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arXiv 2023年

作者： Al-Shaibani, Maged S. Ahmad, Irfan King Fahd University of Petroleum and Minerals Saudi Arabia Information & Computer Science Department KFUP Saudi Arabia SDAIA–KFUPM Joint Research Center for AI Dhahran31261 Saudi Arabia

This paper presents a novel dotless representation of Arabic text as an alternative to the standard Arabic text representation. We delve into its implications through comprehensive analysis across five diverse corpora and four different tokenization techniques. We explore the impact of dotless representation on the relationships between tokenization granularity and vocabulary size and compare them with standard text representation. Moreover, we analyze the information density of dotless versus standard text using text entropy calculations. To delve deeper into the implications of the dotless representation, statistical and neural language models are constructed using the various text corpora and tokenization techniques. A comparative assessment is then made against language models developed using the standard Arabic text representation. This multifaceted analysis provides valuable insights into the potential advantages and challenges associated with the dotless representation. Last but not the least, utilizing parallel corpora, we draw comparisons between the text analysis of Arabic and English to gain further insights. Our findings shed light on the potential benefits of dotless representation for various NLP tasks, paving the way for further exploration for Arabic natural language processing. © 2023, CC BY-NC-SA.

关键词： Entropy

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Prediction of Stroke Risk Within 7-Years Follow Up Using Machine Learning Models

Prediction of Stroke Risk Within 7-Years Follow Up Using Mac...

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2023 IEEE EMBS Special Topic Conference on Data science and Engineering in Healthcare, Medicine and Biology, IEEECONF 2023

作者： Tsarapatsani, Konstantina-Helen Sakellarios, Antonis Tsakanikas, Vasilis D. Rudolf, Henrik Trampisch, Hans J. Pezoulas, Vasileios C. Matsopoulos, George K. Fotiadis, Dimitrios I. University of Ioannina Unit of Medical Technology and Intelligent Information Systems Dept. of Materials Science and Engineering IoanninaGR 45110 Greece Biomedical Research Institute Greece Institute for Biostatistics and Informatics in Medicine and Ageing Research University Medical Center Rostock Germany Ruhr University Bochum Department of Medical Informatics Biometry and Epidemiology Bochum Germany National Technical University of Athens School of Electrical and Computer Engineering Athens Greece

ISBN: (纸本)9798350383386

A stroke, also known as brain attack, occurs when blood supply to your brain is interrupted. Primary prevention relies on prompt prediction of a stroke. While currently there are several clinical risk scores, machine learning (ML) models seems to be more suitable tools for accurate prediction of stroke events. Therefore, this work focuses on the prediction of stroke within 7 years follow-up in patients who have not suffered from a stroke or TIA event at baseline. LightGBM (LGBM), Extreme Grading Boosting (XGBoost), Support Vector Machine (SVM) and Decision Tree were employed in the getABI dataset, which includes 5,897 participants. The performance of models was calculated by Accuracy (ACC), Sensitivity (SENS), Specificity (SPE) and area under the receiver operating characteristic curve (AUC) of each model. According to the comparison analysis's results, LGBM has been shown to be the most trustworthy algorithm, with accuracy 68 %. Moreover, sex, age, status of peripheral artery disease (PAD), history of myocardial infarction, angina pectoris, amputation and diabetes and pulse status of different arteries can be used as a simple and cost-effective way to predict *** Relevance: A fatal medical emergency, stroke may be anticipated using artificial intelligence, and the sooner it is predicted, the more cerebrovascular disease occurrences can be avoided. © 2023 IEEE.

