Efficient Method Research Articles

Pulses as Sustainable Protein Sources: Benefits, Drawbacks, and Gaps

Abstract: Pulses have redeemed the interest in terms of a sustainable and healthy diet due to their high protein content. Furthermore, the vitamins, minerals, and phytochemicals they contain also make them more valuable nutritionally. A sustainable diet should improve public health and food security and has a low environmental footprint. Pulses have been considered one of the emerging plant food proteins. Although they have beneficial components such as vitamins, minerals, phytochemicals, and antioxidants, many drawbacks limit their use to consumers and the food industry. One of the drawbacks is their incomplete proteins due to the lack of sulfur-containing amino acids in their protein profile. Furthermore, the protein digestibility of pulses is low due to the antinutritional compounds they contain. The other drawback of pulses is their beany flavor and bitter taste that limits the acceptability of consumers. From the consumer's point of view, the other disadvantage is the laborious and timeconsuming preparation period. : This article provides a concise overview of the current state of knowledge on pulses as sustainable protein sources, underlining the gaps that limit their extensive application in the food industry. Based on the available scientific facts on pulses, finding an efficient method for removing beany flavor and overall sensorial acceptability and antinutritional factors, thus increasing protein digestibility is crucial. The most promising option to increase pulse protein digestibility seems to combine conventional and novel technologies that can modulate digestibility by different mechanisms, such as the removal of antinutritional factors, protein denaturation, and the design of the food matrix.

Unveiling the power of g-C3N4@CR composite photocatalyst in simulated solar light-assisted degradation of fast green dye and nitrophenol

A novel and efficient method has been developed for synthesizing the g-C3N4@CR composite using a simple mingling and heating technique. This method involves the decoration of one-dimensional carmine (CR) onto graphitic carbon nitride (g-C3N4). The light-harvesting composites produced through this process underwent characterization using various spectroscopic techniques, including UV, XRD, FT-IR, Raman, and XPS spectroscopy. To assess the photocatalytic activity of the g-C3N4@CR Composite, its degradation efficiency was investigated by examining its performance in degrading fast green and nitrophenol dyes under outdoor solar light irradiation. The mechanism of degradation for these dyes by the newly designed g-C3N4@CR composite was elucidated through degradation kinetics analysis, involving the determination of parameters such as half-life time, rate constant, and degradation efficiency.

One-Step Multiview Fuzzy Clustering With Collaborative Learning Between Common and Specific Hidden Space Information.

Multiview data are widespread in real-world applications, and multiview clustering is a commonly used technique to effectively mine the data. Most of the existing algorithms perform multiview clustering by mining the commonly hidden space between views. Although this strategy is effective, there are two challenges that still need to be addressed to further improve the performance. First, how to design an efficient hidden space learning method so that the learned hidden spaces contain both shared and specific information of multiview data. Second, how to design an efficient mechanism to make the learned hidden space more suitable for the clustering task. In this study, a novel one-step multiview fuzzy clustering (OMFC-CS) method is proposed to address the two challenges by collaborative learning between the common and specific space information. To tackle the first challenge, we propose a mechanism to extract the common and specific information simultaneously based on matrix factorization. For the second challenge, we design a one-step learning framework to integrate the learning of common and specific spaces and the learning of fuzzy partitions. The integration is achieved in the framework by performing the two learning processes alternately and thereby yielding mutual benefit. Furthermore, the Shannon entropy strategy is introduced to obtain the optimal views weight assignment during clustering. The experimental results based on benchmark multiview datasets demonstrate that the proposed OMFC-CS outperforms many existing methods.

The Quality Improvement Review Board: An Innovative Approach to Oversight of Projects That Do Not Meet Criteria of Human Subject Research.

