Adaptation Layer Research Articles

The efflux system CdfX exports zinc that cannot be transported by ZntA in Cupriavidus metallidurans.

Cupriavidus metallidurans is able to survive exposure to high concentrations of transition metals, but is also able to grow under metal starvation conditions. A prerequisite of cellular zinc homeostasis is a flow equilibrium combining zinc uptake and efflux processes. The mutant strain ∆e4 of the parental plasmid-free strain AE104 with a deletion of all four chromosomally encoded genes of previously known efflux systems ZntA, CadA, DmeF, and FieF was still able to efflux zinc in a pulse-chase experiment, indicating the existence of a fifth efflux system. The gene cdfX, encoding a protein of the cation diffusion facilitator (CDF) family, is located in proximity to the cadA gene, encoding a P-type ATPase. Deletion of cdfX in the ∆e4 mutant resulted in a further decrease in zinc resistance. Pulse-chase experiments with radioactive 65Zn(II) and stable-isotope-enriched 67Zn(II) provided evidence that CdfX was responsible for the residual zinc efflux activity of the mutant strain ∆e4. Reporter gene fusions with cdfX-lacZ indicated that the MerR-type regulator ZntR, the main regulator of zntA expression, was responsible for zinc- and cadmium-dependent upregulation of cdfX expression, especially in mutant cells lacking one or both of the previously characterized efflux systems, ZntA and CadA. Expression of zntR also proved to be controlled by ZntR itself as well as by zinc and cadmium availability. These data indicate that the cdfX-cadA region provides C. metallidurans with a backup system for the zinc-cadmium-exporting P-type ATPase ZntA, with CdfX exporting zinc and CadA cadmium.IMPORTANCEBacteria have evolved the ability to supply the important trace element zinc to zinc-dependent proteins, despite external zinc concentrations varying over a wide range. Zinc homeostasis can be understood as adaptive layering of homeostatic systems, allowing coverage from extreme starvation to extreme resistance. Central to zinc homeostasis is a flow equilibrium of zinc comprising uptake and efflux reactions, which adjusts the cytoplasmic zinc content. This report describes what happens when an imbalance in zinc and cadmium concentrations impairs the central inner-membrane zinc efflux system for zinc by competitive inhibition for this exporter. The problem is solved by activation of Cd-exporting CadA or Zn-exporting CdfX as additional efflux systems.

Extended multi-stream temporal-attention module for skeleton-based human action recognition (HAR)

Graph convolutional networks (GCNs) are an effective skeleton-based human action recognition (HAR) technique. GCNs enable the specification of CNNs to a non-Euclidean frame that is more flexible. The previous GCN-based models still have a lot of issues: (I) The graph structure is the same for all model layers and input data. GCN model's hierarchical structure and human action recognition input diversity make this a problematic approach; (II) Bone length and orientation are understudied due to their significance and variance in HAR. For this purpose, we introduce an Extended Multi-stream Temporal-attention Adaptive GCN (EMS-TAGCN). By training the network topology of the proposed model either consistently or independently according to the input data, this data-based technique makes graphs more flexible and faster to adapt to a new dataset. A spatial, temporal, and channel attention module helps the adaptive graph convolutional layer focus on joints, frames, and features. Hence, a multi-stream framework representing bones, joints, and their motion enhances recognition accuracy. Our proposed model outperforms the NTU RGBD for CS and CV by 0.6% and 1.4%, respectively, while Kinetics-skeleton Top-1 and Top-5 are 1.4% improved, UCF-101 has improved 2.34% accuracy and HMDB-51 dataset has significantly improved 1.8% accuracy. According to the results, our model has performed better than the other models. Our model consistently outperformed other models, and the results were statistically significant that demonstrating the superiority of our model for the task of HAR and its ability to provide the most reliable and accurate results.

Sequential lexicon enhanced bidirectional encoder representations from transformers: Chinese named entity recognition using sequential lexicon enhanced BERT

Lexicon Enhanced Bidirectional Encoder Representations from Transformers (LEBERT) has achieved great success in Chinese Named Entity Recognition (NER). LEBERT performs lexical enhancement with a Lexicon Adapter layer, which facilitates deep lexicon knowledge fusion at the lower layers of BERT. However, this method is likely to introduce noise words and does not consider the possible conflicts between words when fusing lexicon information. To address this issue, we advocate for a novel lexical enhancement method, Sequential Lexicon Enhanced BERT (SLEBERT) for the Chinese NER, which builds sequential lexicon to reduce noise words and resolve the problem of lexical conflict. Compared with LEBERT, it leverages the position encoding of sequential lexicon and adaptive attention mechanism of sequential lexicon to enhance the lexicon feature. Experiments on the four available datasets identified that SLEBERT outperforms other lexical enhancement models in performance and efficiency.

