Traditional Gradient Research Articles

Efficient on-chip training of large-scale optical neural network through block adjoint training algorithm

MZI-based block optical neural networks (BONNs), which utilize block matrix multiplication to achieve large-scale network models, have garnered significant attention but still lack efficient training algorithms. In this article, by calculating the original field and adjoint field for the block matrices in BONNs and directly updating the phase values of all phase shifters within the optical mesh, we propose an on-chip block adjoint training (BAT) algorithm for large-scale BONNs. To demonstrate the effectiveness of our proposed algorithm, the trained BONNs are applied in image classification tasks for MNIST and SVHN datasets. The calculated results demonstrate that the performance of the BAT algorithm (95.915% for the MNIST dataset and 82.64% for the SVHN dataset) is competitive with the traditional gradient algorithm based on artificial neural networks (96.238% and 84.182%), but the BONNs can infer 1.5 times and 1.3 times faster than artificial neural networks, respectively. By studying the influence of the block size and the inputted position of the padded zero signals, we demonstrate that the BAT algorithm based on the BONNs with 12 block sizes can achieve higher performance by adding the padded zero signals to the same side beside the normal inputted signals. Additionally, we demonstrate that substituting the complete weight matrices with unitary matrices to construct BONNs is an efficient way to reduce both the system area and the required trainable parameters. Finally, we demonstrate the relatively good robustness of the BAT algorithm and the imprecision alleviation method by using on-chip retraining. Notably, our proposed BAT algorithm shows excellent potential for more complex tasks and network models.

Optimization design of multi-scale TPMS lattices based on geometric continuity fusion and strain energy driven

3D lattice structure is a kind of advanced metamaterial structure with excellent performance, such as lightweight, high specific strength and stiffness, shock damping, heat dissipation, and electromagnetic shielding capabilities. With the development of additive manufacturing technology, it has been widely concerned and developed rapidly in recent years. In order to further integrate the performance of lattice structures with mesoscale units into multi-scale optimization, this paper proposes an optimization design method for the multi-scale TPMS (Triply Periodic Minimal Surfaces) lattices based on geometric continuity fusion and strain energy driven. Regarding the challenge of geometric continuity problem of complex transition boundaries in multi-TPMS lattices, interface interpolation and density factor optimization methodologies are studied. At the same time, the mapping mechanism between lattice configuration and mechanical properties of multi-TPMS lattices is discussed in view of the requirements of multi-scale design from macro to micro configurations. Then, the multi-TPMS lattice density and configuration fields in the design domain are co-designed with the strain energy driven. Simulation and experimental results show that the mechanical properties of the multi-scale TPMS lattice designed by the proposed method are significantly improved compared with the traditional single-configuration gradient lattice, including the stiffness of the cantilever beam optimized structure is improved by 20.5% and the flexural stiffness of the three-point flexural beam optimized structure is improved by 51.3%.

Embryology outcomes of a device-based sperm separation technique compared to density gradient centrifugation using thawed spermatozoa-a sibling donor oocyte study.

To evaluate whether the ZyMōt™ Multi 850μl sperm separation device (SSD) effectively recovers motile spermatozoa from cryopreserved ejaculates and compare its effect on key embryology outcomes including fertilization, cleavage stage, and total and top-quality blastocyst formation rates to the traditional Density Gradient Centrifugation (DGC) method. In this prospective, single-center, controlled study, we used fresh sibling donor oocytes and non-donor cryopreserved ejaculates. In total, 150 couples participated in this study. At least eight MII donor oocytes were allocated to each couple split into two arms. One arm underwent ICSI with the control DGC-processed sample, and the other arm processed with SSD. No significant difference on fertilization and cleavage stage embryo rates was observed between the two techniques. We observed a significant increase in the percentage of total (SSD: 74.03 ± 23.47% vs. DGC: 67.86 ± 23.92%; p = 0.016) and top-quality (SSD: 66.38 ± 24.94% vs. DGC: 60.98 ± 24.40%; p = 0.035) blastocysts formed post-SSD processing. Sub-analysis showed that this increase remained significant for the WHO-normal group (n = 118), but not for the WHO-abnormal group (n = 32). The SSD was successfully applied in all 150 cases, providing adequate numbers of spermatozoa to undergo ICSI. Additionally, SSD significantly improved blastocyst development rates; however, this was of limited clinical impact considering the minor improvement on the average number of top-quality blastocysts. It can be hypothesized that this positive contribution may be stronger and clinically significant when a larger number of oocytes is used or in homologous oocyte ICSI cycles, where the repair mechanisms of the oocytes may insufficient for promoting healthy embryo development.