This article describes the development of an institutional quality improvement review board (QIRB) as an effective and efficient method for reviewing and overseeing institutional quality improvement (QI) initiatives. QI projects involve the systematic collection and analysis of data and the implementation of interventions designed to improve the quality of clinical care and/or educational programs for a distinct population in a specific setting. QI projects are fundamentally distinct from human subjects research (HuSR); however, the differences between them are subtle and highly nuanced. Determining whether a project meets the definition of QI or qualifies as HuSR, thus requiring institutional review board (IRB) review, can be confusing and frustrating. Nevertheless, this distinction is highly consequential due to the heavy regulatory requirements involved in HuSR and IRB oversight. Making the correct determination of a project's regulatory status is essential before the project begins. Project leaders may not realize that their work meets the definition of HuSR and, therefore, might conduct the project without appropriate IRB review. Therefore, best practices dictate that project leaders should not decide which type of institutional review is appropriate for their projects. In addition, when QI project teams attempt to disseminate the results of their work, documentation of formal review and approval is generally required by peer-reviewed journals and professional organizations. However, institutional review mechanisms are rarely available. Projects that do not meet the definition of HuSR fall outside the purview of IRBs and most institutions do not have an alternative review body. This creates frustration for both project leaders and IRB administrators. Apart from IRB review, a separate process for reviewing QI projects offers several benefits. These include (1) relieving the burden on busy IRB staff; (2) promoting scholarly activity; (3) protecting the institution, project leaders, and participants from HuSR conducted outside of appropriate IRB review; and (4) promoting rigorous QI methods.

Weakly Supervised Temporal Action Localization With Bidirectional Semantic Consistency Constraint.

Weakly supervised temporal action localization (WTAL) aims to classify and localize temporal boundaries of actions for the video, given only video-level category labels in the training datasets. Due to the lack of boundary information during training, existing approaches formulate WTAL as a classification problem, i.e., generating the temporal class activation map (T-CAM) for localization. However, with only classification loss, the model would be suboptimized, i.e., the action-related scenes are enough to distinguish different class labels. Regarding other actions in the action-related scene (i.e., the scene same as positive actions) as co-scene actions, this suboptimized model would misclassify the co-scene actions as positive actions. To address this misclassification, we propose a simple yet efficient method, named bidirectional semantic consistency constraint (Bi-SCC), to discriminate the positive actions from co-scene actions. The proposed Bi-SCC first adopts a temporal context augmentation to generate an augmented video that breaks the correlation between positive actions and their co-scene actions in the inter-video. Then, a semantic consistency constraint (SCC) is used to enforce the predictions of the original video and augmented video to be consistent, hence suppressing the co-scene actions. However, we find that this augmented video would destroy the original temporal context. Simply applying the consistency constraint would affect the completeness of localized positive actions. Hence, we boost the SCC in a bidirectional way to suppress co-scene actions while ensuring the integrity of positive actions, by cross-supervising the original and augmented videos. Finally, our proposed Bi-SCC can be applied to current WTAL approaches and improve their performance. Experimental results show that our approach outperforms the state-of-the-art methods on THUMOS14 and ActivityNet. The code is available at https://github.com/lgzlIlIlI/BiSCC.

CapsRule: Explainable Deep Learning for Classifying Network Attacks.

Despite the potential deep learning (DL) algorithms have shown, their lack of transparency hinders their widespread application. Extracting if-then rules from deep neural networks is a powerful explanation method to capture nonlinear local behaviors. However, existing rule extraction methods suffer from inefficiency, incomprehensibility, infidelity, and not scaling well. Concerning security applications, they are not optimized regarding the decision boundary, data types and ranges, classification tasks, and dataset size. In this article, we propose CapsRule, an effective and efficient rule-based DL explanation method dedicated to classifying network attacks. It extracts high-fidelity rules from the feed-forward capsule network that explains how an input sample is classified. Using precomputed coupling coefficients, the training phase overlaps the rule extraction process to increase efficiency. The activation vector of a capsule can represent semantic intelligence about the attributes of the input sample. The rules extracted from CapsRule address the major concerns of network attack detection. The rules: 1) approximate the nonlinear decision boundary of the underlying data; 2) reduce the number of false positives significantly; 3) increase transparency; and 4) help find errors and noise in the data. We evaluate CapsRule on the CICDDoS2019 dataset that contains over a million of the most advanced Distributed Denial-of-Service (DDoS) attacks. The extensive evaluation shows that it generates accurate, high-fidelity, and comprehensible rules. CapsRule achieves an average accuracy of 99.0% and a false positive rate of 0.70% for reflection- and exploitation-based attacks. We verify that the learned features from the rulesets match our domain-specific knowledge. They also help find flaws in the dataset generation process and erroneous patterns caused by attack simulators.