Fault diagnosis of bearings under variable operating conditions based on domain adaptation

Aiming at the problem of low fault recognition rate caused by different distribution of training samples and test samples and imbalance of various fault data in bearing fault diagnosis, a domain adaptive fault diagnosis method based on improved residual network ( ResNet ) is designed. In the first layer of the diagnosis network, a multi-dimensional convolution structure is used for feature extraction to obtain fault feature information of different dimensions; the local maximum mean difference (LMMD) is used in the domain adaptive layer to align the distribution of the source domain and the target domain to obtain more fine-grained information; the class balance loss function ( CBLoss ) is used to solve the training problem of unbalanced data, and the Adam optimization network is used to realize fault diagnosis. Experimental results show that the proposed improved method can achieve higher diagnosis results under the imbalance of fault sample categories. Experimental verification is carried out on two bearing data sets and collected wind turbine data. The results show that the proposed improved method has certain advantages. The diagnostic performance of the proposed improved method in the case of unbalanced data samples is better than other deep transfer learning methods, and it can be used as an effective cross-condition fault analysis method.

Multifunctional-hierarchical flexible metasurface with simultaneous low infrared emissivity, broadband microwave absorption, and dynamic visible camouflage

Sophisticated multispectral detectors have made single-band camouflage materials ineffective, consequently leading to significant advancements in metasurfaces that possess both infrared (IR), radar, and visible stealth capabilities. However, the mutual constraints of stealth principles across different bands and the demand for environment-adaptive camouflage raise challenges to existing multispectral compatible stealth solutions. Here a multifunctional-hierarchical flexible metasurface (MHFM) including an infrared suppression layer (IRSL), three microwave absorbing layers (MAL), an environmental adaptation layer (EAL), and a total reflective sheet (TRS), was designed to simultaneously achieve IR, radar, and dynamic visible stealth. Unlike the direct stacking of functional layers in existing solutions, the EAL is directly integrated with the first MAL as a part of the absorbing structure. As a proof-of-concept, an MHFM sample with an area of 300 × 300 mm2 and a minimum linewidth of 20 µm is demonstrated. The excellent multispectral camouflage performance is verified in experiments, showing low infrared emissivity (0.229, covering the wavelength of 3∼14 µm), the high absorption efficiency of over 90% in 2.53∼34.56 GHz, and dynamic camouflage in both grassland and desert environments. Our work presents a new solution for adaptive visible camouflage and competitive IR-radar stealth that is prospectively applicable in complex environments.

Infrared and visible image fusion based on hybrid multi-scale decomposition and adaptive contrast enhancement

Effectively fusing infrared and visible images enhances the visibility of infrared target information while capturing visual details. Balancing the brightness and contrast of the fusion image adequately has posed a significant challenge. Moreover, preserving detailed information in fusion images has been problematic. To address these issues, this paper proposes a fusion algorithm based on multi-scale decomposition and adaptive contrast enhancement. Initially, we present a hybrid multi-scale decomposition method aimed at extracting valuable information comprehensively from the source image. Subsequently, we advance an adaptive base layer optimization approach to regulate the brightness and contrast of the resultant fusion image. Lastly, we design a weight mapping rule grounded in saliency detection to integrate small-scale layers, thereby conserving the edge structure within the fusion outcome. Both qualitative and quantitative experimental results affirm the superiority of the proposed method over 11 state-of-the-art image fusion methods. Our method excels in preserving more texture and achieving higher contrast, which proves advantageous for monitoring tasks.

Parent GAN: image generation model for creating parent’s images using children’s images

AbstractIn the past decade, several applications have emerged in predicting children’s images using their parents via Generative Adversarial Networks (GANs). However, no one has tackled the problem of predicting one of the parents using the other parent and their children or answering the question of the possibility of deducing the parent images from the children and other parent image features. It could be used in parental identification cases. Moreover, it could help children who don’t know one of their parents to have a visual representation of their images. To perform this task, several obstacles were overcome, like the small number of parent pairs in the dataset and stabilizing the GANs to produce good-looking images. The proposed method depends on dual GAN architecture in addition to adaptive instance normalization layers and introducing a triple loss function to stabilize further and improve the resulting images. The results were proven using a kinship verification model, a face verification model, and other well-known evaluation metrics, which showed that the generated parent images are of decent quality compared to real parents’ images with affordable computational hardware. As a result, a novel method is developed that could produce unknown parent images.