Helicopter turboshaft engines combustion chamber monitoring neural network method

The article proposes an innovative method for helicopter turboshaft engines’ combustion chamber monitoring based on a neural network, significantly impacting the aircraft engines’ diagnostics and monitoring subject area. This article’s main contribution is to improve defect detections’ accuracy and timeliness, which in turn contributes to the aircraft equipment operations’ reliability and safety. The developed method, leveraging an enhanced adaptive neuro-fuzzy inference system (ANFIS) neuro-fuzzy network with Sugeno-Takagi inference, significantly improves defect detection accuracy and reduces diagnostic errors, thereby enhancing the reliability and safety of flight operations. To implement it, the combustion chamber mathematical model was created based on the heat balance equation, which showed that the fuel combustion completeness coefficient is a diagnostic criterion for defects. It is mathematically substantiated that this coefficient determines combustion efficiency and directly affects thermal energy. The ANFIS with Sugeno-Takagi fuzzy inference architecture has been improved, making it possible to achieve the defects diagnosis and prediction accuracy of 99.65 %. The ANFIS neuro-fuzzy network with Sugeno-Takagi inference proposed in this work improves quality metrics for determining helicopter turboshaft engines’ combustion chamber defects, enhancing the fuel combustion completeness coefficient by 1.01 to 4.64 times. Experimental results show that optimal network training with a loss of 0.35 % is achieved in 60 epochs, which is 2.0 to 9.5 times faster than traditional algorithms (genetic algorithm, traditional backpropagation algorithm, traditional inverse gradient descending method, modified inverse gradient descending method, hybrid algorithm). The absence of hidden T-patterns in the fuel combustion completeness coefficient dynamics (the value does not exceed 10–4) is also substantiated, confirming the proposed method’s high accuracy. The accumulated heat experimental surfaces’ dependences on the dynamics and fuel consumption were obtained, which makes it possible to analyze the defects’ influence on the combustion chamber’s thermal characteristics. The proposed ANFIS network use reduces the 1st and 2nd types’ errors by 1.44…6.15 times when determining combustion chamber defects compared to other architectures and classical methods, such as the least squares method.

Efficient Robot Manipulation via Reinforcement Learning with Dynamic Movement Primitives-Based Policy

Reinforcement learning (RL) that autonomously explores optimal control policies has become a crucial direction for developing intelligent robots while Dynamic Movement Primitives (DMPs) serve as a powerful tool for efficiently expressing robot trajectories. This article explores an efficient integration of RL and DMP to enhance the learning efficiency and control performance of reinforcement learning in robot manipulation tasks by focusing on the forms of control actions and their smoothness. A novel approach, DDPG-DMP, is proposed to address the efficiency and feasibility issues in the current RL approaches that employ DMP to generate control actions. The proposed method naturally integrates a DMP-based policy into the actor–critic framework of the traditional RL approach Deep Deterministic Policy Gradient (DDPG) and derives the corresponding update formulas to learn the networks that properly decide the parameters of DMPs. A novel inverse controller is further introduced to adaptively learn the translation from observed states into various robot control signals through DMPs, eliminating the requirement for human prior knowledge. Evaluated on five robot arm control benchmark tasks, DDPG-DMP demonstrates significant advantages in control performance, learning efficiency, and smoothness of robot actions compared to related baselines, highlighting its potential in complex robot control applications.

