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53. Integrating UAV hyperspectral data and radiative transfer model simulation to quantitatively estimate maize leaf and canopy nitrogen content

Author

Jiating Li, Yufeng Ge, Laila A. Puntel, Derek M. Heeren, Geng Bai, Guillermo R. Balboa, John A. Gamon, Timothy J. Arkebauer, and Yeyin Shi

Subjects
Hyperspectral imager, UAS, Mechanistic model, Machine learning, Crop traits, Physical geography, GB3-5030, Environmental sciences, GE1-350
Abstract

Crop nitrogen (N) content reflects crop nutrient status and plays an important role in precision nutrient management. Accurate crop N content estimation from remote sensing has been well documented. However, the robustness (i.e., the ability of a model to perform consistently across various conditions) of these methods under varied soil conditions or different growth stages has rarely been considered. We proposed a hybrid method that integrates in-situ measurements and the data simulated by a mechanistic model to improve the estimation of maize N content. In-situ data included hyperspectral images collected by Unmanned Aerial Vehicle (UAV), and leaf and canopy N content (LNC and CNC). A mechanistic radiative transfer model (PROSAIL-PRO) was used to generate simulated data, i.e., canopy reflectance paired with target crop traits (i.e., LNC, CNC). We compared the performance from the hybrid method with a machine learning method (Gaussian Process Regression) and six different vegetation indices (VIs) on four in-situ datasets collected at three study sites from 2021 to 2022. Results show that the hybrid method consistently performed the best for LNC estimation across four testing datasets (RRMSE ranging from 10.08% to 10.84%). For CNC estimation, the hybrid method had the best estimation results on two out of the four testing datasets and performed comparably to the best method on the other two datasets (RRMSE ranging from 13.89% to 25.21%). Next, we assessed the estimation robustness of the hybrid method, the machine learning, and the best-VI by comparing the mean (µ) and standard deviation (σ) of RRMSE across diverse water and N treatments (condition #1) and different growth stages (condition #2). Among 16 total cases (two crop traits by four study sites by two conditions), the hybrid method had 11 cases of smallest µ and seven cases of smallest σ, outperforming the machine learning (0/16 for µ, 4/16 for σ) and the best-VI (5/16 for µ, 5/16 for σ). These results underscore the greater robustness of the hybrid method. This study highlights the potential of integrating in-situ measurements and simulated data to improve estimation accuracy and robustness for maize LNC and CNC. The promising performance of the hybrid method suggests its applicability to a broader range of crops and various crop traits.

Published
2024
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59. Diagnostic value of magnetic resonance imaging for patients with periprosthetic joint infection: a systematic review

Author

Chang Shufen, Liu Jinmin, Zhang Xiaohui, and Geng Bin

Subjects
Magnetic resonance imaging, Periprosthetic Joint infection, Diagnosis, Total joint arthroplasty, Diseases of the musculoskeletal system, RC925-935
Abstract

Abstract Purpose The purpose of this study was to provide a critical systematic review of the role of magnetic resonance imaging (MRI) as a noninvasive method to assess periprosthetic joint infections (PJIs). Methods The electronic databases PubMed and EMBASE were searched, since their inception up to March 27, 2022. The included studies evaluated the reproducibility and accuracy of MRI features to diagnose PJIs. The article quality assessment was conducted by the COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) and Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2). Results Among 1909 studies identified in the initial search, 8 studies were eligible for final systematic review. The included studies evaluated the reproducibility and accuracy of MRI features to diagnose PJIs. Seven of 8 studies showed good to excellent reliability, but only one article among them in which accuracy was evaluated had a low risk of bias. The intraclass correlation coefficient (ICC) and Cohen coefficient (κ) varied between 0.44 and 1.00. The accuracy varied between 63.9% and 94.4%. Potential MRI features, such as lamellated hyperintense synovitis, edema, fluid collection, or lymphadenopathy, might be valuable for diagnosing PJIs. Conclusion The quality of the evidence regarding the role of MRI for PJIs diagnosis was low. There is preliminary evidence that MRI has a noteworthy value of distinguishing suspected periprosthetic joint infection in patients with total knee arthroplasty or total hip arthroplasty, but the definition of specific MRI features related to PJIs diagnosis lacks consensus and standardization. Large-scale studies with robust quality were required to help make better clinical decisions in the future.

Published
2023
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60. L’amlitélimab (anticorps anti-OX40 ligand) réduit significativement les taux sériques d’IgE et de LDH ainsi que l’hyperplasie épidermique chez les adultes atteints de dermatite atopique modérée à sévère

Author

Geng, B., Meslin, F., Weidinger, S., Masuda, K., Blauvelt, A., Rahawi, K., O’malley, J.T., Bernigaud, C., Belhechmi, S., and Rynkiewicz, N.

Abstract

L’amlitélimab (SAR445229 ; KY1005) est un anticorps monoclonal anti-OX40 Ligand (OX40L) entièrement humain, non déplétif, qui se lie à l’OX40L sur les cellules présentatrices d’antigène, empêchant l’interaction avec l’OX40 sur les lymphocytes T activés. Chez les adultes atteints de dermatite atopique (DA) modérée à sévère, l’amlitélimab a démontré des améliorations cliniquement significatives des lésions et du prurit par rapport aux patients traités par placebo, avec une diminution du niveau sérique de plusieurs biomarqueurs inflammatoires Th2 et Th17/Th22 pendant 24 semaines, dans la partie 1 de l’étude de phase 2b STREAM-AD. Ici, les effets de l’amlitélimab sur des biomarqueurs supplémentaires liés à la gravité de la maladie et aux caractéristiques cutanées de la dermatite atopique ont été évalués jusqu’à la semaine 24.

Published
2024
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62. Defect‐driven enhancement of electrochemical oxygen evolution on Fe–Co–Al ternary hydroxides

Author

Sun, Y. (Yixuan), Xia, Y. (Yuanyuan), Kuai, L. (Long), Sun, H. (Hongxia), Cao, W. (Wei), Huttula, M. (Marko), Honkanen, A.-P. (Ari-Pekka), Viljanen, M. (Mira), Huotari, S. (Simo), Geng, B. (Baoyou), Sun, Y. (Yixuan), Xia, Y. (Yuanyuan), Kuai, L. (Long), Sun, H. (Hongxia), Cao, W. (Wei), Huttula, M. (Marko), Honkanen, A.-P. (Ari-Pekka), Viljanen, M. (Mira), Huotari, S. (Simo), and Geng, B. (Baoyou)

Abstract

Efficient, abundant and low‐cost catalysts for the oxygen evolution reaction (OER) are required for energy conversion and storage. In this study, a doping–etching route has been developed to access defect rich Fe–Co–Al (Fe–Co–Al‐AE) ternary hydroxide nanosheets for superior electrochemical oxygen evolution. After partial etching of Al, ultrathin Fe3Co2Al2‐AE electrocatalysts with a rich pore structure are obtained with a shift of the cobalt valence state towards higher valence (Co2+→Co3+), along with a substantial improvement in the catalytic performance. Fe3Co2Al2‐AE shows a notably lower overpotential of only 284 mV at a current density of 10 mA cm−2 and double the OER mass activity of the etching‐free Fe3Co2Al2 with an overpotential of 350 mV. Density functional theory shows the leaching of Al changes the rate‐determining step of the OER from conversion of *OOH into O2 on Fe3Co2Al2 to formation of OOH from *O on the Al‐defective catalysts. This work demonstrates an effective route to design and synthesize transition metal electrocatalysts and provides a promising alternative for the further development of oxygen evolution catalysts.

Published
2019

63. Research progress in detection method for bisphenol A in food packaging materials

Author

MA Yuqing, DUAN Wanli, PENG Lei, GENG Bingjie, TANG Lingxuan, WANG Haoneng, and GAO Fangyuan

Subjects
bisphenol a, food packaging material, extraction method, detection method, Medicine
Abstract

As an industrial chemical， bisphenol A is widely used in various food packaging materials. However， it is an endocrine disrupting chemical， which has estrogen activity and can cause certain damage to humans. So far， there are few studies on the detection of bisphenol A in self-heating food packaging materials， and there remains a lack of relevant standard. Therefore， it is necessary to establish a simple， sensitive and efficient method for the detection of bisphenol A in self-heating food. This study briefly introduces the pretreatment methods of bisphenol A， such as ultrasonic extraction， solid phase extraction， accelerated solvent extraction， and detection methods， such as gas chromatography-mass spectrometry， high performance liquid chromatography， fluorescent detection， and electrochemical detection.

