Colpitts Chaotic Oscillator Coupling with a Generalized Memristor

By introducing a generalized memristor into a fourth-order Colpitts chaotic oscillator, a new memristive Colpitts chaotic oscillator is proposed in this paper. The generalized memristor is equivalent to a diode bridge cascaded with a first-order parallel RC filter. Chaotic attractors of the oscillator are numerically revealed from the mathematical model and experimentally captured from the physical circuit. The dynamics of the memristive Colpitts chaotic oscillator is investigated both theoretically and numerically, from which it can be found that the oscillator has a unique equilibrium point and displays complex nonlinear phenomena

X-ray Emission from the Interstellar and Circumgalactic Medium of Elliptical Galaxies based on MACER simulations

Interstellar (ISM) and circumgalactic mediums (CGM) around galaxies are linked to several physical processes that drive galaxy evolution. For example, the X-ray emission from the CGM gas around ellipticals has been linked to the AGN feedback occurring in the host. Upcoming telescopes, such as HUBS with ~2 eV resolution, can provide us with deep insights about the hot gas properties of such galaxies thus constrain these processes. In this project, we discuss X-ray emission of the ISM and CGM of elliptical galaxies simulated using MACER code. We generate X-ray emission data from the MACER simulations with various feedback models and produce mock observations for an instrument with high spectral resolution, which is a necessary step of selecting sources for the future observations with planned mission such as HUBS. More importantly, we establish connections between the physics of AGN and stellar feedback with the emission spectra from the ISM and CGM to investigate the possibility of using observations to constrain feedback models. We fit the X-ray spectra from these simulations with standard fitting procedures and compare the retrieved physical properties with their counterparts from the simulations to understand whether the future high-resolution observations can reliably reveal the properties of the gas in the galaxies.Comment: 10 pages, 13 Figures, accepted in MNRA

PhantomSound: Black-Box, Query-Efficient Audio Adversarial Attack via Split-Second Phoneme Injection

In this paper, we propose PhantomSound, a query-efficient black-box attack toward voice assistants. Existing black-box adversarial attacks on voice assistants either apply substitution models or leverage the intermediate model output to estimate the gradients for crafting adversarial audio samples. However, these attack approaches require a significant amount of queries with a lengthy training stage. PhantomSound leverages the decision-based attack to produce effective adversarial audios, and reduces the number of queries by optimizing the gradient estimation. In the experiments, we perform our attack against 4 different speech-to-text APIs under 3 real-world scenarios to demonstrate the real-time attack impact. The results show that PhantomSound is practical and robust in attacking 5 popular commercial voice controllable devices over the air, and is able to bypass 3 liveness detection mechanisms with >95% success rate. The benchmark result shows that PhantomSound can generate adversarial examples and launch the attack in a few minutes. We significantly enhance the query efficiency and reduce the cost of a successful untargeted and targeted adversarial attack by 93.1% and 65.5% compared with the state-of-the-art black-box attacks, using merely ~300 queries (~5 minutes) and ~1,500 queries (~25 minutes), respectively.Comment: RAID 202

MLIP: Enhancing Medical Visual Representation with Divergence Encoder and Knowledge-guided Contrastive Learning

The scarcity of annotated data has sparked significant interest in unsupervised pre-training methods that leverage medical reports as auxiliary signals for medical visual representation learning. However, existing research overlooks the multi-granularity nature of medical visual representation and lacks suitable contrastive learning techniques to improve the models' generalizability across different granularities, leading to the underutilization of image-text information. To address this, we propose MLIP, a novel framework leveraging domain-specific medical knowledge as guiding signals to integrate language information into the visual domain through image-text contrastive learning. Our model includes global contrastive learning with our designed divergence encoder, local token-knowledge-patch alignment contrastive learning, and knowledge-guided category-level contrastive learning with expert knowledge. Experimental evaluations reveal the efficacy of our model in enhancing transfer performance for tasks such as image classification, object detection, and semantic segmentation. Notably, MLIP surpasses state-of-the-art methods even with limited annotated data, highlighting the potential of multimodal pre-training in advancing medical representation learning

New Insights into Neuroinflammation Involved in Pathogenic Mechanism of Alzheimer’s Disease and Its Potential for Therapeutic Intervention

Alzheimer’s disease (AD) is the most common form of dementia, affecting more than 50 million people worldwide with an estimated increase to 139 million people by 2050. The exact pathogenic mechanisms of AD remain elusive, resulting in the fact that the current therapeutics solely focus on symptomatic management instead of preventative or curative strategies. The two most widely accepted pathogenic mechanisms of AD include the amyloid and tau hypotheses. However, it is evident that these hypotheses cannot fully explain neuronal degeneration shown in AD. Substantial evidence is growing for the vital role of neuroinflammation in AD pathology. The neuroinflammatory hypothesis provides a new, exciting lead in uncovering the underlying mechanisms contributing to AD. This review aims to highlight new insights into the role of neuroinflammation in the pathogenesis of AD, mainly including the involvement of nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB), nucleotide-binding oligomerization domain, leucine-rich repeat-containing protein 3 (NLRP3)/caspase-1 axis, triggering receptor expressed on myeloid cells 2 (TREM2) and cGAS-STING as key influencers in augmenting AD development. The inflammasomes related to the pathways of NF-κB, NLRP3, TREM2, and cGAS-STING as biomarkers of the neuroinflammation associated with AD, as well as an overview of novel AD treatments based on these biomarkers as potential drug targets reported in the literature or under clinical trials, are explored

Adaptive command-filtered finite-time consensus tracking control for single-link flexible-joint robotic multi-agent systems

This article presents a command-filtered finite-time consensus tracking control strategy for the considered single-link flexible-joint robotic multi-agent systems. First, each agent system considered in this article is a nonlinear nonstrict-feedback system with unknown nonlinearities, so the traditional backstepping method cannot be directly applied to the design controller. However, by applying the unique structure of the Gaussian function in radial basis function neural networks, the challenges in controller design caused by the aforementioned nonstrict-feedback system have been overcome. Second, the problem of unknown nonlinearities in the system is solved by the approximation property of radial basis function neural network technology. In addition, the traditional backstepping approach often leads to an “explosion of complexity” resulting from repeated derivation of virtual control signals. Our design addresses this issue by employing command filtering technology, which simplifies the controller design process. Meanwhile, new compensation signals are designed, which successfully eliminate the error influence posed by the filters. It is seen that the control strategy presented in this article can guarantee the tracking errors converge to a small neighborhood of origin in a finite time, and all signals in the closed-loop systems remain bounded. Eventually, the simulation results show the validity of the acquired control scheme