Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal cancer in which the tumor-vascular involvement greatly affects the resectability and, thus, overall survival of patients.
Objective: The artificial pancreas (AP) has shown promising potential in achieving closed-loop glucose control for individuals with type 1 diabetes mellitus (T1DM).
Finally, we applied this approach to develop the first human promoter DNA sequence design model and showed that designed sequences share similar properties with natural promoter sequences.
In this study, a novel hybrid framework combining the controlled physics-informed data generation approach with a deep learning-based prediction model for prognostics is proposed.
While the fundamental framework of point cloud semantic segmentation has been largely overlooked, with most existing approaches rely on the U-Net architecture by default.
no code implementations • • Jieneng Chen, Yingda Xia, Jiawen Yao, Ke Yan, Jianpeng Zhang, Le Lu, Fakai Wang, Bo Zhou, Mingyan Qiu, Qihang Yu, Mingze Yuan, Wei Fang, Yuxing Tang, Minfeng Xu, Jian Zhou, Yuqian Zhao, Qifeng Wang, Xianghua Ye, Xiaoli Yin, Yu Shi, Xin Chen, Jingren Zhou, Alan Yuille, Zaiyi Liu, Ling Zhang
Human readers or radiologists routinely perform full-body multi-organ multi-disease detection and diagnosis in clinical practice, while most medical AI systems are built to focus on single organs with a narrow list of a few diseases.
This paper proposes an interaction and safety-aware motion-planning method for an autonomous vehicle in uncertain multi-vehicle traffic environments.
Experiments show that our method surpasses most basic methods in terms of accuracy on facial expression data sets (e. g., AffectNet, RAF-DB), and it also solves the problem of class imbalance well.
Sampling is an essential part of raw point cloud data processing such as in the popular PointNet++ scheme.
Moreover, our model is more stable for training in a non-adversarial manner, compared to other adversarial based novelty detection methods.
In this paper, we propose a novel contrastive regularization (CR) built upon contrastive learning to exploit both the information of hazy images and clear images as negative and positive samples, respectively.
Ranked #5 on Image Dehazing on RS-Haze
Interestingly, the critical strain and failure mechanism of zigzag direction in MoSi2N4 are almost the same as those of armchair direction in MoS2, while the critical strain and failure mechanism of armchair direction for MoSi2N4 are similar to the ones of zigzag direction for MoS2.
Mesoscale and Nanoscale Physics
no code implementations • 8 Sep 2020 • Jaroslav Adam, Christine Aidala, Aaron Angerami, Benjamin Audurier, Carlos Bertulani, Christian Bierlich, Boris Blok, James Daniel Brandenburg, Stanley Brodsky, Aleksandr Bylinkin, Veronica Canoa Roman, Francesco Giovanni Celiberto, Jan Cepila, Grigorios Chachamis, Brian Cole, Guillermo Contreras, David d'Enterria, Adrian Dumitru, Arturo Fernández Téllez, Leonid Frankfurt, Maria Beatriz Gay Ducati, Frank Geurts, Gustavo Gil da Silveira, Francesco Giuli, Victor P. Goncalves, Iwona Grabowska-Bold, Vadim Guzey, Lucian Harland-Lang, Martin Hentschinski, Timothy J. Hobbs, Jamal Jalilian-Marian, Valery A. Khoze, Yongsun Kim, Spencer R. Klein, Simon Knapen, Mariola Kłusek-Gawenda, Michal Krelina, Evgeny Kryshen, Tuomas Lappi, Constantin Loizides, Agnieszka Luszczak, Magno Machado, Heikki Mäntysaari, Daniel Martins, Ronan McNulty, Michael Murray, Jan Nemchik, Jacquelyn Noronha-Hostler, Joakim Nystrand, Alessandro Papa, Bernard Pire, Mateusz Ploskon, Marius Przybycien, John P. Ralston, Patricia Rebello Teles, Christophe Royon, Björn Schenke, William Schmidke, Janet Seger, Anna Stasto, Peter Steinberg, Mark Strikman, Antoni Szczurek, Lech Szymanowski, Daniel Tapia Takaki, Ralf Ulrich, Orlando Villalobos Baillie, Ramona Vogt, Samuel Wallon, Michael Winn, Keping Xie, Zhangbu Xu, Shuai Yang, Mikhail Zhalov, Jian Zhou
Ultra-peripheral collisions (UPCs) involving heavy ions and protons are the energy frontier for photon-mediated interactions.
High Energy Physics - Phenomenology High Energy Physics - Experiment Nuclear Experiment
First, a spatial transformer - generative adversarial network which consists of convolutional layers and residual units is utilized to solve the initialization issues caused by face detectors, such as rotation and scale variations, to obtain improved face bounding boxes for face alignment.
A set of orthonormal polynomials is proposed for image reconstruction from projection data.
Here we present a new supervised generative stochastic network (GSN) based method to predict local secondary structure with deep hierarchical representations.