Published
2023
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64. Enhancing estimation of cover crop biomass using field-based high-throughput phenotyping and machine learning models

Author

Geng Bai, Katja Koehler-Cole, David Scoby, Vesh R. Thapa, Andrea Basche, and Yufeng Ge

Subjects
aboveground biomass, cover crop, plant phenotyping, machine learning, partial least squares regression, rye, Plant culture, SB1-1110
Abstract

Incorporating cover crops into cropping systems offers numerous potential benefits, including the reduction of soil erosion, suppression of weeds, decreased nitrogen requirements for subsequent crops, and increased carbon sequestration. The aboveground biomass (AGB) of cover crops strongly influences their performance in delivering these benefits. Despite the significance of AGB, a comprehensive field-based high-throughput phenotyping study to quantify AGB of multiple cover crops in the U.S. Midwest has not been found. This study presents a two-year field experiment carried out in Eastern Nebraska, USA, to estimate AGB of five different cover crop species [canola (Brassica napus L.), rye (Secale cereale L.), triticale (Triticale × Triticosecale L.), vetch (Vicia sativa L.), and wheat (Triticum aestivum L.)] using high-throughput phenotyping and Machine Learning (ML) models. Destructive AGB sampling was performed three times during each spring season in 2022 and 2023. An array of morphological, spectral, thermal, and environmental features from the sensors were utilized as feature inputs of ML models. Moderately strong linear correlations between AGB and the selected features were observed. Four ML models, namely Random Forests Regression (RFR), Support Vector Regression (SVR), Partial Least Squares Regression (PLSR), and Artificial Neural Network (ANN), were investigated. Among the four models, PLSR achieved the highest Coefficient of Determination (R2) of 0.84 and the lowest Root Mean Squared Error (RMSE) of 892 kg/ha (Normalized RMSE (NRMSE) = 8.87%), indicating that PLSR could be the most appropriate method for estimating AGB of multiple cover crop species. Feature importance analysis ranked spectral features like Normalized Difference Red Edge (NDRE), Solar-induced Fluorescence (SIF), Spectral Reflectance at 485 nm (R485), and Normalized Difference Vegetation Index (NDVI) as top model features using PLSR. When utilizing fewer feature inputs, ANN exhibited better prediction performance compared to other models. Using morphological and spectral parameters as input features alone led to a R2 of 0.80 and 0.77 for AGB prediction using ANN, respectively. This study demonstrated the feasibility of high-throughput phenotyping and ML techniques for accurately estimating AGB of multiple cover crop species. Further enhancement of model performance could be achieved through additional destructive sampling conducted across multiple locations and years.

Published
2024
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65. Research on personalized recommendation algorithm for micromechanical sensors based on cloud model

Author

Geng Baojun

Subjects
micromechanical sensors, cloud clustering methods, recommendation algorithms, cloud computing models, intra-class clustering techniques, 68u07, Mathematics, QA1-939
Abstract

In order to improve the ability of micromechanical sensors to update data-matching nodes in real-time and speed up the establishment of a neighbor relationship between nodes, this paper proposes a personalized recommendation algorithm for micromechanical sensors based on the cloud model to improve the stability and real-time performance of micromechanical sensors. The algorithm uses the service attribute values of the cloud computing model and cloud clustering method to set feature term labels and establish a cloud service similarity matrix so as to meet the user-matched manufacturing service requirements. The intra-class clustering technique is applied to measure the clustering effect, and the evaluation function of each clustering number is calculated on the basis of considering the time sequence to determine the best clustering result to complete the personalized recommendation path for micromechanical sensors. To verify the application effect of the personalized recommendation algorithm for micromechanical sensors based on the cloud model, experiments are conducted. The results show that the recommendation algorithm in this paper can always control the node energy consumption below 2.5×103, and the average discovery delay is stable between 41-43 seconds. And the sensor response time is 12.1 seconds, and the average absolute deviation value is 0.23, which is nearly 1.3 times smaller than 0.53 and 0.52 of the collaborative recommendation algorithm and hybrid recommendation algorithm. It can be seen that the recommendation algorithm in this paper solves the problem of excessive neighbor discovery delay in the communication process of micromechanical sensors and effectively improves the personalized recommendation performance of micromechanical sensors.

Published
2024
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66. Mesoporous LaMnO3+δ perovskite from spray−pyrolysis with superior performance for oxygen reduction reaction and Zn−air battery

Author

Kuai, L. (Long), Kan, E. (Erjie), Cao, W. (Wei), Huttula, M. (Marko), Ollikkala, S. (Sami), Ahopelto, T. (Taru), Honkanen, A.-P. (Ari-Pekka), Huotari, S. (Simo), Wang, W. (Wenhai), and Geng, B. (Baoyou)

Subjects
LaMnO3+δ, Mesoporous, Oxidation state, Zn-air battery, Oxygen reduction reaction
Abstract

Oxygen reduction reaction (ORR) is the key reaction in various electrochemical energy devices. This work reports an inexpensive mesoporous LaMnO3+δ perovskite for ORR with remarkable activity, synthesized by a facile aerosol-spray assisted approach. The mesoporous LaMnO3+δ material shows a factor of 3.1 higher activity (at 0.9 V vs. RHE) than LaMnO3 obtained from co-precipitation method (LMO-CP). Based on results of x-ray absorption near-edge spectroscopy (XANES), x-ray photoelectron spectroscopy (XPS) and Brunauer–Emmett–Teller (BET) analysis, we conclude that the chemical state of surface Mn and the high surface area are the sources to the notably enhanced activity. The study of Zn-air batteries device further confirmed a Pt/C comparable performance in the practical devices with the novel mesoporous LaMnO3+δ catalyst, where the power density at 200 mA/cm2 is only 2.1% lower than in the battery with same-loaded Pt/C catalyst. Therefore, the high mass activity and low-cost of Mn/La may make LaMnO3+δ further approach to the application of electrochemical devices.

Published
2018

68. Surgical interventions for symptomatic knee osteoarthritis: a network meta-analysis of randomized control trials

Author

Geng Bin, Liu Jinmin, Tian Cong, Tang Yuchen, Zhang Xiaohui, and Xia Yayi

Subjects
Total knee arthroplasty, Unicompartmental knee arthroplasty, High tibial osteotomy, Bicompartmental knee arthroplasty, Bi-unicompartmental knee arthroplasty, Knee joint distraction, Diseases of the musculoskeletal system, RC925-935
Abstract

Abstract Background Multiple surgical interventions exist for the treatment of symptomatic knee osteoarthritis, but the surgeon and patient may often have difficulty deciding which interventions are the best option. Methods We conducted a systematic review to identify randomized clinical trials (RCTs) that compared complications, revisions, reoperations, and functional outcomes among TKA (total knee arthroplasty), UKA (unicompartmental knee arthroplasty), HTO (high tibial osteotomy), BCA (bicompartmental knee arthroplasty), BIU (bi-unicompartmental knee arthroplasty), and KJD (knee joint distraction). The PubMed, Embase, and Cochrane databases were reviewed for all studies comparing two or more surgical interventions. Direct-comparison meta-analysis and network meta-analysis (NMA) were performed to combine direct and indirect evidence. The risk of bias was assessed using the revised Cochrane risk of bias tool for RCTs. Results This NMA and systematic review included 21 studies (17 RCTs), with a total of 1749 patients. The overall risk-of-bias assessment of the RCTs revealed that 7 studies had low risk, 5 had some concerns, and 9 had high risk. SUCRA (the surface under the cumulative ranking curve) rankings revealed that KJD had the greatest risk of appearing postoperative complications, revisions, and reoperations, and UKA or TKA had the lowest risk. The majority of comparisons among various treatments showed no difference for functional outcomes. Conclusion Each surgical intervention is noninferior to other treatments in functional outcomes, but UKA and TKA are better options to treat OA according to SUCRA rankings by comparing complications, revisions, and reoperations. KJD is an imperfect option for treating OA. Other treatments should be carefully considered for each patient in accordance with their actual conditions. However, this conclusion is limited by the selection of reviewed publications and individual variation of surgical indications for patients. Trial registration This study was registered with Research Registry (reviewregistry1395).

