Dynamic Architecture Research Articles

Brain dynamics supported by a hierarchy of complex correlation patterns defining a robust functional architecture

Functional magnetic resonance imaging (fMRI) provides insights into cognitive processes with significant clinical potential. However, delays in brain region communication and dynamic variations are often overlooked in functional network studies. We demonstrate that networks extracted from fMRI cross-correlation matrices, considering time lags between signals, show remarkable reliability when focusing on statistical distributions of network properties. This reveals a robust brain functional connectivity pattern, featuring a sparse backbone of strong 0-lag correlations and weaker links capturing coordination at various time delays. This dynamic yet stable network architecture is consistent across rats, marmosets, and humans, as well as in electroencephalogram (EEG) data, indicating potential universality in brain dynamics. Second-order properties of the dynamic functional network reveal a remarkably stable hierarchy of functional correlations in both group-level comparisons and test-retest analyses. Validation using alcohol use disorder fMRI data uncovers broader shifts in network properties than previously reported, demonstrating the potential of this method for identifying disease biomarkers.

Unveiling the Strategic Defense Mechanisms in Dynamic Heterogeneous Redundancy Architecture

Unveiling the Strategic Defense Mechanisms in Dynamic Heterogeneous Redundancy Architecture

Precise facial landmark detection by Dynamic Semantic Aggregation Transformer

At present, deep neural network methods have played a dominant role in face alignment field. However, they generally use predefined network structures to predict landmarks, which tends to learn general features and leads to mediocre performance, e.g., they perform well on neutral samples but struggle with faces exhibiting large poses or occlusions. Moreover, they cannot effectively deal with semantic gaps and ambiguities among features at different scales, which may hinder them from learning efficient features. To address the above issues, in this paper, we propose a Dynamic Semantic-Aggregation Transformer (DSAT) for more discriminative and representative feature (i.e., specialized feature) learning. Specifically, a Dynamic Semantic-Aware (DSA) model is first proposed to partition samples into subsets and activate the specific pathways for them by estimating the semantic correlations of feature channels, making it possible to learn specialized features from each subset. Then, a novel Dynamic Semantic Specialization (DSS) model is designed to mine the homogeneous information from features at different scales for eliminating the semantic gap and ambiguities and enhancing the representation ability. Finally, by integrating the DSA model and DSS model into our proposed DSAT in both dynamic architecture and dynamic parameter manners, more specialized features can be learned for achieving more precise face alignment. It is interesting to show that harder samples can be handled by activating more feature channels. Extensive experiments on popular face alignment datasets demonstrate that our proposed DSAT outperforms state-of-the-art models in the literature. Our code is available at https://github.com/GERMINO-LiuHe/DSAT.

Unleashing the power of deep neural networks: An interactive exploration of static and dynamic architectures

Unleashing the power of deep neural networks: An interactive exploration of static and dynamic architectures

The subcutaneous adipose transcriptome identifies a molecular signature of insulin resistance shared with visceral adipose.

The objective of this study was to identify the transcriptional landscape of insulin resistance (IR) in subcutaneous adipose tissue (SAT) in humans across the spectrum of obesity. We used SAT RNA sequencing in 220 individuals with metabolic phenotyping. We identified a 35-gene signature with high predictive accuracy for homeostatic model of IR that was expressed across a variety of non-immune cell populations. We observed primarily "protective" IR associations for adipocyte transcripts and "deleterious" associations for macrophage transcripts, as well as a high concordance between SAT and visceral adipose tissue (VAT). Multiple SAT genes exhibited dynamic expression 5 years after weight loss surgery and with insulin stimulation. Using available expression quantitative trait loci in SAT and/or VAT, we demonstrated similar genetic effect sizes of SAT and VAT on type 2 diabetes and BMI. SAT is conventionally viewed as a metabolic buffer for lipid deposition during positive energy balance, whereas VAT is viewed as a dominant contributor to and prime mediator of IR and cardiometabolic disease risk. Our results implicate a dynamic transcriptional architecture of IR that resides in both immune and non-immune populations in SAT and is shared with VAT, nuancing the current VAT-centric concept of IR in humans.

