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Publisher Correction: Deciphering DNA variant-associated aberrant splicing with the aid of RNA sequencing

Nature genetics 2023年第7期55卷 1251页

作者： Bin Zhang Xin Gao Computer Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia. KAUST Computational Bioscience Research Center King Abdullah University of Science and Technology Thuwal Saudi Arabia. Computer Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia. xin.gao@kaust.edu.sa. KAUST Computational Bioscience Research Center King Abdullah University of Science and Technology Thuwal Saudi Arabia. xin.gao@kaust.edu.sa.

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A Finite Difference Method for Space Fractional Differential Equations with Variable Diffusivity Coefficient

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Communications on Applied Mathematics and Computation 2020年第4期2卷 671-688页

作者： K.A.Mustapha K.M.Furati O.M.Knio O.P.Le Maître Department of Mathematics and Statistics King Fahd University of Petroleum and MineralsDhahran 31261Kingdom of Saudi Arabia Computer ElectricalMathermatical Sciences and Engineering DivisionKing Abdullah University of Science and TechnologyThuwal 23955Kingdom of Saudi Arabia CNRS LIMSIUniversitéParis-SaclayCampus Universitaire-BP 13391403 OrsayFrance

Anomalous diffusion is a phenomenon that cannot be modeled accurately by second-order diffusion equations,but is better described by fractional diffusion *** nonlocal nature of the fractional diffusion operators makes substantially more difficult the mathemati-cal analysis of these models and the establishment of suitable numerical *** paper proposes and analyzes the first finite difference method for solving variable-coefficient one-dimensional(steady state)fractional differential equations(DEs)with two-sided fractional derivatives(FDs).The proposed scheme combines first-order forward and backward Euler methods for approximating the left-sided FD when the right-sided FD is approximated by two consecutive applications of the first-order backward Euler *** scheme reduces to the standard second-order central difference in the absence of *** existence and uniqueness of the numerical solution are proved,and truncation errors of order h are demonstrated(h denotes the maximum space step size).The numerical tests illustrate the global 0(h)accu-racy,except for nonsmooth cases which,as expected,have deteriorated convergence rates.

关键词： Two-sided fractional derivatives Variable coefcients Finite diferences

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At the Junction Between Deep Learning and Statistics of Extremes: Formalizing the Landslide Hazard Definition

Journal of Geophysical Research: Machine Learning and Comput...

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Journal of Geophysical Research: Machine Learning and Computation 2024年第3期1卷

作者： Ashok Dahal Raphaël Huser Luigi Lombardo Faculty of Geo-Information Science and Earth Observation (ITC) University of Twente Enschede The Netherlands Statistics Program Computer Electrical and Mathematical Sciences and Engineering (CEMSE) Division King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia

The most adopted definition of landslide hazard combines spatial information about landslide location (susceptibility), threat (intensity), and frequency (return period). Only the first two elements are usually considered and estimated when working over vast areas. Even then, separate models constitute the standard, with frequency being rarely investigated. Frequency and intensity are dependent on each other because larger events occur less frequently and vice versa. However, due to the lack of multi-temporal inventories and joint statistical models, modeling such properties via a unified hazard model has always been challenging and has yet to be attempted. Here, we develop a unified model to estimate landslide hazard at the slope unit level to address such gaps. We employed deep learning, combined with extreme-value theory to analyze an inventory of 30 years of observed rainfall-triggered landslides in Nepal and assess landslide hazard for multiple return periods. We also use our model to further explore landslide hazard for the same return periods under different climate change scenarios up to the end of the century. Our model performs excellently (with an accuracy of 0.78 and an area under the curve of 0.86) and can be used to model landslide hazard in a unified manner. Geomorphologically, we find that under climate change scenarios (SSP245 and SSP585), landslide hazard is likely to increase up to two times on average in the lower Himalayan regions (Siwalik and lower Himalayas; ≈110–3,500 m) while remaining the same in the middle Himalayan region (≈3,500–5,000 m) whilst decreasing slightly in the upper Himalayan region (≳5,000 m) areas. A Landslide hazard model is developed by combining deep learning and the extended generalized Pareto distribution from extreme value theory Thirty years of past observations are used to model the current landslide hazard scenario under different return periods of precipitation Landslide hazard is predicted for multiple return peri