Published
2023
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69. Bagging Improves the Performance of Deep Learning-Based Semantic Segmentation with Limited Labeled Images: A Case Study of Crop Segmentation for High-Throughput Plant Phenotyping

Author

Yinglun Zhan, Yuzhen Zhou, Geng Bai, and Yufeng Ge

Subjects
field-based high-throughput plant phenotyping (FHTPP), high-resolution RGB image, semantic segmentation, deep learning, bagging, Chemical technology, TP1-1185
Abstract

Advancements in imaging, computer vision, and automation have revolutionized various fields, including field-based high-throughput plant phenotyping (FHTPP). This integration allows for the rapid and accurate measurement of plant traits. Deep Convolutional Neural Networks (DCNNs) have emerged as a powerful tool in FHTPP, particularly in crop segmentation—identifying crops from the background—crucial for trait analysis. However, the effectiveness of DCNNs often hinges on the availability of large, labeled datasets, which poses a challenge due to the high cost of labeling. In this study, a deep learning with bagging approach is introduced to enhance crop segmentation using high-resolution RGB images, tested on the NU-Spidercam dataset from maize plots. The proposed method outperforms traditional machine learning and deep learning models in prediction accuracy and speed. Remarkably, it achieves up to 40% higher Intersection-over-Union (IoU) than the threshold method and 11% over conventional machine learning, with significantly faster prediction times and manageable training duration. Crucially, it demonstrates that even small labeled datasets can yield high accuracy in semantic segmentation. This approach not only proves effective for FHTPP but also suggests potential for broader application in remote sensing, offering a scalable solution to semantic segmentation challenges. This paper is accompanied by publicly available source code.

Published
2024
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70. The influence of up-wave barge motion on the water resonance at a narrow gap between two rectangular barges under waves in the sea

Author

Jin, R, Ning, DZ, Bai, Wei, Geng, B, Jin, R, Ning, DZ, Bai, Wei, and Geng, B

Abstract

A three-dimensional time-domain potential flow model is developed and applied to simulate the wave resonance in a gap between two side-by-side rectangular barges. A fourth-order predict-correct method is implemented to update free surface boundary conditions. The response of an up-wave barge is predicted by solving the motion equation with the Newmark-β method. Following the validation of the developed numerical model for wave radiation and diffraction around two side-by-side barges, the influence of up-wave barge motion on the gap surfaceresonance is investigated in two different locations of the up-wave barge relative to the back-wave barge at various frequencies. The results reveal that the freely floating up-wave barge significantly influences the resonance frequency and the resonance wave amplitude. Simultaneously, the up-wave barge located in the middle of the back-wave barge leads to a reduction in the resonance wave amplitude and motion response when compared with other configurations.

Published
2018

71. Mass-production of mesoporous MnCo₂O₄ spinels with manganese(IV)- and cobalt(II)-rich surfaces for superior bifunctional oxygen electrocatalysis

Author

Wang, W. (Wenhai), Kuai, L. (Long), Cao, W. (Wei), Huttula, M. (Marko), Ollikkala, S. (Sami), Ahopelto, T. (Taru), Honkanen, A. (Ari‐Pekka), Huotari, S. (Simo), Yu, M. (Mengkang), and Geng, B. (Baoyou)

Abstract

A mesoporous MnCo₂O₄ electrode material is made for bifunctional oxygen electrocatalysis. The MnCo₂O₄ exhibits both Co₃O₄-like activity for oxygen evolution reaction (OER) and Mn₂O₃-like performance for oxygen reduction reaction (ORR). The potential difference between the ORR and OER of MnCo₂O₄ is as low as 0.83 V. By XANES and XPS investigation, the notable activity results from the preferred MnIV- and CoII-rich surface. The electrode material can be obtained on large-scale with the precise chemical control of the components at relatively low temperature. The surface state engineering may open a new avenue to optimize the electrocatalysis performance of electrode materials. The prominent bifunctional activity shows that MnCo₂O₄ could be used in metal–air batteries and/or other energy devices.

Published
2017

72. MiR-34a affects G2 arrest in prostate cancer PC3 cells via Wnt pathway and inhibits cell growth and migration.

Author

DONG, B., XU, G.-C., LIU, S.-T., LIU, T., and GENG, B.

Abstract

OBJECTIVE: To investigate the effect and mechanism of miRNA-34a overexpression on proliferation and migration of PC3 prostate cancer cells. PATIENTS AND METHODS: Benign prostatic hyperplasia tissue (30 cases), prostate cancer tissue (30 cases), and prostate paracancerous tissue (30 cases) were collected. Levels of miRNA-34a in these fresh tissues were measured by fluorescence quantitative PCR. PC3 cells were divided into non-loaded group and overexpression group. Cells in the non-loaded group were transfected with non-loaded plasmid. Cells in the overexpression group were transfected with miRNA-34a plasmid, and the miRNA-34a level was determined by fluorescence quantitative PCR to confirm the overexpression. Cell proliferation was analyzed by CCK-8 assay. Cell migration rate was measured by cell scratch assay. Flow cytometry was used to detect apoptosis and analyze cell cycle. Western blot was used to measure the expression levels of β-catenin, E-cadherin and Vimentin. RESULTS: The expression level of miRNA-34a in prostate cancer tissue was significantly lower than that in prostate paracancerous tissue. Dual-Luciferase reporter gene assay was used to analyze the transcriptional activity of Wnt1 gene. The proliferation and migration of PC3 cells were significantly decreased after overexpression of miRNA-34a, and the differences were statistically significant compared with those in the non-loaded group (p<0.05). Flow cytometry analysis showed that in the overexpression group, the apoptotic rate, as well as the proportion of cells in the G2 phase, was significantly higher than that in the non-loaded group (p<0.05). The β-catenin level in the nucleus of PC3 cells was significantly reduced after overexpression of miRNA-34a. The total protein levels of β-catenin and Vimentin were significantly decreased, whereas the level of E-cadherin in the overexpression group was apparently increased, compared with that in the non-loaded group. The Dual-Luciferase reporter gene showed a decrease in the relative fluorescence intensity of Wnt1 after overexpression of miR-34a (p<0.05). CONCLUSIONS: Overexpression of miRNA-34a inhibits Wnt/β-catenin pathway by regulating the transcriptional activity of Wnt1, thereby regulating the proliferation and migration of PC3 cells and promoting apoptosis. [ABSTRACT FROM AUTHOR]

Published
2020

76. Micro-Raman and infrared properties of SnO[sub 2] nanobelts synthesized from Sn and SiO[sub 2] powders.

Author

Peng, X. S., Zhang, L. D., Meng, G. W., Tian, Y. T., Lin, Y., Geng, B. Y., and Sun, S. H.