Tuning Structural Organization via Molecular Design and Hierarchical Assembly to Develop Supramolecular Thermoresponsive Hydrogels.

The cellular microenvironment is composed of a dynamic hierarchical fibrillar architecture providing a variety of physical and bioactive signals to the surrounding cells. This dynamicity, although common in biology, is a challenge to control in synthetic matrices. Here, responsive synthetic supramolecular monomers were designed that are able to assemble into hierarchical fibrous structures, combining supramolecular fiber formation via hydrogen bonding interactions, with a temperature-responsive hydrophobic collapse, resulting in cross-linking and hydrogel formation. Therefore, amphiphilic molecules were synthesized, composed of a hydrogen bonding ureido-pyrimidinone (UPy) unit, a hydrophobic alkyl spacer, and a hydrophilic oligo(ethylene glycol) tail. The temperature responsive behavior was introduced by functionalizing these supramolecular amphiphiles with a relatively short poly(N-isopropylacrylamide) (PNIPAM) chain (M n ∼ 2.5 or 5.5 kg/mol). To precisely control the assembly of these monomers, the length of the alkyl spacer between the UPy moiety and PNIPAM was varied in length. A robust sol-gel transition, with the dodecyl UPy-PNIPAM molecule, was obtained, with a network elasticity enhancing over 2000 times upon heating above room temperature. The UPy-PNIPAM compounds with shorter alkyl spacers were already hydrogels at room temperature. The sol-gel transition of the dodecyl UPy-PNIPAM hydrogelator could be tuned by the incorporation of different UPy-functionalized monomers. Furthermore, we demonstrated the suitability of this system for microfluidic cell encapsulation through a convenient temperature sol-gel transition. Our results indicate that this novel thermoresponsive supramolecular system offers a modular platform to study and guide single-cell behavior.

Intelligent dynamic heterogeneous redundancy architecture for IoT systems

Intelligent dynamic heterogeneous redundancy architecture for IoT systems

SNNtrainer3D: Training Spiking Neural Networks Using a User-Friendly Application with 3D Architecture Visualization Capabilities

Spiking Neural Networks have gained significant attention due to their potential for energy efficiency and biological plausibility. However, the reduced number of user-friendly tools for designing, training, and visualizing Spiking Neural Networks hinders widespread adoption. This paper presents the SNNtrainer3D v1.0.0, a novel software application that addresses these challenges. The application provides an intuitive interface for designing Spiking Neural Networks architectures, with features such as dynamic architecture editing, allowing users to add, remove, and edit hidden layers in real-time. A key innovation is the integration of Three.js for three-dimensional visualization of the network structure, enabling users to inspect connections and weights and facilitating a deeper understanding of the model’s behavior. The application supports training on the Modified National Institute of Standards and Technology dataset and allows the downloading of trained weights for further use. Moreover, it lays the groundwork for future integration with physical memristor technology, positioning it as a crucial tool for advancing neuromorphic computing research. The advantages of the development process, technology stack, and visualization are discussed. The SNNtrainer3D represents a significant step in making Spiking Neural Networks more accessible, understandable, and easier for Artificial Intelligence researchers and practitioners.

A Dynamic Controller Architecture for Wheeled Mobile Robot Trajectory Tracking Utilizing Feedback Linearization and State Feedback

A Dynamic Controller Architecture for Wheeled Mobile Robot Trajectory Tracking Utilizing Feedback Linearization and State Feedback

Thermodynamic analysis of an enhanced ejector vapor injection refrigeration cycle for CO2 transcritical operation at low evaporating temperatures

