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The 20-nm Skyrmion Generated at Room Temperature by Spin-Orbit Torques

Chinese Physics Letters 2022年第1期39卷 40-45页

作者： Jiahao Liu Zidong Wang Teng Xu Hengan Zhou Le Zhao Soong-Guen Je Mi-Young Im Liang Fang Wanjun Jiang State Key Laboratory of Low-Dimensional Quantum Physics and Department of Physics Tsinghua UniversityBeijing 100084China Frontier Science Center for Quantum Information Tsinghua UniversityBeijing 100084China Institute for Quantum Information&State Key Laboratory of High Performance Computing College of ComputerNational University of Defense TechnologyChangsha 410073China Center for X-ray Optics Lawrence Berkeley National LaboratoryCyclotron RoadBerkeleyCA 94720USA Department of Physics Chonnam National UniversityGwangju61186Republic of Korea

The discovery of magnetic skyrmions provides a promising pathway for developing functional spintronic memory and logic devices. Towards the future high-density memory application, nanoscale skyrmions with miniaturized diameters, ideally down to 20 nm are required. Using x-ray magnetic circular dichroism transmission microscopy,nanoscale skyrmions are observed in the [Pt/Co/Ir]15 multilayer at room temperature. In particular, small skyrmions with minimum diameters approaching 20 nm could be generated by the current-induced spin-orbit torques. Through implementing material specific parameters, the dynamic process of skyrmion generation is further investigated by performing micromagnetic simulations. According to the simulation results, we find that both the tube-like Neel-type skyrmions and the bobber-like Neel-type skyrmions can be electrically generated. In particular, the size of the bobber-like Neel-type skyrmions can be effectively reduced by the spin-orbit torques,which leads to the formation of 20 nm Neel-type skyrmions. Our findings could be important for understanding the formation dynamics of nanoscale Neel-type spin textures, skyrmions and bobber in particular, which could also be useful for promoting nanoscale skyrmionic memories and logic devices.

关键词： temperature diameters approaching

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Twisted topology and Bipolar Non-Hermitian Skin Effect induced by long-range asymmetric coupling

arXiv

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arXiv 2023年

作者： Rafi-Ul-Islam, S.M. Siu, Zhuo Bin Sahin, Haydar Razo, Md Saddam Hossain Jalil, Mansoor B.A. Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore Institute of High Performance Computing A*STAR Singapore138632 Singapore

We investigate the twisted topology of the complex eigenspectrum of a one-dimensional non-Hermitian system under the influence of long-range unidirectional coupling. Unlike the complex energy spectrum of the conventional Hatano-Nelson chain, which takes the form of a single loop with a topological winding index of a definite sign, the introduction of long-range unidirectional hopping results in the creation of multiple twisted loops. These twisted loops exhibit opposite signs of the topological winding index, which correlate to alternating clockwise and anticlockwise energy windings. The simultaneous presence of both signs of the winding index translates into a bipolar non-Hermitian skin effect (NHSE), which challenges the conventional wisdom that the NHSE localization is dependent on the direction of the dominant nearest-neighbor interactions. In this bipolar NHSE, the exponents of the complex energy eigenvectors corresponding to clockwise and anti-clockwise windings, lie inside and outside of the complex unit circle, respectively. Interestingly, at the intersections of oppositely oriented energy loops where the sign of the topological winding index flips, the energy becomes real-valued, leading to a suppression of the NHSE. This marks the emergence of Bloch-like contact points, where both the bipolar NHSE and the traditional NHSE vanish. Based on the non-Hermitian model we provide analytical insights into the effects of long-range unidirectional coupling to the winding topology of its complex energy spectra and their broader implications for the field of condensed matter physics. © 2023, CC BY.

关键词： Topology

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A Heuristic Sparsification Method to Improve the performance of Neural Networks on Autism Spectrum Disorder Identification Tasks 13

A Heuristic Sparsification Method to Improve the Performance...

