171 research outputs found
Deep recurrent spiking neural networks capture both static and dynamic representations of the visual cortex under movie stimuli
In the real world, visual stimuli received by the biological visual system
are predominantly dynamic rather than static. A better understanding of how the
visual cortex represents movie stimuli could provide deeper insight into the
information processing mechanisms of the visual system. Although some progress
has been made in modeling neural responses to natural movies with deep neural
networks, the visual representations of static and dynamic information under
such time-series visual stimuli remain to be further explored. In this work,
considering abundant recurrent connections in the mouse visual system, we
design a recurrent module based on the hierarchy of the mouse cortex and add it
into Deep Spiking Neural Networks, which have been demonstrated to be a more
compelling computational model for the visual cortex. Using Time-Series
Representational Similarity Analysis, we measure the representational
similarity between networks and mouse cortical regions under natural movie
stimuli. Subsequently, we conduct a comparison of the representational
similarity across recurrent/feedforward networks and image/video training
tasks. Trained on the video action recognition task, recurrent SNN achieves the
highest representational similarity and significantly outperforms feedforward
SNN trained on the same task by 15% and the recurrent SNN trained on the image
classification task by 8%. We investigate how static and dynamic
representations of SNNs influence the similarity, as a way to explain the
importance of these two forms of representations in biological neural coding.
Taken together, our work is the first to apply deep recurrent SNNs to model the
mouse visual cortex under movie stimuli and we establish that these networks
are competent to capture both static and dynamic representations and make
contributions to understanding the movie information processing mechanisms of
the visual cortex
Accelerated Particle Swarm Optimization for Photovoltaic Maximum Power Point Tracking under Partial Shading Conditions
This paper presents an accelerated particle swarm optimization (PSO)-based maximum power point tracking (MPPT) algorithm to track global maximum power point (MPP) of photovoltaic (PV) generation under partial shading conditions. Conventional PSO-based MPPT algorithms have common weaknesses of a long convergence time to reach the global MPP and oscillations during the searching. The proposed algorithm includes a standard PSO and a perturb-and-observe algorithm as the accelerator. It has been experimentally tested and compared with conventional MPPT algorithms. Experimental results show that the proposed MPPT method is effective in terms of high reliability, fast dynamic response, and high accuracy in tracking the global MPP
SwG-former: Sliding-window Graph Convolutional Network Integrated with Conformer for Sound Event Localization and Detection
Sound event localization and detection (SELD) is a joint task of sound event
detection (SED) and direction of arrival (DoA) estimation. SED mainly relies on
temporal dependencies to distinguish different sound classes, while DoA
estimation depends on spatial correlations to estimate source directions. To
jointly optimize two subtasks, the SELD system should extract spatial
correlations and model temporal dependencies simultaneously. However, numerous
models mainly extract spatial correlations and model temporal dependencies
separately. In this paper, the interdependence of spatial-temporal information
in audio signals is exploited for simultaneous extraction to enhance the model
performance. In response, a novel graph representation leveraging graph
convolutional network (GCN) in non-Euclidean space is developed to extract
spatial-temporal information concurrently. A sliding-window graph (SwG) module
is designed based on the graph representation. It exploits sliding-windows with
different sizes to learn temporal context information and dynamically
constructs graph vertices in the frequency-channel (F-C) domain to capture
spatial correlations. Furthermore, as the cornerstone of message passing, a
robust Conv2dAgg function is proposed and embedded into the SwG module to
aggregate the features of neighbor vertices. To improve the performance of SELD
in a natural spatial acoustic environment, a general and efficient SwG-former
model is proposed by integrating the SwG module with the Conformer. It exhibits
superior performance in comparison to recent advanced SELD models. To further
validate the generality and efficiency of the SwG-former, it is seamlessly
integrated into the event-independent network version 2 (EINV2) called
SwG-EINV2. The SwG-EINV2 surpasses the state-of-the-art (SOTA) methods under
the same acoustic environment
Deep Spiking Neural Networks with High Representation Similarity Model Visual Pathways of Macaque and Mouse
Deep artificial neural networks (ANNs) play a major role in modeling the
visual pathways of primate and rodent. However, they highly simplify the
computational properties of neurons compared to their biological counterparts.
Instead, Spiking Neural Networks (SNNs) are more biologically plausible models
since spiking neurons encode information with time sequences of spikes, just
like biological neurons do. However, there is a lack of studies on visual
pathways with deep SNNs models. In this study, we model the visual cortex with
deep SNNs for the first time, and also with a wide range of state-of-the-art
deep CNNs and ViTs for comparison. Using three similarity metrics, we conduct
neural representation similarity experiments on three neural datasets collected
from two species under three types of stimuli. Based on extensive similarity
analyses, we further investigate the functional hierarchy and mechanisms across
species. Almost all similarity scores of SNNs are higher than their
counterparts of CNNs with an average of 6.6%. Depths of the layers with the
highest similarity scores exhibit little differences across mouse cortical
regions, but vary significantly across macaque regions, suggesting that the
visual processing structure of mice is more regionally homogeneous than that of
macaques. Besides, the multi-branch structures observed in some top mouse
brain-like neural networks provide computational evidence of parallel
processing streams in mice, and the different performance in fitting macaque
neural representations under different stimuli exhibits the functional
specialization of information processing in macaques. Taken together, our study
demonstrates that SNNs could serve as promising candidates to better model and
explain the functional hierarchy and mechanisms of the visual system.Comment: Accepted by Proceedings of the 37th AAAI Conference on Artificial
Intelligence (AAAI-23
Generative Watermarking Against Unauthorized Subject-Driven Image Synthesis
Large text-to-image models have shown remarkable performance in synthesizing
high-quality images. In particular, the subject-driven model makes it possible
to personalize the image synthesis for a specific subject, e.g., a human face
or an artistic style, by fine-tuning the generic text-to-image model with a few
images from that subject. Nevertheless, misuse of subject-driven image
synthesis may violate the authority of subject owners. For example, malicious
users may use subject-driven synthesis to mimic specific artistic styles or to
create fake facial images without authorization. To protect subject owners
against such misuse, recent attempts have commonly relied on adversarial
examples to indiscriminately disrupt subject-driven image synthesis. However,
this essentially prevents any benign use of subject-driven synthesis based on
protected images.