Optimal layer selection for latent data augmentation

While data augmentation (DA) is generally applied to input data, several studies have reported that applying DA to hidden layers in neural networks, i.e., feature augmentation, can improve performance. However, in previous studies, the layers to which DA is applied have not been carefully considered, often being applied randomly and uniformly or only to a specific layer, leaving room for arbitrariness. Thus, in this study, we investigated the trends of suitable layers for applying DA in various experimental configurations, e.g., training from scratch, transfer learning, various dataset settings, and different models. In addition, to adjust the suitable layers for DA automatically, we propose the adaptive layer selection (AdaLASE) method, which updates the ratio to perform DA for each layer based on the gradient descent method during training. The experimental results obtained on several image classification datasets indicate that the proposed AdaLASE method altered the ratio as expected and achieved high overall test accuracy.

End-to-End Electrocardiogram Signal Transformation from Continuous-Wave Radar Signal Using Deep Learning Model with Maximum-Overlap Discrete Wavelet Transform and Adaptive Neuro-Fuzzy Network Layers

End-to-End Electrocardiogram Signal Transformation from Continuous-Wave Radar Signal Using Deep Learning Model with Maximum-Overlap Discrete Wavelet Transform and Adaptive Neuro-Fuzzy Network Layers

Dynamic Layer Skipping for Large Language Models on Natural Language Understanding Tasks and Machine Translation Using Reinforcement Learning

Large Language Models (LLMs) demonstrate remarkable proficiency in various natural language processing (NLP) tasks. However, their extensive size, resulting from the inclusion of billions of parameters across multiple layers, presents significant challenges regarding storage, training, and inference. Traditional methodologies such as model pruning and distillation are employed to decrease the size of these models, but these techniques often result in a compromise on performance retention. In this work, we propose a novel framework that uses dynamic layer skipping for different samples to accelerate the inference speed of LLMs. First, we add an adapter layer at each transformer layer to predict whether to skip the next layer or not, and we propose layer skip pretraining to recover the model’s performance. Second, we propose using reinforcement learning (RL) to optimize the model and design several strategies to stabilize the training. Extensive experiments on four natural language understanding (NLU) datasets and three machine translation datasets and ablation studies show that our method achieves SOTA performance among layer skipping methods on LLMs.

An efficient isogeometric buckling optimization framework for multi-patch laminated shells: Leveraging 3D solid elements, lamination parameters and penalty method

Buckling is a common failure mode of laminated shell structures during service, and isogeometric analysis methods are highly suitable for the buckling analysis due to their accurate geometric modeling capability and high-order continuity. However, the isogeometric buckling analysis for a multi-patch structure and its optimal design still face challenges in computational efficiency. To address this issue, this paper proposes an integrated framework for the buckling optimization of multi-patch laminated shells, which leverages 3D solid elements, lamination parameters, and the penalty coupling method. Solid-shell elements have advantages in shell analysis due to their simplicity, absence of rotational degrees of freedom, and adaptive layering in the thickness direction. Lamination parameters simplify the optimization of fiber orientations in laminated shells by condensing the design variable space and improving the convexity of the optimization problem. The penalty method can handle the coupling in multiple patches at a relatively low cost. Nevertheless, lamination parameters are rarely applied to the case of solid-shell elements. In addition, despite using lamination parameters, the number of design variables for multi-patch structures remains significantly large. To tackle these issues, this study derives the solid-shell formulation in lamination parameters and the sensitivity analysis formulae in the reverse mode. Experiment validation is conducted to assess the accuracy of the proposed method based on lamination parameters with solid elements, the feasibility of the multi-patch optimization model, and the computational efficiency of optimization sensitivity based on the reverse mode compared to the forward counterpart common in the literature.

Mapping the Path of Digital Transformation: The Role of Leadership in Building Adaptive Organizational Culture

The development movement continues towards the digital transformation era, marked by rapid advances in information and computer technology (ICT) and the emergence of social media platforms that change the order of life to be more practical and accessible and deny the geographical distance between people. These changes require universities to adapt to the demands of rapid development. This study aims to explore the strategies carried out by university leaders in facing the challenges of the digital era and map the extent to which the transformation of university organizational culture is operated in the face of digital technology pressure. The research used a qualitative approach with a multi-site format in three private universities. Data analysis applies constant comparative analysis, which ends with a simultaneous analysis step. The results showed that university leaders are ready to implement an open strategy in facing the challenges of the digital era by utilizing these technological advances. However, there are differences in the utilization of network assets owned by institutions and the knowledge and experience of leaders. Organizational culture transformation is operated to the extent that institutions can master digital technology to optimize the performance of institutional services. Universities' engagement with digital technology does not disrupt the resilience of their organizational culture because it only leads to an adaptive surface layer.