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13th International Conference on Information Science and Technology, ICIST 2023

作者： Zhang, Fangyu Wei, Yanjie Lin, Zhenli Pan, Yi Liu, Jin Centre for High Performance Computing Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Department of Ophthalmology Shenzhen University General Hospital Shenzhen China Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China School of Computer Science and Engineering Central South University Changsha China

ISBN: (纸本)9798350313925

Deep learning (DL) has been widely used in the detection of neurological diseases such as Autism spectrum disorder (ASD). Most studies in this field manually designed and adjusted the hyperparameters of DL models, but due to the huge search space, few studies have attempted to optimize the topology of neural networks which is another important factor related to the performance of DL models. This paper proposes a heuristic sparsification method named the Evolutionary Connection Sparsification Algorithm (ECSA) which is used to optimize the topology of fully connected neural networks (FC-NNs) and generate the corresponding sparsely connected neural networks (SC-NNs). The algorithm is based on the workflow of the genetic algorithm but with improved coding strategy, fitness evaluation approach, crossover and mutation operator, and including an effective convergence acceleration strategy. Experiments show that the SC-NNs generated by ECSA significantly outperform the original FC-NNs in terms of accuracy, F1-score, and AUC, and the model complexities are greatly reduced. © 2023 IEEE.

关键词： Topology

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DrugProtKGE: Weakly Supervised Knowledge Graph Embedding for highly-Effective Drug-Protein Interaction Representation

DrugProtKGE: Weakly Supervised Knowledge Graph Embedding for...

引用

2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Qiu, Yanlong Wang, Siqi Yang, Xi Qiu, Xinyuan Wu, Chengkun Cui, Yingbo Yang, Canqun National University of Defense Technology Institute for Quantum Information State Key Laboratory of High-Performance Computing College of Computer Science Hunan Changsha410073 China National Supercomputer Center in Tianjin Tianjin300457 China National University of Defense Technology National Key Laboratory of Parallel and Distributed Computing College of Computer Science Hunan Changsha410073 China National University of Defense Technology Department of Biology and Chemistry College of Science Hunan Changsha410073 China

ISBN: (纸本)9798350337488

With the exponential growth of biomedical knowledge in unstructured text repositories such as PubMed, it is imminent to establish a knowledge graph-style, efficient searchable and targeted database that can support the need of information retrieval from researchers and clinicians. To mine knowledge from graph databases, most previous methods view a triple in a graph (see Fig. 1) as the basic processing unit and embed the triplet element (i.e. drugs/chemicals, proteins/genes and their interaction) as separated embedding matrices, which cannot capture the semantic correlation among triple elements. To remedy the loss of semantic correlation caused by disjoint embeddings, we propose a novel approach to learn triple embeddings by combining entities and interactions into a unified representation. Furthermore, traditional methods usually learn triple embeddings from scratch, which cannot take advantage of the rich domain knowledge embedded in pre-trained models, and is also another significant reason for the fact that they cannot distinguish the differences implied by the same entity in the multi-interaction triples. In this paper, we propose a novel fine-tuning based approach to learn better triple embeddings by creating weakly supervised signals from pre-trained knowledge graph embeddings. The method automatically samples triples from knowledge graphs and estimates their pairwise similarity from pre-trained embedding models. The triples are then fed pairwise into a Siamese-like neural architecture, where the triple representation is fine-tuned in the manner bootstrapped by triple similarity scores. Finally, we demonstrate that triple embeddings learned with our method can be readily applied to several downstream applications (e.g. triple classification and triple clustering). We evaluated the proposed method on two open-source drug-protein knowledge graphs constructed from PubMed abstracts, as provided by BioCreative. Our method achieves consistent improvement in both t

关键词： Drug-Protein Interaction Knowledge Graph Embedding Triple Embedding Weakly Supervised Learning

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engineering higher-Order Dirac and Weyl Semimetallic phase in 3D Topolectrical Circuits

arXiv

引用

arXiv 2023年

作者： Rafi-Ul-Islam, S.M. Siu, Zhuo Bin Sahin, Haydar Jalil, Mansoor B.A. Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore Institute of High Performance Computing A*STAR Singapore138632 Singapore

We propose a 3D topolectrical (TE) network that can be tuned to realize various higher-order topological gapless and chiral phases. We first study a higher-order Dirac semimetal phase that exhibits a hinge-like Fermi arc linking the Dirac points. This circuit can be extended to host highly tunable first- and second-order Weyl semimetal phases by introducing a non-reciprocal resistive coupling in the x − y plane that breaks time reversal symmetry. The first- and second-order Weyl points are connected by zero-admittance surface and hinge states, respectively. We also study the emergence of first- and second-order chiral modes induced by resistive couplings between similar nodes in the z-direction. These modes respectively occur in the midgap of the surface and hinge admittance bands in our circuit model without the need for any external magnetic field. Copyright © 2023, The Authors. All rights reserved.

