Gating Mechanism Research Articles

Semantic-guided attention and adaptive gating for document-level relation extraction

In natural language processing, document-level relation extraction is a complex task that aims to predict the relationships among entities by capturing contextual interactions from an unstructured document. Existing graph- and transformer-based models capture long-range relational facts across sentences. However, they still cannot fully exploit the semantic information from multiple interactive sentences, resulting in the exclusion of influential sentences for related entities. To address this problem, a novel Semantic-guided Attention and Adaptively Gated (SAAG) model is developed for document-level relation extraction. First, a semantic-guided attention module is designed to guide sentence representation by assigning different attention scores to different words. The multihead attention mechanism is then used to capture the attention of different subspaces further to generate a document context representation. Finally, the SAAG model exploits the semantic information by leveraging a gating mechanism that can dynamically distinguish between local and global contexts. The experimental results demonstrate that the SAAG model outperforms previous models on two public datasets.

FINE VESSEL SEGMENTATION WITH REFINEMENT GATE IN DEEP LEARNING ARCHITECTURES

Automated vessel segmentation is essential in diagnosing eye-related disorders and monitoring progressive retinal diseases. State-of-the-art methods have achieved excellent results in this field, but very few have considered the post-processing of feature maps. As a result, there is often a lack of small and fine vessels or discontinuities in segmented vessels. To address this issue, this study introduces a novel post-processing technique called the refinement gate, which works with a deep learning model during training. The refinement gate enhances contextual information to extract important features from feature maps better. The proposed technique is applied with U-net architecture and placed after every convolution block in the encoder path. Visual and statistical comparisons demonstrate the robustness of the proposed method using three publicly available datasets, namely: the DRIVE DB, the STARE DB, and CHASE_DB1 datasets, showing significant improvements to segment weak and tiny vessels. The reported results confirm the potential of the model to be used as a segmentation tool in the medical field. This study is the first to propose such a gating mechanism without additional trainable parameters or standalone networks as in other literature.

A Systematic Literature Review on The Role of LSTM Networks in Capturing Temporal Dependencies in Data Mining Algorithms

Data mining plays a crucial role in extracting meaningful insights from large and complex data sets, with broad applications in sectors like finance, health care, and market analysis. Traditional techniques—such as classification, clustering, association rule mining, regression, and anomaly detection—are effective for analyzing structured data but struggle with sequential data due to the challenges of modeling temporal dependencies. Long Short Term Memory (LSTM) networks, a specialized form of Recurrent Neural Networks (RNNs), provide a solution to these challenges. By incorporating memory cells and gating mechanisms, LSTM effectively manage long-term dependencies and address issues like vanishing and exploding gradients. This paper reviews the impact of LSTM networks on data mining, analyzing over 60 key publications. By synthesizing concepts and recent advancements, the review underscores how LSTMs enhance the ability of data mining algorithms to capture and predict temporal patterns, reflecting current research trends and innovations

PKCδ is an activator of neuronal mitochondrial metabolism that mediates the spacing effect on memory consolidation.

Relevance-based selectivity and high energy cost are two distinct features of long-term memory (LTM) formation that warrant its default inhibition. Spaced repetition of learning is a highly conserved cognitive mechanism that can lift this inhibition. Here, we questioned how the spacing effect integrates experience selection and energy efficiency at the cellular and molecular levels. We showed in Drosophila that spaced training triggers LTM formation by extending over several hours an increased mitochondrial metabolic activity in neurons of the associative memory center, the mushroom bodies (MBs). We found that this effect is mediated by PKCδ, a member of the so-called 'novel PKC' family of enzymes, which uncovers the critical function of PKCδ in neurons as a regulator of mitochondrial metabolism for LTM. Additionally, PKCδ activation and translocation to mitochondria result from LTM-specific dopamine signaling on MB neurons. By bridging experience-dependent neuronal circuit activity with metabolic modulation of memory-encoding neurons, PKCδ signaling binds the cognitive and metabolic constraints underlying LTM formation into a unified gating mechanism.

