Article
Mathematics, Interdisciplinary Applications
Wang Yi, Xue Yu, Wang Xue, Cen Bing-ling, Qiao Yan-feng
Summary: The paper presents a two-layer coupled oscillator model, in which the driven layer transitions from unsynchronized state to synchronized state with parameter changes, revealing the existence of chimer state. The dynamic behavior of the system has been studied and the critical value for the state transition has been identified.
CHAOS SOLITONS & FRACTALS
(2021)
Article
Mathematics, Interdisciplinary Applications
Raul I. Sosa, Damian H. Zanette
Summary: The collective dynamics of an ensemble of globally coupled, externally forced, identical mechanical oscillators with cubic nonlinearity is analyzed, revealing the multistability of the ensemble. As the coupling strength grows, the solutions with two internally synchronized clusters are replaced by a state of full synchronization. The distribution of oscillators between the clusters and the relative prevalence of the two stable solutions are key factors in the system's behavior.
INTERNATIONAL JOURNAL OF BIFURCATION AND CHAOS
(2021)
Article
Mathematics, Applied
ShuaiLiu, XinYue Chen, ChengGui Yao, ZiQin Zhang
Summary: This study investigates the effect of network structure on system dynamics by studying a circular network of coupled Landau-Stuart oscillators. The stability of stationary states and different splay states, as well as global synchronization, are analyzed using linear stability analysis. The analysis shows that the network connectivity has a significant impact on the synchronous frequency and stability of global synchronization.
COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION
(2022)
Article
Polymer Science
Kai Li, Haiyang Wu, Biao Zhang, Yuntong Dai, Yong Yu
Summary: Self-oscillating coupled machines have the advantages of absorbing energy from the external environment and maintaining their own motion, contributing to the exploration of synchronization and clustering. This paper constructs a coupling and synchronization model of two self-oscillators connected by springs based on a thermally responsive LCE spring self-oscillator in a linear temperature field. The results show that the system exhibits two synchronization modes: in-phase mode and anti-phase mode, and the work done by the driving force compensates for the damping dissipation of the system, maintaining self-oscillation.
Article
Engineering, Mechanical
Saureesh Das, Rashmi Bhardwaj
Summary: The study focuses on the complex dynamics and synchronization of two coupled Duffing-type circuits using recurrence quantification analysis. Various RQA parameters reveal chaotic transitions in the oscillatory behavior of a Duffing oscillator driven by a sinusoidal voltage source. The critical parameter for chaos synchronization in systems of two-way coupled Duffing-type oscillators is determined using simulated recurrence plots.
NONLINEAR DYNAMICS
(2021)
Article
Engineering, Mechanical
Zhiyang Gu, Chengli Fan, Dengxiu Yu, Zhen Wang
Summary: A distributed optimal control algorithm based on adaptive neural network is proposed for the synchronized control problem of a class of second-order nonlinear coupled harmonic oscillators. By establishing the coupling relationship, fitting the unknown nonlinearity, designing virtual and actual controllers, and designing cost and HJB functions, the optimal consistent control of the oscillators is achieved.
NONLINEAR DYNAMICS
(2023)
Article
Chemistry, Analytical
Xiufeng Guo, Pengchun Rao, Zhaoyan Wu
Summary: This paper investigates the fixed-time synchronization problem of a Kuramoto-oscillator network in the presence of a pacemaker. The synchronization criteria for phase agreement and frequency synchronization are presented using distributed control strategies and Lyapunov stability analysis. The upper bounds of synchronization time are obtained, and numerical simulations confirm the effectiveness of the derived results.
Article
Engineering, Electrical & Electronic
Eunseon Yu, Amogh Agrawal, Dongqi Zheng, Mengwei Si, Minsuk Koo, Peide D. Ye, Sumeet Kumar Gupta, Kaushik Roy
Summary: Coupled-oscillatory networks using ferroelectric field-effect transistors (FeFETs) are shown to be energy-efficient and effective for optimization tasks. The FeFET oscillator circuits with optimized biasing schemes achieve wider synchronization range and significantly lower energy consumption compared to previous FeFET-based oscillators. FeFET coupled-oscillatory network also demonstrates promising performance in edge detection tasks, closely following the ideal output with consideration of non-idealities and process variations.