关键词： Timing circuits

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Cultural-Aware AI Model for Emotion Recognition

Cultural-Aware AI Model for Emotion Recognition

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Iranian Machine Vision and Image Processing (MVIP)

作者： Mehrdad Baradaran Payam Zohari Abtin Mahyar Hossein Motamednia Dara Rahmati Saeid Gorgin Department of Computer and Data Sciences Shahid Beheshti University Tehran Iran Department of Computer Engineering Khaje Nasir University Of Technology Tehran Iran High Performance Computing Laboratory School of Computer Science Institute for Research in Fundamental Sciences Tehran Iran Faculty of Computer Science and Engineering Shahid Beheshti University Tehran Iran Department of Computer Engineering Chosun University Gwangju South Korea

Emotion AI is a research domain that aims to understand human emotions from visual or textual data. However, existing methods often ignore the influence of cultural diversity on emotional interpretation. In this paper, we propose a multimodal deep learning model that integrates cultural awareness into emotion recognition. Our model uses images as the primary data source and comments from individuals across different regions as the secondary data source. Our results show that our model achieves robust performance across various scenarios. Our contribution is to introduce a novel fusion approach that bridges cultural gaps and fosters a more nuanced understanding of emotions. Due to the best of our knowledge, few works are using this approach, for Emotion AI, combining different types of data sources and models. We evaluate our model on the ArtELingo dataset, which contains image-comment pairs with Chinese, Arabic, and English annotations. The experimental results in the evaluation phase demonstrate an impressive 80% recognition accuracy for the model that merges image-text features.

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Improving the Reliability of FPGA CRO PUFs

Improving the Reliability of FPGA CRO PUFs

引用

International Conference on Field Programmable Logic and Applications

作者： Hayden Cook Zephram Tripp Brad Hutchings Jeffrey Goeders Department of Electrical and Computer Engineering Brigham Young University Provo Utah USA NSF Center for Space High-performance and Resilient Computing (SHREC)

This paper presents a novel technique that greatly improves the reliability of FPGA-based CRO PUFs. We improve upon existing CRO implementations and increase the number of configurations per CLB tile from 16 384 to 1.1 × 10 12 • To maximize reliability, each CRO pair must be configured to maximize its frequency difference. This requires using a novel technique that reduces the configuration search space from 1.1 × 10 12 to 256. Our CRO PUF achieves 100% reliability within the FPGA's maximum rated voltages. We believe that this is the first FPGA PUF that can achieve this level of reliability without the use of post-processing. We also show that in some cases, our CRO may be reliable enough to omit the ECC that is usually required in PUF-based key generation circuits. This allows our CRO PUF to provide the reliability required for key generation while reducing the latency, complexity, and area overhead of ECC algorithms.

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Prediction by Machine Learning Analysis of Genomic Data Phenotypic Frost Tolerance in Perccottus glenii

arXiv

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arXiv 2024年

作者： Fan, Lilin Chai, Xuqing Tian, Zhixiong Qiao, Yihang Wang, Zhen Zhang, Yifan School of Computer Information and Engineering Henan Normal University Xinxiang453000 China Henan Engineering Laboratory of Smart Business and Internet of Things Technology China Henan Normal University High Performance Computing Center China