关键词： Forecasting

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Implicit Learning of Scene Geometry from Poses for Global Localization

arXiv

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arXiv 2023年

作者： Altillawi, Mohammad Li, Shile Prakhya, Sai Manoj Liu, Ziyuan Serrat, Joan The AI Robotics & Simulation team Huawei Munich Research Center Germany Department of Computer Science Universitat Autònoma de Barcelona Cerdanyola del Vallès Bellaterra08193 Spain

Global visual localization estimates the absolute pose of a camera using a single image, in a previously mapped area. Obtaining the pose from a single image enables many robotics and augmented/virtual reality applications. Inspired by latest advances in deep learning, many existing approaches directly learn and regress 6 DoF pose from an input image. However, these methods do not fully utilize the underlying scene geometry for pose regression. The challenge in monocular relocalization is the minimal availability of supervised training data, which is just the corresponding 6 DoF poses of the images. In this paper, we propose to utilize these minimal available labels (.i.e, poses) to learn the underlying 3D geometry of the scene and use the geometry to estimate the 6 DoF camera pose. We present a learning method that uses these pose labels and rigid alignment to learn two 3D geometric representations (X, Y, Z coordinates) of the scene, one in camera coordinate frame and the other in global coordinate frame. Given a single image, it estimates these two 3D scene representations, which are then aligned to estimate a pose that matches the pose label. This formulation allows for the active inclusion of additional learning constraints to minimize 3D alignment errors between the two 3D scene representations, and 2D re-projection errors between the 3D global scene representation and 2D image pixels, resulting in improved localization accuracy. During inference, our model estimates the 3D scene geometry in camera and global frames and aligns them rigidly to obtain pose in real-time. We evaluate our work on three common visual localization datasets, conduct ablation studies, and show that our method exceeds state-of-the-art regression methods’ pose accuracy on all datasets. © 2023, CC BY-NC-SA.

关键词： Geometry

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A Variational Bayesian Perspective on MIMO Detection with Low-Resolution ADCs

A Variational Bayesian Perspective on MIMO Detection with Lo...

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Asilomar Conference on Signals, Systems & computers

作者： Ly V. Nguyen A. Lee Swindlehurst Duy H.N. Nguyen Computational Science Research Center San Diego State University CA USA Department of Electrical Engineering and Computer Science University of California Irvine CA USA Department of Electrical and Computer Engineering San Diego State University CA USA

This paper proposes data detection methods for massive multiple-input multiple-output (MIMO) systems with low-resolution analog-to-digital converters (ADCs) based on the variational Bayes (VB) inference framework. We derive matched-filter quantized VB (MF-QVB) and linear minimum mean-squared error quantized VB (LMMSE-QVB) detection methods assuming the channel state information (CSI) is available. Unlike conventional VB-based detection methods that assume knowledge of the second-order statistics of the additive noise, we propose to float the noise variance/covariance matrix as an unknown random variable that is used to account for both the noise and the residual inter-user interference. Finally, we show via numerical results that the proposed VB-based methods provide robust performance and also significantly outperform existing methods.

关键词： computers Additive noise Channel estimation Interference MIMO Random variables Bayes methods

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Radiomics model based on computed tomography images for prediction of radiation-induced optic neuropathy following radiotherapy of brain and head and neck tumors

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Heliyon 2025年第1期11卷 e41409页

作者： Nafchi, Elham Raiesi Fadavi, Pedram Amiri, Sepideh Cheraghi, Susan Garousi, Maryam Nabavi, Mansoureh Daneshi, Iman Gomar, Marzieh Molaie, Malihe Nouraeinejad, Ali Department of Radiation Sciences Faculty of Allied Medicine Iran University of Medical Sciences Tehran Iran Department of Radiation Oncology School of Medicine Iran University of Medical Science Tehran Iran Department of Information Technology Faculty of Electrical and Computer Engineering University of Tehran Tehran Iran Radiation Biology Research Center Iran University of Medical Sciences Tehran Iran Radiation Oncology Research Center (RORC) Cancer Institute Tehran University of Medical Sciences Tehran Iran Department of Clinical Oncology Haft-e-Tir Hospital Iran University of Medical Science Tehran Iran Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Department of Optometry and Vision Science School of Rehabilitation Tehran University of Medical Science Tehran Iran