PKCδ is an activator of neuronal mitochondrial metabolism that mediates the spacing effect on memory consolidation

Relevance-based selectivity and high energy cost are two distinct features of long-term memory (LTM) formation that warrant its default inhibition. Spaced repetition of learning is a highly conserved cognitive mechanism that can lift this inhibition. Here, we questioned how the spacing effect integrates experience selection and energy efficiency at the cellular and molecular levels. We showed in Drosophila that spaced training triggers LTM formation by extending over several hours an increased mitochondrial metabolic activity in neurons of the associative memory center, the mushroom bodies (MBs). We found that this effect is mediated by PKCδ, a member of the so-called ‘novel PKC’ family of enzymes, which uncovers the critical function of PKCδ in neurons as a regulator of mitochondrial metabolism for LTM. Additionally, PKCδ activation and translocation to mitochondria result from LTM-specific dopamine signaling on MB neurons. By bridging experience-dependent neuronal circuit activity with metabolic modulation of memory-encoding neurons, PKCδ signaling binds the cognitive and metabolic constraints underlying LTM formation into a unified gating mechanism.

Triplet extraction network with dual gating mechanism and dependency-oriented attention

Triplet extraction network with dual gating mechanism and dependency-oriented attention

Mechanisms of Action Underlying Conductance-modifying Positive Allosteric Modulators of the NMDA Receptor.

NMDA receptors (NMDARs) are ionotropic glutamate receptors that mediate a slow, Ca2+-permeable component of fast excitatory neurotransmission. Modulation of NMDAR function has the potential for disease modification as NMDAR dysfunction has been implicated in neurodevelopment, neuropsychiatric, neurological, and neurodegenerative disorders. We recently described the thieno[2,3-d]pyrimidin-4-one (EU1622) class of positive allosteric modulators, including several potent and efficacious analogs. Here we have used electrophysiological recordings from Xenopus oocytes, HEK cells, and cultured cerebellar and cortical neurons to determine the mechanisms of action of a representative member of this class of modulator. EU1622-240 enhances current response to saturating agonist (doubling response amplitude at 0.2-0.5 µM), slows the deactivation time course following rapid removal of glutamate, increases open probability, enhances co-agonist potency, and reduces single channel conductance. We also show that EU1622-240 can transform NMDARs so that they can be opened when only glutamate or glycine is bound. EU1622-240-bound NMDARs channels activated by a single agonist (glutamate or glycine) open to a unique conductance level with different pore properties and Mg2+ sensitivity, in contrast to channels arising from activation of NMDARs with both co-agonists bound. These data demonstrate that previously hypothesized distinct gating steps can be controlled by glutamate and glycine binding and shows that the 1622-series modulators enable glutamate- or glycine-bound NMDARs to generate open conformations with different pore properties. The properties of this class of allosteric modulators present intriguing therapeutic opportunities for the modulation of circuit function. Significance Statement NMDA receptors are expressed throughout the CNS and are permeable to calcium. EU1622-240 increases open probability and agonist potency, while reducing single channel conductance and prolonging the deactivation time course. EU1622-240 allows NMDA receptor activation by the binding of one co-agonist (glycine or glutamate), which produces channels with distinct properties. Evaluation of this modulator provides insight into gating mechanisms and may lead to the development of new therapeutic strategies.

Abrupt and spontaneous strategy switches emerge in simple regularised neural networks.

Humans sometimes have an insight that leads to a sudden and drastic performance improvement on the task they are working on. Sudden strategy adaptations are often linked to insights, considered to be a unique aspect of human cognition tied to complex processes such as creativity or meta-cognitive reasoning. Here, we take a learning perspective and ask whether insight-like behaviour can occur in simple artificial neural networks, even when the models only learn to form input-output associations through gradual gradient descent. We compared learning dynamics in humans and regularised neural networks in a perceptual decision task that included a hidden regularity to solve the task more efficiently. Our results show that only some humans discover this regularity, whose behaviour was marked by a sudden and abrupt strategy switch that reflects an aha-moment. Notably, we find that simple neural networks with a gradual learning rule and a constant learning rate closely mimicked behavioural characteristics of human insight-like switches, exhibiting delay of insight, suddenness and selective occurrence in only some networks. Analyses of network architectures and learning dynamics revealed that insight-like behaviour crucially depended on a regularised gating mechanism and noise added to gradient updates, which allowed the networks to accumulate "silent knowledge" that is initially suppressed by regularised gating. This suggests that insight-like behaviour can arise from gradual learning in simple neural networks, where it reflects the combined influences of noise, gating and regularisation. These results have potential implications for more complex systems, such as the brain, and guide the way for future insight research.