IEEE ELECTRON DEVICE LETTERS
(2021)
Article
Multidisciplinary Sciences
Kyra L. Kadhim, Ann M. Hermundstad, Kevin S. Brown
Summary: Identifying coordinated activity in complex systems is crucial for understanding their structure and function. The study on networks of pulse-coupled oscillators revealed three broad classes of behaviors: temporally-irregular, temporally-regular, and chimeric. These classes exhibit different collective spiking statistics and interactions between nodes, providing insights into the dynamics of such systems.
Article
Mathematics, Applied
Georgi S. Medvedev, Matthew S. Mizuhara, Andrew Phillips
Summary: In this study, we investigate a system of coupled phase oscillators driven by random intrinsic frequencies near a saddle-node on invariant circle bifurcation. The system undergoes a phase transition and changes its qualitative properties of collective dynamics under the variation of control parameters. By using Ott-Antonsen reduction and geometric techniques for ordinary differential equations, we identify heteroclinic bifurcation in a family of vector fields on a cylinder, which explains the change in collective dynamics. Specifically, we demonstrate that heteroclinic bifurcation separates two topologically distinct families of limit cycles: contractible limit cycles before bifurcation and noncontractible ones after bifurcation. Both families are stable in the model at hand.
Article
Acoustics
Prashanth Gurunath Shivakumar, Somer Bishop, Catherine Lord, Shrikanth Narayanan
Summary: Automatic inference of paralinguistic information from speech, such as age, is an important area of research with many technological applications. In this paper, a novel technique is proposed for automatic speaker age estimation in children by exploiting temporal variability present in children's speech. Phone durations are used as biomarkers of children's age. Experimental results demonstrate the robustness and portability of the proposed features over multiple domains of varying signal conditions.
JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA
(2022)
Article
Engineering, Mechanical
Chunlai Li, Xuan Wang, Jianrong Du, Zhijun Li
Summary: This paper investigates the electrical activity and synchronization of a bi-neuron network coupled by Hindmarsh-Rose and tabu learning models with Chua Corsage Memristor (CCM). The study reveals the correlation between the initial value-related state switching of the network and the equilibrium states of CCM. The synchronization behavior of the network, dependent on the coupling strength, external stimuli, and system parameters, is analyzed by measuring the phase difference and synchronization factor.
NONLINEAR DYNAMICS
(2023)
Article
Engineering, Electrical & Electronic
Yan Zong, Xuewu Dai, Pep Canyelles-Pericas, Zhiwei Gao, Wai Pang Ng, Krishna Busawon, Richard Binns
Summary: This paper proposes a new protocol for synchronizing low-accuracy and large-drifting clocks in a high-disturbance wireless network. It utilizes a data packet-coupled synchronization scheme to adjust drifting clocks using a proportional control-based correction scheme. Experimental results demonstrate that the proposed protocol achieves and maintains robust time synchronization on internal RC oscillator clocks.
IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS
(2023)
Article
Mathematics, Applied
Henrique M. M. Oliveira, Sara Perestrelo
Summary: This paper investigates the effects of perturbations and interactions of Andronov clocks on the orbits, and finds that the resulting perturbed orbits are very close to the unperturbed orbits. The Arnold tongues for Huygens coupling are obtained through experiments.
JOURNAL OF DIFFERENCE EQUATIONS AND APPLICATIONS
(2022)
Article
Computer Science, Information Systems
June-Woo Kim, Hyekyung Yoon, Ho-Young Jung
Summary: This paper addresses the poor performance of the elderly in automatic speech recognition by proposing a neural network-based voice conversion framework with unsupervised phonology clustering. The study aims to enhance speech recognition accuracy for minority speakers, such as the elderly, through spectral feature adaptation.
Article
Neurosciences
Sophie Roberts, Rachel M. Bruce, Louise Lim, Hayley Woodgate, Kate Ledingham, Storm Anderson, Diego L. Lorca-Puls, Andrea Gajardo-Vidal, Alexander P. Leff, Thomas M. H. Hope, David W. Green, Jennifer T. Crinion, Cathy J. Price
Summary: Research shows that early speech and language therapy after a stroke has beneficial effects on speaking ability, with the number of hours of early therapy positively related to long-term recovery. This has important implications for future studies aiming to predict individual patients' speech outcomes and their response to therapy.