关键词： landslides statistics of extremes deep learning climate change modeling Nepal

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Greedy Algorithms for Decision Trees with Hypotheses

arXiv

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arXiv 2022年

作者： Azad, Mohammad Chikalov, Igor Hussain, Shahid Moshkov, Mikhail Zielosko, Beata Department of Computer Science College of Computer and Information Sciences Jouf University Sakaka72441 Saudi Arabia Intel Corporation 5000 W Chandler Blvd ChandlerAZ85226 United States Department of Computer Science School of Mathematics and Computer Science Institute of Business Administration University Road Karachi75270 Pakistan Computer Electrical and Mathematical Sciences & Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia Institute of Computer Science Faculty of Science and Technology University of Silesia in Katowice B¸edzinska 39 Sosnowiec41-200 Poland

We investigate at decision trees that incorporate both traditional queries based on one attribute and queries based on hypotheses about the values of all attributes. Such decision trees are similar to ones studied in exact learning, where membership and equivalence queries are allowed. We present greedy algorithms based on diverse uncertainty measures for construction of above decision trees and discuss results of computer experiments on various data sets from the UCI ML Repository and randomly generated Boolean functions. We also study the length and coverage of decision rules derived from the decision trees constructed by greedy algorithms. Copyright © 2022, The Authors. All rights reserved.

关键词： Decision trees

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Front Cover: Ethylene Oligomerization: Unraveling the Roles of Ni Sites, Acid Sites, and Zeolite Pore Topology through Continuous and Pulsed Reactions (ChemCatChem 5/2024)

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ChemCatChem 2024年第5期16卷

作者： Omar Abed Dr. Hend Omar Mohamed Dr. Idoia Hita Dr. Vijay Velisoju Dr. Natalia Morlanés Dr. Omar El Tall Prof. Pedro Castaño Multiscale Reaction Engineering KAUST Catalysis Center (KCC) King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia KAUST Catalysis Center King Abdullah University of Science and Technology Thuwal 23955-6900 Saudi Arabia KAUST Core Labs King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia Chemical Engineering Program Physical Science and Engineering (PSE) Division King Abdullah University of Science and Technology Thuwal 23955-6900 Saudi Arabia

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Creation and annihilation of magnetic skyrmions for neuromorphic computing applications

TechRxiv

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TechRxiv 2021年

作者： Lone, Aijaz H. Ganguly, Arnab Divyanshu Amara, Selma Das, Gobind Fariborzi, Hossein Computer Electrical and Mathematical Science and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia Department of Physics Khalifa University Abu Dhabi12788 United Arab Emirates

In this work we present the creation, annihilation and dynamics of a topologically protected magnetic structure, a skyrmion, for neuromorphic computing application. We study the effect of Dzyaloshinskii Moriya interaction (DMI) and surface anisotropy on the skyrmion density. The relation between skyrmion annihilation threshold anisotropy Kth and DMI coefficient is evaluated. Furthermore, the skyrmion diameter dependence on these two parameters is studied. Using MOKE analysis we study the effect of external magnetic field on the skyrmion density and predict the threshold magnetic field for the transition of magnetic texture from Labriynth domains to skyrmions. These results are further supported by the MuMax simulations. The spin orbit torque SOT manipulation of skyrmion size and density is also presented for skyrmion applications in the race-track memory and neuromorphic computing. Motivated by the results, we propose a Skyrmionic neuromorphic device and using SOT switching mechanism, show its applicability as spintronic synapse and neuron. The MuMax simulations are coupled to the Non- Equilibrium Green’s Function formalism to model the neuron and synapse behavior. Finally, we conclude with the possibility of using these devices for pattern recognition and other unconventional computing paradigms. © 2021, CC BY.