Working memory gating in obesity is moderated by striatal dopaminergic gene variants.

Everyday life requires an adaptive balance between distraction-resistant maintenance of information and the flexibility to update this information when needed. These opposing mechanisms are proposed to be balanced through a working memory gating mechanism. Prior research indicates that obesity may elevate the risk of working memory deficits, yet the underlying mechanisms remain elusive. Dopaminergic alterations have emerged as a potential mediator. However, current models suggest these alterations should only shift the balance in working memory tasks, not produce overall deficits. The empirical support for this notion is currently lacking, however. To address this gap, we pooled data from three studies (N = 320) where participants performed a working memory gating task. Higher BMI was associated with overall poorer working memory, irrespective of whether there was a need to maintain or update information. However, when participants, in addition to BMI level, were categorized based on certain putative dopamine-signaling characteristics (single-nucleotide polymorphisms [SNPs]; specifically, Taq1A and DARPP-32), distinct working memory gating effects emerged. These SNPs, primarily associated with striatal dopamine transmission, appear to be linked with differences in updating, specifically, among high-BMI individuals. Moreover, blood amino acid ratio, which indicates central dopamine synthesis capacity, combined with BMI shifted the balance between distractor-resistant maintenance and updating. These findings suggest that both dopamine-dependent and dopamine-independent cognitive effects exist in obesity. Understanding these effects is crucial if we aim to modify maladaptive cognitive profiles in individuals with obesity.

Working memory gating in obesity is moderated by striatal dopaminergic gene variants

Everyday life requires an adaptive balance between distraction-resistant maintenance of information and the flexibility to update this information when needed. These opposing mechanisms are proposed to be balanced through a working memory gating mechanism. Prior research indicates that obesity may elevate the risk of working memory deficits, yet the underlying mechanisms remain elusive. Dopaminergic alterations have emerged as a potential mediator. However, current models suggest these alterations should only shift the balance in working memory tasks, not produce overall deficits. The empirical support for this notion is currently lacking, however. To address this gap, we pooled data from three studies (N = 320) where participants performed a working memory gating task. Higher BMI was associated with overall poorer working memory, irrespective of whether there was a need to maintain or update information. However, when participants, in addition to BMI level, were categorized based on certain putative dopamine-signaling characteristics (single-nucleotide polymorphisms [SNPs]; specifically, Taq1A and DARPP-32), distinct working memory gating effects emerged. These SNPs, primarily associated with striatal dopamine transmission, appear to be linked with differences in updating, specifically, among high-BMI individuals. Moreover, blood amino acid ratio, which indicates central dopamine synthesis capacity, combined with BMI shifted the balance between distractor-resistant maintenance and updating. These findings suggest that both dopamine-dependent and dopamine-independent cognitive effects exist in obesity. Understanding these effects is crucial if we aim to modify maladaptive cognitive profiles in individuals with obesity.

Intensified greenhouse gas prediction: Configuring Gate with Fine-Tuning Shifts with Bi-LSTM and GRU System