Vision transformers inference acceleration based on adaptive layer normalization

Over the past decade, deep neural networks (DNNs) have been widely used due to their remarkable accuracy in real-world applications. However, this increase in accuracy often results in computationally expensive models and high memory usage, leading to longer prediction latencies and exorbitant release costs. In this paper, we propose a new adaptive layer normalization (ALN) algorithm for transformer models, which tackles the computational, and memory problems encountered by traditional layer normalization (LN) techniques. The proposed method computes and stores statistical moments during the training and uses them directly during the inference phase, allowing the normalization layer to be merged with the nearest linear layer. The result is a significant acceleration in inference time, by up to 29%. In classification tasks, our evaluations on the ImageNet dataset show an improvement in accuracy of 0.1%, while maintaining comparable accuracy in object detection tasks on the COCO reference dataset. The proposed ALN algorithm is a simple and effective solution for improving the inference time of pre-trained transformer models, making it a valuable tool for natural language processing and computer vision tasks.

A coded knowledge distillation framework for image classification based on adaptive JPEG encoding

In knowledge distillation (KD), a lightweight student model yields enhanced test accuracy by mimicking the behavior of a pre-trained large model (teacher). However, the cumbersome teacher model often makes over-confident responses, resulting in poor generalization when presented with unseen data. Consequently, a student trained by such a teacher also inherits this problem. To mitigate this issue, in this paper, we present a new framework of KD dubbed coded knowledge distillation (CKD) in which the student is trained to mimic instead the behavior of a coded teacher. Compared to the teacher in KD, the coded teacher in CKD has an additional adaptive encoding layer in the front, which adaptively encodes an input image into a compressed version (using JPEG encoding for instance) and then feeds the compressed input image to the pre-trained teacher. Comprehensive experimental results show the effectiveness of CKD over KD. In addition, we extend the deployment of a coded teacher to other knowledge transfer methods, showcasing its ability to enhance test accuracy across these methods.

A Comparative Analysis Employing Adaptive Layers of RCNN Technique and Transfer Learning Pre-Trained Networks

A Comparative Analysis Employing Adaptive Layers of RCNN Technique and Transfer Learning Pre-Trained Networks

Gated ensemble of spatio-temporal mixture of experts for multi-task learning in ride-hailing system

Ride-hailing system requires efficient management of dynamic demand and supply to ensure optimal service delivery, pricing strategies, and operational efficiency. Designing spatio-temporal forecasting models separately in a task-wise and city-wise manner to forecast demand and supply-demand gap in a ride-hailing system poses a burden for the expanding transportation network companies. Therefore, a multi-task learning architecture is proposed in this study by developing gated ensemble of spatio-temporal mixture of experts network (GESME-Net) with convolutional recurrent neural network (CRNN), convolutional neural network (CNN), and recurrent neural network (RNN) for simultaneously forecasting these spatio-temporal tasks in a city as well as across different cities. Furthermore, a task adaptation layer is integrated with the architecture for learning joint representation in multi-task learning and revealing the contribution of the input features utilized in prediction. The proposed architecture is tested with data from Didi Chuxing for: (i) simultaneously forecasting demand and supply-demand gap in Beijing, and (ii) simultaneously forecasting demand across Chengdu and Xian. In both scenarios, models from our proposed architecture outperformed the single-task and multi-task deep learning benchmarks and ensemble-based machine learning algorithms.

A Graph Reinforcement Learning Framework for Neural Adaptive Large Neighbourhood Search

Adaptive Large Neighbourhood Search (ALNS) is a popular metaheuristic with renowned efficiency in solving combinatorial optimisation problems. However, despite 18 years of intensive research into ALNS, the design of an effective adaptive layer for selecting operators to improve the solution remains an open question. In this work, we isolate this problem by formulating it as a Markov Decision Process, in which an agent is rewarded proportionally to the improvement of the incumbent. We propose Graph Reinforcement Learning for Operator Selection (GRLOS), a method based on Deep Reinforcement Learning and Graph Neural Networks, as well as Learned Roulette Wheel (LRW), a lightweight approach inspired by the classic Roulette Wheel adaptive layer. The methods, which are broadly applicable to optimisation problems that can be represented as graphs, are comprehensively evaluated on 5 routing problems using a large portfolio of 28 destroy and 7 repair operators. Results show that both GRLOS and LRW outperform the classic selection mechanism in ALNS, owing to the operator choices being learned in a prior training phase. GRLOS is also shown to consistently achieve better performance than a recent Deep Reinforcement Learning method due to its substantially more flexible state representation. The evaluation further examines the impact of the operator budget and type of initial solution, and is applied to problem instances with up to 1000 customers. The findings arising from our extensive benchmarking bear relevance to the wider literature of hybrid methods combining metaheuristics and machine learning.