Improved DDPG algorithm-based path planning for unmanned surface vehicles

As a promising mode of water transportation, unmanned surface vehicles (USVs) are used in various fields owing to their small size, high flexibility, favorable price, and other advantages. Traditional navigation algorithms are affected by various path planning issues. To address the limitations of the traditional deep deterministic policy gradient (DDPG) algorithm, namely slow convergence speed and sparse reward and punishment functions, we proposed an improved DDPG algorithm for USV path planning. First, the principle and workflow of the DDPG deep reinforcement learning (DRL) algorithm are described. Second, the improved method (based on the USVs kinematic model) is proposed, and a continuous state and action space is designed. The reward and punishment function are improved, and the principle of collision avoidance at sea is introduced. Dynamic region restriction is added, distant obstacles in the state space are ignored, and the nearby obstacles are observed to reduce the number of algorithm iterations and save computational resources. The introduction of a multi-intelligence approach combined with a prioritized experience replay mechanism accelerates algorithm convergence, thereby increasing the efficiency and robustness of training. Finally, through a combination of theory and simulation, the DDPG DRL is explored for USV obstacle avoidance and optimal path planning.

Modified Noise-Immune Fuzzy Neural Network for Solving the Quadratic Programming With Equality Constraint Problem.

Quadratic programming with equality constraint (QPEC) problems have extensive applicability in many industries as a versatile nonlinear programming modeling tool. However, noise interference is inevitable when solving QPEC problems in complex environments, so research on noise interference suppression or elimination methods is of great interest. This article proposes a modified noise-immune fuzzy neural network (MNIFNN) model and use it to solve QPEC problems. Compared with the traditional gradient recurrent neural network (TGRNN) and traditional zeroing recurrent neural network (TZRNN) models, the MNIFNN model has the advantage of inherent noise tolerance ability and stronger robustness, which is achieved by combining proportional, integral, and differential elements. Furthermore, the design parameters of the MNIFNN model adopt two disparate fuzzy parameters generated by two fuzzy logic systems (FLSs) related to the residual and residual integral term, which can improve the adaptability of the MNIFNN model. Numerical simulations demonstrate the effectiveness of the MNIFNN model in noise tolerance.

Fractional-order spike-timing-dependent gradient descent for multi-layer spiking neural networks

Accumulated detailed knowledge about the neuronal activities in human brains has brought more attention to bio-inspired spiking neural networks (SNNs). In contrast to non-spiking deep neural networks (DNNs), SNNs can encode and transmit spatiotemporal information more efficiently by exploiting biologically realistic and low-power event-driven neuromorphic architectures. However, the supervised learning of SNNs still remains a challenge because the spike-timing-dependent plasticity (STDP) of connected spiking neurons is difficult to implement and interpret in existing backpropagation learning schemes. This paper proposes a fractional-order spike-timing-dependent gradient descent (FO-STDGD) learning model by considering a derived nonlinear activation function that describes the relationship between the quasi-instantaneous firing rate and the temporal membrane potentials of nonleaky integrate-and-fire neurons. The training strategy can be generalized to any fractional orders between 0 and 2 since the FO-STDGD incorporates the fractional gradient descent method into the calculation of spike-timing-dependent loss gradients. The proposed FO-STDGD model is tested on the MNIST and DVS128 Gesture datasets and its accuracy under different network structure and fractional orders is analyzed. It can be found that the classification accuracy increases as the fractional order increases, and specifically, the case of fractional order 1.9 improves by 155 % relative to the case of fractional order 1 (traditional gradient descent). In addition, our scheme demonstrates the state-of-the-art computational efficacy for the same SNN structure and training epochs.