Analyzing the genome sequence of the only fish species known to possess freezing tolerance, Perccottus glenii, is crucial for understanding how organisms adapt to extreme environments. Traditional biological analysis methods are time-consuming and have limited accuracy. Therefore, we will employ machine learning techniques to analyze the genomic sequences of Perccottus glenii, using Neodontobutis hainanens as a comparison group to identify differential genes. Firstly, we propose five gene sequence vectorization methods and a method for handling super-long gene sequences, and we compare three vectorization methods: sequential encoding, One-Hot encoding, and K-mer encoding, to identify the optimal encoding method. Secondly, we construct four classification models: random forest, LightBGM, XGBoost, and decision tree. The dataset used for these models is derived from the NCBI database. We determine the optimal K-value for the K-mer encoding method, and extract and vectorize the gene sequence matrix, with the best-performing model, random forest, achieving a classification accuracy of up to 99.98%. Finally, we use SHAP values to perform interpretability analysis on the classification models, and through tenfold cross-validation and AUC metrics, we identify the top 10 features that contribute the most to the model’s classification accuracy, thereby recognizing the differential genes for freezing tolerance phenotype in Perccottus glenii. We validate the identified differential genes using the biological software BLAST. Conclusion: Our study demonstrates that machine learning methods can replace traditional manual methods for identifying differential genes associated with freezing tolerance phenotype in Perccottus glenii. Copyright © 2024, The Authors. All rights reserved.

关键词： Gene encoding

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Recommender System for Social Media: Research Challenges and Future Applications 5

Recommender System for Social Media: Research Challenges and...

引用

5th International Conference on Transdisciplinary AI, TransAI 2023

作者： Ge, Mouzhi Pilato, Giovanni Persia, Fabio D'Auria, Daniela Deggendorf Institute of Technology Faculty of European Campus Rottal-Inn Deggendorf Germany Institute for High Performance Computing and Networking National Research Council of Italy Palermo Italy University of l'Aquila Department of Information Engineering Computer Science and Mathematics L'Aquila Italy University of Bozen-Bolzano Faculty of Computer Science Free Bozen-Bolzano Italy

ISBN: (纸本)9798350358018

Social networks have evolved as a ubiquitous means of communication in our daily lives. Users consistently generate a large amount of data on social media platforms, which on the other hand has increased the complexity when selecting relevant social media content. Thus, recommender system has been emerged as a solution to provide personalized and user-centric media recommendations. However, social media has significantly broadened the scope of available inputs such as social sensing data and new types of social media. It not only offers new research opportunities for social media recommender systems, but also indicates that various domains may benefit from the application of social media recommender systems. Therefore, this paper conducts a comprehensive survey of state-of-the-art research between social media and recommender systems. The survey results identify a set of research challenges on how social media recommender systems can be applied in various domains such as healthcare and route planning. © 2023 IEEE.

关键词： Recommender systems

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Dynamic Allocation Hypernetwork with Adaptive Model Recalibration for Federated Continual Learning

arXiv

引用

arXiv 2025年

作者： Qi, Xiaoming Zhang, Jingyang Fu, Huazhu Yang, Guanyu Li, Shuo Jin, Yueming Department of Biomedical Engineering Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore Ministry of Education Nanjing China Institute of High Performance Computing A*STAR Singapore Departments Biomedical Engineering and Computer and Data Science Case Western Reserve University Cleveland United States

Federated continual learning (FCL) offers an emerging pattern to facilitate the applicability of federated learning (FL) in real-world scenarios, where tasks evolve dynamically and asynchronously across clients, especially in medical scenario. Existing server-side FCL methods in nature domain construct a continually learnable server model by client aggregation on all-involved tasks. However, they are challenged by: (1) Catastrophic forgetting for previously learned tasks, leading to error accumulation in server model, making it difficult to sustain comprehensive knowledge across all tasks. (2) Biased optimization due to asynchronous tasks handled across different clients, leading to the collision of optimization targets of different clients at the same time steps. In this work, we take the first step to propose a novel server-side FCL pattern in medical domain, Dynamic Allocation Hypernetwork with adaptive model recalibration (FedDAH). It is to facilitate collaborative learning under the distinct and dynamic task streams across clients. To alleviate the catastrophic forgetting, we propose a dynamic allocation hypernetwork (DAHyper) where a continually updated hypernetwork is designed to manage the mapping between task identities and their associated model parameters, enabling the dynamic allocation of the model across clients. For the biased optimization, we introduce a novel adaptive model recalibration (AMR) to incorporate the candidate changes of historical models into current server updates, and assign weights to identical tasks across different time steps based on the similarity for continual optimization. Extensive experiments on the AMOS dataset demonstrate the superiority of our FedDAH to other FCL methods on sites with different task streams. The code is available:https://***/jinlab-imvr/FedDAH. © 2025, CC BY-SA.

关键词： Federated learning

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