In this paper, we take a different angle and aim at protection without
sacrificing the utility of protected images for general synthesis purposes.
Specifically, we propose GenWatermark, a novel watermark system based on
jointly learning a watermark generator and a detector. In particular, to help
the watermark survive the subject-driven synthesis, we incorporate the
synthesis process in learning GenWatermark by fine-tuning the detector with
synthesized images for a specific subject. This operation is shown to largely
improve the watermark detection accuracy and also ensure the uniqueness of the
watermark for each individual subject. Extensive experiments validate the
effectiveness of GenWatermark, especially in practical scenarios with unknown
models and text prompts (74% Acc.), as well as partial data watermarking (80%
Acc. for 1/4 watermarking). We also demonstrate the robustness of GenWatermark
to two potential countermeasures that substantially degrade the synthesis
quality
Neocortical activity is stimulus- and scale-invariant
Mounting evidence supports the hypothesis that the cortex operates near a critical state, defined as the transition point between order (large-scale activity) and disorder (small-scale activity). This criticality is manifested by power law distribution of the size and duration of spontaneous cascades of activity, which are referred as neuronal avalanches. The existence of such neuronal avalanches has been confirmed by several studies both in vitro and in vivo, among different species and across multiple spatial scales. However, despite the prevalence of scale free activity, still very little is known concerning whether and how the scale-free nature of cortical activity is altered during external stimulation. To address this question, we performed in vivo two-photon population calcium imaging of layer 2/3 neurons in primary visual cortex of behaving mice during visual stimulation and conducted statistical analyses on the inferred spike trains. Our investigation for each mouse and condition revealed power law distributed neuronal avalanches, and irregular spiking individual neurons. Importantly, both the avalanche and the spike train properties remained largely unchanged for different stimuli, while the cross-correlation structure varied with stimuli. Our results establish that microcircuits in the visual cortex operate near the critical regime, while rearranging functional connectivity in response to varying sensory inputs
Electrochemically primed functional redox mediator generator from the decomposition of solid state electrolyte.
Recent works into sulfide-type solid electrolyte materials have attracted much attention among the battery community. Specifically, the oxidative decomposition of phosphorus and sulfur based solid state electrolyte has been considered one of the main hurdles towards practical application. Here we demonstrate that this phenomenon can be leveraged when lithium thiophosphate is applied as an electrochemically "switched-on" functional redox mediator-generator for the activation of commercial bulk lithium sulfide at up to 70 wt.% lithium sulfide electrode content. X-ray adsorption near-edge spectroscopy coupled with electrochemical impedance spectroscopy and Raman indicate a catalytic effect of generated redox mediators on the first charge of lithium sulfide. In contrast to pre-solvated redox mediator species, this design decouples the lithium sulfide activation process from the constraints of low electrolyte content cell operation stemming from pre-solvated redox mediators. Reasonable performance is demonstrated at strict testing conditions
A switched reluctance motor torque ripple reduction strategy with deadbeat current control
This paper presents a switched reluctance motor (SRM) torque ripple reduction strategy with deadbeat current control. In this method, the SRM torque is indirectly controlled by the phase current. The deadbeat control method can predict the duty cycle of the switching signal for the next control period according to current error, and achieve an accurate current tracking. Thus, SRM torque control error can be reduced significantly. The feasibility and effectiveness of the proposed strategy have been verified in both simulation and experimental studies
A switched reluctance motor torque ripple reduction strategy with deadbeat current control and active thermal management
This paper presents a switched reluctance motor
(SRM) torque ripple reduction strategy with deadbeat current
control and active thermal management. In this method, the
SRM torque is indirectly controlled by the phase current. A
deadbeat current control method is used to improve the SRM
phase current control accuracy, so that SRM torque control
error can be reduced significantly. According to the online
measurement of the power switching device temperature, the
switching frequency will be reduced to prevent the SRM power
converter from being damaged by over-temperature. The
feasibility and effectiveness of the proposed strategy have been
verified in both simulation and experimental studies
Acceleration and displacement dynamic response laws of a shallow-buried bifurcated tunnel
In order to obtain the seismic dynamic laws of the acceleration and displacement of a shallow-buried bifurcated tunnel, the analysis of the numerical simulation was carried out by MIDAS-GTS/NX software. The results of the numerical simulation were verified by a shaking table model test. The results show that: (1) the numerical simulation and shaking table test coincide with each other in terms of variation law and peak value. The results of the numerical simulation are credible. (2) For different tunnel cross-section, the response of acceleration and displacement are significant difference. (3) The seismic response of the small distance tunnel (Section 6) is intense. The seismic response laws of the small distance tunnel are significant difference from other type tunnels. The seismic response of the measuring point at the middle rock column is intense. (4) Along the axis of the tunnel, the displacement of the tunnel firstly increases and then decreases. The displacement of the measuring point at the middle rock column is intense, which is in accordance with the law of the acceleration response. The seismic response laws of the tunnel are significantly affected by the middle rock column. The section structure size has a significant effect on the dynamic response of the tunnel
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