Indonesia-Japan Economic Partnership Agreement (IJEPA): a response to a regime change. by Siti Tiefryani Fahlyah

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Modeling the change of beach profile under tsunami waves : a comparison of selected sediment transport models by Li, Linlin, Huang, Zhenhua

“…In contrast to the efforts made to develop hydrodynamic models for large-scale tsunami propagation and run-up, little has been done to develop, test, and validate sediment transport models used to simulate tsunami-induced sediment movement. …”
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Enhancing reservoir operations with charged system search (CSS) algorithm: Accounting for sediment accumulation and multiple scenarios by Almubaidin, Mohammad Abdullah Abid, Ahmed, Ali Najah, Abdul Malek, Marlinda, A. Mahmoud, Moamin, Sherif, Mohsen, El-Shafie, Ahmed

“…The simulation of monthly sediment values in the Mujib reservoir showed that sediment accumulation accounts for 14.6% of the reservoir's volume at the end of 2019. …”
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FAST: An Optimization Framework for Fast Additive Segmentation in Transparent ML by Liu, Brian, Mazumder, Rahul

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Continual semantic segmentation via image and latent space consistency by Wang, Zhichao

“…In this thesis, my continual-learning research process is introduced in detail, including a novel method and two regulators, which contribute to anti-forgetting Result in continual learning in the semantic segmentation area. Firstly a real-time semantic segmentation model called ERFnet is evaluated, then based on this network and Cityscapes dataset, a model-recall method is proposed which could significantly reduce the catastrophic forgetting which happens in the process of continual learning in the semantic segmentation area; inspired by mentors, 2 regulators are also conducted which were expected to further improve performance (one regulator is come up by mentors and another is by myself). …”
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Segmentation and analysis of cement particles in cement paste with deep learning by Qian, Hanjie, Li, Ye, Yang, Jianfei, Xie, Lihua, Tan, Kang Hai

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Segment anything: real-time AI model interaction and visualization by Wang, Jingwei

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Automatic knee segmentation from multi-contrast MR images by Zhang, Kunlei

“…Also, the proposed cartilage segmentation scheme can be applied to the segmentation of other joint structures. …”
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The Japanese economy in crises : a time series segmentation study by Cheong, Siew Ann, Fornia, Robert Paulo, Lee, Gladys Hui Ting, Kok, Jun Liang, Yim, Woei Shyr, Xu, Danny Yuan, Zhang, Yiting

“…The authors performed a comprehensive time series segmentation study on the 36 Nikkei Japanese industry indices from 1 January 1996 to 11 June 2010. …”
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3D-aware instance segmentation and tracking in egocentric videos by Bhalgat, Y, Tschernezki, V, Laina, I, Henriques, JF, Vedaldi, A, Zisserman, A

“…Leveraging our tracked instance segmentations, we showcase downstream applications in 3D object reconstruction and amodal video object segmentation in these egocentric settings.…”

Humanising GrabCut: learning to segment humans using the Kinect by Gulshan, V, Lempitsky, V, Zisserman, A

“…To this end we investigate learning to automatically segment humans from cluttered images (without depth information) given a bounding box. …”

Placing objects in context via inpainting for out-of-distribution segmentation by De Jorge, P, Volpi, R, Dokania, PK, Torr, PHS, Rogez, G

“…When deploying a semantic segmentation model into the real world, it will inevitably encounter semantic classes that were not seen during training. …”

Using multiple segmentations to discover objects and their extent in image collections by Russell, BC, Efros, AA, Sivic, J, Freeman, WT, Zisserman, A

“…Given a large dataset of images, we seek to automatically determine the visually similar object and scene classes together with their image segmentation. To achieve this we combine two ideas: (i) that a set of segmented objects can be partitioned into visual object classes using topic discovery models from statistical text analysis; and (ii) that visual object classes can be used to assess the accuracy of a segmentation. …”

Segmentation of magnetic resonance images for assessing neonatal brain maturation by Wang, S

“…We also investigate atlas-based whole brain segmentation that generates the binary mask for the region of interest. …”

DiffuSeg: domain-driven diffusion for medical image segmentation by Zhang, L, Wu, F, Bronik, K, Papiez, BW

“…Initially, we apply our method to the MNIST dataset and subsequently adapt it for use with medical image segmentation datasets, such as retinal fundus images for vessel segmentation and MRI images for heart segmentation. …”

Empowerment or Reinforcement of Gender Regime: Rethinking Social Enterprises’ Intentions and Impact through Gender Lens by Tymosiewicz, Natalia Elizabeth

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Response time characterization of Organic Rankine Cycle evaporators for dynamic regime analysis with fluctuating load by Jiménez-Arreola, Manuel, Wieland, Christoph, Romagnoli, Alessandro

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Fostering motivation in TVET students: the role of learner-paced segments and computational thinking in digital video learning by Wan Nor Ashiqin Wan Ali, Wan Ahmad Jaafar Wan Yahaya, Syed Zulkarnain Syed Idrus, Mohd Noorul Fakhri Yaacob

“…This study aims to address this gap by examining how learner-paced predefined segments and CT algorithmic thinking can impact TVET students' perceived motivation. …”
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Modeling mass removal and sediment deposition in stormwater ponds using floating treatment islands: a computational approach by Xavier, Manoel L. M., Janzen, Johannes G., Nepf, Heidi

“…Moreover, the FTI configuration exerts a more pronounced influence on mass removal through FTIs than through sediment deposition alone. In cases where both processes occur simultaneously, the presence of FTIs lead to higher mass removal, primarily attributed to the FTIs themselves, particularly in the initial segment. …”
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Uncertainty Quantification in Deep Learning Models of G-Computation for Outcome Prediction under Dynamic Treatment Regimes by Deng, Leon

“…G-Net is a neural network framework that implements g-computation, a causal inference method for making counterfactual predictions and estimating treatment effects under dynamic and time-varying treatment regimes. Two G-Net models have been successfully implemented: one that uses recurrent neural networks (RNNs) as its predictors, and one that uses transformer encoders (G-Transformer). …”
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