The main drawback associated with CO2 refrigeration systems is related to their performance reduction during transcritical operation at warm climate conditions, which may be compensated by better cycle architectures such as the split-cycle with subcooling or the flash-tank configuration, among others. Specifically, the use of standard gas-ejectors together with parallel compressors provides even better efficiency improvements, not being able to use them with low-temperature evaporators to prevent the triple point inside the ejector. This paper proposes an enhanced cycle with a gas ejector for two-stage compressor architectures with vapor injection from the flash-tank, which is able to operate at low evaporating temperatures and that provides a greater performance improvement the more severe the climate conditions are. The methodology conducted is based on a thermodynamic analysis that includes parametric evaluation and cycle optimization, comparing the results to a conventional CO2 transcritical cycle with flash-tank and dynamic vapor injection architecture. The main results show that a maximum Coefficient of Performance improvement of 17.5% is achievable for transcritical operation at -40 °C evaporating temperature. The compressor displacement capacity required with the enhanced cycle is up to 9% lower for the same refrigeration demand, reducing the electrical consumption as well as the compressor expenditure. Moreover, greater vapor injection mass flow rates are obtained by the gas-ejector injection with discharge temperature reductions up to 18%, enhancing the system reliability.

Efficient Recurrent Neural Networks via Importance-Based Sparsification

Embedded sensing devices that are battery-operated and resource-limited are increasingly utilizing recurrent neural networks (RNNs) to attain real-time perception of the complex environment. Nevertheless, the majority of RNNs typically handle substantial amounts of time-series data, resulting in computational intensity and high energy consumption. Hence, the development of energy-efficient RNN models is a crucial focus. This paper introduces IS-RNN, an energy-efficient RNN model featuring importance-based sparsification, aimed at enhancing the energy efficiency of RNN-based in-sensor inference. The principal motivation for this research arises from the recognition that time series data obtained from sensor sensing often exhibit inherent redundancy. Leveraging input sequence sparsification allows for the elimination of invalid input accesses and computations, thus optimizing efficiency. As a result, optimizing the sparsity of the input sequence through adaptive control allows us to improve inference performance while adhering to energy constraints. In particular, we build an importance score analysis method to quantify the contribution of each input toward the RNN inference performance. Building upon the proposed quantitative importance score analysis, we introduce a dynamic efficient RNN architecture, which enables the model to make decisions based on important data to obtain the highest accuracy under energy constraints. Moreover, we introduce an adaptive RNN inference method to enhance prediction accuracy under energy constraints by making a trade-off between computational cost and model performance. We evaluated IS-RNN on five datasets and the results show that it achieves a higher inference accuracy with the same amount of data processed compared to the state-of-the-art methods.

NAKAGIN RESIDENTIAL TOWER IN TOKYO, JAPAN: A FUTURISTIC ARCHITECTURAL CONCEPT OF THE MID-20TH CENTURY AND REALITY

The work is devoted to a retrospective analysis of the historical and social conditions of the construction, existence and dismantling of the futuristic Nakagin capsule tower. The Nakagin Capsule Tower was built in 1970-1972 in central Tokyo by architect Kisho Kurokawa and was similar to the modernist building of the Shizuoka Press Center by architect Kenzo Tange. Both buildings appeared as a practical illustration of the concept of architectural metabolism, that is, the possibility of giving architecture the ability of living organisms to grow and renew themselves, adapting to the external environment. Initially, the building was intended for a hotel, but finally it received a mixed function of temporary and permanent accommodation. The structure of the tower consisted of a stationary reinforced concrete vertical core, on which residential capsules were suspended with the possibility of replacement. However, in reality, the Nakagin tower was unable to adapt to changing external conditions. During 50 years of operation, the capsules were never replaced, as the owners could not replace them at once, and the individual replacement was technically difficult. This led to the complete deterioration of the Nakagin Tower and its dismantling in 2022. Attempts to reconstruct the object did not receive adequate attention and sufficient funding. During the dismantling of the building, the public association of Nakagin Capsule Tower Preservation and Restoration Project managed to remove 23 capsules for their further repair with the aim of turning them into separate art objects and exhibition pavilions. Thus, it can be stated that the futuristic idea of the Nakagin residential tower, which was far ahead of its time during construction, did not stand the test of reality. Despite this, this project in many ways laid the foundations of modern concepts of dynamic architecture.