Path Planning in Complex Environments Using Attention-Based Deep Deterministic Policy Gradient

The traditional Deep Deterministic Policy Gradient (DDPG) algorithm frequently exhibits a notable reduction in success rate when transferred to new environments after being trained in complex simulation settings. To address these issues, this paper adopts a Multi-Environment (Multi-Env) parallel training approach and integrates Multi-Head Attention (MHA) and Prioritized Experience Replay (PER) into the DDPG framework, optimizing the reward function to form the MAP-DDPG algorithm. This approach enhances the algorithm’s generalization capability and execution efficiency. Through comparative training and testing of the DDPG and MAP-DDPG algorithms in both simulation and real-world environments, the experimental results demonstrate that MAP-DDPG significantly improves generalization and execution efficiency over the DDPG algorithm. Specifically, in simulation environment tests, the MAP-DDPG algorithm achieved an average 30% increase in success rate and reduced the average time to reach the target point by 23.7 s compared to the DDPG algorithm. These results indicate that the MAP-DDPG algorithm significantly enhances path planning generalization and execution efficiency, providing a more effective solution for path planning in complex environments.

EVENT-TRIGGERED CONTROL OF MILLING PHOTOELECTRIC TRACKING SERVO SYSTEMS BASED ON MULTI-INNOVATION INTEREST PARAMETER IDENTIFICATION

Given the coupling of speed and current and the nonlinearity of electronic devices, the photoelectric tracking servo system for milling machines make it difficult to determine the model parameters, and traditional proportional–integral (PI) control hardly meets the high-performance requirements of milling machine optoelectronic tracking servo systems, especially in the development of system networking. In this study, an event-triggered intelligent PI position control strategy based on a multi-innovation identification model was proposed to improve the low control accuracy and dynamic performance caused by the strong coupling and nonlinearity in the optoelectronic tracking servo system of computerized numerical control (CNC) milling machines. A discredite model of the system was established through a multi-innovation identification model, and the PI control parameter was quickly determined using an improved multiverse optimization (IMVO) algorithm. At the same time, an event-triggering mechanism was introduced, thus reducing the number of controller triggers and saving system resources while ensuring the dynamic performance of the system. Finally, experiment results were compared with typical second-order system engineering design PI (SSED-PI) control, pole placement PI (PP-PI) control, and multiverse optimization (MVO)-PI control. Results demonstrate that the proposed multi-innovation stochastic gradient identification model fully utilizes the historical turning angle information of the optoelectronic tracking servo system and has higher accuracy than traditional stochastic gradient identification (parameter accuracy improved by 6.9 times, quantization error reduced by 6.7 times). The proposed event-triggered IMVO-PI (ET-IMVO-PI) has a triggering frequency of 3.5% compared with time-triggered IMVO-PI, with an overshoot of less than 0.5%, which can meet the needs of most engineering practices (less than 5%). Compared with event-triggered SSED-PI, PP-PI, and IMVO-PI, ET-IMVO-PI has higher dynamic performance and fewer triggering times, which can effectively meet the requirements of high-performance network control. The proposed method serves a crucial theoretical guide and important reference for the upgrading and transformation of the photoelectric tracking servo system of CNC milling machines. Keywords: Event-triggered control, Multiple innovative parameter identification, Multiverse optimization PI, Optoelectronic tracking servo system

Mapless Path Planning for Mobile Robot Based on Improved Deep Deterministic Policy Gradient Algorithm.

In the traditional Deep Deterministic Policy Gradient (DDPG) algorithm, path planning for mobile robots in mapless environments still encounters challenges regarding learning efficiency and navigation performance, particularly adaptability and robustness to static and dynamic obstacles. To address these issues, in this study, an improved algorithm frame was proposed that designs the state and action spaces, and introduces a multi-step update strategy and a dual-noise mechanism to improve the reward function. These improvements significantly enhance the algorithm's learning efficiency and navigation performance, rendering it more adaptable and robust in complex mapless environments. Compared to the traditional DDPG algorithm, the improved algorithm shows a 20% increase in the stability of the navigation success rate with static obstacles along with a 25% reduction in pathfinding steps for smoother paths. In environments with dynamic obstacles, there is a remarkable 45% improvement in success rate. Real-world mobile robot tests further validated the feasibility and effectiveness of the algorithm in true mapless environments.