Subjects
TIN compounds, RAMAN effect
Abstract

Rutile structured SnO[sub 2] nanobelts have been synthesized from the mixture of Sn powders and SiO[sub 2] nanoparticle powders. Each nanobelt has a uniform width of about several hundred nanometers and a thickness of about tens of nanometers along its entire length. Micro-Raman spectrum measurement on the SnO[sub 2] nanobelts shows that the first-order Raman A[sub 1g] mode (632.9 cm[sup -1]) is very strong, and two weak Raman bands 498 and 694 cm[sup -1] seem to correspond to infrared (IR)-active longitudinal optical (LO) and transverse optical (TO) of A[sub 2u] modes. In addition, the IR spectrum of the SnO[sub 2] nanobelts shows the A[sub 2u] (LO) (701.9 cm[sup -1]) and E[sub u (1)] (TO) (634.5 cm[sup -1]) modes and one surface mode (565.2 cm[sup -1]). The IR-active bands in the Raman spectrum and the surface mode in IR spectrum, which may be due to the nanoscale morphology of the nanobelts. [ABSTRACT FROM AUTHOR]

Published
2003
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77. Logical Resolving-Based Methodology for Efficient Reliability Analysis

Author

Zhengguang Tang, Cong Li, Hailong You, Xingming Liu, Yu Wang, Yong Dai, Geng Bai, and Xiaoling Lin

Subjects
reliability simulation acceleration, bias temperature instability (BTI), stress probability evaluation, static timing analysis, Mechanical engineering and machinery, TJ1-1570
Abstract

With the CMOS technology downscaling to the deep nanoscale, the aging effects of devices degrade circuit performance and even lead to functional failure. The stress analysis is critical to evaluate the influence of aging effects on digital circuits. Some related analytical work has recently focused on reliability-aware circuit analysis. Nevertheless, the aging dependence among different devices is not considered, which will induce errors of degradation evaluation in the digital circuit. In order to improve the accuracy of reliability-aware static timing analysis, an improved analytical method is proposed by employing logical resolving. Experimental results show that the proposed method has a better evaluation accuracy of aging path delay than traditional strategies. For aging timing evaluation on aging paths, excessive pessimism can be reduced by employing the proposed method. And, a 378× speedup is achieved while having a 0.56% relative error compared with precise SPICE simulation. Moreover, the circuit performance sacrifice of an aging-aware synthesis flow with the proposed method can be decreased. Due to the high efficiency and high accuracy, the proposed method can meet the speed demands of large-scale digital circuit reliability analysis while achieving transistor simulation accuracy.

Published
2023
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80. Search for gravitational waves from intermediate mass binary black holes