NEUROPSYCHOLOGICAL REHABILITATION
(2022)
Review
Behavioral Sciences
Luca Tarasi, Jelena Trajkovic, Stefano Diciotti, Giuseppe di Pellegrino, Francesca Ferri, Mauro Ursino, Vincenzo Romei
Summary: The brain is a predictive machine, with different predictive strategies observed from autism spectrum disorders (ASD) to schizophrenic spectrum disorders (SSD). ASD is characterized by rigid perceptual inference shaped by incoming sensory information, while individuals with SSD tend to overestimate the precision of their prior models. Brain oscillations are considered pivotal in understanding how top-down predictions integrate bottom-up input, with oscillatory disturbances potentially leading to maladjustments in the predictive mechanism.
NEUROSCIENCE AND BIOBEHAVIORAL REVIEWS
(2022)
Article
Biochemistry & Molecular Biology
Miriam Schirru, Florence Veronneau-Veilleux, Fahima Nekka, Mauro Ursino
Summary: This study investigates the role of dopamine-dependent pathways in flexible action selection and learning mechanisms of striatal synapses. The findings suggest that controlling phasic dopamine changes can lead to successful reversal learning, providing insights into the mechanisms of dopamine changes during flexible behavior.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2022)
Editorial Material
Neurosciences
Mauro Ursino, Elisa Magosso, Manuela Petti
Article
Neurosciences
Mauro Ursino, Nicole Cesaretti, Gabriele Pirazzini
Summary: Oscillatory activity is crucial for working memory, and cross-frequency coupling between theta and gamma oscillations is believed to be a core mechanism for multi-item memory. This study presents an original neural network model based on oscillating neural masses to investigate working memory mechanisms. The model can address various memory problems, including partial information reconstruction, simultaneous maintenance of multiple items, and ordered sequence reconstruction from an initial cue. The trained network demonstrates the ability to desynchronize up to nine items and replicate sequences using gamma and theta rhythms. Reduction in certain parameters can mimic memory impairments. The isolated network can randomly recover previously learned sequences and link them together using item similarity.
COGNITIVE NEURODYNAMICS
(2023)
Article
Audiology & Speech-Language Pathology
Catherine Doogan, Renaad Al Balushi, Beth Gooding, Jennifer Crinion, Alexander Leff
Summary: This study examines the effectiveness of goal-setting for people with aphasia participating in an Intensive Comprehensive Aphasia Programme (ICAP). The results show statistically significant and clinically meaningful improvements in various goal categories. The qualitative analysis reveals the goals that people with aphasia wanted to achieve through ICAP.
Article
Behavioral Sciences
Amalie H. Munk, Elisabeth B. Starup, Matthew A. Lambon Ralph, Alex P. Leff, Randi Starrfelt, Ro J. Robotham
Summary: Cerebral achromatopsia is a rare acquired color perception impairment caused by brain injury, with most cases involving bilateral or right hemisphere lesions. It differs from congenital color blindness by affecting perception of all colors. A study on stroke patients with posterior cerebral artery involvement found that 22% of patients showed significant color discrimination problems, with bilateral lesions associated with more severe impairments.
Review
Clinical Neurology
Neena R. Singh, Alexander P. Leff
Summary: The purpose of this review is to summarize the studies published from 2017 to 2022 on rehabilitation approaches for hemispatial inattention and identify common themes to guide future research. Immersive virtual reality approaches to visual stimulation are well tolerated but have not yet shown clinically relevant improvements. Dynamic auditory stimulation shows promise and has high potential for implementation. Robotic interventions are limited by cost and may be most suitable for patients with co-occurring hemiparesis. Regarding brain stimulation, rTMS has moderate effects while tDCS studies have yielded disappointing results so far. Drugs targeting the dopaminergic system often demonstrate moderate beneficial effects, but predicting responders and non-responders remains a challenge.
CURRENT NEUROLOGY AND NEUROSCIENCE REPORTS
(2023)
Article
Engineering, Biomedical
Davide Borra, Matteo Filippini, Mauro Ursino, Patrizia Fattori, Elisa Magosso
Summary: This study compared the performance of fully-connected, convolutional, and recurrent neural networks in motor decoding and found that convolutional neural networks (CNNs) were the most effective choice. CNNs showed improved performance in low data scenarios and provided insights about the encoding properties and functional roles of brain regions.
JOURNAL OF NEURAL ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Mauro Ursino, Gabriele Pirazzini
Summary: This study explores the hierarchical organization of semantic memory using an attractor network model. The model utilizes gamma-band synchronization to facilitate information processing and feature binding, and creates a taxonomy, distinguishes concepts, and discriminates features through training. Sensitivity analysis reveals the robustness of the network, but also identifies conditions that may lead to confusion. The analysis emphasizes the role of GABAergic interneurons and inhibitory-excitatory balance in feature synchronization.
