Article
Engineering, Mechanical
Junwei Sun, Yongxing Ma, Zicheng Wang, Yanfeng Wang
Summary: This paper reports on the dynamic analysis and cryptographic applications of hyperbolic memristor-coupled neurons. A memristor model is proposed and its locally active property is verified. A 5D hyperbolic memristor-coupled neuron is constructed and its boundedness and Hamiltonian energy are analyzed. The nonlinear behavior of the neuron, such as coexistence bifurcation mode and coexistence attractor, is revealed.
NONLINEAR DYNAMICS
(2023)
Article
Engineering, Mechanical
Halgurd Taher, Daniele Avitabile, Mathieu Desroches
Summary: We report a detailed analysis on the emergence of bursting in a recently developed neural mass model that includes short-term synaptic plasticity. The study reveals the importance of synaptic dynamics in bursting activity and the complex process of bursting initiation.
NONLINEAR DYNAMICS
(2022)
Review
Thermodynamics
Biao Wang, Yanwei Hu, Yurong He, Nikolay Rodionov, Jiaqi Zhu
Summary: This review investigates the dynamic instabilities of gas-liquid two-phase flow boiling in micro-channels from both theoretical and experimental perspectives. Various methods to suppress instabilities are summarized, and further research directions are proposed. Experimental evidence is provided for three primary dynamic instabilities in micro-channels, and the effects of instabilities on flow pattern and heat transfer coefficient are discussed.
APPLIED THERMAL ENGINEERING
(2022)
Article
Mathematics, Interdisciplinary Applications
A. Dlamini, E. F. Doungmo Goufo
Summary: In this paper, a generalized chaotic system with multiple scrolls is studied, analyzed, and simulated using analytical, numerical, and experimental circuit elements. The system exhibits chaotic and hyperchaotic dynamics, as well as hidden attractors. Simulations demonstrate that the system displays fractal features combined with chaotic behavior.
CHAOS SOLITONS & FRACTALS
(2023)
Review
Chemistry, Physical
Pascal Friederich, Florian Hase, Jonny Proppe, Alan Aspuru-Guzik
Summary: This paper discusses how machine-learned potentials break the limitations of system-size or accuracy, how active-learning will aid their development, how they are applied, and how they may become a more widely used approach.
Article
Psychology, Multidisciplinary
Sophie Aerdker, Jing Feng, Gregor Schoener
Summary: This study provides new empirical evidence that shows motor behavior early in development has similar characteristics of habituation, dishabituation, and Spencer-Thompson dishabituation as seen in infant perception and cognition. Moreover, a unified neural dynamic model is proposed to explain the choice preferences in A not B motor decision tasks.
FRONTIERS IN PSYCHOLOGY
(2022)
Article
Energy & Fuels
Wei Gao, Jinghu Yang, Yong Mu, Fuqiang Liu, Shaolin Wang, Qianpeng Zhao, Kaixing Wang, Cunxi Liu, Gang Xu
Summary: This study experimentally investigates the ignition transient process on a staged partially premixed annular combustor, focusing on the impact of bulk velocity and equivalence ratio on flame propagation characteristics and light-round speed. The findings reveal wrinkled turbulent flame fronts and a large span of flame fragment azimuthal propagation due to swirling air in the combustor. The light-round process can be decomposed into three distinct phases, with bulk velocity and equivalence ratio playing a positive role under certain conditions.
Article
Physics, Multidisciplinary
Yuta Murakami, Kento Uchida, Akihisa Koga, Koichiro Tanaka, Philipp Werner
Summary: This article reveals the significant effect of strong spin-charge coupling on high-harmonic generation in Mott insulators. The study shows that the HHG signal is greatly enhanced with decreasing temperature, which is an anomalous behavior due to a cooperative effect between the spin-charge coupling and the thermal ensemble, as well as the temperature-dependent coherence between charge carriers. The research argues that the peculiar temperature dependence of HHG is a generic feature of Mott insulators and can be controlled through the Coulomb interaction and dimensionality of the system.