IntroductionOn a global scale, climate change refers to persistent alterations in weather conditions and temperature patterns. These modifications have far-reaching implications across the world. GHGs (Greenhouse Gases) play a crucial role in driving climate change. Most of these emissions originate from human activities, particularly those contributing to releasing CO2 and CH4. In the conventional approach, identifying emissions involves recognizing and quantifying the sources and amounts of GHG released into the atmosphere. However, this manual identification method has limitations, including being time-consuming, relying on incomplete resources, prone to human error, and lacking scalability and coverage.MethodologyTo address these challenges, a technology-based system is necessary for effectively identifying GHG emissions. The proposed method utilized the configuration of a gating mechanism incorporating fine-tuning shifts in the Bi-LSTM-GRU algorithm to predict GHG emissions in top-emitting countries. The PRIMAP-host dataset is used in the respective method comprising subsector data such as CO2, CH4, and N2O to attain this. In the presented model, Bi-LSTM is used to capture significant features, handle vanishing gradient problems, etc., because of its process in both directions. Conversely, it is limited by overfitting and long-term dependencies.Results and discussionGRU is used with Bi-LSTM to address the issue for the advantages of memory efficiency, handling long-term dependencies, rapid training process and minimizes the overfitting by infusion of GRU in the input layer of BiLSTM with tuning process in the BiLSTM. Here, the configuration of gates with fine-tuning shifts to improve the prediction performance. Moreover, the efficiency of the proposed method is calculated with performance metrics. Where RMSE value is 0.0288, MAPE is 0.0007, and the R-Square value is 0.99. In addition, internal and external comparisons are carried out to reveal the greater performance of the respective research.

Molecular mechanism of GIRK2 channel gating modulated by cholesteryl hemisuccinate.

Cholesterol, an essential lipid of cell membranes, regulates G protein-gated inwardly rectifying potassium (GIRK) channel activity. Previous studies have shown that cholesterol activates GIRK2 homotetrameric channels, which are expressed in dopaminergic neurons of the brain. Deletion of GIRK2 channels affects both GIRK2 homo- and heterotetrames and can lead to abnormal neuronal excitability, including conditions such as epilepsy and addiction. A 3.5Å cryo-EM structure of GIRK2 in complex with CHS (cholesteryl hemisuccinate) and PIP2 (phosphatidylinositol 4,5-bisphosphate) has been solved. This structure provides the opportunity to study GIRK2 channel gating dynamics regulated by cholesterol using gating molecular dynamics (GMD) simulations. In the present study, we conducted microsecond-long GMD simulations on the GIRK2 channel in its APO, PIP2, and PIP2/CHS bound states, followed by systematic analysis to gain molecular insights into how CHS modulates GIRK2 channel gating. We found that CHS binding facilitates GIRK2 channel opening, with 43 K+ ion permeation events observed, compared to 0 and 2 K+ ion permeation events for GIRK2-APO and GIRK2/PIP2, respectively. Binding of CHS to the GIRK2 channel enhances PIP2 and channel interactions, which is consistent with previous experimental results. The negatively charged PIP2 alters the internal electrostatic potential field in the channel and lowers the negative free energy barrier for K+ ion permeation.

Understanding Power Gating Mechanism Based on Workload Classification of Modern Heterogeneous Many-Core Mobile Platform in the Dark Silicon Era

The rapid progress in mobile computing necessitates energy efficient solutions to support substantially diverse and complex workloads. Heterogeneous many core platforms are progressively being adopted in contemporary embedded implementations for high performance at low power cost estimations. These implementations experience diverse workloads that offer drastic opportunities to improve energy efficiency. In this paper, we propose a novel per core power gating (PCPG) approach based on workload classifications (WLC) for drastic energy cost minimization in the dark silicon era. Core of our paradigm is to use an integrated sleep mode management based on workloads classification indicated by the performance counters. A number of real applications benchmark (PARSEC) are adopted as a practical example of diverse workloads, including memory- and CPU-intensive ones. In this paper, these applications are exercised on Samsung Exynos 5422 heterogeneous many core system showing up to 37% to 110% energy efficient when compared with our most recent published work, and ondemand governor, respectively. Furthermore, we illustrate low-complexity and low-cost runtime per core power gating algorithm that consistently maximize IPS/Watt at all state space.