Author

J. Abadie, 1, Abbott, a. B. P., Abbott, a. R., Abbott, a. T. D., Abernathy, a. M., Accadia, a. T., Acernese, b. F., Adams, b. C., Adhikari, a. R., Affeldt, a. C., Agathos, a. M., Agatsuma, b. K., Ajith, a. P., Allen, a. B., Amador Ceron, a. E., Amariutei, a. D., Anderson, a. S. B., Anderson, a. W. G., Arai, a. K., Arain, a. M. A., Araya, a. M. C., Aston, a. S. M., Astone, a. P., 14a, Atkinson, b. D., Aufmuth, a. P., Aulbert, a. C., Aylott, a. B. E., Babak, a. S., Baker, a. P., Ballardin, a. G., Ballmer, b. S., Barayoga, a. J. C. B., Barker, a. D., Barone, a. F., Barr, b. B., Barsotti, a. L., Barsuglia, a. M., Barton, b. M. A., Bartos, a. I., Bassiri, a. R., Bastarrika, a. M., Basti, a. A., 23a, 23b, Batch, b. J., Bauchrowitz, a. J., Th S. Bauer, a. T.h. S. Bauer, Bebronne, b. M., Beck, b. D., Behnke, a. B., Bejger, a. M., 25c, Beker, b. M. G., Bell, b. A. S., Belletoile, a. A., Belopolski, b. I., Benacquista, a. M., Berliner, a. J. M., Bertolini, a. A., Betzwieser, a. J., Beveridge, a. N., Beyersdorf, a. P. T., Bilenko, a. I. A., Billingsley, a. G., Birch, a. J., Biswas, a. R., Bitossi, a. M., Bizouard, b. M. A., 29a, Black, b. E., Blackburn, a. J. K., Blackburn, a. L., Blair, a. D., Bland, a. B., Blom, a. M., Bock, b. O., Bodiya, a. T. P., Bogan, a. C., Bondarescu, a. R., Bondu, a. F., 33b, Bonelli, b. L., Bonnand, b. R., Bork, b. R., Born, a. M., Boschi, a. V., Bose, b. S., Bosi, a. L., 36a, Bouhou, a. B., Braccini, b. S., Bradaschia, b. C., Brady, b. P. R., Braginsky, a. V. B., Branchesi, a. M., 37a, 37b, Brau, b. J. E., Breyer, a. J., Briant, a. T., Bridges, b. D. O., Brillet, a. A., 33a, Brinkmann, b. M., Brisson, a. V., Britzger, b. M., Brooks, a. A. F., Brown, a. D. A., Bulik, a. T., 25b, Bulten, b. H. J., Buonanno, b. A., Burguet Castell, a. J., Buskulic, a. D., Buy, b. C., Byer, b. R. L., Cadonati, a. L., Cagnoli, a. G., Camp, b. J. B., Campsie, a. P., Cannizzo, a. J., Cannon, a. K., Canuel, a. B., Cao, b. J., Capano, a. C. D., Carbognani, a. F., Carbone, b. L., Caride, a. S., Caudill, a. S., Cavaglia, a. M., Cavalier, a. F., Cavalieri, b. R., Cella, b. G., Cepeda, b. C., Cesarini, a. E., Chaibi, b. O., Chalermsongsak, b. T., Charlton, a. P., Chassande Mottin, a. E., Chelkowski, b. S., Chen, a. W., Chen, a. X., Chen, a. Y., Chincarini, a. A., Chiummo, b. A., Cho, b. H., Chow, a. J., Christensen, a. N., Chua, a. S. S. Y., Chung, a. C. T. Y., Chung, a. S., Ciani, a. G., Clara, a. F., Clark, a. D. E., Clark, a. J., Clayton, a. J. H., Cleva, a. F., Coccia, b. E., 55a, 55b, Cohadon, b. P. F., Colacino, b. C. N., Colas, b. J., Colla, b. A., 14b, Colombini, b. M., Conte, b. A., Conte, b. R., Cook, a. D., Corbitt, a. T. R., Cordier, a. M., Cornish, a. N., Corsi, a. A., Costa, a. C. A., Coughlin, a. M., Coulon, a. J. P., Couvares, b. P., Coward, a. D. M., Cowart, a. M., Coyne, a. D. C., Creighton, a. J. D. E., Creighton, a. T. D., Cruise, a. A. M., Cumming, a. A., Cunningham, a. L., Cuoco, a. E., Cutler, b. R. 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V., 29b, Lormand, b. M., Losurdo, a. G., Lough, b. J., Luan, a. J., Lubinski, a. M., Lu¨ck, a. H., Lundgren, a. A. P., Macdonald, a. E., Machenschalk, a. B., Macinnis, a. M., Macleod, a. D. M., Mageswaran, a. M., Mailand, a. K., Majorana, a. E., Maksimovic, b. I., Man, b. N., Mandel, b. I., Mandic, a. V., Mantovani, a. M., 23c, Marandi, b. A., Marchesoni, a. F., Marion, a. F., Ma´rka, b. S., Ma´rka, a. Z., Markosyan, a. A., Maros, a. E., Marque, a. J., Martelli, b. F., Martin, b. I. W., Martin, a. R. M., Marx, a. J. N., Mason, a. K., Masserot, a. A., Matichard, b. F., Matone, a. L., Matzner, a. R. A., Mavalvala, a. N., Mazzolo, a. G., Mccarthy, a. R., Mcclelland, a. D. E., Mcguire, a. S. C., Mcintyre, a. G., Mciver, a. J., Mckechan, a. D. J. A., Mcwilliams, a. S., Meadors, a. G. D., Mehmet, a. M., Meier, a. T., Melatos, a. A., Melissinos, a. A. C., Mendell, a. G., Mercer, a. R. A., Meshkov, a. S., Messenger, a. C., Meyer, a. M. S., Miao, a. H., Michel, a. C., Miller, b. J., Minenkov, a. Y., Mitrofanov, b. V. P., Mitselmakher, a. G., Mittleman, a. R., Miyakawa, a. O., Moe, a. B., Mohan, a. M., Mohanty, b. S. D., Mohapatra, a. S. R. P., Moraru, a. D., Moreno, a. G., Morgado, a. N., Morgia, b. A., Mori, b. T., Morriss, a. S. R., Mossavi, b. K., Mours, a. B., Mow Lowry, b. C. M., Mueller, a. C. L., Mueller, a. G., Mukherjee, a. S., Mullavey, a. A., Mu¨ ller Ebhardt, a. H., Munch, a. J., Murphy, a. D., Murray, a. P. G., Mytidis, a. A., Nash, a. T., Naticchioni, a. L., Necula, b. V., Nelson, a. J., Newton, a. G., Nguyen, a. T., Nishizawa, a. A., Nitz, a. A., Nocera, a. F., Nolting, b. D., Normandin, a. M. E., Nuttall, a. L., Ochsner, a. E., O’Dell, a. J., Oelker, a. E., Ogin, a. G. H., Oh, a. J. J., Oh, a. S. H., O’Reilly, a. B., O’Shaughnessy, a. R., Osthelder, a. C., Ott, a. C. D., Ottaway, a. D. J., Ottens, a. R. S., Overmier, a. H., Owen, a. B. J., Page, a. A., Pagliaroli, a. G., Palladino, b. L., Palomba, b. C., Pan, b. Y., Pankow, a. C., Paoletti, a. 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M. R., Smith, a. R. J. E., Smith Lefebvre, a. N. D., Somiya, a. K., Sorazu, a. B., Soto, a. J., Speirits, a. F. C., Sperandio, a. L., Stefszky, b. M., Stein, a. A. J., Stein, a. L. C., Steinert, a. E., Steinlechner, a. J., Steinlechner, a. S., Steplewski, a. S., Stochino, a. A., Stone, a. R., Strain, a. K. A., Strigin, a. S. E., Stroeer, a. A. S., Sturani, a. R., Stuver, b. A. L., Summerscales, a. T. Z., Sung, a. M., Susmithan, a. S., Sutton, a. P. J., Swinkels, a. B., Tacca, b. M., Taffarello, b. L., 59c, Talukder, b. D., Tanner, a. D. B., Tarabrin, a. S. P., Taylor, a. J. R., Taylor, a. R., Thomas, a. P., Thorne, a. K. A., Thorne, a. K. S., Thrane, a. E., Thu¨ring, a. A., Tokmakov, a. K. V., Tomlinson, a. C., Toncelli, a. A., Tonelli, b. M., Torre, b. O., Torres, b. C., Torrie, a. C. I., Tournefier, a. E., Travasso, b. F., Traylor, a. G., Tseng, a. K., Ugolini, a. D., Vahlbruch, a. H., Vajente, a. G., van den Brand, b. J. F. J., Van Den Broeck, b. C., van der Putten, b. S., van Veggel, b. A. A., Vass, a. S., Vasuth, a. M., Vaulin, b. R., Vavoulidis, a. M., Vecchio, b. A., Vedovato, A.g., Veitch, b. J., Veitch, a. P. J., Veltkamp, a. C., Verkindt, a. D., Vetrano, b. F., Vicere, b. A., Villar, b. A. E., Vinet, a. J. Y., Vitale, b. S., Vitale, a. S., Vocca, b. H., Vorvick, a. C., Vyatchanin, a. S. P., Wade, a. A., Wade, a. L., Wade, a. M., Waldman, a. S. J., Wallace, a. L., Wan, a. Y., Wang, a. M., Wang, a. X., Wang, a. Z., Wanner, a. A., Ward, a. R. L., Was, b. M., Weinert, b. M., Weinstein, a. A. J., Weiss, a. R., Wen, a. L., Wessels, a. P., West, a. M., Westphal, a. T., Wette, a. K., Whelan, a. J. T., Whitcomb, a. S. E., White, a. D. J., Whiting, a. B. F., Wilkinson, a. C., Willems, a. P. A., Williams, a. L., Williams, a. R., Willke, a. B., Winkelmann, a. L., Winkler, a. W., Wipf, a. C. C., Wiseman, a. A. G., Wittel, a. H., Woan, a. G., Wooley, a. R., Worden, a. J., Yakushin, a. I., Yamamoto, a. H., Yamamoto, a. K., 59d, A, Yancey, b. C. C., Yang, a. H., Yeaton Massey, a. D., Yoshida, a. S., Yu, a. P., Yvert, a. M., Zadroz´ny, b. A., Zanolin, b. M., Zendri, a. J. P., Zhang, b. F., Zhang, a. L., Zhang, a. W., Zhao, a. C., Zotov, a. N., Zucker, a. M. E., J. Zweizig, a, J. Zweizig, PINTO, INNOCENZO, CALLONI, ENRICO, DE ROSA, ROSARIO, GARUFI, FABIO, MILANO, LEOPOLDO, MOSCA, simona, PARISI, MARIA, Laboratoire d'Annecy de Physique des Particules (LAPP), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS), Unité Scientifique de la Station de Nançay (USN), Observatoire des Sciences de l'Univers en région Centre (OSUC), Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université d'Orléans (UO)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire de Paris, Université Paris sciences et lettres (PSL)-Université d'Orléans (UO)-Centre National de la Recherche Scientifique (CNRS), Laboratoire de l'Accélérateur Linéaire (LAL), Université Paris-Sud - Paris 11 (UP11)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS), Institut de Physique de Rennes (IPR), Université de Rennes (UR)-Centre National de la Recherche Scientifique (CNRS), Laboratoire des matériaux avancés (LMA), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS), Astrophysique Relativiste Théories Expériences Métrologie Instrumentation Signaux (ARTEMIS), Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire de la Côte d'Azur, COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Université Côte d'Azur (UCA)-Université Côte d'Azur (UCA)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Observatoire de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire des Sciences de l'Univers en région Centre (OSUC), Université Paris sciences et lettres (PSL)-Université d'Orléans (UO)-Centre National de la Recherche Scientifique (CNRS)-Université d'Orléans (UO), Centre National de la Recherche Scientifique (CNRS)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Université Paris-Sud - Paris 11 (UP11), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon, Centre National de la Recherche Scientifique (CNRS)-Observatoire de la Côte d'Azur, Université Côte d'Azur (UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Université Côte d'Azur (UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA), The LIGO Scientific Collaboration, The Virgo Collaboration, Laboratoire d'Annecy de Physique des Particules (LAPP/Laboratoire d'Annecy-le-Vieux de Physique des Particules), APC - Cosmologie, Physique Corpusculaire et Cosmologie - Collège de France (PCC), Collège de France (CdF)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS)-Collège de France (CdF)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS)-AstroParticule et Cosmologie (APC (UMR_7164)), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Observatoire de Paris, PSL Research University (PSL)-PSL Research University (PSL)-Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Observatoire de Paris, PSL Research University (PSL)-PSL Research University (PSL)-Université Paris Diderot - Paris 7 (UPD7), Université de Lyon-Université de Lyon-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), AstroParticule et Cosmologie (APC (UMR_7164)), Université Nice Sophia Antipolis (... - 2019) (UNS), Université Côte d'Azur (UCA)-Université Côte d'Azur (UCA)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire de la Côte d'Azur, Université Côte d'Azur (UCA)-Centre National de la Recherche Scientifique (CNRS), APC - Gravitation (APC-Gravitation), PSL Research University (PSL)-PSL Research University (PSL)-Université Paris Diderot - Paris 7 (UPD7)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Observatoire de Paris, PSL Research University (PSL)-PSL Research University (PSL)-Université Paris Diderot - Paris 7 (UPD7)-Max-Planck-Institut für Gravitationsphysik ( Albert-Einstein-Institut ) (AEI), Max-Planck-Gesellschaft-Max-Planck-Gesellschaft, Laboratoire d'Annecy de Physique des Particules ( LAPP/Laboratoire d'Annecy-le-Vieux de Physique des Particules ), Institut National de Physique Nucléaire et de Physique des Particules du CNRS ( IN2P3 ) -Université Savoie Mont Blanc ( USMB [Université de Savoie] [Université de Chambéry] ) -Centre National de la Recherche Scientifique ( CNRS ), Physique Corpusculaire et Cosmologie - Collège de France ( PCC ), Collège de France ( CdF ) -Institut National de Physique Nucléaire et de Physique des Particules du CNRS ( IN2P3 ) -Université Paris Diderot - Paris 7 ( UPD7 ) -Centre National de la Recherche Scientifique ( CNRS ) -Collège de France ( CdF ) -Institut National de Physique Nucléaire et de Physique des Particules du CNRS ( IN2P3 ) -Université Paris Diderot - Paris 7 ( UPD7 ) -Centre National de la Recherche Scientifique ( CNRS ) -AstroParticule et Cosmologie ( APC - UMR 7164 ), Centre National de la Recherche Scientifique ( CNRS ) -Institut National de Physique Nucléaire et de Physique des Particules du CNRS ( IN2P3 ) -Observatoire de Paris-Université Paris Diderot - Paris 7 ( UPD7 ) -Commissariat à l'énergie atomique et aux énergies alternatives ( CEA ) -Centre National de la Recherche Scientifique ( CNRS ) -Observatoire de Paris-Commissariat à l'énergie atomique et aux énergies alternatives ( CEA ), Laboratoire de l'Accélérateur Linéaire ( LAL ), Université Paris-Sud - Paris 11 ( UP11 ) -Institut National de Physique Nucléaire et de Physique des Particules du CNRS ( IN2P3 ) -Centre National de la Recherche Scientifique ( CNRS ), Institut de Physique de Rennes ( IPR ), Université de Rennes 1 ( UR1 ), Université de Rennes ( UNIV-RENNES ) -Université de Rennes ( UNIV-RENNES ) -Centre National de la Recherche Scientifique ( CNRS ), Laboratoire des matériaux avancés ( LMA ), Université Claude Bernard Lyon 1 ( UCBL ), Université de Lyon-Université de Lyon-Institut National de Physique Nucléaire et de Physique des Particules du CNRS ( IN2P3 ) -Centre National de la Recherche Scientifique ( CNRS ) -Centre National de la Recherche Scientifique ( CNRS ), AstroParticule et Cosmologie ( APC - UMR 7164 ), Centre National de la Recherche Scientifique ( CNRS ) -Institut National de Physique Nucléaire et de Physique des Particules du CNRS ( IN2P3 ) -Observatoire de Paris-Université Paris Diderot - Paris 7 ( UPD7 ) -Commissariat à l'énergie atomique et aux énergies alternatives ( CEA ), Astrophysique Relativiste Théories Expériences Métrologie Instrumentation Signaux ( ARTEMIS ), Université Nice Sophia Antipolis ( UNS ), Université Côte d'Azur ( UCA ) -Université Côte d'Azur ( UCA ) -Institut national des sciences de l'Univers ( INSU - CNRS ) -Observatoire de la Côte d'Azur, Université Côte d'Azur ( UCA ) -Centre National de la Recherche Scientifique ( CNRS ), APC - Gravitation ( APC-Gravitation ), Centre National de la Recherche Scientifique ( CNRS ) -Institut National de Physique Nucléaire et de Physique des Particules du CNRS ( IN2P3 ) -Observatoire de Paris-Université Paris Diderot - Paris 7 ( UPD7 ) -Commissariat à l'énergie atomique et aux énergies alternatives ( CEA ) -Centre National de la Recherche Scientifique ( CNRS ) -Institut National de Physique Nucléaire et de Physique des Particules du CNRS ( IN2P3 ) -Observatoire de Paris-Université Paris Diderot - Paris 7 ( UPD7 ) -Commissariat à l'énergie atomique et aux énergies alternatives ( CEA ) -Max-Planck-Institut für Gravitationsphysik (Albert-Einstein-Institut), Max-Planck-Institut-Max-Planck-Institut, (Astro)-Particles Physics, Mathematical Analysis, J. 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F., Finn, b. L. S., Fiori, a. I., Fisher, b. R. P., Flaminio, a. R., Flanigan, b. M., Foley, a. S., Forsi, a. E., Forte, a. L. A., Fotopoulos, b. N., Fournier, a. J. D., Franc, b. J., Frasca, b. S., Frasconi, b. F., Frede, b. M., Frei, a. M., Frei, a. Z., Freise, a. A., Frey, a. R., Fricke, a. T. T., Friedrich, a. D., Fritschel, a. P., Frolov, a. V. V., Fujimoto, a. M. K., Fulda, a. P. J., Fyffe, a. M., Gair, a. J., Galimberti, a. M., Gammaitoni, b. L., 36b, Garcia, a. J., Garufi, Fabio, Ga´spa´r, b. M. E., Gemme, b. G., Geng, b. R., Genin, a. E., Gennai, b. A., Gergely, b. L. A. ´ ., Ghosh, a. S., Giaime, a. J. A., Giampanis, a. S., Giardina, a. K. D., Giazotto, a. A., Gil, b. S., Gill, a. C., Gleason, a. J., Goetz, a. E., Goggin, a. L. M., Gonza´lez, a. G., Gorodetsky, a. M. L., Goßler, a. S., Gouaty, a. R., Graef, b. C., Graff, a. P. B., Granata, a. M., Grant, b. A., Gras, a. S., Gray, a. C., Gray, a. N., Greenhalgh, a. R. J. S., Gretarsson, a. A. M., Greverie, a. C., Grosso, b. 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H., Woan, a. G., Wooley, a. R., Worden, a. J., Yakushin, a. I., Yamamoto, a. H., Yamamoto, a. K., 59d, A, Yancey, b. C. C., Yang, a. H., Yeaton Massey, a. D., Yoshida, a. S., Yu, a. P., Yvert, a. M., Zadroz´ny, b. A., Zanolin, b. M., Zendri, a. J. P., Zhang, b. F., Zhang, a. L., Zhang, a. W., Zhao, a. C., Zotov, a. N., Zucker, a. M. E., J. Zweizig, A, J., Zweizig, and Pinto, Innocenzo