关键词： Tunnel junctions

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Efficient automatic segmentation for multi-level pulmonary arteries: The PARSE challenge

arXiv

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arXiv 2023年

作者： Luo, Gongning Wang, Kuanquan Liu, Jun Li, Shuo Liang, Xinjie Li, Xiangyu Gan, Shaowei Wang, Wei Dong, Suyu Wang, Wenyi Yu, Pengxin Liu, Enyou Wei, Hongrong Wang, Na Guo, Jia Li, Huiqi Zhang, Zhao Zhao, Ziwei Gao, Na An, Nan Pakzad, Ashkan Rangelov, Bojidar Dou, Jiaqi Tian, Song Liu, Zeyu Wang, Yi Sivalingam, Ampatishan Punithakumar, Kumaradevan Qiu, Zhaowen Gao, Xin School of Computer Science and Technology Harbin Institute of Technology Harbin150001 China Department of Computer and Data Science Department of Biomedical Engineering Case Western Reserve University Cleveland44106 United States Institute of Information and Computer Engineering NorthEast Forestry University Harbin150040 China Infervision Medical Technology Co. Ltd. Beijing China Sensetime Shanghai China Beijing Institute of Technology Beijing China Peking University Beijing China Northeastern University Shenyang China Shanghai Jiao Tong University Shanghai China London United Kingdom Center for Biomedical Imaging Research Tsinghua University Beijing China Philips Healthcare China Chongqing University Chongqing China University of Alberta Edmonton Canada Computer Electrical and Mathematical Sciences & Engineering Division King Abdullah University of Science and Technology 4700 KAUST Thuwal23955 Saudi Arabia

Efficient automatic segmentation of multi-level (i.e. main and branch) pulmonary arteries (PA) in CTPA images plays a significant role in clinical applications. However, most existing methods concentrate only on main PA or branch PA segmentation separately and ignore segmentation efficiency. Besides, there is no public large-scale dataset focused on PA segmentation, which makes it highly challenging to compare the different methods. To benchmark multi-level PA segmentation algorithms, we organized the first Pulmonary ARtery SEgmentation (PARSE) challenge. On the one hand, we focus on both the main PA and the branch PA segmentation. On the other hand, for better clinical application, we assign the same score weight to segmentation efficiency (mainly running time and GPU memory consumption during inference) while ensuring PA segmentation accuracy. We present a summary of the top algorithms and offer some suggestions for efficient and accurate multi-level PA automatic segmentation. We provide the PARSE challenge as open-access for the community to benchmark future algorithm developments at https://***/Parse2022/. Copyright © 2023, The Authors. All rights reserved.

关键词： Benchmarking

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A nonlinear adaptive resilient observer for fouling detection and localization in direct contact membrane distillation systems

A nonlinear adaptive resilient observer for fouling detectio...

引用

Control technology and Applications (CCTA),

作者： Yasmine Marani Tania Camelia Touati Messaoud Chakir Taous Meriem Laleg-Kirati Ecole Nationale Polytechnique Algiers Algeria Computer Electrical and Mathematical Sciences and Engineering Division (CEMSE) King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia

ISBN: (纸本)9781665436441

Because freshwater source exhaustion is a growing issue, the use of seawater desalination technologies is constantly increasing. More attention is drawn to Direct Contact Membrane Distillation (DCMD), thanks to its high rejection factor and low energy consumption, which makes it a promising sustainable solution for water desalination. Nevertheless, the DCMD system is prone to membrane fouling which alters its produced water’s properties, deteriorates the membrane performance, and induces huge operation and maintenance costs if not detected in early stages. Considering the above, the present paper proposes a new approach based on an adaptive resilient observer, not only to detect fouling in DCMD systems but also to localize it with high accuracy to further reduce the maintenance costs. The paper starts by recalling the DCMD model in presence of fouling that was developed using the thermal-electrical analogy and the lumped capacitance method. Then, under Lipschitz conditions of the nonlinear terms, the design of the nonlinear adaptive resilient observer is presented to estimate simultaneously the system’s states and the fouling thermal resistance. The proposed observer’s convergence proof is given using the Lyapunov method, from which a set of linear matrix inequalities (LMI) is derived in order to obtain the observer’s gain. To demonstrate the effectiveness of the proposed method compared to the classical adaptive observer-based method, the states and parameter estimation are compared through numerical simulations under both observer designs. Simulation results reveal that the nonlinear adaptive resilient observer outperforms the adaptive observer.