PHYSICAL REVIEW LETTERS
(2022)
Article
Physics, Multidisciplinary
Chunlai Li, Yongyan Yang, Jianrong Du, Zhen Chen
Summary: In this study, a simple chaotic circuit was constructed by introducing a new magnetic flux-controlled memristor model. The system's basic dynamics were studied theoretically and numerically, revealing complex nonlinear behavior such as coexistence bifurcation, transient dynamics, and anti-monotonicity. The memristor chaotic circuit has the unique feature of enabling modulation of attractor position and amplitude by adjusting initial conditions.
EUROPEAN PHYSICAL JOURNAL-SPECIAL TOPICS
(2021)
Article
Materials Science, Multidisciplinary
Aditya Vasudevan, Yuri Lubomirsky, Chih-Hung Chen, Eran Bouchbinder, Alain Karma
Summary: Recent research has shown that the oscillatory instability and tip-splitting instability in 2D dynamic fracture are influenced by the nonlinear elastic and dissipation length scales, demonstrating the universality of these instabilities.
JOURNAL OF THE MECHANICS AND PHYSICS OF SOLIDS
(2021)
Article
Neurosciences
Peter A. Robinson, James A. Henderson, Natasha C. Gabay, Kevin M. Aquino, Tara Babaie-Janvier, Xiao Gao
Summary: Spectral analysis based on neural field theory is used to analyze dynamic connectivity by examining the physical eigenmodes that are the building blocks of brain dynamics. The study demonstrates that functional connectivity is dynamic, dominated by a few eigenmodes, and that common artifacts introduced by statistical analyses can be avoided by using spectral analysis with eigenmodes. Eigenmodes, unlike artificially discretized resting state networks, overlap and provide directly interpretable insights related to brain structure and function.
FRONTIERS IN HUMAN NEUROSCIENCE
(2021)
Article
Physics, Fluids & Plasmas
Matteo Paoluzzi
Summary: This article examines the role of nonequilibrium terms in active field theories in describing active phase separation, particularly at critical points. Despite their irrelevance at the critical point, these terms still contribute to nontrivial scaling of the entropy production rate.
Article
Mathematics, Applied
Harald Garcke, Patrik Knopf, Sema Yayla
Summary: This article investigates a Cahn-Hilliard model with dynamic boundary conditions, showing convergence of solutions as the kinetic rate increases, establishing the existence and stability of global attractors, and constructing exponential attractors.
NONLINEAR ANALYSIS-THEORY METHODS & APPLICATIONS
(2022)
Article
Physics, Multidisciplinary
Lun Yue, Mette B. Gaarde
Summary: Anomalous high-harmonic generation (HHG) in certain solids is caused by a Berry-curvature-induced anomalous current, but is often contaminated by harmonics from interband coherences. This study presents an ab initio methodology to characterize the anomalous HHG mechanism and identifies two unique properties of the anomalous harmonic yields: an overall increase with laser wavelength and pronounced minima at specific laser wavelengths and intensities. These properties can be utilized to distinguish anomalous harmonics from competing HHG mechanisms and enable the experimental identification and time-domain control of pure anomalous harmonics and reconstruction of Berry curvatures.
PHYSICAL REVIEW LETTERS
(2023)
Article
Computer Science, Software Engineering
Li Ma, Xiaoyu Li, Jing Liao, Xuan Wang, Qi Zhang, Jue Wang, Pedro V. Sander
Summary: The authors introduce Neural Parameterization (NeP) as a hybrid representation combining the advantages of implicit and explicit methods, achieving photo-realistic rendering with fine-grained editing capabilities. By parameterizing 3D geometry into 2D texture space, they separate geometry and appearance, enhancing the operability of scene editing.
