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1

Caro, Victor, Jou-Hui Ho, Scarlet Witting, and Felipe Tobar. "Modeling Neonatal EEG Using Multi-Output Gaussian Processes." IEEE Access 10 (2022): 32912–27. http://dx.doi.org/10.1109/access.2022.3159653.

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2

Ingram, Martin, Damjan Vukcevic, and Nick Golding. "Multi‐output Gaussian processes for species distribution modelling." Methods in Ecology and Evolution 11, no. 12 (2020): 1587–98. http://dx.doi.org/10.1111/2041-210x.13496.

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3

Rodrigues, Filipe, Kristian Henrickson, and Francisco C. Pereira. "Multi-Output Gaussian Processes for Crowdsourced Traffic Data Imputation." IEEE Transactions on Intelligent Transportation Systems 20, no. 2 (2019): 594–603. http://dx.doi.org/10.1109/tits.2018.2817879.

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4

Vasudevan, Shrihari, Arman Melkumyan, and Steven Scheding. "Efficacy of Data Fusion Using Convolved Multi-Output Gaussian Processes." Journal of Data Science 13, no. 2 (2021): 341–68. http://dx.doi.org/10.6339/jds.201504_13(2).0007.

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5

Truffinet, Olivier, Karim Ammar, Jean-Philippe Argaud, Nicolas Gérard Castaing, and Bertrand Bouriquet. "Adaptive sampling of homogenized cross-sections with multi-output gaussian processes." EPJ Web of Conferences 302 (2024): 02010. http://dx.doi.org/10.1051/epjconf/202430202010.

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In another talk submitted to this conference, we presented an efficient new framework based on multi-outputs gaussian processes (MOGP) for the interpolation of few-groups homogenized cross-sections (HXS) inside deterministic core simulators. We indicated that this methodology authorized a principled selection of interpolation points through adaptive sampling. We here develop this idea by trying simple sampling schemes on our problem. In particular, we compare sample scoring functions with and without integration of leave-one-out errors, and obtained with single-output and multi-output gaussian
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Ramirez, Wilmer Ariza, Juš Kocijan, Zhi Quan Leong, Hung Duc Nguyen, and Shantha Gamini Jayasinghe. "Dynamic System Identification of Underwater Vehicles Using Multi-Output Gaussian Processes." International Journal of Automation and Computing 18, no. 5 (2021): 681–93. http://dx.doi.org/10.1007/s11633-021-1308-x.

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7

Truffinet, Olivier, Karim Ammar, Jean-Philippe Argaud, Nicolas Gérard Castaing, and Bertrand Bouriquet. "Multi-output gaussian processes for the reconstruction of homogenized cross-sections." EPJ Web of Conferences 302 (2024): 02006. http://dx.doi.org/10.1051/epjconf/202430202006.

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Deterministic nuclear reactor simulators employing the prevalent two-step scheme often generate a substantial amount of intermediate data at the interface of their two subcodes, which can impede the overall performance of the software. The bulk of this data comprises “few-groups homogenized cross-sections” or HXS, which are stored as tabulated multivariate functions and interpolated inside the core simulator. A number of mathematical tools have been studied for this interpolation purpose over the years, but few meet all the challenging requirements of neutronics computation chains: extreme acc
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Lu, Chi-Ken, and Patrick Shafto. "Conditional Deep Gaussian Processes: Multi-Fidelity Kernel Learning." Entropy 23, no. 11 (2021): 1545. http://dx.doi.org/10.3390/e23111545.

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Deep Gaussian Processes (DGPs) were proposed as an expressive Bayesian model capable of a mathematically grounded estimation of uncertainty. The expressivity of DPGs results from not only the compositional character but the distribution propagation within the hierarchy. Recently, it was pointed out that the hierarchical structure of DGP well suited modeling the multi-fidelity regression, in which one is provided sparse observations with high precision and plenty of low fidelity observations. We propose the conditional DGP model in which the latent GPs are directly supported by the fixed lower
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Torres-Valencia, Cristian, Álvaro Orozco, David Cárdenas-Peña, Andrés Álvarez-Meza, and Mauricio Álvarez. "A Discriminative Multi-Output Gaussian Processes Scheme for Brain Electrical Activity Analysis." Applied Sciences 10, no. 19 (2020): 6765. http://dx.doi.org/10.3390/app10196765.

