The emerging field of topological acoustics provides exciting possibilities for controlling sound propagation with extraordinary robustness. Conventional topological acoustic waveguides built from topological edge sta...
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The emerging field of topological acoustics provides exciting possibilities for controlling sound propagation with extraordinary robustness. Conventional topological acoustic waveguides built from topological edge states, which arise solely from the nontrivial topology in the momentum space, usually have restricted shapes due to their crystalline structures. Here, we show that, using an acoustic topological system with both nontrivial topologies in the real and momentum spaces, free-form acoustic topological waveguides can be constructed. These topological waveguides support disclination-localized states, caused by the interplay between topological lattice defects and the valley-Hall topology. As demonstrated in our simulations and experiments, such disclination waveguides can take arbitrary shapes and form open arcs within the lattice, which are not possible for previous crystalline acoustic topological waveguides. Furthermore, by increasing the number of topological lattice defects, we can realize more than one topological waveguide in one lattice. They join at the topological lattice defect and form a topological waveguide network, enabling more complicated functionalities such as beam splitting. Our results point to a promising direction for free-form acoustic topological devices with the advantages of embedded forms, easy cascading, and high robustness.
Diabetic retinopathy is a critical eye condition that,if not treated,can lead to vision *** methods of diagnosing and treating the disease are time-consuming and ***,machine learning and deep transfer learning(DTL)tec...
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Diabetic retinopathy is a critical eye condition that,if not treated,can lead to vision *** methods of diagnosing and treating the disease are time-consuming and ***,machine learning and deep transfer learning(DTL)techniques have shown promise in medical applications,including detecting,classifying,and segmenting diabetic *** advanced techniques offer higher accuracy and *** Diagnosis(CAD)is crucial in speeding up classification and providing accurate disease ***,these technological advancements hold great potential for improving the management of diabetic *** study’s objective was to differentiate between different classes of diabetes and verify the model’s capability to distinguish between these *** robustness of the model was evaluated using other metrics such as accuracy(ACC),precision(PRE),recall(REC),and area under the curve(AUC).In this particular study,the researchers utilized data cleansing techniques,transfer learning(TL),and convolutional neural network(CNN)methods to effectively identify and categorize the various diseases associated with diabetic retinopathy(DR).They employed the VGG-16CNN model,incorporating intelligent parameters that enhanced its *** outcomes surpassed the results obtained by the auto enhancement(AE)filter,which had an ACC of over 98%.The manuscript provides visual aids such as graphs,tables,and techniques and frameworks to enhance *** study highlights the significance of optimized deep TL in improving the metrics of the classification of the four separate classes of *** manuscript emphasizes the importance of using the VGG16CNN classification technique in this context.
Message Passing Graph Neural Networks (MPGNNs) have emerged as the preferred method for modeling complex interactions across diverse graph entities. While the theory of such models is well understood, their aggregatio...
Quantile regression (QR) is a powerful tool for estimating one or more conditional quantiles of a target variable Y given explanatory features X.A limitation of QR is that it is only defined for scalar target variable...
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Constrained multi-objective optimization problems (CMOPs) are prevalent in real-world applications, dealing with multi-objective and constraint functions. Multiobjective evolutionary algorithm based on decomposition w...
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We present the development of a bias compensating reinforcement learning(RL)algorithm that optimizes thermal comfort(by minimizing tracking error)and control utilization(by penalizing setpoint deviations)in a multi-zo...
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We present the development of a bias compensating reinforcement learning(RL)algorithm that optimizes thermal comfort(by minimizing tracking error)and control utilization(by penalizing setpoint deviations)in a multi-zone heating,ventilation,and air-conditioning(HVAC)lab facility subject to unmeasurable disturbances and unknown *** is shown that the presence of unmeasurable disturbance results in an inconsistent learning equation in traditional RL controllers leading to parameter estimation bias(even with integral action support),and in the extreme case,the divergence of the learning *** demonstrate this issue by applying the popular Q-learning algorithm to linear quadratic regulation(LQR)of a multi-zone HVAC environment and showing that,even with integral support,the algorithm exhibits bias issue during the learning phase when the HVAC disturbance is unmeasurable due to unknown heat gains,occupancy variations,light sources,and outside weather *** address this difficulty,we present a bias compensating learning equation that learns a lumped bias term as a result of disturbances(and possibly other sources)in conjunction with the optimal control *** results show that the proposed scheme not only recovers the bias-free optimal control parameters but it does so without explicitly learning the dynamic model or estimating the disturbances,demonstrating the effectiveness of the algorithm in addressing the above challenges.
This work introduces SPAR, a compiler framework based on MLIR, tailored for deep learning inference applications. Alongside SPAR, we present SPAR HL, an extensible Instruction Set Architecture (ISA) designed to seamle...
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Developing multi-label classification models under significant class imbalance, and when annotating data requires expert-level knowledge remains a major challenge. Additionally, interdependency and correlation among l...
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The widely used ReLU is favored for its hardware efficiency, as the implementation at inference is a one bit sign case, yet suffers from issues such as the "dying ReLU" problem, where during training, neuron...
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This paper presents a novel data-driven control algorithm that coordinates a large aggregation of heterogeneous thermostatically controlled loads (TCLs) with unknown temperature dynamics and disturbance distributions ...
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