243 research outputs found
Supplemental Material, sj-pdf-1-bsa-10.26599_BSA.2021.9050013 - Hippocampal resting-state functional connectivity with the mPFC and DLPFC moderates and mediates the association between education level and memory function in subjective cognitive decline
No full textSupplemental Material, sj-pdf-1-bsa-10.26599_BSA.2021.9050013 for Hippocampal resting-state functional connectivity with the mPFC and DLPFC moderates and mediates the association between education level and memory function in subjective cognitive decline by Shurui Xu, Qianqian Sun, Ming Li, Jia Luo, Guiyan Cai, Ruilin Chen, Lin Zhang and Jiao Liu in Brain Science Advances</p
<b>Supplemental Material - A Nomogram for Predicting the Risk of Deep Vein Thrombosis in Patients With Acute Ischemic Stroke During the COVID-19</b>
No full textSupplemental Material for A Nomogram for Predicting the Risk of Deep Vein Thrombosis in Patients With Acute Ischemic Stroke During the COVID-19 by Jie Zhang, Shurui Zhang, Ge Song, Shimeng Zhuang, Hua Li, Lisi An, Yan Meng, Jiayu Fan, and Lijuan Wang in Angiology.</p
State of Charge Estimation of Li-Ion Battery Based on Adaptive Sliding Mode Observer
No full textAs the main power source of new energy electric vehicles, the accurate estimation of State of Charge (SOC) of Li-ion batteries is of great significance for accurately estimating the vehicle’s driving range, prolonging the battery life, and ensuring the maximum efficiency of the whole battery pack. In this paper, the ternary Li-ion battery is taken as the research object, and the Dual Polarization (DP) equivalent circuit model with temperature-varying parameters is established. The parameters of the Li-ion battery model at ambient temperature are identified by the forgetting factor least square method. Based on the state space equation of power battery SOC, an adaptive Sliding Mode Observer is used to study the estimation of the State of Charge of the power battery. The SOC estimation results are fully verified at low temperature (0 °C), normal temperature (25 °C), and high temperature (50 °C). The simulation results of the Urban Dynamometer Driving Schedule (UDDS) show that the SOC error estimated at low temperature and high temperature is within 2%, and the SOC error estimated at normal temperature is less than 1%, The algorithm has the advantages of accurate estimation, fast convergence, and strong robustness
A 3-Mode Wide Operational Range Reconfigurable Regulating Rectifier for Wireless Power Transfer
No full textThis article presents a novel wide operational range reconfigurable regulating rectifier for wireless power transfer. The proposed 1X/2X/3X rectifier achieves wide range voltage regulation without global loop control to minimize the area occupation. Compared with previous work, more working modes and greater magnification allow the proposed rectifier to regulate smaller signal, which extends voltage regulation range. A novel local loop control system is proposed for voltage rectification with three modes. The local loop adaptively senses the duty cycle of mode signal to determine which two working modes the rectifier should work with and configure the rectifier in these two modes. Then the rectifier are switching between two working modes according to the comparison result with reference voltage. Also, the change of which two working modes are also triggered by a window comparator to make sure the change happens when the output voltage is far away from reference voltage. The system is designed and simulated in a 0.18um BCD technology. The measurement results show that the proposed system can rectify wide-range input AC power to a regulated output. The achieved voltage conversion ratio (VCR) is 0.95-2.68 with a peak power conversion efficiency (PCE) of 87.4%.Electrical Engineerin
Perfect AI Mimicry and the Epistemology of Consciousness: A Solipsistic Dilemma
No full textRapid advances in artificial intelligence necessitate a re-examination of the epistemological foundations upon which we attribute consciousness. As AI systems increasingly mimic human behavior and interaction with high fidelity, the concept of a "perfect mimic"—an entity empirically indistinguishable from a human through observation and interaction—shifts from hypothetical to technologically plausible. This paper argues that such developments pose a fundamental challenge to the consistency of our mind-recognition practices. Consciousness attributions rely heavily, if not exclusively, on empirical evidence derived from behavior and interaction. If a perfect mimic provides evidence identical to that of humans, any refusal to grant it equivalent epistemic status must invoke inaccessible factors, such as qualia, substrate requirements, or origin. Selectively invoking such factors risks a debilitating dilemma: either we undermine the rational basis for attributing consciousness to others (epistemological solipsism), or we accept inconsistent reasoning. I contend that epistemic consistency demands we ascribe the same status to empirically indistinguishable entities, regardless of metaphysical assumptions. The perfect mimic thus acts as an epistemic mirror, forcing critical reflection on the assumptions underlying intersubjective recognition in light of advancing AI. This analysis carries significant implications for theories of consciousness and ethical frameworks concerning artificial agents
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Architecture, Modeling, and Optimization of Photonic Neural Network Accelerators