Model of oil pipeline tiny defects detection based on DDPM gated parallel convolutional swin transformer

Abstract To address the challenges of difficult detection of minute magnetic flux leakage (MFL) defects, insufficient inspection data, and low detection accuracy, the denoising diffusion probabilistic model (DDPM) gate dilated parallel convolution swin transformer (DGPST) is proposed. First, we introduce a DDPM-based data generation model, successfully generating a large quantity of diverse and rich MFL defect samples. Second, a gated parallel convolution layer is introduced into the backbone network. This strategy uses the characteristics of dilated convolution to broaden the receptive field of the model, thus enhancing the integration ability of global information. The addition of gating mechanism enables the model to adjust the calculation of attention weight based on broader context information in advance, which not only complicates the shortcomings of window self-attention in global dependence understanding, but also effectively suppress irrelevant calculation. Finally, the loss function of H Intersection over Union is introduced to improve the mean average precision. Following these enhancements, DGPST attains a satisfactory outcome in detecting tiny defects within the MFL problem. Experimental data indicates the accuracy of the algorithm reaches 95.6% and the delay is reduced to 7.6 ms.

A Predictive Model for Sintering Ignition Temperature Based on a CNN-LSTM Neural Network with an Attention Mechanism

The sintering ignition process parameters fluctuate frequently and significantly, resulting in large variations in ignition temperature, which in severe cases can exceed 200 °C. This not only increases gas consumption but also affects the quality of the sinter. Because the intelligent control model based on feedback mechanisms struggles to deal with high-frequency fluctuation conditions over time, the prediction of sintering ignition temperature using feedforward regulation is attracting increasing attention. Given the multi-variable, time-sequential and strongly coupled characteristics of the sintering ignition process, a convolutional neural network (CNN) and a long short-term memory (LSTM) network are deeply integrated, with an attention mechanism incorporated to develop the sintering ignition temperature prediction model, enabling the accurate prediction of the ignition temperature. The research demonstrates that the combination of a CNN and the attention mechanism effectively addresses the challenges posed by the multi-variable and strongly coupled nature of sintering ignition data to predictive modeling. The LSTM network resolves the sequential data issues through its gating mechanism. As a result, the coefficient of determination (R2 ) of the CNN_LSTM-Attention model in predicting the sintering ignition temperature can reach 0.97, with a mean absolute error (MAE) as low as 10.23 °C. The predicted values closely match the actual values, achieving a hit rate of 93% within the acceptable error range. These performance metrics are significantly superior to those of the CNN-Attention and LSTM-Attention models, greatly enhancing the control accuracy of the ignition temperature.

GBMPhos: A Gating Mechanism and Bi-GRU-Based Method for Identifying Phosphorylation Sites of SARS-CoV-2 Infection.

Phosphorylation, a reversible and widespread post-translational modification of proteins, is essential for numerous cellular processes. However, due to technical limitations, large-scale detection of phosphorylation sites, especially those infected by SARS-CoV-2, remains a challenging task. To address this gap, we propose a method called GBMPhos, a novel method that combines convolutional neural networks (CNNs) for extracting local features, gating mechanisms to selectively focus on relevant information, and a bi-directional gated recurrent unit (Bi-GRU) to capture long-range dependencies within protein sequences. GBMPhos leverages a comprehensive set of features, including sequence encoding, physicochemical properties, and structural information, to provide an in-depth analysis of phosphorylation sites. We conducted an extensive comparison of GBMPhos with traditional machine learning algorithms and state-of-the-art methods. Experimental results demonstrate the superiority of GBMPhos over existing methods. The visualization analysis further highlights its effectiveness and efficiency. Additionally, we have established a free web server platform to help researchers explore phosphorylation in SARS-CoV-2 infections. The source code of GBMPhos is publicly available on GitHub.

Shift-left approach for Vulnerability Management in SDLC

Security vulnerabilities in software development can lead to severe risks if not addressed promptly. By adopting a shift-left approach and implementing gating mechanisms in Continuous Integration/Continuous Delivery (CI/CD) pipelines, organizations can mitigate the impact of high and critical vulnerabilities early in the Software Development Life Cycle (SDLC). This paper discusses a practical methodology for integrating security gates into the CI/CD pipeline to prevent the release of software with critical security flaws. Keywords —Vulnerability Management, Shift-Left Security, CI/CD Pipeline, High Vulnerabilities, Critical Vulnerabilities, Security Gating, SDLC, GitLab, Automation.