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The study of brain electrical activity (BEA) from different cognitive conditions has attracted a lot of interest in the last decade due to the high number of possible applications that could be generated from it. In this work, a discriminative framework for BEA via electroencephalography (EEG) is proposed based on multi-output Gaussian Processes (MOGPs) with a specialized spectral kernel. First, a signal segmentation stage is executed, and the channels from the EEG are used as the model outputs. Then, a novel covariance function within the MOGP known as the multispectral mixture kernel (MOSM)
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Bae, Joonho, and Jinkyoo Park. "Count-based change point detection via multi-output log-Gaussian Cox processes." IISE Transactions 52, no. 9 (2019): 998–1013. http://dx.doi.org/10.1080/24725854.2019.1676937.

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Ariza Ramirez, Wilmer, Zhi Quan Leong, Hung Nguyen, and Shantha Gamini Jayasinghe. "Non-parametric dynamic system identification of ships using multi-output Gaussian Processes." Ocean Engineering 166 (October 2018): 26–36. http://dx.doi.org/10.1016/j.oceaneng.2018.07.056.

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Liu, Yiqi, Yongping Pan, Daoping Huang, and Qilin Wang. "Fault prognosis of filamentous sludge bulking using an enhanced multi-output gaussian processes regression." Control Engineering Practice 62 (May 2017): 46–54. http://dx.doi.org/10.1016/j.conengprac.2017.02.003.

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13

Garcia, H. F., A. Davey, A. E. Salazar-Jimenez, M. A. Alvarez, and E. Vasquez Osorio. "PP01.14 FEASIBILITY OF USING MULTI-OUTPUT GAUSSIAN PROCESSES TO MODEL ANATOMICAL CHANGES FOR PAEDIATRIC APPLICATIONS." Physica Medica 125 (September 2024): 103584. http://dx.doi.org/10.1016/j.ejmp.2024.103584.

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14

Zhang, Zhao, and Junsheng Ren. "Non-parametric dynamics modeling for unmanned surface vehicle using spectral metric multi-output Gaussian processes learning." Ocean Engineering 292 (January 2024): 116491. http://dx.doi.org/10.1016/j.oceaneng.2023.116491.

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15

Phillips, Toby R. F., Claire E. Heaney, Ellyess Benmoufok, et al. "Multi-Output Regression with Generative Adversarial Networks (MOR-GANs)." Applied Sciences 12, no. 18 (2022): 9209. http://dx.doi.org/10.3390/app12189209.

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Regression modelling has always been a key process in unlocking the relationships between independent and dependent variables that are held within data. In recent years, machine learning has uncovered new insights in many fields, providing predictions to previously unsolved problems. Generative Adversarial Networks (GANs) have been widely applied to image processing producing good results, however, these methods have not often been applied to non-image data. Seeing the powerful generative capabilities of the GANs, we explore their use, here, as a regression method. In particular, we explore th
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Wang, Zhenglang, Zao Feng, Zhaojun Ma, and Jubo Peng. "A Multi-Output Regression Model for Energy Consumption Prediction Based on Optimized Multi-Kernel Learning: A Case Study of Tin Smelting Process." Processes 12, no. 1 (2023): 32. http://dx.doi.org/10.3390/pr12010032.