Cyclical Curriculum Learning.

Artificial neural networks (ANNs) are inspired by human learning. However, unlike human education, classical ANN does not use a curriculum. Curriculum learning (CL) refers to the process of ANN training in which samples are used in a meaningful order. When using CL, training begins with a subset of the dataset and new samples are added throughout the training, or training begins with the entire dataset and the number of samples used is reduced. With these changes in training dataset size, better results can be obtained with curriculum, anti-curriculum, or random-curriculum methods than the vanilla method. However, a generally efficient CL method for various architectures and datasets is not found. In this article, we propose cyclical CL (CCL), in which the data size used during training changes cyclically rather than simply increasing or decreasing. Instead of using only the vanilla method or only the curriculum method, using both methods cyclically like in CCL provides more successful results. We tested the method on 18 different datasets and 15 architectures in image and text classification tasks and obtained more successful results than no-CL and existing CL methods. We also have shown theoretically that it is less erroneous to apply CL and vanilla cyclically instead of using only CL or only the vanilla method. The code of the cyclical curriculum is available at https://github.com/CyclicalCurriculum/Cyclical-Curriculum.

Prototype-Guided Memory Replay for Continual Learning.

Continual learning (CL) is a machine learning paradigm that accumulates knowledge while learning sequentially. The main challenge in CL is catastrophic forgetting of previously seen tasks, which occurs due to shifts in the probability distribution. To retain knowledge, existing CL models often save some past examples and revisit them while learning new tasks. As a result, the size of saved samples dramatically increases as more samples are seen. To address this issue, we introduce an efficient CL method by storing only a few samples to achieve good performance. Specifically, we propose a dynamic prototype-guided memory replay (PMR) module, where synthetic prototypes serve as knowledge representations and guide the sample selection for memory replay. This module is integrated into an online meta-learning (OML) model for efficient knowledge transfer. We conduct extensive experiments on the CL benchmark text classification datasets and examine the effect of training set order on the performance of CL models. The experimental results demonstrate the superiority our approach in terms of accuracy and efficiency.

Revisiting Realistic Test-Time Training: Sequential Inference and Adaptation by Anchored Clustering Regularized Self-Training.

Deploying models on target domain data subject to distribution shift requires adaptation. Test-time training (TTT) emerges as a solution to this adaptation under a realistic scenario where access to full source domain data is not available, and instant inference on the target domain is required. Despite many efforts into TTT, there is a confusion over the experimental settings, thus leading to unfair comparisons. In this work, we first revisit TTT assumptions and categorize TTT protocols by two key factors, i.e., whether testing data is sequentially streamed and whether source model is allowed to be trained with modified loss function. Among the multiple protocols, we adopt a realistic sequential test-time training (sTTT) protocol, under which we develop a test-time anchored clustering (TTAC) approach to enable stronger test-time feature learning. TTAC discovers clusters in both source and target domains and matches the target clusters to the source ones to improve adaptation. When source domain information is strictly absent (i.e., source-free) we further develop an efficient method to infer source domain distributions for anchored clustering. Finally, self-training (ST) has demonstrated great success in learning from unlabeled data and we empirically figure out that applying ST alone to TTT is prone to confirmation bias. Therefore, a more effective TTT approach is introduced by regularizing self-training with anchored clustering, and the improved model is referred to as TTAC++. We demonstrate that, under all TTT protocols, TTAC++ consistently outperforms the state-of-the-art methods on five TTT datasets, including corrupted target domain, selected hard samples, synthetic-to-real adaptation and adversarially attacked target domain. We hope this work will provide a fair benchmarking of TTT methods, and future research should be compared within respective protocols.

Target-agnostic identification of human antibodies to Plasmodium falciparum sexual forms reveals cross stage recognition of glutamate-rich repeats.