COGNITIVE COMPUTATION
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Davide Borra, Matteo Filippini, Mauro Ursino, Patrizia Fattori, Elisa Magosso
Summary: Neural decoding is crucial for Brain-Computer Interfaces (BCIs) and convolutional neural networks (CNNs) have emerged as powerful tools for decoding neural activity. CNNs have shown promising results in decoding electroencephalographic signals, but their applications in single-neuron decoding require further validation and research.
MACHINE LEARNING, OPTIMIZATION, AND DATA SCIENCE, LOD 2022, PT II
(2023)
Article
Audiology & Speech-Language Pathology
Alexander Leff, Catherine Doogan, John Bentley, Bani Makkar, Luisa Zenobi-Bird, Amy Sherman, Simon Grobler, Jennifer Crinion
Summary: The field of human expert performance teaches us that high quality, high-dose guided practice is required to make large gains in cognitively driven acts. The same seems to be true for people with acquired brain injury. Intensive Comprehensive Aphasia Programmes (ICAPs) are one way to address the chronic under-dosing of therapy that most people with aphasia experience.
Meeting Abstract
Clinical Neurology
L. Taylor, J. Bentley, A. Sherman, B. Makkar, L. Zenobi-Bird, C. Doogan, J. Crinion, A. Leff
INTERNATIONAL JOURNAL OF STROKE
(2023)
Meeting Abstract
Clinical Neurology
H. Ozkan, G. Ambler, G. Banerjee, S. Browning, A. Leff, D. Werring, R. Simister
INTERNATIONAL JOURNAL OF STROKE
(2023)
Meeting Abstract
Clinical Neurology
T. Langford, V. Fleming, J. Crinion, A. Leff
INTERNATIONAL JOURNAL OF STROKE
(2023)
Article
Computer Science, Artificial Intelligence
Hamdan Abdellatef, Lina J. Karam
Summary: This paper proposes performing the learning and inference processes in the compressed domain to reduce computational complexity and improve speed of neural networks. Experimental results show that modified ResNet-50 in the compressed domain is 70% faster than traditional spatial-based ResNet-50 while maintaining similar accuracy. Additionally, a preprocessing step with partial encoding is suggested to improve resilience to distortions caused by low-quality encoded images. Training a network with highly compressed data can achieve good classification accuracy with significantly reduced storage requirements.
Article
Computer Science, Artificial Intelligence
Victor R. Barradas, Yasuharu Koike, Nicolas Schweighofer
Summary: Inverse models are essential for human motor learning as they map desired actions to motor commands. The shape of the error surface and the distribution of targets in a task play a crucial role in determining the speed of learning.
Article
Computer Science, Artificial Intelligence
Ting Zhou, Hanshu Yan, Jingfeng Zhang, Lei Liu, Bo Han
Summary: We propose a defense strategy that reduces the success rate of data poisoning attacks in downstream tasks by pre-training a robust foundation model.
Article
Computer Science, Artificial Intelligence
Hao Sun, Li Shen, Qihuang Zhong, Liang Ding, Shixiang Chen, Jingwei Sun, Jing Li, Guangzhong Sun, Dacheng Tao
Summary: In this paper, the convergence rate of AdaSAM in the stochastic non-convex setting is analyzed. Theoretical proof shows that AdaSAM has a linear speedup property and decouples the stochastic gradient steps with the adaptive learning rate and perturbed gradient. Experimental results demonstrate that AdaSAM outperforms other optimizers in terms of performance.
Article
Computer Science, Artificial Intelligence
Juntong Yun, Du Jiang, Li Huang, Bo Tao, Shangchun Liao, Ying Liu, Xin Liu, Gongfa Li, Disi Chen, Baojia Chen
Summary: In this study, a dual manipulator grasping detection model based on the Markov decision process is proposed. By parameterizing the grasping detection model of dual manipulators using a cross entropy convolutional neural network and a full convolutional neural network, stable grasping of complex multiple objects is achieved. Robot grasping experiments were conducted to verify the feasibility and superiority of this method.