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Energy consumption forecasting plays an important role in energy management, conservation, and optimization in manufacturing companies. Aiming at the tin smelting process with multiple types of energy consumption and a strong coupling with energy consumption, the traditional prediction model cannot be applied to the multi-output problem. Moreover, the data collection frequency of different processes is inconsistent, resulting in few effective data samples and strong nonlinearity. In this paper, we propose a multi-kernel multi-output support vector regression model optimized based on a differen
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Lee, L. A., K. S. Carslaw, K. J. Pringle, G. W. Mann, and D. V. Spracklen. "Emulation of a complex global aerosol model to quantify sensitivity to uncertain parameters." Atmospheric Chemistry and Physics 11, no. 23 (2011): 12253–73. http://dx.doi.org/10.5194/acp-11-12253-2011.

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Abstract. Sensitivity analysis of atmospheric models is necessary to identify the processes that lead to uncertainty in model predictions, to help understand model diversity through comparison of driving processes, and to prioritise research. Assessing the effect of parameter uncertainty in complex models is challenging and often limited by CPU constraints. Here we present a cost-effective application of variance-based sensitivity analysis to quantify the sensitivity of a 3-D global aerosol model to uncertain parameters. A Gaussian process emulator is used to estimate the model output across m
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Nikolaidis, Efstratios, Anastassios N. Perakis, and Michael G. Parsons. "Probabilistic Torsional Vibration Analysis of a Motor Ship Propulsion Shafting System: The Input-Output Problem." Journal of Ship Research 31, no. 01 (1987): 41–52. http://dx.doi.org/10.5957/jsr.1987.31.1.41.

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A probabilistic approach to the torsional vibration analysis of a marine diesel engine propulsion shafting system is developed. The diesel engine and propeller torsional excitation are modeled probabilistically. The statistical properties of the resulting torsional vibratory shear stress in each element of the shafting system are determined by solving the corresponding input-output problem. The shafting system is considered as a multi-input linear system with the propeller and the cylinder torsional excitation as inputs and the torsional vibratory stress as the output. Under the assumption tha
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19

Pastrana-Cortés, Julián David, Julian Gil-Gonzalez, Andrés Marino Álvarez-Meza, David Augusto Cárdenas-Peña, and Álvaro Angel Orozco-Gutiérrez. "Scalable and Interpretable Forecasting of Hydrological Time Series Based on Variational Gaussian Processes." Water 16, no. 14 (2024): 2006. http://dx.doi.org/10.3390/w16142006.

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Accurate streamflow forecasting is crucial for effectively managing water resources, particularly in countries like Colombia, where hydroelectric power generation significantly contributes to the national energy grid. Although highly interpretable, traditional deterministic, physically-driven models often suffer from complexity and require extensive parameterization. Data-driven models like Linear Autoregressive (LAR) and Long Short-Term Memory (LSTM) networks offer simplicity and performance but cannot quantify uncertainty. This work introduces Sparse Variational Gaussian Processes (SVGPs) fo
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Lee, L. A., K. S. Carslaw, K. Pringle, G. W. Mann, and D. V. Spracklen. "Emulation of a complex global aerosol model to quantify sensitivity to uncertain parameters." Atmospheric Chemistry and Physics Discussions 11, no. 7 (2011): 20433–85. http://dx.doi.org/10.5194/acpd-11-20433-2011.

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Abstract. Sensitivity analysis of atmospheric models is necessary to identify the processes that lead to uncertainty in model predictions, to help understand model diversity, and to prioritise research. Assessing the effect of parameter uncertainty in complex models is challenging and often limited by CPU constraints. Here we present a cost-effective application of variance-based sensitivity analysis to quantify the sensitivity of a 3-D global aerosol model to uncertain parameters. A Gaussian process emulator is used to estimate the model output across multi-dimensional parameter space using i
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Lee, Seung Hwan. "Optimization of Cold Metal Transfer-Based Wire Arc Additive Manufacturing Processes Using Gaussian Process Regression." Metals 10, no. 4 (2020): 461. http://dx.doi.org/10.3390/met10040461.