Research on the artistic form and image expression effect of public sculpture based on intelligent and parametric design

With the modernization of cities, public sculptures are constantly being designed and constructed. The artistic form and image expression effect of sculpture based on intelligent and parametric design needs to be designed and developed to guide and assist the construction of sculpture. This paper applies the NAS architecture search method to explore the field of image expression effect models. Through the end-to-end search of the experiment designed in this paper, the separable convolution lightweight design is used, and the new model AestheticNet is used to predict the image form effect score distribution. Secondly, this paper proposes optimization strategies combining image expression effect theory and convolutional neural network, including improvement of Loss function self-weighted Loss, two-dimensional Attention mechanism – introduction of CBAM, and adaptive pooling layer. Optimization of several aspects, such as adaptive input. Finally, the validation set is compared with other existing image-morphological effect model algorithms, which proves the effectiveness of the customized search scheme. It demonstrates the efficacy of the AestheticNet model compared to other algorithms by validating its prediction of public sculpture image form effect ratings. The artistic form using intelligent and parametric design methodologies may improve. Image expression of sculptures may be enhanced by applying the image form effect model, which should be pervasive. We can use it to intelligently and parametrically guide the design and manufacture of sculptures.

The world without, the world within and the space between: Amanda Dalton’s adaptation of Nosferatu for BBC Radio 3

Made for BBC Radio 3, The Midnight Cry of the Deathbird (2012) is an adaptation of F. W. Murnau’s German expressionist silent film Nosferatu (1922), which serves as both a target text (of Bram Stoker’s novel Dracula, 1897) and a source text (of The Midnight Cry). Thus, complex layers of adaptation and audience expectations are evoked through medium-specific qualities, such as the ability of radio to represent the immaterial or disembodied and multiple spaces at the same time, including a between-world along inside/outside worlds. Radio can exist almost entirely in the mind, effortlessly navigating between outer and inner dimensions. This article explores questions of narration, embodiment/non-corporeality and inner space. Using Scott McCloud’s concept of radio as a ‘mono-sensory medium’, it centres primarily on the characters of the Nosferatu (a bodiless essence of contagion) and Roger, a friendly Everyman who serves as the listener’s radio guide.

An Adaptive Migration Collaborative Network for Multimodal Image Classification.

The multispectral (MS) and the panchromatic (PAN) images belong to different modalities with specific advantageous properties. Therefore, there is a large representation gap between them. Moreover, the features extracted independently by the two branches belong to different feature spaces, which is not conducive to the subsequent collaborative classification. At the same time, different layers also have different representation capabilities for objects with large size differences. In order to dynamically and adaptively transfer the dominant attributes, reduce the gap between them, find the best shared layer representation, and fuse the features of different representation capabilities, this article proposes an adaptive migration collaborative network (AMC-Net) for multimodal remote-sensing (RS) images classification. First, for the input of the network, we combine principal component analysis (PCA) and nonsubsampled contourlet transformation (NSCT) to migrate the advantageous attributes of the PAN and the MS images to each other. This not only improves the quality of images themselves, but also increases the similarity between the two images, thereby reducing the representational gap between them and the pressure on the subsequent classification network. Second, for the interaction on the feature migrate branch, we design a feature progressive migration fusion unit (FPMF-Unit) based on the adaptive cross-stitch unit of correlation coefficient analysis (CCA), which can make the network automatically learn the features that need to be shared and migrated, aiming to find the best shared-layer representation for multifeature learning. And we design an adaptive layer fusion mechanism module (ALFM-Module), which can adaptively fuse features of different layers, aiming to clearly model the dependencies among multiple layers for different sized objects. Finally, for the output of the network, we add the calculation of the correlation coefficient to the loss function, which can make the network converge to the global optimum as much as possible. The experimental results indicate that AMC-Net can achieve competitive performance. And the code for the network framework is available at: https://github.com/ru-willow/A-AFM-ResNet.