A naturally acquired human monoclonal antibody recognizes proteins expressed at different stages of the Plasmodium falciparum lifecycle through affinity-matured homotypic interactions with glutamate-rich repeats.

Neural-Network-Based Predefined-Time Adaptive Consensus in Nonlinear Multi-Agent Systems With Switching Topologies.

A predefined-time adaptive consensus control strategy is developed for a class of multi-agent systems containing unknown nonlinearity. The unknown dynamics and switching topologies are simultaneously considered to adapt to actual scenarios. The time required for tracking error convergence can be easily adjusted using the proposed time-varying decay functions. An efficient method is proposed to determine the expected convergence time. Subsequently, the predefined time is adjustable by regulating the parameters of the time-varying functions (TVFs). The neural network (NN) approximation technique is used to address the issue of unknown nonlinear dynamics through predefined-time consensus control. The Lyapunov stability theory testifies that the predefined-time tracking error signals are bounded and convergent. The feasibility and effectiveness of the proposed predefined-time consensus control scheme are demonstrated through the simulation results.

Antibiotic Potentiation Through Phytochemical-Based Efflux Pump Inhibitors to Combat Multidrug Resistance Bacteria.

Antimicrobial resistance development poses a significant danger to the efficacy of antibiotics, which were once believed to be the most efficient method for treating infections caused by bacteria. Antimicrobial resistance typically involves various mechanisms, such as drug inactivation or modification, drug target modification, drug uptake restriction, and drug efflux, resulting in decreased antibiotic concentrations within the cell. Antimicrobial resistance has been associated with efflux Pumps, known for their capacity to expel different antibiotics from the cell non-specifically. This makes EPs fascinating targets for creating drugs to combat antimicrobial resistance (AMR). The varied structures of secondary metabolites (phytomolecules) found in plants have positioned them as a promising reservoir of efflux pump inhibitors. These inhibitors act as modifiers of bacterial resistance and facilitate the reintroduction of antibiotics that have lost clinical effectiveness. Additionally, they may play a role in preventing the emergence of multidrug resistant strains. The objective of this review article is to discuss the latest studies on plant-based efflux pump inhibitors such as terpenoids, alkaloids, flavonoids, glycosides, and tetralones. It highlighted their potential in enhancing the effectiveness of antibiotics and combating the development of multidrug resistance. Efflux pump inhibitors (EPIs) derived from botanical sources, including compounds like lysergol, chanaoclavine, niazrin, 4-hydroxy-α-tetralone, ursolic acid, phytol, etc., as well as their partially synthesized forms, have shown significant potential as practical therapeutic approaches in addressing antimicrobial resistance caused by efflux pumps. Further, several phyto-molecules and their analogs demonstrated superior potential for reversing drug resistance, surpassing established agents like reserpine, niaziridin, etc. Conclusion: This review found that while the phyto-molecules and their derivatives did not possess notable antimicrobial activity, their combination with established antibiotics significantly reduced their minimum inhibitory concentration (MIC). Specific molecules, such as chanaoclavine and niaziridin, exhibited noteworthy potential in reversing the effectiveness of drugs, resulting in a reduction of the MIC of tetracycline by up to 16 times against the tested strain of bacteria. These molecules inhibited the efflux pumps responsible for drug resistance and displayed a stronger affinity for membrane proteins. By employing powerful EPIs, these molecules can selectively target and obstruct drug efflux pumps. This targeted approach can significantly augment the strength and efficacy of older antibiotics against various drug resistant bacteria, given that active drug efflux poses a susceptibility for nearly all antibiotics.

Fast Bayesian Functional Regression for Non-Gaussian Spatial Data

Functional generalized linear models (FGLM) have been widely used to study the relations between non-Gaussian response and functional covariates. However, most existing works for FGLM assume independence among observations and therefore they are of limited applicability for correlated data. A particularly important example is spatial functional data, where we observe functions over spatial domains, such as the age population curve or temperature curve at each areal unit. In this paper, we extend FGLM by incorporating spatial random effects. Especially, we study the relationship between the non-Gaussian response variable and functional covariates that are spatially observed. However, such model has computational and inferential challenges. The high-dimensional spatial random effects cause the slow mixing of Markov chain Monte Carlo (MCMC) algorithms. Furthermore, spatial confounding can lead to bias in parameter estimates and inflate their variances. To address these issues, we propose an efficient Bayesian method using a sparse reparameterization of high-dimensional random effects. We also study the average coverage probabilities of the credible intervals of functional parameters. We apply our methods to simulated and real data examples, including malaria incidence data and US COVID-19 data. The proposed method is fast while providing accurate functional estimates.