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Wire and arc additive manufacturing (WAAM) is among the most promising additive manufacturing techniques for metals because it yields high productivity at low raw material costs. However, additional post-processing is required to remove redundant surface material from components manufactured by the WAAM process, and thus the productivity decreases. To increase productivity, multi-variable process parameters need to be optimized, including thermo-mechanical effects caused by high deposition rates. When the process is modeled, deposit shape and productivity are challenging to quantify due to unc
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Caballero, Gabriel, Alejandro Pezzola, Cristina Winschel, et al. "Synergy of Sentinel-1 and Sentinel-2 Time Series for Cloud-Free Vegetation Water Content Mapping with Multi-Output Gaussian Processes." Remote Sensing 15, no. 7 (2023): 1822. http://dx.doi.org/10.3390/rs15071822.

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Optical Earth Observation is often limited by weather conditions such as cloudiness. Radar sensors have the potential to overcome these limitations, however, due to the complex radar-surface interaction, the retrieving of crop biophysical variables using this technology remains an open challenge. Aiming to simultaneously benefit from the optical domain background and the all-weather imagery provided by radar systems, we propose a data fusion approach focused on the cross-correlation between radar and optical data streams. To do so, we analyzed several multiple-output Gaussian processes (MOGP)
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Shi, Yan, and Zhenzhou Lu. "Dynamic reliability analysis for structure with temporal and spatial multi-parameter." Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 233, no. 6 (2019): 1002–13. http://dx.doi.org/10.1177/1748006x19853413.

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For efficiently estimating the dynamic failure probability of the structure with random variables, stochastic processes and temporal and spatial multi-parameter, an estimation strategy is presented based on the random field transformation. The random field transformation focusing on the dynamic reliability with only one time parameter is further investigated, and it is extended to temporal and spatial multi-parameter issue, which simulates the output as multi-dimensional Gaussian random field. Also, the active learning Kriging method is used to construct the surrogate models for the mean funct
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Kamhi, Souha, Shuai Zhang, Mohamed Ait Amou, et al. "Multi-Classification of Motor Imagery EEG Signals Using Bayesian Optimization-Based Average Ensemble Approach." Applied Sciences 12, no. 12 (2022): 5807. http://dx.doi.org/10.3390/app12125807.

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Motor Imagery (MI) classification using electroencephalography (EEG) has been extensively applied in healthcare scenarios for rehabilitation aims. EEG signal decoding is a difficult process due to its complexity and poor signal-to-noise ratio. Convolutional neural networks (CNN) have demonstrated their ability to extract time–space characteristics from EEG signals for better classification results. However, to discover dynamic correlations in these signals, CNN models must be improved. Hyperparameter choice strongly affects the robustness of CNNs. It is still challenging since the manual tunin
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Das Mou, Trisha, Saadia Binte Alam, Md Hasibur Rahman, Gautam Srivastava, Mahady Hasan, and Mohammad Faisal Uddin. "Multi-Range Sequential Learning Based Dark Image Enhancement with Color Upgradation." Applied Sciences 13, no. 2 (2023): 1034. http://dx.doi.org/10.3390/app13021034.

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Images under low-light conditions suffer from noise, blurring, and low contrast, thus limiting the precise detection of objects. For this purpose, a novel method is introduced based on convolutional neural network (CNN) dual attention unit (DAU) and selective kernel feature synthesis (SKFS) that merges with the Retinex theory-based model for the enhancement of dark images under low-light conditions. The model mentioned in this paper is a multi-scale residual block made up of several essential components equivalent to an onward convolutional neural network with a VGG16 architecture and various
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Carter, Jeremy, Erick A. Chacón-Montalván, and Amber Leeson. "Bayesian hierarchical model for bias-correcting climate models." Geoscientific Model Development 17, no. 14 (2024): 5733–57. http://dx.doi.org/10.5194/gmd-17-5733-2024.