Deep reinforcement learning-based low-latency task offloading for mobile-edge computing networks

Wireless Mobile Edge Computing (MEC) is an emerging solution to facilitate users’ computing. However, there are still some drawbacks, such as slow learning speed and high task latency. To overcome these difficulties, a deep reinforcement learning (DRL)-based task offloading (DRTO) framework is proposed, taking into account the time-varying nature of wireless channels and the unpredictability of task arrivals in multi-user MEC systems. Many deep neural networks (DNNs) are also used as scalable solutions to quickly determine the best offloading strategy by combining the perceptual capabilities of deep learning with the decision making capabilities of reinforcement learning (RL). Additionally, the concept of a minimum upper bound on the target Q-value is utilized to address the value overestimation problem that occurs during offload policy updates. This approach aims to minimize the latency of offload decisions by considering different channel environments, variable latency constraints among many users, and limited computational resources. Simulation results show that the algorithm reduces computation time and latency while achieving near-optimal performance compared to traditional Deep Deterministic Policy Gradient (DDPG) and Q-learning optimization methods. In addition, DRTO achieves more than 99.6% computational speedup compared to current almost ideal benchmarking techniques. Thus, the DRTO computational offload approach offers great potential for reducing overall system latency and improving the user experience.

Deep learning-assisted research on high-performance electrolyte for zinc-ion capacitors

Zinc-ion capacitors (ZICs) have garnered significant attention due to their ability to balance energy density and power density. The properties of electrolytes greatly influence the electrochemical performance of ZICs, and the oxidation potential (OP) of electrolytes can be used to determine the most suitable electrolyte for ZICs. However, traditional methods for exploring OP are time-consuming and inefficient, making it difficult to effectively determine the most suitable electrolyte for ZICs. Herein, we propose a machine learning-assisted method that combines deep learning with the traditional machine learning algorithm Gradient Boosting Regression (GBR), which we refer to as DeepGBR. This method can accurately predict the OP of electrolytes even when dealing with complex features and small datasets. Furthermore, with the assistance of deep learning, the accuracy of the predictions improves as the dataset size increases. We further validated the model with four common electrolyte molecules, finding that the predicted results are highly consistent with the theoretical values. The results show that acetonitrile has a high OP, which can achieve high energy density and long-cycle stability when used as an electrolyte solvent for ZICs. This work provides a certain reference for the database modeling strategy of electrolytes for electrochemical energy storage devices.

Novel Step Size for Unconstrained Optimization

The step length size is the basis of gradient descent method with the desirable conjugate property. The aim of this document is to present a fresh gradient descent technique that calculates the step size based on a basic estimation of the Hessian of the function being minimized. The algorithm that results falls within the category of gradient descent methods with linear convergence characteristics. Our study includes numerical data that demonstrates the significant advantages of our approach compared to traditional gradient descent methods.

A novel subgrid-scale stress model considering the influence of combustion on turbulence: A priori and a posteriori assessment

Most classical turbulence models were proposed and developed based on non-reacting flows without considering the effects of combustion on turbulence. The flow structure in turbulent combustion is more complex, creating challenges to the applicability of traditional turbulence models. Given this, a novel flame surface and k-equation-based gradient model (FKGM) considering combustion effects is proposed for the modeling of the subgrid-scale (SGS) stress in large eddy simulation (LES). The SGS stress is calculated based on the SGS kinetic energy (kSGS) and normalized velocity gradient. The velocity gradient incorporates first-order gradients at multiple stencil locations and considers the anisotropy of the stress near the flame surface. The FKGM model is first validated by the a priori analysis based on the direct numerical simulation (DNS) database of a premixed swirling flame. The closure terms of the kSGS equation are well validated, and the predicted SGS stress using the FKGM model achieves good agreement with the filtered DNS results. In the a posteriori LES study, the FKGM model demonstrates better performance compared with the traditional dynamic Smagorinsky model and velocity gradient model, providing more accurate predictions for mean and fluctuation velocities. The error analysis for SGS kinetic energy is further conducted by comparing the LES results with the DNS data, and the results indicate that the underestimation of the generation term of the kSGS equation is the main source of error.