Fuzzy Attention Neural Network to Tackle Discontinuity in Airway Segmentation.

Airway segmentation is crucial for the examination, diagnosis, and prognosis of lung diseases, while its manual delineation is unduly burdensome. To alleviate this time-consuming and potentially subjective manual procedure, researchers have proposed methods to automatically segment airways from computerized tomography (CT) images. However, some small-sized airway branches (e.g., bronchus and terminal bronchioles) significantly aggravate the difficulty of automatic segmentation by machine learning models. In particular, the variance of voxel values and the severe data imbalance in airway branches make the computational module prone to discontinuous and false-negative predictions, especially for cohorts with different lung diseases. The attention mechanism has shown the capacity to segment complex structures, while fuzzy logic can reduce the uncertainty in feature representations. Therefore, the integration of deep attention networks and fuzzy theory, given by the fuzzy attention layer, should be an escalated solution for better generalization and robustness. This article presents an efficient method for airway segmentation, comprising a novel fuzzy attention neural network (FANN) and a comprehensive loss function to enhance the spatial continuity of airway segmentation. The deep fuzzy set is formulated by a set of voxels in the feature map and a learnable Gaussian membership function. Different from the existing attention mechanism, the proposed channel-specific fuzzy attention addresses the issue of heterogeneous features in different channels. Furthermore, a novel evaluation metric is proposed to assess both the continuity and completeness of airway structures. The efficiency, generalization, and robustness of the proposed method have been proved by training on normal lung disease while testing on datasets of lung cancer, COVID-19, and pulmonary fibrosis.

Robotic Frameless Stereotactic Aspiration with Thrombolysis for Primary Pontine Hemorrhage: A Therapeutic Evaluation of a Retrospective Cohort Study.

There is still no consensus on whether primary pontine hemorrhage (PPH) should be managed conservatively or treated promptly via surgical evacuation of the hematoma. The purpose of this study was to assess the therapeutic effect of robotic frameless stereotactic aspiration with thrombolysis in the treatment of PPH. A total of 39 patients with PPH treated between January 2012 and November 2016 were included in the study. Sixteen patients underwent frameless stereotactic surgical treatment (ST group) and 23 patients underwent conservative treatment (CT group). Clinical and radiologic parameters were assessed, and the patient outcomes were analyzed over a 6-month follow-up period. Surgical treatment did not result in any intracranial infections, or complications. Baseline characteristics did not significantly differ between the two groups. At discharge, the average Glasgow Coma Scale (GCS) score and the overall Glasgow Outcome Scale (GOS) score were significantly higher in the ST group compared to the CT group (p < 0.05). The mortality rate (GOS score 1) was significantly lower in the ST group (18.75%, 3/16) than in the CT group (52.17%, 12/23). For patients with hematoma volumes of 5 to 10 mL or GCS scores of 6 to 8, following treatment, the ST group exhibited markedly higher GOS scores in comparison to the CT group. Our study suggests that robotic frameless stereotactic aspiration with thrombolysis is a safe and efficient method for the treatment of PPH. Patients with hematomas of 5 to 10 mL or GCS scores of 6 to 8 could benefit from surgery.

Improving Robustness of Intent Detection Under Adversarial Attacks: A Geometric Constraint Perspective.