ACM TRANSACTIONS ON GRAPHICS
(2022)
Article
Computer Science, Artificial Intelligence
Evelina Dineva, Gregor Schoener
CONNECTION SCIENCE
(2018)
Article
Engineering, Electrical & Electronic
Julien N. P. Martel, Lorenz K. Mueller, Stephen J. Carey, Jonathan Mueller, Yulia Sandamirskaya, Piotr Dudek
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS
(2018)
Article
Neurosciences
Eva Hansen, Britta Grimme, Hendrik Reimann, Gregor Schoener
EXPERIMENTAL BRAIN RESEARCH
(2018)
Article
Computer Science, Cybernetics
Valere Martin, Hendrik Reimann, Gregor Schoener
BIOLOGICAL CYBERNETICS
(2019)
Article
Engineering, Electrical & Electronic
Giacomo Indiveri, Yulia Sandamirskaya
IEEE SIGNAL PROCESSING MAGAZINE
(2019)
Article
Computer Science, Artificial Intelligence
Jan Tekuelve, Adrien Fois, Yulia Sandamirskaya, Gregor Schoener
FRONTIERS IN NEUROROBOTICS
(2019)
Article
Engineering, Electrical & Electronic
Hajar Asgari, Babak Mazloom-Nezhad Maybodi, Yulia Sandamirskaya
INTERNATIONAL JOURNAL OF CIRCUIT THEORY AND APPLICATIONS
(2020)
Article
Engineering, Electrical & Electronic
Dongchen Liang, Raphaela Kreiser, Carsten Nielsen, Ning Qiao, Yulia Sandamirskaya, Giacomo Indiveri
IEEE JOURNAL ON EMERGING AND SELECTED TOPICS IN CIRCUITS AND SYSTEMS
(2019)
Article
Computer Science, Artificial Intelligence
Llewyn Salt, David Howard, Giacomo Indiveri, Yulia Sandamirskaya
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2020)
Article
Engineering, Electrical & Electronic
Hajar Asgari, Babak Mazloom-Nezhad Maybodi, Raphaela Kreiser, Yulia Sandamirskaya
IEEE JOURNAL ON EMERGING AND SELECTED TOPICS IN CIRCUITS AND SYSTEMS
(2020)
Article
Engineering, Electrical & Electronic
Mike Davies, Andreas Wild, Garrick Orchard, Yulia Sandamirskaya, Gabriel A. Fonseca Guerra, Prasad Joshi, Philipp Plank, Sumedh R. Risbud
Summary: Neuromorphic computing aims to develop chips inspired by biological neural circuits to process new knowledge, adapt, behave, and learn in real time at low power levels, with recent advancements showing promising results with Intel's Loihi processor. Compelling neuromorphic networks using spike-based hardware demonstrate significantly lower latency and energy consumption compared to state-of-the-art conventional approaches, solving diverse problems representative of brain-like computation.
PROCEEDINGS OF THE IEEE
(2021)
Article
Computer Science, Artificial Intelligence
Jan Tekulve, Gregor Schoener
Summary: This article examines the neural process organization required for autonomous learning, including the decision and control of learning, the construction of learning data representations, and the selection of neural substrate for learning new patterns. It demonstrates how a neural dynamic network can learn new beliefs from single experiences and use them to satisfy desires.
IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS
(2022)
Review
Robotics
Fengyuan Liu, Sweety Deswal, Adamos Christou, Yulia Sandamirskaya, Mohsen Kaboli, Ravinder Dahiya
Summary: Touch is a complex sensing modality that enables us to feel and perceive the world through the large number of receptors in our skin. The touch-sensing capability of current robots is far behind humans, therefore there is a need for neural-like hardware for electronic skin (e-skin). This paper discusses the hardware implementations of various computational building blocks for e-skin and how they can potentially achieve human skin-like or peripheral nervous system-like functionalities.
Article
Robotics
Yulia Sandamirskaya, Mohsen Kaboli, Jorg Conradt, Tansu Celikel
Summary: This viewpoint provides an overview of recent insights from neuroscience that could enhance signal processing in artificial neural networks on chip, unlocking innovative applications in robotics and autonomous intelligent systems.
Article
Neurosciences
Rachid Ramadan, Cora Hummert, Jean-Stephane Jokeit, Gregor Schoener
Summary: The study focuses on the characteristics of descending activation patterns in targeted hand movements, revealing that these patterns have different temporal structures at different movement speeds to adapt to changes in interaction torques and muscle dynamics.
JOURNAL OF NEUROPHYSIOLOGY
(2022)
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.
