Motivated by the principle of stochastic resonance, we investigate the noise-boosted activations within both channel attention mechanisms of convolutional networks and gated linear unit (GLU)-based feedforward networks (FFNs) of Vision Transformers (ViTs) under attention-based visual processing frameworks. Specifically, we replace conventional ReLU or ReLU-based GLU (ReGLU) activations with noise-boosted variants, which incorporate learnable noise scale parameters during training. Experiments on the CIFAR-10 and STL-10 image classifications, Kvasir-SEG medical image segmentation, and Cityscapes semantic segmentation show significant improvements over conventional baselines across diverse attention architectures. The learnable noise scale parameters in activations converge to non-zero values after training, demonstrating the existence of stochastic resonance in deep attention mechanisms. These results indicate that controlled noise injection can enhance information transfer efficiency of neural networks, and establish a coherent framework that connects the theoretical principle of stochastic resonance with its practical applicability in attention-based visual processing.

Background:Robust and accurate prediction of cardiovascular disease (CVD) risk facilitates early intervention to benefit patients. The intricate relationship between mental health disorders and CVD is widely recognized. However, existing models often overlook psychological factors, relying on a limited set of clinical and lifestyle parameters, or being developed on restricted population subsets. Objectives:This study aims to assess the impact of integrating psychological data into a novel machine learning (ML) approach on enhancing CVD prediction performance. Methods:Using a comprehensive UK Biobank data set (n = 375,145), the correlation between CVD and traditional and psychological risk factors was examined. CVD included hypertensive disease, ischemic heart disease, heart failure, and arrhythmias. An ensemble ML model containing 5 constituent algorithms (decision tree, random forest, XGBoost, support vector machine, and deep neural network) was tested for its ability to predict CVD based on 2 training data sets: using traditional CVD risk factors alone, or using a combination of traditional and psychological risk factors. Results:A total of 375,145 subjects with normal health status and with CVD were included. The ensemble ML model could predict CVD with 71.31% accuracy using traditional CVD risk factors alone. However, by adding psychological factors to the training data, accuracy increased to 85.13%. The accuracy and robustness of the ensemble ML model outperformed all 5 constituent learning algorithms. Conclusions:Incorporating mental health assessment data within an ensemble ML model results in a significantly improved, highly accurate, CVD prediction model, outperforming traditional risk factor prediction alone.

Backdoor attacks on deep learning (DL) models emerge as the most worrisome security threats to their secure and safe usage, especially for security-sensitive tasks. Great efforts have been devoted to thwarting backdoor attacks by devising detection or prevention countermeasures. By default, these countermeasures are designed and evaluated on models with full-precision parameters (e.g., floating32). It is unclear whether they are immediately applicable to mitigate backdoor attacks in the quantized model that are being pervasively deployed on mobile devices and Internet of Things (IoT) devices to save resources (i.e. power and memory) and reduce latency and privacy risks.This work, for the first time, initializes the critical examination of the robustness or applicability of existing state-of-the-art (SOTA) DL backdoor defenses for detecting or preventing backdoor attacks on quantized models. Based on extensive evaluations of four representative defenses (Neural Cleanse, ABS, Fine-Pruning and Trojan Signature) with three datasets (CIFAR10, GTSRB, and STL10), we found that only Neural Cleanse’s defensive robustness is generally independent of model quantization, while all others exhibit degraded effectiveness or failures against quantized models (in particular, widely used int-8 and 1-bit models), especially when the model is quantized to be 1-bit. The identified main failure reason is that these defenses are based on examining the weight values of the model or the activation values of the neuron to identify or prevent the backdoor, often using the ranking as a step. Quantization with a small bit width leads to less fine-grained discrete values (e.g., 1-bit quantization only possesses two value elements of -1 and +1), rendering ranking effectiveness deteriorate in this case. Note that the quantization not only applies to the weight but also to activation, thus making these defenses less robust or trivially fail. This work highlights the demand for devising backdoor defenses that are generic to different quantization formats on top of the default full-precision model.