Deep neural networks (DNNs)-based natural language processing (NLP) systems are vulnerable to being fooled by adversarial examples presented in recent studies. Intent detection tasks in dialog systems are no exception, however, relatively few works have been attempted on the defense side. The combination of linear classifier and softmax is widely used in most defense methods for other NLP tasks. Unfortunately, it does not encourage the model to learn well-separated feature representations. Thus, it is easy to induce adversarial examples. In this article, we propose a simple, yet efficient defense method from the geometric constraint perspective. Specifically, we first propose an M-similarity metric to shrink variances of intraclass features. Intuitively, better geometric conditions of feature space can bring lower misclassification probability (MP). Therefore, we derive the optimal geometric constraints of anchors within each category from the overall MP (OMP) with theoretical guarantees. Due to the nonconvex characteristic of the optimal geometric condition, it is hard to satisfy the traditional optimization process. To this end, we regard such geometric constraints as manifold optimization processes in the Stiefel manifold, thus naturally avoiding the above challenges. Experimental results demonstrate that our method can significantly improve robustness compared with baselines, while retaining the excellent performance on normal examples.

Efficient Method for Solving Underwater Electromagnetic Fields Generated by Radiation Sources in Seawater

Efficient Method for Solving Underwater Electromagnetic Fields Generated by Radiation Sources in Seawater

Perturbed Progressive Learning for Semisupervised Defect Segmentation.

Recently, with the development of intelligent manufacturing, the demand for surface defect inspection has been increasing. Deep learning has achieved promising results in defect inspection. However, due to the rareness of defect data and the difficulties of pixelwise annotation, the existing supervised defect inspection methods are too inferior to be implemented in practice. To solve the problem of defect segmentation with few labeled data, we propose a simple and efficient method for semisupervised defect segmentation (SSDS), named perturbed progressive learning (PPL). On the one hand, PPL decouples the predictions of student and teacher networks as well as alleviates overfitting on noisy pseudo-labels. On the other hand, PPL encourages consistency across various perturbations in a broader stagewise scope, alleviating drift caused by the noisy pseudo-labels. Specifically, PPL contains two training stages. In the first stage, the teacher network gives the unlabeled data with pseudo-labels that are divided into the easy and hard groups. The labeled data and the unlabeled data in the easy group with their perturbation are both used to train for a better-performing student network. In the second stage, the unlabeled data in the hard group are predicted by the obtained student network, so the refined pseudo-labeled data are enlarged. All the pseudo-labeling data and labeled data with their perturbation are used to retrain the student network, progressively improving the defect feature representation. We build a mobile screen defect dataset (MSDD-3) with three classes of defects. PPL is implemented on MSDD-3 as well as other public datasets. Extensive experimental results demonstrate that PPL significantly surpasses the state-of-the-art methods across all evaluation partition protocols.

The Block-Correlated Pseudo Marginal Sampler for State Space Models

Pseudo Marginal Metropolis-Hastings (PMMH) is a general approach to Bayesian inference when the likelihood is intractable, but can be estimated unbiasedly. Our article develops an efficient PMMH method that scales up better to higher dimensional state vectors than previous approaches. The improvement is achieved by the following innovations. First, a novel block version of PMMH that works with multiple particle filters is proposed. Second, the trimmed mean of the unbiased likelihood estimates of the multiple particle filters is used. Third, the article develops an efficient auxiliary disturbance particle filter, which is necessary when the bootstrap disturbance filter is inefficient, but the state transition density cannot be expressed in closed form. Fourth, a novel sorting algorithm, which is as effective as previous approaches but significantly faster than them, is developed to preserve the correlation between the logs of the likelihood estimates at the current and proposed parameter values. The performance of the sampler is investigated empirically by applying it to nonlinear Dynamic Stochastic General Equilibrium models with relatively high state dimensions and with intractable state transition densities and to multivariate GARCH diffusion-driven volatility in the mean models. Although we only apply the method to state space models, the approach will be useful in a wide range of applications such as large panel data models and stochastic differential equation models with mixed effects.

Synthesis of Deuterated Compounds by Flow Chemistry.

Development of the efficient and practical method for the synthesis of deuterated compounds which occupies the broadest area among stable isotopes is one of the most essential issues toward the industrial advance and building a sustainable society. This review describes recent advances in deuteration reactions, where the continuous flow chemistry plays pivotal roles for the successful installation of deuterium atom into diverse organic frameworks, opening new fields of isotope-based synthetic chemistry.