Subjects
Nuclear and High Energy Physics, Physics and Astronomy (miscellaneous), Stellar mass, Astrophysics::High Energy Astrophysical Phenomena, FOS: Physical sciences, General Relativity and Quantum Cosmology (gr-qc), Astrophysics, Astrophysics::Cosmology and Extragalactic Astrophysics, FORMS, 01 natural sciences, General Relativity and Quantum Cosmology, Gravitational waves, [ PHYS.GRQC ] Physics [physics]/General Relativity and Quantum Cosmology [gr-qc], Settore FIS/05 - Astronomia e Astrofisica, interferometers, Binary black hole, 0103 physical sciences, Astronomy, Astrophysics and Cosmology, 010303 astronomy & astrophysics, Astrophysics::Galaxy Astrophysics, QC, LIGO Scientific Collaboration, QB, Physics, [PHYS]Physics [physics], Solar mass, 010308 nuclear & particles physics, Gravitational wave, 5TH, Settore FIS/01 - Fisica Sperimentale, Astronomy, Mass ratio, LIGO, 3. Good health, [PHYS.GRQC]Physics [physics]/General Relativity and Quantum Cosmology [gr-qc], GROWTH, Stellar black hole, [PHYS.ASTR]Physics [physics]/Astrophysics [astro-ph], CLUSTERS, INSPIRALLING COMPACT BINARIES
Abstract

We present the results of a weakly modeled burst search for gravitational waves from mergers of non-spinning intermediate mass black holes (IMBH) in the total mass range 100--450 solar masses and with the component mass ratios between 1:1 and 4:1. The search was conducted on data collected by the LIGO and Virgo detectors between November of 2005 and October of 2007. No plausible signals were observed by the search which constrains the astrophysical rates of the IMBH mergers as a function of the component masses. In the most efficiently detected bin centered on 88+88 solar masses, for non-spinning sources, the rate density upper limit is 0.13 per Mpc^3 per Myr at the 90% confidence level., Comment: 13 pages, 4 figures: data for plots and archived public version at https://dcc.ligo.org/cgi-bin/DocDB/ShowDocument?docid=62326, see also the public announcement at http://www.ligo.org/science/Publication-S5IMBH/

Published
2012

82. A High-Reliability 12T SRAM Radiation-Hardened Cell for Aerospace Applications

Author

Ruxue Yao, Hongliang Lv, Yuming Zhang, Xu Chen, Yutao Zhang, Xingming Liu, and Geng Bai

Subjects
multi-node upset, read stability, single-event effect, static random-access memory (SRAM), write ability, Mechanical engineering and machinery, TJ1-1570
Abstract

The static random-access memory (SRAM) cells used in the high radiation environment of aerospace have become highly vulnerable to single-event effects (SEE). Therefore, a 12T SRAM-hardened circuit (RHB-12T cell) for the soft error recovery is proposed using the radiation hardening design (RHBD) concept. To verify the performance of the RHB-12T, the proposed cell is simulated by the 28 nm CMOS process and compared with other hardened cells (Quatro-10T, WE-Quatro-12T, RHM-12T, RHD-12T, and RSP-14T). The simulation results show that the RHB-12T cell can recover not only from single-event upset caused by their sensitive nodes but also from single-event multi-node upset caused by their storage node pairs. The proposed cell exhibits 1.14×/1.23×/1.06× shorter read delay than Quatro-10T/WE-Quatro-12T/RSP-14T and 1.31×/1.11×/1.18×/1.37× shorter write delay than WE-Quatro-12T/RHM-12T/RHD-12T/RSP-14T. It also shows 1.35×/1.11×/1.04× higher read stability than Quatro-10T/RHM-12T/RHD-12T and 1.12×/1.04×/1.09× higher write ability than RHM-12T/RHD-12T/RSP-14T. All these improvements are achieved at the cost of a slightly larger area and power consumption.