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Abstract. Climate models, derived from process understanding, are essential tools in the study of climate change and its wide-ranging impacts. Hindcast and future simulations provide comprehensive spatiotemporal estimates of climatology that are frequently employed within the environmental sciences community, although the output can be afflicted with bias that impedes direct interpretation. Post-processing bias correction approaches utilise observational data to address this challenge, although they are typically criticised for not being physically justified and not considering uncertainty in
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Rauh, Andreas, Stefan Wirtensohn, Patrick Hoher, Johannes Reuter, and Luc Jaulin. "Reliability Assessment of an Unscented Kalman Filter by Using Ellipsoidal Enclosure Techniques." Mathematics 10, no. 16 (2022): 3011. http://dx.doi.org/10.3390/math10163011.

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The Unscented Kalman Filter (UKF) is widely used for the state, disturbance, and parameter estimation of nonlinear dynamic systems, for which both process and measurement uncertainties are represented in a probabilistic form. Although the UKF can often be shown to be more reliable for nonlinear processes than the linearization-based Extended Kalman Filter (EKF) due to the enhanced approximation capabilities of its underlying probability distribution, it is not a priori obvious whether its strategy for selecting sigma points is sufficiently accurate to handle nonlinearities in the system dynami
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Ziemer, Paulo G. P., Carlos A. Bulant, José I. Orlando, et al. "Automated lumen segmentation using multi-frame convolutional neural networks in intravascular ultrasound datasets." European Heart Journal - Digital Health 1, no. 1 (2020): 75–82. http://dx.doi.org/10.1093/ehjdh/ztaa014.

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Abstract Aims Assessment of minimum lumen areas in intravascular ultrasound (IVUS) pullbacks is time-consuming and demands adequately trained personnel. In this work, we introduce a novel and fully automated pipeline to segment the lumen boundary in IVUS datasets. Methods and results First, an automated gating is applied to select end-diastolic frames and bypass saw-tooth artefacts. Second, within a machine learning (ML) environment, we automatically segment the lumen boundary using a multi-frame (MF) convolutional neural network (MFCNN). Finally, we use the theory of Gaussian processes (GPs)
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Abubakar, Ahmad, Mahmud M. Jibril, Carlos F. M. Almeida, Matheus Gemignani, Mukhtar N. Yahya, and Sani I. Abba. "A Novel Hybrid Optimization Approach for Fault Detection in Photovoltaic Arrays and Inverters Using AI and Statistical Learning Techniques: A Focus on Sustainable Environment." Processes 11, no. 9 (2023): 2549. http://dx.doi.org/10.3390/pr11092549.

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Fault detection in PV arrays and inverters is critical for ensuring maximum efficiency and performance. Artificial intelligence (AI) learning can be used to quickly identify issues, resulting in a sustainable environment with reduced downtime and maintenance costs. As the use of solar energy systems continues to grow, the need for reliable and efficient fault detection and diagnosis techniques becomes more critical. This paper presents a novel approach for fault detection in photovoltaic (PV) arrays and inverters, combining AI techniques. It integrates Elman neural network (ENN), boosted tree
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Losanno, E., M. Badi, S. Wurth, et al. "Bayesian optimization of peripheral intraneural stimulation protocols to evoke distal limb movements." Journal of Neural Engineering 18, no. 6 (2021): 066046. http://dx.doi.org/10.1088/1741-2552/ac3f6c.

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Abstract Objective. Motor neuroprostheses require the identification of stimulation protocols that effectively produce desired movements. Manual search for these protocols can be very time-consuming and often leads to suboptimal solutions, as several stimulation parameters must be personalized for each subject for a variety of target motor functions. Here, we present an algorithm that efficiently tunes peripheral intraneural stimulation protocols to elicit functionally relevant distal limb movements. Approach. We developed the algorithm using Bayesian optimization (BO) with multi-output Gaussi
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Yeh, Shuan-Tai, and Xiaosong Du. "Optimal Tilt-Wing eVTOL Takeoff Trajectory Prediction Using Regression Generative Adversarial Networks." Mathematics 12, no. 1 (2023): 26. http://dx.doi.org/10.3390/math12010026.

