oriented activity-travel choices (ATC) modeling is a principal part of travel demand analysis. Traditional econometric and rule-based methods require explicit model structures and complex domain knowledge. While several recent studies used machine learning models, especially adversarial inverse reinforcement learning (IRL) models, to learn potential ATC patterns with less expert-designed settings, they lack a clear representation of rational ATC behavior. In this study, we propose a data-driven IRL framework based on the maximum causal approach to minimize f-divergences between expert and agent state marginal distributions, which provides a more sample-efficient measurement. In addition, we specify a separate state-only reward function and derive an analytical gradient of the f-divergence objective with respect to reward parameters to ensure good convergences. The method can recover a stationary reward function, which assures the agent to get close to the expert behavior when training from scratch. We validate the proposed model using cellular signaling data from Chongqing, China by comparing with baseline models (behavior cloning, policy-based, and reward-based models) in aspects of policy performance comparison, reward recovery, and reward transfer tasks. The experiment results indicate that the proposed model outperforms existing methods and is relatively less sensitive to the number of expert demonstrations. Qualitative analyses are provided on the fundamental ATC preferences on different features given the reward function recovered from the observed mobility trajectories, and on the learning behaviors under different choices of f-divergence.
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