One-pot construction of gradient colloidal gel coating for stable and efficient emulsion separation

Gradient gel coatings possess superior interfacial bonding strength and surface wettability, providing new ideas for the structure design of separation materials. However, simply and effectively combining colloidal gels that have excellent separation potential with gradient structure design remains a great challenge. Herein, a polymerization-induced microphase separation strategy based on metal ions is developed for constructing colloidal gel coatings with gradient structure on the stainless steel mesh. The colloidal gel is formed by hydrogen-bond complexation and microphase separation of acrylamide and methacrylic acid during polymerization. The metal ions generated by the hydrogen evolution corrosion of stainless steel mesh can not only be used to construct a continuous gradient structure by influencing the microphase separation process but also enhance the mechanical properties and stability of the gel coating. The preparation strategies of traditional gradient materials are limited in the manufacturing and application processes due to their complicated preparation procedures and potential requirement for special equipment assistance. In contrast, this strategy can simply and efficiently prepare colloidal gel coatings with continuous gradient structure by one-pot method under strict monomer ratio. Its unique colloid network demonstrates better wettability and permeability than conventional polymer gel. The prepared gradient colloidal gel coating exhibits excellent separation efficiency (above 99.9 %) and water flux recovery rate (≈98.75 %) in separating surfactant-stabilized emulsion. Therefore, this work provides new insights into the design and preparation of gradient colloidal gels, promoting their development in the field of water treatment.

Multi-morphological design of TPMS-based microchannels for thermal performance optimization

Higher heat flux density in more compact and powerful electronic systems calls for further innovative solutions. Microchannel heat sinks based on triply periodic minimal surface (TPMS) can help dissipate highly concentrated heat, ensuring the system’s safety and longevity. Some researches indicate that compared to traditional TPMS structures, multi-morphology TPMS structures can further improve performances and adapt to local functional requirements. However, the morphology distribution has not been analyzed from a thermal performance perspective. In this study, a multi-morphological TPMS-based microchannel design method for thermal performance optimization is proposed. Firstly, the thermal properties of different TPMS structures are analyzed using the homogenization method, which provides a theoretical basis for TPMS distributions and their transition boundaries. Then, a beta growth algorithm is proposed to smoothly transition these regions with complex transition boundaries. A series of designed microchannels show that this method can not only greatly increase design freedom but also automatically guarantee the geometric continuity of heterogeneous TPMS structures. Finally, the optimized microchannel and the traditional gradient microchannel are compared in a finite element analysis simulation. And the results show that the optimized structure’s cooling efficiency increased by 4.4%, and its cooling uniformity improved by 15%, proving that properly arranging the TPMS morphology can further enhance thermal performance.

Prediction of Health Status of Small-Tailed Cold Sheep Based on Improved BP Neural Network

According to related research, different body temperatures, heart rates, and locomotor behaviors of small-tailed cold goats can represent the physical condition of the goats themselves and are used as direct evidence for evaluating the physical health status of small-tailed cold goats. In this paper, we designed and tested a system for predicting the health status of small-tailed cold sheep based on wearable information monitoring technology. To test the system, sheep wearable devices were worn on 36 small-tailed cold sheep of different ages and inconsistent health conditions at different time points from May to October. A SLBAS-BP neural network model for predicting the health condition of small-tailed cold sheep was established using the collected and processed data, which overcame the problem that the traditional gradient descent method in the BP neural network is prone to fall into local optimization leading to insufficient prediction ability. The correct prediction rates of the improved BP neural network for the four health conditions of healthy, sub-healthy, fever, and disease were 98.4%, 94.5%, 90.4%, and 98.7%, respectively, and the average correct prediction rate of the four conditions was 5.8% higher than that before the improvement, reaching 95.2%.