Epileptic seizure prediction via multidimensional transformer and recurrent neural network fusion

BackgroundEpilepsy is a prevalent neurological disorder in which seizures cause recurrent episodes of unconsciousness or muscle convulsions, seriously affecting the patient’s work, quality of life, and health and safety. Timely prediction of seizures is critical for patients to take appropriate therapeutic measures. Accurate prediction of seizures remains a challenge due to the complex and variable nature of EEG signals. The study proposes an epileptic seizure model based on a multidimensional Transformer with recurrent neural network(LSTM-GRU) fusion for seizure classification of EEG signals.MethodologyFirstly, a short-time Fourier transform was employed in the extraction of time-frequency features from EEG signals. Second, the extracted time-frequency features are learned using the Multidimensional Transformer model. Then, LSTM and GRU are then used for further learning of the time and frequency characteristics of the EEG signals. Next, the output features of LSTM and GRU are spliced and categorized using the gating mechanism. Subsequently, seizure prediction is conducted.ResultsThe model was tested on two datasets: the Bonn EEG dataset and the CHB-MIT dataset. On the CHB-MIT dataset, the average sensitivity and average specificity of the model were 98.24% and 97.27%, respectively. On the Bonn dataset, the model obtained about 99% and about 98% accuracy on the binary classification task and the tertiary upper classification task, respectively.ConclusionThe findings of the experimental investigation demonstrate that our model is capable of exploiting the temporal and frequency characteristics present within EEG signals.

Advanced Wind Power Forecasting

The popularity of LSTM networks in wind prediction is due to their inherent capabilities in handling the long-term dependencies of sequential data. This normally outperforms traditional methods like ARIMA. However, current work has illustrated that EMD-based approaches outperform LSTM models in many critical performance metrics. Although it is very powerful in gathering historical patterns due to the enhanced gating mechanisms, LSTM has a lot of challenges regarding computational efficiency and flexibility. EMD models overwhelmingly outperformed the LSTM model in computational time by probably about 20-25%, thereby solving a weakness that may be one of the intrinsic weaknesses of the LSTM model. In addition, the EMD models improved by about 2-4% in accuracy as compared to LSTM, thus evidencing better ability in the prediction of the wind data. On feature extraction, EMD did better, improving by 7% over LSTM. Moreover, EMD has better robustness and adaptability, with improvements of 6% and 4%, respectively. This introduction of EMD does bring a bit of an increase in model complexity, this disadvantage is rather insignificant when compared with the huge advantages it brings in efficiency and accuracy. While an LSTM network is very good at modeling a long-term relationship, it suffers from some problems in handling nonlinear interactions and continuous series data, thus its effectiveness may be limited in some cases of forecasting. In contrast, EMD has a clear advantage in real-time applications owing to enhanced efficiency and precision. From the results obtained in this study, it becomes easy to envision that the EMD methodology is un-equivocally better than LSTM in all the critical aspects, such as computational efficiency, accuracy in prediction, feature extraction, resilience, and adaptability when used individually. Results indicate that EMD has a more constructive methodology toward wind pattern predictions, beating LSTM on many measures of performance. The advantages presented should be maximized in future studies aimed at further improving and optimizing a wind-forecasting system, with special attention toward EMD's benefits in gaining more accurate and efficient forecasts.

Auditory processing up to cortex is maintained during sleep spindles

Abstract Sleep spindles are transient 11–16 Hz brain oscillations generated by thalamocortical circuits. Their role in memory consolidation is well established, but how they play a role in sleep continuity and protection of memory consolidation against interference is unclear. One theory posits that spindles or a neural refractory period following their offset act as a gating mechanism, blocking sensory information en route to the cortex at the level of the thalamus. An alternative model posits that spindles do not participate in the suppression of neural responses to sound, although they can be produced in response to sound. We present evidence from three experiments using electroencephalography and magnetoencephalography in humans that examine different evoked responses in the presence of and following sleep spindles. The results provide convergent empirical evidence suggesting that auditory processing up to cortex is maintained during sleep spindles, and their refractory periods.