Content-addressable memory using selective-charging and adaptive-discharging scheme for low-power hardware search engine

Single clock cycle access feature of content-addressable memory (CAM) suits well for high-speed parallel content search operation in data-intensive hardware search engines. The diverse applications span from accelerating databases and routing networks to processing images, implementing machine learning, processing biomedical data, and compressing data. Nevertheless, the CAM macro consumes significant energy due to the high switching of most match-lines (MLs), which comprise CAM words, during parallel access. Segmented ML schemes reduced power yet the cell and ML delay, and the extra sequential cycles affect search-speed. A novel selective-charging and adaptive-discharging (SCAD) scheme in the form of dynamic ML architecture is proposed to reduce CAM power consumption at no extra cycle cost. Additionally, a full-swing CAM cell forms the basis of storage and comparison-evaluation to lessen ML delay. Based on 45-nm technology under 1-V supply, the proposed 64 × 32-bit and 256 × 144-bit SCAD-CAM arrays dissipate only 0.45–0.46 fJ/bit/search energy and achieve high-speed. Compared to CAMs based on low-power ML schemes, viz., low-swing precharge, division and control, and master–slave, and the conventional CAM as baseline design, the SCAD-CAM reduces 13.49%–89.35% energy-delay. The average-power reduction of 1.8×–2.4× establishes the SCAD-CAM as a promising memory architecture for emerging search-intensive applications involving large-scale data workloads.

Perspective Article: Space-time dynamics of genome replication studied with super-resolved microscopy.

Genome replication requires duplication of the complete set of DNA sequences together with nucleosomes and epigenetic signatures. Notwithstanding profound knowledge on mechanistic details of DNA replication, major problems of genome replication have remained unresolved. In this perspective article, we consider the accessibility of replication machines to all DNA sequences in due course, the maintenance of functionally important positional and structural features of chromatid domains during replication, and the rapid transition of CTs into prophase chromosomes with two chromatids. We illustrate this problem with EdU pulse-labeling (10 min) and chase experiments (80 min) performed with mouse myeloblast cells. Following light optical serial sectioning of nuclei with 3D structured illumination microscopy (SIM), seven DNA intensity classes were distinguished as proxies for increasing DNA compaction. In nuclei of cells fixed immediately after the pulse-label, we observed a relative under-representation of EdU-labeled DNA in low DNA density classes, representing the active nuclear compartment (ANC), and an over-representation in high density classes representing the inactive nuclear compartment (INC). Cells fixed after the chase revealed an even more pronounced shift to high DNA intensity classes. This finding contrasts with previous studies of the transcriptional topography demonstrating an under-representation of epigenetic signatures for active chromatin and RNAPII in high DNA intensity classes and their over-representation in low density classes. We discuss these findings in the light of current models viewing CDs either as structural chromatin frameworks or as phase-separated droplets, as well as methodological limitations that currently prevent an integration of this contrasting evidence for the spatial nuclear topography of replication and transcription into a common framework of the dynamic nuclear architecture.

Pose detection and docking control for autonomous dynamic docking mechanism with non-cooperative targets

PurposeThis study aims to address the issue of random movement and non coordination between docking mechanisms and locking mechanisms, and proposes a comprehensive dynamic docking control architecture that integrates perception, planning, and motion control.Design/methodology/approachFirstly, the proposed dynamic docking control architecture uses laser sensors and a charge-coupled device camera to perceive the pose of the target. The sensor data are mapped to a high-dimensional potential field space and fused to reduce interference caused by detection noise. Next, a new potential function based on multi-dimensional space is developed for docking path planning, which enables the docking mechanism based on Stewart platform to rapidly converge to the target axis of the locking mechanism, which improves the adaptability and terminal docking accuracy of the docking state. Finally, to achieve precise tracking and flexible docking in the final stage, the system combines a self-impedance controller and an impedance control algorithm based on the planned trajectory.FindingsExtensive simulations and experiments have been conducted to validate the effectiveness of the dynamic docking system and its control architecture. The results indicate that even if the target moves randomly, the system can successfully achieve accurate, stable and flexible dynamic docking.Originality/valueThis research can provide technical guidance and reference for docking task of unmanned vehicles under the ground conditions. It can also provide ideas for space docking missions, such as space simulator docking.