Published
2023
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83. Query expansion by spatial co-occurrence for image retrieval

Author

Li, Y, Geng, B, Zha, ZJ, Tao, D, and Xu, C

Abstract

The well-known bag-of-features (BoF) model is widely utilized for large scale image retrieval. However, BoF model lacks the spatial information of visual words, which is informative for local features to build up meaningful visual patches. To compensate for the spatial information loss, in this paper, we propose a novel query expansion method called Spatial Co-occurrence Query Expansion (SCQE), by utilizing the spatial co-occurrence information of visual words mined from the database images to boost the retrieval performance. In offline phase, for each visual word in the vocabulary, we treat the visual words that are frequently co-occurred with it in the database images as neighbors, base on which a spatial co-occurrence graph is built. In online phase, a query image can be expanded with some spatial co-occurred but unseen visual words according to the spatial co-occurrence graph, and the retrieval performance can be improved by expanding these visual words appropriately. Experimental results demonstrate that, SCQE achieves promising improvements over the typical BoF baseline on two datasets comprising 5K and 505K images respectively. Copyright 2011 ACM.

Published
2011

84. Fatigue Strength Assessment of the River-Sea Bulk Carrier Based on Spectral Analysis Method.

Author

Xie, Y. H., Zhang, J. P., Wang, W., Li, G. Q., and Geng, B. Y.

Abstract

This paper focuses on the fatigue strength of river-sea direct link ships based on spectral method. A 45000 DWT river-sea bulk carrier is taken as an example, hydrodynamic analysis method and finite element analysis method are adopted using SESAM software to study its wave induced loads and bending-torsional strength of whole ship structure. Fatigue check points are chosen according to the stress and deformation distribution of hull structure. The spectral-based fatigue analysis is applied to gain four typical nodes' fatigue strength in two loading conditions, and their accumulative fatigue damage and life are obtained. Hot spots' stress response spectral and fatigue life spectral in per sea state are drawn to analyze the effect of different sea states on fatigue damage. Sub model technique is applied and fractions of time in loaded condition and ballast condition are taken into account during analysis process. [ABSTRACT FROM AUTHOR]

Published
2016

85. Manifold regularized multitask learning for semi-supervised multilabel image classification

Author

Luo, Y, Tao, D, Geng, B, Xu, C, Maybank, SJ, Luo, Y, Tao, D, Geng, B, Xu, C, and Maybank, SJ

Abstract

It is a significant challenge to classify images with multiple labels by using only a small number of labeled samples. One option is to learn a binary classifier for each label and use manifold regularization to improve the classification performance by exploring the underlying geometric structure of the data distribution. However, such an approach does not perform well in practice when images from multiple concepts are represented by high-dimensional visual features. Thus, manifold regularization is insufficient to control the model complexity. In this paper, we propose a manifold regularized multitask learning (MRMTL) algorithm. MRMTL learns a discriminative subspace shared by multiple classification tasks by exploiting the common structure of these tasks. It effectively controls the model complexity because different tasks limit one another's search volume, and the manifold regularization ensures that the functions in the shared hypothesis space are smooth along the data manifold. We conduct extensive experiments, on the PASCAL VOC'07 dataset with 20 classes and the MIR dataset with 38 classes, by comparing MRMTL with popular image classification algorithms. The results suggest that MRMTL is effective for image classification. © 1992-2012 IEEE.

Published
2013

86. FTACMT study protocol: a multicentre, double-blind, randomised, placebo-controlled trial of faecal microbiota transplantation for autism spectrum disorder

Author

Li Ling, Wei Wei, Li Lin, Li Ke, Ye Chen, Li Ning, Wang Yu, Zhang Xin, Yang Rong, Zhang Xueying, Cui Jiaqu, Chen Qiyi, Qin Huanlong, Du Yasong, Zhao Xiaoxin, Lu Jubao, Lv Xiaoqiong, Ma Chunlian, Chen Shidong, Kuang Guifang, Zhao Dongmei, Fang Shuanfeng, Zhang Xujing, Yang Binrang, Wang Yanxia, Yuan Song, Zhou Xiang, Zhang Beihua, Jiang Lin, Ji Hong, Yan Yinmei, Yan Peihua, Huang Linsheng, Zhang Shaoyi, Ma Chenhuan, Miao Yun, Ling Wenqi, Geng Bojing, Feng Xueying, Li Huilin, Liu Liyan, Wu Weijia, Li Dexin, and Jiang Qianru

Subjects
Medicine
Published
2022
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87. Parallel lasso for large-scale video concept detection

Author

Geng, B, Li, Y, Tao, D, Wang, M, Zha, ZJ, Xu, C, Geng, B, Li, Y, Tao, D, Wang, M, Zha, ZJ, and Xu, C

Abstract

Existing video concept detectors are generally built upon the kernel based machine learning techniques, e.g., support vector machines, regularized least squares, and logistic regression, just to name a few. However, in order to build robust detectors, the learning process suffers from the scalability issues including the high-dimensional multi-modality visual features and the large-scale keyframe examples. In this paper, we propose parallel lasso (Plasso) by introducing the parallel distributed computation to significantly improve the scalability of lasso (the regularized least squares). We apply the parallel incomplete Cholesky factorization to approximate the covariance statistics in the preprocess step, and the parallel primal-dual interior-point method with the Sherman-Morrison-Woodbury formula to optimize the model parameters. For a dataset with samples in a -dimensional space, compared with lasso, Plasso significantly reduces complexities from the original for computational time and for storage space to and respectively, if the system has $m$ processors and the reduced dimension is much smaller than the original dimension. Furthermore, we develop the kernel extension of the proposed linear algorithm with the sample reweighting schema, and we can achieve similar time and space complexity improvements [time complexity from to and the space complexity from to for a dataset with training examples]. Experimental results on TRECVID video concept detection challenges suggest that the proposed method can obtain significant time and space savings for training effective detectors with limited communication overhead. © 2006 IEEE.

Published
2012

88. Query difficulty estimation for image retrieval

Author

Li, Y, Geng, B, Yang, L, Xu, C, Bian, W, Li, Y, Geng, B, Yang, L, Xu, C, and Bian, W

Abstract

Query difficulty estimation predicts the performance of the search result of the given query. It is a powerful tool for multimedia retrieval and receives increasing attention. It can guide the pseudo relevance feedback to rerank the image search results and re-write the query by suggesting "easy" alternatives to obtain better search results. Many techniques to estimate the query difficulty have been proposed in the textual information retrieval, but directly employing them for image search will result in poor performance. That is because image query is more complex with spatial or structural information, and the well-known semantic gap induces extra burdens for accurate estimations. In this paper, we propose a query difficulty estimation approach by analyzing the top ranked images obtained by ad hoc retrieval models. Specifically, we seamlessly integrate the language model based clarity score, the spatial consistency of local descriptors and the appearance consistency of global features. Experimental results demonstrate that the query difficulty estimated by the proposed algorithm correlates well with the actual retrieval performance. Two applications of query difficulty estimation, namely guided pseudo relevance feedback (GPRF) and selective query refinement (SQR), are also proposed from both system and user perspectives. Experimental results show that both strategies further boost the retrieval performance. © 2012 Elsevier B.V..