No full textAs artificial intelligence (AI) continues to advance, so too must the computational architectures that support it. Traditional Complementary Metal-Oxide-Semiconductor (CMOS)-based accelerators, while having served as the backbone of computing for decades, are now encountering significant limitations due to the slowing of Moore’s Law. Photonic neural network accelerators emerge as a promising alternative, offering high-speed, parallel optical computations that can significantly enhance performance and energy efficiency.Despite their potential, photonic computing systems face significant challenges, particularly related to the conversion overhead from digital to analog signals and vice versa. The conversion overhead can drastically reduce the energy efficiency of photonic neural network accelerators. This dissertation addresses these challenges by introducing cross-layer optimizations and co-design strategies that span computing methods, circuits, architectures, and algorithms. Specifically, we focus on reducing the conversion overhead through innovative design and optimization techniques.Part 1: We introduce an innovative use of free-space optical systems, specifically 4F systems, to accelerate CNNs. By leveraging Fourier optics, we reduce the complexity of convolution operations from O(N^2) to O(N), a feat unachievable by traditional electronic systems. This part includes the design, construction, and optimization of free-space optical CNN accelerators, demonstrating significant performance improvements and energy efficiency through experimental evaluations on datasets such as MNIST and CIFAR-10. Part 2: We delve into on-chip photonic neural network accelerators, presenting two pioneering architectures: PhotoFourier and ReFOCUS. PhotoFourier leverages the Joint Transform Correlator (JTC) approach to perform convolutions with reduced complexity and fewer photonic components. ReFOCUS builds upon this with innovative features like optical buffers and wavelength-division multiplexing (WDM), further improving energy and area efficiency. We demonstrate that these on-chip designs outperform contemporary photonic and CMOS accelerators in terms of throughput, power efficiency, and energy-delay product (EDP).Part 3: We focus on algorithmic innovations and theoretical analysis to enhance the efficiency of photonic and analog computing systems. We propose a weight pool compression algorithm that reduces storage requirements and memory traffic, enabling efficient deployment of large neural networks. This compression algorithm also has a promising synergy with analog and photonic neural network accelerators, which could avoid or drastically reduce the conversion overhead of weights. Additionally, since the ADC power is heavily dependent on the bitwidth (ADC resolution), we develop a comprehensive analytical model for partial sum precision requirements, optimizing the trade-offs between accuracy and energy efficiency in analog neural network accelerators.Experimental JTC challenges and findings: We also included a chapter dedicated to the challenges and non-idealities we observed in our JTC hardware prototype. We further provide sensitivity analysis for various non-linearity of the photodetectors. The goal of this section is to complement the architecture work with some experimental analysis and findings, and provide insights and directions for future work. Through our contributions, we advance the field of photonic neural network accelerators, providing new architectures, modeling techniques, and optimization strategies. Our findings demonstrate the potential of photonic technologies to achieve high-performance, energy-efficient AI computations, thereby addressing critical challenges in modern computing hardware and paving the way for future advancements in this rapidly evolving domain
Perfect AI Mimicry and the Epistemology of Consciousness: A Solipsistic Dilemma
No full textRapid advances in artificial intelligence necessitate a re-examination of the epistemological foundations upon which we attribute consciousness. As AI systems increasingly mimic human behavior and interaction with high fidelity, the concept of a "perfect mimic"—an entity empirically indistinguishable from a human through observation and interaction—shifts from hypothetical to technologically plausible. This paper argues that such developments pose a fundamental challenge to the consistency of our mind-recognition practices. Consciousness attributions rely heavily, if not exclusively, on empirical evidence derived from behavior and interaction. If a perfect mimic provides evidence identical to that of humans, any refusal to grant it equivalent epistemic status must invoke inaccessible factors, such as qualia, substrate requirements, or origin. Selectively invoking such factors risks a debilitating dilemma: either we undermine the rational basis for attributing consciousness to others (epistemological solipsism), or we accept inconsistent reasoning. I contend that epistemic consistency demands we ascribe the same status to empirically indistinguishable entities, regardless of metaphysical assumptions. The perfect mimic thus acts as an epistemic mirror, forcing critical reflection on the assumptions underlying intersubjective recognition in light of advancing AI. This analysis carries significant implications for theories of consciousness and ethical frameworks concerning artificial agents
3D hierarchical porous current collector via deposition-dealloying method for lithium metal anode
No full textIn order to inhibit dendritic growth and improve the cycle stability of lithium metal anodes (LMAs), a 3D Nanoporous Nickel Foam (NP-NF) collector with hierarchical porous structure is designed through a simple modification strategy of Ni Foam (NF). The strategy only involves two steps, i.e. electrodeposition of metal zinc and chemical dealloying to evolve nanoporous structure. The obtained NP-NF possesses hierarchical pores. The large pores of several hundreds of micrometers from Ni foam could facilitate fast Li+ transport in dynamics. The mesopores on the surface of 100 nm to 1 μm could provide spatial confinement for Li deposition. The increased specific surface area could also reduce the local current density of electrode and consequently suppress the growth of dendrites. In addition, the in-situ formed lithophilic NiO on the 3D NP-NF surface can uniformly induce Li+ deposition. Compared to the Ni foam skeleton, 3D NP-NF in LMAs presents a significantly improved Li plating/string stability with a high Coulombic efficiency of 95 % after 350 cycles with plating capacity of 1 mAh cm−2 at a current density of 1 mA cm−2. 3D NP-NF@Li|Li cell shows an ultra-low overpotential of 18 mV during the 500 cycles (1000 h) at a current density of 1 mA cm−2. The 3D NP-NF@Li|LiFePO4 can stably cycle for 300 times with the capacity retention of above 80 % at 1C. This work demonstrates that constructing a micro-nano 3D porous structure collector can inhibit dendritic growth and improve lifespan of LMAs
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