A Differentially Private Framework for the Dynamic Heterogeneous Redundant Architecture System in Cyberspace

With the development of information technology, tremendous vulnerabilities and backdoors have evolved, causing inevitable and severe security problems in cyberspace. To fix them, the endogenous safety and security (ESS) theory and one of its practices, the Dynamic Heterogeneous Redundant (DHR) architecture, are proposed. In the DHR architecture, as an instance of the multi-heterogeneous system, a decision module is designed to obtain intermediate results from heterogeneous equivalent functional executors. However, privacy-preserving is not paid attention to in the architecture, which may cause privacy breaches without compromising the ESS theory. In this paper, based on differential privacy (DP), a theoretically rigorous privacy tool, we propose a privacy-preserving DHR framework called DP-DHR. Gaussian random noise is injected into each (online) executor output in DP-DHR to guarantee DP, but it also makes the decision module unable to choose the final result because each executor output is potentially correct even if it is compromised by adversaries. To weaken this disadvantage, we propose the advanced decision strategy and the hypersphere clustering algorithm to classify the perturbed intermediate results into two categories, candidates and outliers, where the former is closer to the correct value than the latter. Finally, the DP-DHR is proven to guarantee DP, and the experimental results also show that the utility is not sacrificed for the enhancement of privacy by much (a ratio of 4–7% on average), even in the condition of some executors (less than one-half) being controlled by adversaries.

Mammalian IRE1α dynamically and functionally coalesces with stress granules.

Upon endoplasmic reticulum (ER) stress, activation of the ER-resident transmembrane protein kinase/endoribonuclease inositol-requiring enzyme 1 (IRE1) initiates a key branch of the unfolded protein response (UPR) through unconventional splicing generation of the transcription factor X-box-binding protein 1 (XBP1s). Activated IRE1 can form large clusters/foci, whose exact dynamic architectures and functional properties remain largely elusive. Here we report that, in mammalian cells, formation of IRE1α clusters is an ER membrane-bound phase separation event that is coupled to the assembly of stress granules (SGs). In response to different stressors, IRE1α clusters are dynamically tethered to SGs at the ER. The cytosolic linker portion of IRE1α possesses intrinsically disordered regions and is essential for its condensation with SGs. Furthermore, disruption of SG assembly abolishes IRE1α clustering and compromises XBP1 mRNA splicing, and such IRE1α-SG coalescence engenders enrichment of the biochemical components of the pro-survival IRE1α-XBP1 pathway during ER stress. Our findings unravel a phase transition mechanism for the spatiotemporal assembly of IRE1α-SG condensates to establish a more efficient IRE1α machinery, thus enabling higher stress-handling capacity.

Exploring the dynamic three-dimensional chromatin architecture and transcriptional landscape in goose liver tissues underlying metabolic adaptations induced by a high-fat diet