Published
2012

89. Difficulty guided image retrieval using linear multiple feature embedding

Author

Li, Y, Geng, B, Tao, D, Zha, ZJ, Yang, L, Xu, C, Li, Y, Geng, B, Tao, D, Zha, ZJ, Yang, L, and Xu, C

Abstract

Existing image retrieval systems suffer from a performance variance for different queries. Severe performance variance may greatly degrade the effectiveness of the subsequent query-dependent ranking optimization algorithms, especially those that utilize the information mined from the initial search results. In this paper, we tackle this problem by proposing a query difficulty guided image retrieval system, which can predict the queries' ranking performance in terms of their difficulties and adaptively apply ranking optimization approaches. We estimate the query difficulty by comprehensively exploring the information residing in the query image, the retrieval results, and the target database. To handle the high-dimensional and multi-model image features in the large-scale image retrieval setting, we propose a linear multiple feature embedding algorithm which learns a linear transformation from a small set of data by integrating a joint subspace in which the neighborhood information is preserved. The transformation can be effectively and efficiently used to infer the subspace features of the newly observed data in the online setting. We prove the significance of query difficulty to image retrieval by applying it to guide the conduction of three retrieval refinement applications, i.e., reranking, federated search, and query suggestion. Thorough empirical studies on three datasets suggest the effectiveness and scalability of the proposed image query difficulty estimation algorithm, as well as the promising of the image difficulty guided retrieval system. © 1999-2012 IEEE.

Published
2012

90. DAML: Domain adaptation metric learning

Author

Geng, B, Tao, D, Xu, C, Geng, B, Tao, D, and Xu, C

Abstract

The state-of-the-art metric-learning algorithms cannot perform well for domain adaptation settings, such as cross-domain face recognition, image annotation, etc., because labeled data in the source domain and unlabeled ones in the target domain are drawn from different, but related distributions. In this paper, we propose the domain adaptation metric learning (DAML), by introducing a data-dependent regularization to the conventional metric learning in the reproducing kernel Hilbert space (RKHS). This data-dependent regularization resolves the distribution difference by minimizing the empirical maximum mean discrepancy between source and target domain data in RKHS. Theoretically, by using the empirical Rademacher complexity, we prove risk bounds for the nearest neighbor classifier that uses the metric learned by DAML. Practically, learning the metric in RKHS does not scale up well. Fortunately, we can prove that learning DAML in RKHS is equivalent to learning DAML in the space spanned by principal components of the kernel principle component analysis (KPCA). Thus, we can apply KPCA to select most important principal components to significantly reduce the time cost of DAML. We perform extensive experiments over four well-known face recognition datasets and a large-scale Web image annotation dataset for the cross-domain face recognition and image annotation tasks under various settings, and the results demonstrate the effectiveness of DAML. © 2011 IEEE.

Published
2011

91. Shared feature extraction for semi-supervised image classification

Author

Luo, Y, Tao, D, Geng, B, Xu, C, Maybank, S, Luo, Y, Tao, D, Geng, B, Xu, C, and Maybank, S

Abstract

Multi-task learning (MTL) plays an important role in image analysis applications, e.g. image classification, face recognition and image annotation. That is because MTL can estimate the latent shared subspace to represent the common features given a set of images from different tasks. However, the geometry of the data probability distribution is always supported on an intrinsic image sub-manifold that is embedded in a high dimensional Euclidean space. Therefore, it is improper to directly apply MTL to multiclass image classification. In this paper, we propose a manifold regularized MTL (MRMTL) algorithm to discover the latent shared subspace by treating the high-dimensional image space as a submanifold embedded in an ambient space. We conduct experiments on the PASCAL VOC'07 dataset with 20 classes and the MIR dataset with 38 classes by comparing MRMTL with conventional MTL and several representative image classification algorithms. The results suggest that MRMTL can properly extract the common features for image representation and thus improve the generalization performance of the image classification models. Copyright 2011 ACM.

Published
2011

92. Difficulty guided image retrieval using linear multiview embedding

Author

Li, Y, Geng, B, Zha, ZJ, Tao, D, Yang, L, Xu, C, Li, Y, Geng, B, Zha, ZJ, Tao, D, Yang, L, and Xu, C

Abstract

Existing image retrieval systems suffer from a radical performance variance for different queries. The bad initial search results for "difficult" queries may greatly degrade the performance of their subsequent refinements, especially the refinement that utilizes the information mined from the search results, e.g., pseudo relevance feedback based reranking. In this paper, we tackle this problem by proposing a query difficulty guided image retrieval system, which selectively performs reranking according to the estimated query difficulty. To improve the performance of both reranking and difficulty estimation, we apply multiview embedding (ME) to images represented by multiple different features for integrating a joint subspace by preserving the neighborhood information in each feature space. However, existing ME approaches suffer from both "out of sample" and huge computational cost problems, and cannot be applied to online reranking or offline large-scale data processing for practical image retrieval systems. Therefore, we propose a linear multiview embedding algorithm which learns a linear transformation from a small set of data and can effectively infer the subspace features of new data. Empirical evaluations on both Oxford and 500K ImageNet datasets suggest the effectiveness of the proposed difficulty guided retrieval system with LME. Copyright 2011 ACM.

Published
2011

93. Bregman divergence-based regularization for transfer subspace learning

Author

Si, S, Tao, D, Geng, B, Si, S, Tao, D, and Geng, B

Abstract

The regularization principals [31] lead approximation schemes to deal with various learning problems, e.g., the regularization of the norm in a reproducing kernel Hilbert space for the ill-posed problem. In this paper, we present a family of subspace learning algorithms based on a new form of regularization, which transfers the knowledge gained in training samples to testing samples. In particular, the new regularization minimizes the Bregman divergence between the distribution of training samples and that of testing samples in the selected subspace, so it boosts the performance when training and testing samples are not independent and identically distributed. To test the effectiveness of the proposed regularization, we introduce it to popular subspace learning algorithms, e.g., principal components analysis (PCA) for cross-domain face modeling; and Fisher's linear discriminant analysis (FLDA), locality preserving projections (LPP), marginal Fisher's analysis (MFA), and discriminative locality alignment (DLA) for cross-domain face recognition and text categorization. Finally, we present experimental evidence on both face image data sets and text data sets, suggesting that the proposed Bregman divergence-based regularization is effective to deal with cross-domain learning problems. © 2010 IEEE.

Published
2010

94. Ensemble Manifold Regularization

Author

Huttenlocher, D, Medioni, G, Rehg, J, Geng, B, Tao, D, Xu, C, Yang, L, Hua, X, Huttenlocher, D, Medioni, G, Rehg, J, Geng, B, Tao, D, Xu, C, Yang, L, and Hua, X

Abstract

We propose an automatic approximation of the intrinsic manifold for general semi-supervised learning problems. Unfortunately, it is not trivial to define an optimization function to obtain optimal hyperparameters. Usually, pure cross-validation is considered but it does not necessarily scale up. A second problem derives from the suboptimality incurred by discrete grid search and overfitting problems. As a consequence, we developed an ensemble manifold regularization (EMR) framework to approximate the intrinsic manifold by combining several initial guesses. Algorithmically, we designed EMR very carefully so that it (a) learns both the composite manifold and the semi-supervised classifier jointly; (b) is fully automatic for learning the intrinsic manifold hyperparameters implicitly; (c) is conditionally optimal for intrinsic manifold approximation under a mild and reasonable assumption; and (d) is scalable for a large number of candidate manifold hyperparameters, from both time and space perspectives. Extensive experiments over both synthetic and real datasets show the effectiveness of the proposed framework.

Published
2009

96. Discovery of Selective and Potent Inhibitors of Gram-positive Bacterial Thymidylate Kinase (TMK): Compund 16

Author

Martinez-Botella, G., primary, Breen, J., additional, Duffy, J., additional, Dumas, J., additional, Geng, B., additional, Gowers, I., additional, Green, O., additional, Guler, S., additional, Hentemann, M., additional, Hernandez-Juan, F., additional, Joseph-McCarthy, D., additional, Kawatkar, S., additional, Larsen, N., additional, Lazari, O., additional, Loch, J., additional, and Macritchie, J., additional

Published
2012
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