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Electric vertical takeoff and landing (eVTOL) aircraft have attracted tremendous attention nowadays due to their flexible maneuverability, precise control, cost efficiency, and low noise. The optimal takeoff trajectory design is a key component of cost-effective and passenger-friendly eVTOL systems. However, conventional design optimization is typically computationally prohibitive due to the adoption of high-fidelity simulation models in an iterative manner. Machine learning (ML) allows rapid decision making; however, new ML surrogate modeling architectures and strategies are still desired to
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Paugnat, Hadrien, Tuan Do, Abhimat K. Gautam, et al. "New Evidence for a Flux-independent Spectral Index of Sgr A* in the Near-infrared." Astrophysical Journal 977, no. 2 (2024): 228. https://doi.org/10.3847/1538-4357/ad8ac6.

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Abstract In this work, we measure the spectral index of Sagittarius A* (Sgr A*) between the H (1.6 μm) and K ′ (2.2 μm) broadband filters in the near-infrared (NIR), sampling over a factor ∼40 in brightness, the largest range probed to date by a factor ∼3. Sgr A*-NIR is highly variable, and studying the spectral index α (with F ν ∝ ν α ) is essential to determine the underlying emission mechanism. For example, variations in α with flux may arise from shifts in the synchrotron cutoff frequency, changes in the distribution of electrons, or multiple concurrent emission mechanisms. We investigate
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Williams, M., A. D. Richardson, M. Reichstein, et al. "Improving land surface models with FLUXNET data." Biogeosciences Discussions 6, no. 2 (2009): 2785–835. http://dx.doi.org/10.5194/bgd-6-2785-2009.

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Abstract. There is a growing consensus that land surface models (LSMs) that simulate terrestrial biosphere exchanges of matter and energy must be better constrained with data to quantify and address their uncertainties. FLUXNET, an international network of sites that measure the land surface exchanges of carbon, water and energy using the eddy covariance technique, is a prime source of data for model improvement. Here we outline a multi-stage process for fusing LSMs with FLUXNET data to generate better models with quantifiable uncertainty. First, we describe FLUXNET data availability, and its
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Williams, M., A. D. Richardson, M. Reichstein, et al. "Improving land surface models with FLUXNET data." Biogeosciences 6, no. 7 (2009): 1341–59. http://dx.doi.org/10.5194/bg-6-1341-2009.

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Abstract. There is a growing consensus that land surface models (LSMs) that simulate terrestrial biosphere exchanges of matter and energy must be better constrained with data to quantify and address their uncertainties. FLUXNET, an international network of sites that measure the land surface exchanges of carbon, water and energy using the eddy covariance technique, is a prime source of data for model improvement. Here we outline a multi-stage process for "fusing" (i.e. linking) LSMs with FLUXNET data to generate better models with quantifiable uncertainty. First, we describe FLUXNET data avail
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Ohrelius, Mathilda, Rakel Lindstrom, and Göran Lindbergh. "Simplified Physics-Based Battery Model for Stationary Energy-Storage Applications." ECS Meeting Abstracts MA2024-01, no. 2 (2024): 249. http://dx.doi.org/10.1149/ma2024-012249mtgabs.

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To perform safe battery operation and avoid unnecessary degradation, battery models are needed. How complex, or close to reality the model should be, depends on the purpose of the model and the computational power available. The possibility for accurate parameterization before model implementation is another crucial aspect for the validity. Physics-based models have the advantage to solve for internal battery states, which includes important information to avoid accelerated degradation and to evaluate state of health. But the parameterization effort can be extensive and the computational cost
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Tang, Zhe, Sihao Li, Kyeong Soo Kim, and Jeremy S. Smith. "Multi-Dimensional Wi-Fi Received Signal Strength Indicator Data Augmentation Based on Multi-Output Gaussian Process for Large-Scale Indoor Localization." Sensors 24, no. 3 (2024): 1026. http://dx.doi.org/10.3390/s24031026.