BackgroundGoose, descendants of migratory ancestors, have undergone extensive selective breeding, resulting in their remarkable ability to accumulate fat in the liver and exhibit a high tolerance for significant energy intake. As a result, goose offers an excellent model for studying obesity, metabolic disorders, and liver diseases in mammals. Although the impact of the three-dimensional arrangement of chromatin within the cell nucleus on gene expression and transcriptional regulation is widely acknowledged, the precise functions of chromatin architecture reorganization during fat deposition in goose liver tissues still need to be fully comprehended.ResultsIn this study, geese exhibited more pronounced changes in the liver index and triglyceride (TG) content following the consumption of the high-fat diet (HFD) than mice without significant signs of inflammation. Additionally, we performed comprehensive analyses on 10 goose liver tissues (5 HFD, 5 normal), including generating high-resolution maps of chromatin architecture, conducting whole-genome gene expression profiling, and identifying H3K27ac peaks in the livers of geese and mice subjected to the HFD. Our results unveiled a multiscale restructuring of chromatin architecture, encompassing Compartment A/B, topologically associated domains, and interactions between promoters and enhancers. The dynamism of the three-dimensional genome architecture, prompted by the HFD, assumed a pivotal role in the transcriptional regulation of crucial genes. Furthermore, we identified genes that regulate chromatin conformation changes, contributing to the metabolic adaptation process of lipid deposition and hepatic fat changes in geese in response to excessive energy intake. Moreover, we conducted a cross-species analysis comparing geese and mice exposed to the HFD, revealing unique characteristics specific to the goose liver compared to a mouse. These chromatin conformation changes help elucidate the observed characteristics of fat deposition and hepatic fat regulation in geese under conditions of excessive energy intake.ConclusionsWe examined the dynamic modifications in three-dimensional chromatin architecture and gene expression induced by an HFD in goose liver tissues. We conducted a cross-species analysis comparing that of mice. Our results contribute significant insights into the chromatin architecture of goose liver tissues, offering a novel perspective for investigating mammal liver diseases.

DLformer: A Dynamic Length Transformer-Based Network for Efficient Feature Representation in Remaining Useful Life Prediction.

Representation learning-based remaining useful life (RUL) prediction plays a crucial role in improving the security and reducing the maintenance cost of complex systems. Despite the superior performance, the high computational cost of deep networks hinders deploying the models on low-compute platforms. A significant reason for the high cost is the computation of representing long sequences. In contrast to most RUL prediction methods that learn features of the same sequence length, we consider that each time series has its characteristics and the sequence length should be adjusted adaptively. Our motivation is that an "easy" sample with representative characteristics can be correctly predicted even when short feature representation is provided, while "hard" samples need complete feature representation. Therefore, we focus on sequence length and propose a dynamic length transformer (DLformer) that can adaptively learn sequence representation of different lengths. Then, a feature reuse mechanism is developed to utilize previously learned features to reduce redundant computation. Finally, in order to achieve dynamic feature representation, a particular confidence strategy is designed to calculate the confidence level for the prediction results. Regarding interpretability, the dynamic architecture can help human understand which part of the model is activated. Experiments on multiple datasets show that DLformer can increase up to 90% inference speed, with less than 5% degradation in model accuracy.

ALZHEIMER’S DISEASE DETECTION USING DEEP LEARNING

Image processing is a technique for applying various procedures to a picture in order to improve it or extract some relevant information from it. It is a kind of signal processing where the input is an image and the output may either be another picture or features or characteristics related to that image. Image processing is one of the technologies that is currently expanding quickly. It is a primary subject of research in both the engineering and computer science fields. For dementia, Alzheimer's disease (AD) is the most prevalent kind. It often shows itself as a steadily declining memory and cognitive function, which makes it difficult for the affected person to live independently and has a significant negative influence on both the affected personand society. At the moment, AD diagnosis depends on medical history analysis, blood testing, behavior analysis, brain imaging, and cognitive testing. But because these processes are arbitrary and uneven, it is difficult to provide a precise forecast for the early phases of AD challenging. A dynamic dual-graph fusion convolutional network is suggested in this paper to enhance the accuracy of Alzheimer's disease (AD) diagnosis. The key contributions of the paper are as follows: The proposed architecture can dynamically adjust the graph structure for GCN to produce better diagnosis outcomes by learning the optimal underlying latent graph, propose a novel dynamic GCN architecture, which is an end-to-end pipeline for diagnosing AD, incorporate feature graph learning and dynamic graph learning, giving those useful features of subjects more weight while reducing the weights of other noise features. Experiments show that our approach achieves great classification results in AD diagnosis while offering flexibility and stability. This project is implemented for the Alzheimer disease classification using Graph convolutional network architecture like CNN with LSTM layer and fit the model in the training and testing and deploy the model for getting the test image output and then compare the accuracy of the model for getting the most perfect model for medical image classification and get theaccuracy of CNN- LSTM layer with 98%.