Article
Computer Science, Artificial Intelligence
Miaohui Zhang, Kaifang Li, Jianxin Ma, Xile Wang
Summary: This paper proposes an unsupervised person re-identification (Re-ID) method that uses two asymmetric networks to generate pseudo-labels for each other by clustering and updates and optimizes the pseudo-labels through alternate training. It also designs similarity compensation and similarity suppression based on the camera ID of pedestrian images to optimize the similarity measure. Extensive experiments show that the proposed method achieves superior performance compared to state-of-the-art unsupervised person re-identification methods.
Article
Computer Science, Artificial Intelligence
Florian Bacho, Dominique Chu
Summary: This paper proposes a new approach called the Forward Direct Feedback Alignment algorithm for supervised learning in deep neural networks. By combining activity-perturbed forward gradients, direct feedback alignment, and momentum, this method achieves better performance and convergence speed compared to other local alternatives to backpropagation.
Article
Computer Science, Artificial Intelligence
Xiaojian Ding, Yi Li, Shilin Chen
Summary: This research paper addresses the limitations of recursive feature elimination (RFE) and its variants in high-dimensional feature selection tasks. The proposed algorithms, which introduce a novel feature ranking criterion and an optimal feature subset evaluation algorithm, outperform current state-of-the-art methods.
Article
Computer Science, Artificial Intelligence
Naoko Koide-Majima, Shinji Nishimoto, Kei Majima
Summary: Visual images observed by humans can be reconstructed from brain activity, and the visualization of arbitrary natural images from mental imagery has been achieved through an improved method. This study provides a unique tool for directly investigating the subjective contents of the brain.
Article
Computer Science, Artificial Intelligence
Huanjie Tao, Qianyue Duan
Summary: In this paper, a hierarchical attention network with progressive feature fusion is proposed for facial expression recognition (FER), addressing the challenges posed by pose variation, occlusions, and illumination variation. The model achieves enhanced performance by aggregating diverse features and progressively enhancing discriminative features.
Article
Computer Science, Artificial Intelligence
Zhenyi Wang, Pengfei Yang, Linwei Hu, Bowen Zhang, Chengmin Lin, Wenkai Lv, Quan Wang
Summary: In the face of the complex landscape of deep learning, we propose a novel subgraph-level performance prediction method called SLAPP, which combines graph and operator features through an innovative graph neural network called EAGAT, providing accurate performance predictions. In addition, we introduce a mixed loss design with dynamic weight adjustment to improve predictive accuracy.
Article
Computer Science, Artificial Intelligence
Yiyang Yin, Shuangling Luo, Jun Zhou, Liang Kang, Calvin Yu-Chian Chen
Summary: Medical image segmentation is crucial for modern healthcare systems, especially in reducing surgical risks and planning treatments. Transanal total mesorectal excision (TaTME) has become an important method for treating colon and rectum cancers. Real-time instance segmentation during TaTME surgeries can assist surgeons in minimizing risks. However, the dynamic variations in TaTME images pose challenges for accurate instance segmentation.
Article
Computer Science, Artificial Intelligence
Teng Cheng, Lei Sun, Junning Zhang, Jinling Wang, Zhanyang Wei
Summary: This study proposes a scheme that combines the start-stop point signal features for wideband multi-signal detection, called Fast Spectrum-Size Self-Training network (FSSNet). By utilizing start-stop points to build the signal model, this method successfully solves the difficulty of existing deep learning methods in detecting discontinuous signals and achieves satisfactory detection speed.
Article
Computer Science, Artificial Intelligence
Wenming Wu, Xiaoke Ma, Quan Wang, Maoguo Gong, Quanxue Gao
Summary: The layer-specific modules in multi-layer networks are critical for understanding the structure and function of the system. However, existing methods fail to accurately characterize and balance the connectivity and specificity of these modules. To address this issue, a joint learning graph clustering algorithm (DRDF) is proposed, which learns the deep representation and discriminative features of the multi-layer network, and balances the connectivity and specificity of the layer-specific modules through joint learning.
Article
Computer Science, Artificial Intelligence
Guanghui Yue, Guibin Zhuo, Weiqing Yan, Tianwei Zhou, Chang Tang, Peng Yang, Tianfu Wang
Summary: This paper proposes a novel boundary uncertainty aware network (BUNet) for precise and robust colorectal polyp segmentation. BUNet utilizes a pyramid vision transformer encoder to learn multi-scale features and incorporates a boundary exploration module (BEM) and a boundary uncertainty aware module (BUM) to handle boundary areas. Experimental results demonstrate that BUNet outperforms other methods in terms of performance and generalization ability.