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Location fingerprinting using Received Signal Strength Indicators (RSSIs) has become a popular technique for indoor localization due to its use of existing Wi-Fi infrastructure and Wi-Fi-enabled devices. Artificial intelligence/machine learning techniques such as Deep Neural Networks (DNNs) have been adopted to make location fingerprinting more accurate and reliable for large-scale indoor localization applications. However, the success of DNNs for indoor localization depends on the availability of a large amount of pre-processed and labeled data for training, the collection of which could be t
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Ito, Takamitsu, Hernan E. Garcia, Zhankun Wang, et al. "Underestimation of multi-decadal global O2 loss due to an optimal interpolation method." Biogeosciences 21, no. 3 (2024): 747–59. http://dx.doi.org/10.5194/bg-21-747-2024.

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Abstract. The global ocean's oxygen content has declined significantly over the past several decades and is expected to continue decreasing under global warming, with far-reaching impacts on marine ecosystems and biogeochemical cycling. Determining the oxygen trend, its spatial pattern, and uncertainties from observations is fundamental to our understanding of the changing ocean environment. This study uses a suite of CMIP6 Earth system models to evaluate the biases and uncertainties in oxygen distribution and trends due to sampling sparseness. Model outputs are sub-sampled according to the sp
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Karuppiah, Krishnaveni, Iniya Murugan, Murugesan Sepperumal, and Siva Ayyanar. "A dual responsive probe based on bromo substituted salicylhydrazone moiety for the colorimetric detection of Cd2+ ions and fluorometric detection of F‒ ions: Applications in live cell imaging." International Journal of Bioorganic and Medicinal Chemistry 1, no. 1 (2021): 1–9. http://dx.doi.org/10.55124/bmc.v1i1.20.

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A new fluorimetric and colorimetric dual-mode probe, 4-bromo-2-(hydrazonomethyl) phenol (BHP) has been synthesized and successfully utilized for the recognition of Cd2+/F‒ ions in DMSO/H2O (9:1, v/v) system. The probe displays dual channel of detection via fluorescence enhancement and colorimetric changes upon binding with F‒ and Cd2+ ions respectively. The Job’s plot analysis, ESI-MS studies, Density Functional Theoretical (DFT) calculations, 1H NMR and 19F NMR titration results were confirmed and highly supported the 1:1 binding stoichiometry of the probe was complexed with Cd2+/F‒ ions. Fur
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Bagde, Vandana, and Dethe C. G. "Performance improvement of space diversity technique using space time block coding for time varying channels in wireless environment." International Journal of Intelligent Unmanned Systems 10, no. 2/3 (2020): 278–86. http://dx.doi.org/10.1108/ijius-04-2019-0026.

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PurposeA recent innovative technology used in wireless communication is recognized as multiple input multiple output (MIMO) communication system and became popular for quicker data transmission speed. This technology is being examined and implemented for the latest broadband wireless connectivity networks. Though high-capacity wireless channel is identified, there is still requirement of better techniques to get increased data transmission speed with acceptable reliability. There are two types of systems comprising of multi-antennas placed at transmitting and receiving sides, of which first is
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40

Chung, Seokhyun, and Raed Al Kontar. "Federated Multi-output Gaussian Processes." Technometrics, July 24, 2023, 1–27. http://dx.doi.org/10.1080/00401706.2023.2238834.

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41

Joukov, Vladimir, and Dana Kulic. "Fast Approximate Multi-output Gaussian Processes." IEEE Intelligent Systems, 2022, 1. http://dx.doi.org/10.1109/mis.2022.3169036.

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42

Joukov, Vladimir, and Dana Kulic. "Fast Approximate Multi-output Gaussian Processes." IEEE Intelligent Systems, 2022, 1. http://dx.doi.org/10.1109/mis.2022.3169036.