Transitioning from a fossil-fuel-dependent economy to one based on renewable energy requires significant investment and technological advancement. While wind and solar technologies provide lower cost electricity, enhanced energy storage and transmission infrastructure come at a cost for managing renewable intermittency. Energy storage systems vary in characteristics and costs, and future grids will incorporate multiple technologies, yet the optimal combination of storage technologies and the role of interconnectors in alleviating storage needs are not widely explored. This study focuses on optimal generation-storage capacity requirements to elucidate associated investments. We propose a multitimescale storage solution consisting of three storage categories and an interconnector between Australia's eastern and western grids. Subsequently, through an extensive sensitivity analysis, we investigate the impact of specific storage technologies and cost variations. Our findings demonstrate that the proposed interconnector offers a cost-effective solution, reducing generation and storage power capacity needs by 6 and 14%, respectively, resulting in 4% savings on overall investment costs. Moreover, the study's sensitivity analysis reveals that wind generation provides 50-70% of the energy demand for the least-cost solution. Despite storage inefficiencies, long-duration storage would need to be deployed to support power capacity for 2-4 days, representing 15-40% of peak demand, depending on future technology costs. Subsequently, achieving a fully renewable electricity sector in Australia requires a significant expansion of generation and storage infrastructure, with a 13-fold increase in storage power capacity and a 40-fold increase in storage energy capacity compared to existing levels.

In laser science and industry, considerable effort is directed toward designing fibers for fiber laser and fiber amplifier applications, each of which offers a particular advantage over the others. Evanescently coupled multicore fibers, however, have been studied less extensively due to the relatively small mode area in the single-mode regime. Here, by proposing a new structure with stress-applying parts in a 37-core fiber and optimizing this structure through a comprehensive framework, we present 21 solutions characterized by large-mode-area and high beam quality in the single-mode, single-polarization regime. Different fiber designs are optimal for different output parameters. In one design, the mode area can significantly increase to above 880 μm2, which is comparable with that of photonic-crystal fibers. Moreover, besides the single-mode operation, the beam quality factor (M2 factor) of the fundamental mode is considered an output parameter in the bent state and is improved up to 1.05 in another design. A comprehensive tolerance analysis is then performed to assess the performance of the designs under deviations from normal conditions. Moreover, in spite of the shifts in the loss of modes, the proposed high beam quality LMA fibers maintain single-polarization, single-mode operation across a wide range of core pitches, bending orientation angles, and bending radius deviations. Our results highlight the potential of multicore fibers for the efficient operation of fiber lasers and amplifiers.

This article presents a framework based on a flow-based programming (FBP) paradigm to design data-stream processing applications for Nuclear Physics (NP). The developed framework encourages a functional decomposition of the overall data-processing application into small monofunctional artifacts that are easy to understand, develop, and debug. The fact that these artifacts (actors) are programmatically independent means that they can be scaled and optimized independently, which is difficult for monolithic application components. One of the advantages of this approach is fault tolerance, where independent actors can come and go in the data stream without stopping or crashing the entire application. Because actors are loosely coupled and data carries context, they can run in heterogeneous environments and utilize wide-ranging accelerators. This article describes the main design concepts of this framework, presenting a proof-of-concept application and the results of processing on-beam calorimeter streaming data.

Digital realization of neuron models, especially implementation on a field programmable gate array (FPGA), is one of the key objectives of neuromorphic research, because the effective hardware realization of the biological neural networks plays a crucial role in implementing the behaviors of the brain for future applications. In this paper, a hybrid FitzHugh Nagumo-Morris Lecar (FNML) neuron model with electromagnetic flux coupling is considered, and two multiplierless piecewise linear (PWL) models, which have similar behaviors to the biological neuron, are presented. A comparison between digital implementation results of the original FNML and PWL models illustrates that, the PWL1 model provides a 65% speed-up with an overall saving (in FPGA resources) of 66.2%, and the PWL2 model yields a 71% speed-up with an overall saving of 78.2%.

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Inaccuracies have been reported in pulse oximetry measurements taken from people who identified as Black. Here, we identify substantial ethnic disparities in the population numbers within 12 pulse oximetry databases, which may affect the testing of new oximetry devices and impact patient outcomes.

Injecting artificial noise into a feedforward threshold neural network allows it to become trainable by gradientbased methods and also enlarges the parameter space as well as the range of synaptic weights. This configuration constitutes a stochastic-resonance-based threshold neural network, where the noise level can adaptively converge to a nonzero optimal value for finding a local minimum of the loss criterion. We prove theoretically that the injected noise plays the role of a generalized Tikhonov regularizer for training the designed threshold network. Experiments on regression and classification problems demonstrate that the generalization of the stochasticresonance-based threshold network is improved by the injection of noise. The feasibility of injecting noise into the threshold neural network opens up the potential for adaptive stochastic resonance in machine learning.