Purpose: We aimed to build a machine learning-based model to predict radiation-induced optic neuropathy in patients who had treated head and neck cancers with radiotherapy. Materials and methods: To measure radiation-induced optic neuropathy, the visual evoked potential values were obtained in both case and control groups and compared. Radiomics features were extracted from the area segmented which included the right and left optic nerves and chiasm. We integrated CT image features with dosimetric and clinical data subsequently, ranked 5 supervised ML models Bernoulli Naive Bayes, Decision Tree, Gradient Boosting Decision Trees, K-Nearest Neighbor, and Random Forest on 4 input datasets to predict radiation-induced visual complications classifiers by implementing 5-fold cross-validation. The F1 score, accuracy, sensitivity, specificity, and area under the ROC curve were compared to access prediction capability. Results: radiation-induced optic neuropathy affected 31 % of the patients. 856 radiomic characteristics were extracted and selected from each segmented area. Decision Tree and Random Forest with the highest AUC (97 % and 95 % respectively) among the five classifiers were the most powerful algorithms to predict radiation-induced optic neuropathy. Chiasm with higher sensitivity and precision was able to predict radiation-induced optic neuropathy better than right or left optic nerve or combination of all radiomic features. Conclusion: We found that combination of radiomic, dosimetric, and clinical factors can predict radiation-induced optic neuropathy after radiation treatment with high accuracy. To acquire more reliable results, it is recommended to conduct visual evoked potential tests before and after radiation therapy, with multiple follow-up courses and more patients. © 2024 The Authors

关键词： Computed tomography Machine learning Radiation-induced optic neuropathy Radiomics Visual evoked potential

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An interactive virtual reality system based on leap motion controller for hand motor rehabilitation

An interactive virtual reality system based on leap motion c...

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International Conference on Image and Signal Processing and their Applications (ISPA)

作者： Amal Bouatrous Abdelkrim Meziane Nadia Zenati Chafiaa Hamitouche Computer Science Department USTHB University Algiers Algeria IMT-Atlantique Brest France LaTIM Brest France Research Center on Scientific and Technical Information (CERIST) Algiers Algeria Centre for Development of Advanced Technologies (CDTA) Algiers Algeria

ISBN: (数字)9798350309249

ISBN: (纸本)9798350309256

Virtual reality (VR) technology is increasingly used in a variety of applications, including healthcare, where it has shown promise as a functional rehabilitation tool after stroke to provide an immersive and interactive environment for patients. The use of VR in rehabilitation allows for task-specific training, provides feedback, and allows for the simulation of real-life activities. This approach can lead to improvements in motor function, cognitive skills, and overall quality of life for patients. In this paper, we present a VR-based system for hand rehabilitation after stroke. This system was designed and developed in close collaboration with clinicians specialized in functional rehabilitation. It consists of a serious game represented by a virtual environment simulating a real-life activity, in which the patient is encouraged to interact with the virtual objects that comprise the virtual environment while playing the game, performing various tasks necessary to achieve their rehabilitation goals. The system uses a Leap motion controller as a motion tracking device, which captures the patient’s movements and transmits them to the virtual environment. To increase patient motivation and engagement, the virtual environment provides immediate feedback on the patient’s movements and performance in real time. This system was tested on a group of patients to evaluate its acceptability and usability.

关键词： Performance evaluation Training Tracking Virtual environments Collaboration Games Stroke (medical condition)

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Syncopated Dynamical Decoupling for Suppressing Crosstalk in Quantum Circuits

arXiv

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arXiv 2024年

作者： Evert, Bram Izquierdo, Zoe Gonzalez Sud, James Hu, Hong-Ye Grabbe, Shon Rieffel, Eleanor G. Reagor, Matthew J. Wang, Zhihui Rigetti Computing BerkeleyCA United States NASA Ames Research Center Moffett FieldCA United States USRA Moffett FieldCA United States University of Chicago Computer Science Department IL United States

Theoretically understanding and experimentally characterizing and modifying the underlying Hamiltonian of a quantum system is of utmost importance in achieving high-fidelity quantum gates for quantum computing. In this work, we explore the use of dynamical decoupling (DD) in characterizing undesired two-qubit couplings as well as the underlying single-qubit decoherence, and in suppressing them. We develop a syncopated dynamical decoupling technique which protects against decoherence and selectively targets unwanted two-qubit interactions, overcoming both significant hurdles to achieving precise quantum control and realizing quantum computing on many hardware prototypes. On a transmon-qubit-based superconducting quantum device, we identify separate white and 1/f noise components underlying the single-qubit decoherence and a static ZZ coupling between pairs of qubits. We suppress these errors using syncopated dynamical decoupling in two-qubit benchmarking experiments and significantly boost performance in a realistic algorithmic quantum circuit. Copyright © 2024, The Authors. All rights reserved.