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43

Ma, Chunchao, and Mauricio A. Álvarez. "Large scale multi-output multi-class classification using Gaussian processes." Machine Learning, February 8, 2023. http://dx.doi.org/10.1007/s10994-022-06289-3.

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AbstractMulti-output Gaussian processes (MOGPs) can help to improve predictive performance for some output variables, by leveraging the correlation with other output variables. In this paper, our main motivation is to use multiple-output Gaussian processes to exploit correlations between outputs where each output is a multi-class classification problem. MOGPs have been mostly used for multi-output regression. There are some existing works that use MOGPs for other types of outputs, e.g., multi-output binary classification. However, MOGPs for multi-class classification has been less studied. The
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44

Huynh, Nhan, and Mike Ludkovski. "Multi-output Gaussian processes for multi-population longevity modelling." Annals of Actuarial Science, May 17, 2021, 1–28. http://dx.doi.org/10.1017/s1748499521000142.

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Abstract We investigate joint modelling of longevity trends using the spatial statistical framework of Gaussian process (GP) regression. Our analysis is motivated by the Human Mortality Database (HMD) that provides unified raw mortality tables for nearly 40 countries. Yet few stochastic models exist for handling more than two populations at a time. To bridge this gap, we leverage a spatial covariance framework from machine learning that treats populations as distinct levels of a factor covariate, explicitly capturing the cross-population dependence. The proposed multi-output GP models straight
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45

Chung, Seokhyun, Raed Al Kontar, and Zhenke Wu. "Weakly Supervised Multi-output Regression via Correlated Gaussian Processes." INFORMS Journal on Data Science, July 11, 2022. http://dx.doi.org/10.1287/ijds.2022.0018.

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Multi-output regression seeks to borrow strength and leverage commonalities across different but related outputs in order to enhance learning and prediction accuracy. A fundamental assumption is that the output/group membership labels for all observations are known. This assumption is often violated in real applications. For instance, in healthcare data sets, sensitive attributes such as ethnicity are often missing or unreported. To this end, we introduce a weakly supervised multi-output model based on dependent Gaussian processes. Our approach is able to leverage data without complete group l
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46

Akbari, Behzad, and Haibin Zhu. "Tracking Dependent Extended Targets Using Multi-Output Spatiotemporal Gaussian Processes." IEEE Transactions on Intelligent Transportation Systems, 2022, 1–14. http://dx.doi.org/10.1109/tits.2022.3154926.

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47

Cheng, Li-Fang, Bianca Dumitrascu, Gregory Darnell, et al. "Sparse multi-output Gaussian processes for online medical time series prediction." BMC Medical Informatics and Decision Making 20, no. 1 (2020). http://dx.doi.org/10.1186/s12911-020-1069-4.

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48

Chen, Zexun, Jun Fan, and Kuo Wang. "Multivariate gaussian processes: definitions, examples and applications." METRON, January 27, 2023. http://dx.doi.org/10.1007/s40300-023-00238-3.

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AbstractGaussian processes occupy one of the leading places in modern statistics and probability theory due to their importance and a wealth of strong results. The common use of Gaussian processes is in connection with problems related to estimation, detection, and many statistical or machine learning models. In this paper, we propose a precise definition of multivariate Gaussian processes based on Gaussian measures on vector-valued function spaces, and provide an existence proof. In addition, several fundamental properties of multivariate Gaussian processes, such as stationarity and independe
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49

Lartaud, Paul, Philippe Humbert, and and Josselin Garnier. "Multi-Output Gaussian Processes for Inverse Uncertainty Quantification in Neutron Noise Analysis." Nuclear Science and Engineering, February 1, 2023, 1–24. http://dx.doi.org/10.1080/00295639.2022.2143705.

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50

Campos-Taberner, Manuel, María Amparo Gilabert, Sergio Sánchez-Ruiz, et al. "Global carbon fluxes using multi-output Gaussian processes regression and MODIS products." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2024, 1–11. http://dx.doi.org/10.1109/jstars.2024.3413184.

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