We propose a scheme for a quantum game based on performing an EPR-type experiment and in which each player’s spatial directional choices are considered as their strategies. A classical mixed-strategy game is recovered by restricting the players’ choices to specific spatial trajectories. We show that for players’ directional choices for which the Bell-CHSH inequality is violated, the players’ payoffs in the quantum game have no mapping within the classical mixed-strategy game. The scheme provides a more direct link between classical and quantum games.

We study, fabricate and characterize the properties of hollow antiresonant (AR) terahertz waveguides ideal for terahertz transmission. We model and measure transmission loss as the antiresonant tube thickness is varied from 0.45 to 0.55 mm with constant fiber dimensions. The experimental results confirm that the 3D printed AR waveguide can guide terahertz wave with low loss and illustrate spectral features near the resonant frequencies. The aim of this work is to develop a low-loss, low cost and simple terahertz waveguides with transmission behavior adjusted by a single parameters, the cladding tube thickness.

Self-repair is fundamental to biological neural networks. In a neural network with a large number of elements, the probability of failure of each component increases. With the breakdown of each part, there may be a significant difference in the final results that will be completely affected by this defect. The existence of a process for detecting the error and compensating it by recruiting healthy elements leads to improved performance. This is where adjacent synapses proxy faulty synapses to avoid disturbances in the network function, thereby compensating the incurred error. In the present research, a self-repairing analog circuit is designed based on an astrocyte-neuron interaction and new synapse architecture. The designed circuit builds upon a software model of an astrocyte-neuron network with the proven ability to detect errors and undertake self-repair. The results obtained from our circuit show that, when an error occurs in the synapses associated with a neuron, the currents within functioning synapses of the same neuron increase. This increase is made by receiving feedback from adjacent astrocytes and other synapses. The process maintains the network function, compensating incurred errors in the network, presenting a neural network-based analog circuit with self-repairing capability, while considering the effect of astrocytes. In this paper, extensive simulation results using HSPICE with 0.35 μm CMOS technology are provided for the evaluation of the proposed circuit.

The growth of solid tumours relies on an ever-increasing supply of oxygen and nutrients that are delivered via vascular networks. Tumour vasculature includes endothelial cell lined angiogenesis and the less common cancer cell lined vasculogenic mimicry (VM). To study and compare the development of vascular networks formed during angiogenesis and VM (represented here by breast cancer and pancreatic cancer cell lines) a number of in vitro assays were utilised. From live cell imaging, we performed a large-scale automated extraction of network parameters and identified properties not previously reported. We show that for both angiogenesis and VM, the characteristic network path length reduces over time; however, only endothelial cells increase network clustering coefficients thus maintaining small-world network properties as they develop. When compared to angiogenesis, the VM network efficiency is improved by decreasing the number of edges and vertices, and also by increasing edge length. Furthermore, our results demonstrate that angiogenic and VM networks appear to display similar properties to road traffic networks and are also subject to the well-known Braess paradox. This quantitative measurement framework opens up new avenues to potentially evaluate the impact of anti-cancer drugs and anti-vascular therapies.

Remaining useful life (RUL) prediction methods for rotating machines have been successfully developed in recent decades. More attention should be paid to predictions with inconsistent data distributions under different conditions. To solve this problem, this article proposes a new RUL prediction method that includes two phases. In the first phase, degradation features are extracted from both the training and testing data sets using probabilistic principal component analysis (PPCA). In the second phase, additive white Gaussian noise (AWGN) is intentionally injected into the degradation features; thereafter, the features that are mixed with manually injected noise are imported into a bidirectional long short-term memory (BLSTM) network. The AWGN can enhance the robustness of the RUL prediction method and achieve prediction for machines under different conditions. In contrast to most deep learning-based RUL prediction methods, the training samples are intentionally “polluted” by manually injected noise. The effectiveness of the proposed method is validated using the C-MAPSS lifetime data set for aeroengines and compared with the effectiveness of state-of-the-art approaches.

For two-player quantum games, a Nash equilibrium consists of a pair of unitary operators. Here we present a scheme for such games in which each player's strategy consists of choosing the orientation of a unit vector and Nash equilibria of the game are directional pairs. Corresponding classical games are then recovered from constraints placed on each player's directional choices.