Transcatheter aortic valve replacement outcomes in patients with high gradient versus low ejection fraction low gradient severe aortic stenosis: A meta-analysis of randomized controlled trials

BackgroundThe outcome of Low Flow-Low Gradient (LF-LG) severe aortic stenosis (AS) patients who underwent Transcatheter Aortic Valve Replacement (TAVR) procedure is not well defined. We conducted a systematic review of the literature to compare the outcomes of TAVR in LF-LG AS patients to the more traditional high gradient (HG) aortic stenosis. MethodsWe comprehensively searched for controlled randomized and non-randomized studies from 4 online databases. We are presenting the data using risk ratios (95 % confidence intervals) and measuring heterogeneity using Higgins' I2 index. ResultsOur analysis included 4380 patients with 3425 HG patients and 955 LF-LG patients from 6 cohort (5 retrospective and 1 prospective) studies. When compared to LFLG; TAVR was associated with significantly lower 30 days mortality in HG patients (5.1 % vs 7.4 %; relative risk [RR]: 0.55; 95 % confidence interval [CI]: 0.35 to 0.86; p < 0.01). Similar findings were also observed in 12-month cardiovascular (CV) mortality (5.5 % vs. 10.4 %; RR: 0.47; 95 % CI: 0.38 to 0.60; p < 0.01 and 12-month all-cause mortality (15.9 % vs 20.9 %; RR: 0.70; 95 % CI: 0.49 to 1.00; p < 0.05). There was no significant difference in myocardial infarction (MI) after TAVR between HG and LF-LG at 30 days (0.16 % vs. 0.95 %; p < 0.09) or 12 months (0.43 % vs. 0.95 %; p = 0.20). Similarly, there was no difference in stroke rates at 30 days (2.9 % vs. 2.86 %) or at 12 months (3.6 % vs. 3.06 %). Conclusions and relevancePatients with LF-LG severe AS who underwent TAVR had worse 1-year all-cause mortality, 30-day all-cause, and 1-year CV mortality when compared to TAVR in HG severe AS. There was no difference in MI or stroke rates. Therefore, with heart team discussion and informed patient decision regarding the risk and benefit, TAVR would still offer better outcomes in LFLG AS compared to conservative medical management.

Evolutionary Strategies AI Addresses Multiple Technical Challenges in Deep Learning Deployment: Proof-of-Principle Demonstration for Neuroblastoma Brain Metastasis Detection.

Two significant obstacles hinder the advancement of Radiology AI. The first is the challenge of overfitting, where small training data sets can result in unreliable outcomes. The second challenge is the need for more generalizability, the lack of which creates difficulties in implementing the technology across various institutions and practices. A recent innovation, deep neuroevolution (DNE), has been introduced to tackle the overfitting issue by training on small data sets and producing accurate predictions. However, the generalizability of DNE has yet to be proven. This paper strives to overcome this barrier by demonstrating that DNE can achieve satisfactory results in diverse external validation sets. The main innovation of the work is thus showing that DNE can generalize to varied outside data. Our example use case is predicting brain metastasis from neuroblastoma, emphasizing the importance of AI with limited data sets. Despite image collection and labeling advancements, rare diseases will always constrain data availability. We optimized a convolutional neural network (CNN) with DNE to demonstrate generalizability. We trained the CNN with 60 MRI images and tested it on a separate diverse collection of images from over 50 institutions. For comparison, we also trained with the more traditional stochastic gradient descent (SGD) method, with the two variants of (1) training from scratch and (2) transfer learning. Our results show that DNE demonstrates excellent generalizability with 97% accuracy on the heterogeneous testing set, while neither form of SGD could reach 60% accuracy. DNE's ability to generalize from small training sets to external and diverse testing sets suggests that it or similar approaches may play an integral role in improving the clinical performance of AI.