关键词： Benchmarking

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ARcode: HPC Application Recognition Through Image-encoded Monitoring Data

arXiv

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arXiv 2023年

作者： Li, Jie Cook, Brandon Chen, Yong Department of Computer Science Texas Tech University LubbockTX United States National Energy Research Scientific Computing Center Lawrence Berkeley National Laboratory BerkeleyCA United States

Knowing HPC applications of jobs and analyzing their performance behavior play important roles in system management and optimizations. The existing approaches detect and identify HPC applications through machine learning models. However, these approaches rely heavily on the manually extracted features from resource utilization data to achieve high prediction accuracy. In this study, we propose an innovative application recognition method, ARcode, which encodes job monitoring data into images and leverages the automatic feature learning capability of convolutional neural networks to detect and identify applications. Our extensive evaluations based on the dataset collected from a large-scale production HPC system show that ARcode outperforms the state-of-the-art methodology by up to 18.87% in terms of accuracy at high confidence thresholds. For some specific applications (BerkeleyGW and e3sm), ARcode outperforms by over 20% at a confidence threshold of 0.8. © 2023, CC BY-NC-SA.

关键词： Convolutional neural networks

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Brain Surface Can Predict Fiber Connections

Brain Surface Can Predict Fiber Connections

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IEEE International Symposium on Biomedical Imaging

作者： Zili Kang Yifan Lv Mengshen He Yiheng Liu Tianming Liu Bao Ge School of Physics & Information Technology Shaanxi Normal University Xi’an China Department of Computer Science and Bioimaging Research Center Cortical Architecture Imaging and Discovery Lab The University of Georgia GA USA

It has been of great interest to study relationship between white matter and gray matter in brain science. Previous studies have found that that the formation of gray matter is closely related to white matter. This paper attempts explore the relationship between cerebral cortex and white matter from the perspective of brain image, which tries to predict fiber connection information by using brain cortical information alone. The extensive experiments on HCP dataset demonstrate that brain cortical surface can be used to predict the presence or absence of fibers between any two cortical regions with about 80% accuracy, furtherly, it can be used to predict the fractional anisotropy (FA) value for each fiber, the experiments show that the FA value can be also predicted with a percentage error of about 20%. These results demonstrate that the brain surface can be used to predict fiber connection information, and there is a close relationship between the gray matter and white matter fiber. This work provides a new perspective for further study of the relationship between cerebral cortex and axonal fiber.

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Intelligent Polarization Reconfigurable Surface for S-Band Applications

Intelligent Polarization Reconfigurable Surface for S-Band A...

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Antennas and Propagation Society International Symposium

作者： Muhammad Sumaid Wajeeh Hassan Ahmad Javed Adnan Nadeem Nosherwan Shoaib David Chatzichristodoulou Symeon Nikolaou School of Electrical Engineering and Computer Science (SEECS) National University of Sciences and Technology (NUST) Islamabad Pakistan Department of Electrical Engineering Frederick Research Center (FRC) Frederic University Nicosia Cyprus

To realize the concept of reconfigurable (cross/circular) polarization of the reflected wave, a reflective intelligent polarization reconfigurable surface (IPRS), operating in the S-band (3.8 GHz) is presented. IPRS consists of periodic unit cells consisting of square shaped structures with a diagonal element and two PIN diodes as switching elements. The simultaneous biasing of the PIN diodes allows the switching of the polarization state of the reflected wave to either cross or circular polarization. The incident wave is linearly polarized, and with the diodes in OFF state, cross polarization is achieved, while with the diodes in ON state, circular polarization is caused on the reflected wave.

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