关键词： Location awareness Temperature distribution Adaptive systems Costs Thermal resistance Simulation Desalination

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Compact microwave based water-cut sensor suitable for downhole installation

Compact microwave based water-cut sensor suitable for downho...

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European Conference on Microwave

作者： Muhammad Akram Karimi Muhammad Arsalan Atif Shamim Computer Electrical and Mathematical Science and Engineering (CEMSE) Division King Abdullah University of Science and Technology (KAUST) Thuwal Kingdom of Saudi Arabia Saudi Aramco Dhahran Kingdom of Saudi Arabia

ISBN: (数字)9782874870590

ISBN: (纸本)9781728170398

Water is a by-product of oil extraction process whose amount needs to be determined accurately to optimize the oil production process. Almost 99.9% of the instruments used to determine water content in oil, are installed on the top surface outside an oil well. With the top surface water-fraction or watercut (WC) meters, it is impossible to locate the water producing zones inside the well because oil (and water) is produced from multiple branches inside a well. Ideally, a downhole WC sensor needs to be installed with every branch of the well for better water management. This paper presents a compact microwave based WC sensor which is compatible with an existing downhole system. Sensor consists of a λ/4 open stub based spiral resonator. The stub is printed on a dielectric core residing in the center of a metallic pipe. The stub is fed with a microstrip feedline which has a dedicated ring-shaped ground plane. Mixture flowing between the central core and the metallic pipe changes the dielectric properties and the resonance frequency of this uniquely designed sensor. WC sensing phenomenon has been validated both in simulations as well as inside a flow loop with measured sensitivity of over 200%.

关键词： Microwave measurement Sensitivity Oils Optical wavelength conversion Predictive models Microwave oscillators Surface treatment

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Cosh-DiT: Co-Speech Gesture Video Synthesis via Hybrid Audio-Visual Diffusion Transformers

arXiv

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arXiv 2025年

作者： Sun, Yasheng Xu, Zhiliang Zhou, Hang Guan, Jiazhi Yang, Quanwei Wang, Kaisiyuan Liang, Borong Li, Yingying Feng, Haocheng Wang, Jingdong Liu, Ziwei Hideki, Koike Center of Excellence for Generative AI King Abdullah University of Science and Technology Jeddah Thuwal23955-6900 Saudi Arabia Baidu VIS Shangdi 10th Street Beijing100085 China School of Computing Tokyo Institute of Technology Ookayama Meguro-ku Tokyo152-8550 Japan Department of Computer Science and Technology Tsinghua University Haidian District Shuangqing Road Beijing100084 China Department of Electronic Engineering and Information Science University of Science and Technology of China Baohe District JinZhai Road Anhui Hefei230026 China S-Lab Nanyang Technological University 50 Nanyang Avenue 639798 Singapore

Co-speech gesture video synthesis is a challenging task that requires both probabilistic modeling of human gestures and the synthesis of realistic images that align with the rhythmic nuances of speech. To address these challenges, we propose Cosh-DiT, a Co-speech gesture video system with hybrid Diffusion Transformers that perform audio-to-motion and motion-to-video synthesis using discrete and continuous diffusion modeling, respectively. First, we introduce an audio Diffusion Transformer (Cosh-DiT-A) to synthesize expressive gesture dynamics synchronized with speech rhythms. To capture upper body, facial, and hand movement priors, we employ vector-quantized variational autoencoders (VQ-VAEs) to jointly learn their dependencies within a discrete latent space. Then, for realistic video synthesis conditioned on the generated speech-driven motion, we design a visual Diffusion Transformer (Cosh-DiT-V) that effectively integrates spatial and temporal contexts. Extensive experiments demonstrate that our framework consistently generates lifelike videos with expressive facial expressions and natural, smooth gestures that align seamlessly with speech. © 2025, CC BY.

关键词： Vector quantization

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