Salient Features Research Articles

Hue Guidance Network for Single Image Reflection Removal.

Reflection from glasses is ubiquitous in daily life, but it is usually undesirable in photographs. To remove these unwanted noises, existing methods utilize either correlative auxiliary information or handcrafted priors to constrain this ill-posed problem. However, due to their limited capability to describe the properties of reflections, these methods are unable to handle strong and complex reflection scenes. In this article, we propose a hue guidance network (HGNet) with two branches for single image reflection removal (SIRR) by integrating image information and corresponding hue information. The complementarity between image information and hue information has not been noticed. The key to this idea is that we found that hue information can describe reflections well and thus can be used as a superior constraint for the specific SIRR task. Accordingly, the first branch extracts the salient reflection features by directly estimating the hue map. The second branch leverages these effective features, which can help locate salient reflection regions to obtain a high-quality restored image. Furthermore, we design a new cyclic hue loss to provide a more accurate optimization direction for the network training. Experiments substantiate the superiority of our network, especially its excellent generalization ability to various reflection scenes, as compared with state-of-the-arts both qualitatively and quantitatively. Source codes are available at https://github.com/zhuyr97/HGRR.

OptNet-Fake: Fake News Detection in Socio-Cyber Platforms Using Grasshopper Optimization and Deep Neural Network

Exposure to half-truths or lies has the potential to undermine democracies, polarize public opinion, and promote violent extremism. Identifying the veracity of fake news is a challenging task in distributed and disparate cyber-socio platforms. To enhance the trustworthiness of news on these platforms, in this article, we put forward a fake news detection model, OptNet-Fake. The proposed model is architecturally a hybrid that uses a meta-heuristic algorithm to select features based on usefulness and trains a deep neural network to detect fake news in social media. The <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$d$</tex-math> </inline-formula> -D feature vectors for the textual data are initially extracted using the term frequency inverse document frequency (TF-IDF) weighting technique. The extracted features are then directed to a modified grasshopper optimization (MGO) algorithm, which selects the most salient features in the text. The selected features are then fed to various convolutional neural networks (CNNs) with different filter sizes to process them and obtain the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$</tex-math> </inline-formula> -gram features from the text. These extracted features are finally concatenated for the detection of fake news. The results are evaluated for four real-world fake news datasets using standard evaluation metrics. A comparison with different meta-heuristic algorithms and recent fake news detection methods is also done. The results distinctly endorse the superior performance of the proposed OptNet-Fake model over contemporary models across various datasets.

Interaction-and-Response Network for Distantly Supervised Relation Extraction.

Distantly supervised relation extraction (DSRE) aims to identify semantic relations from massive plain texts. A broad range of the prior research has leveraged a series of selective attention mechanisms over sentences in a bag to extract relation features without considering dependencies among the relation features. As a result, potential discriminative information existed in the dependencies is ignored, causing a decline in the performance of extracting entity relations. In this article, we focus on going beyond the selective attention mechanisms and propose a new framework termed interaction-and-response network (IR-Net) that adaptively recalibrates the features of sentence, bag, and group levels by explicitly modeling interdependencies among the features on each level. The IR-Net consists of a series of interactive and responsive modules throughout feature hierarchy, seeking to strengthen its power of learning salient discriminative features for distinguishing entity relations. We conduct extensive experiments on three benchmark DSRE datasets, including NYT-10, NYT-16, and Wiki-20m. The experimental results demonstrate that the IR-Net brings obvious improvements in performance when comparing ten state-of-the-art DSRE methods for entity relation extraction.

Content-Aware Rectified Activation for Zero-Shot Fine-Grained Image Retrieval.

Fine-grained image retrieval mainly focuses on learning salient features from the seen subcategories as discriminative embedding while neglecting the problems behind zero-shot settings. We argue that retrieving fine-grained objects from unseen subcategories may rely on more diverse clues, which are easily restrained by the salient features learnt from seen subcategories. To address this issue, we propose a novel Content-aware Rectified Activation model, which enables this model to suppress the activation on salient regions while preserving their discrimination, and spread activation to adjacent non-salient regions, thus mining more diverse discriminative features for retrieving unseen subcategories. Specifically, we construct a content-aware rectified prototype (CARP) by perceiving semantics of salient regions. CARP acts as a channel-wise non-destructive activation upper bound and can be selectively used to suppress salient regions for obtaining the rectified features. Moreover, two regularizations are proposed: 1) a semantic coherency constraint that imposes a restriction on semantic coherency of CARP and salient regions, aiming at propagating the discriminative ability of salient regions to CARP, 2) a feature-navigated constraint to further guide the model to adaptively balance the discrimination power of rectified features and the suppression power of salient features. Experimental results on fine-grained and product retrieval benchmarks demonstrate that our method consistently outperforms the state-of-the-art methods.

Structure Suture Learning-Based Robust Multiview Palmprint Recognition.

Low-quality palmprint images will degrade the recognition performance, when they are captured under the open, unconstraint, and low-illumination conditions. Moreover, the traditional single-view palmprint representation methods have been difficult to express the characteristics of each palm strongly, where the palmprint characteristics become weak. To tackle these issues, in this article, we propose a structure suture learning-based robust multiview palmprint recognition method (SSL_RMPR), which comprehensively presents the salient palmprint features from multiple views. Unlike the existing multiview palmprint representation methods, SSL_RMPR introduces a structure suture learning strategy to produce an elastic nearest neighbor graph (ENNG) on the reconstruction errors that simultaneously exploit the label information and the latent consensus structure of the multiview data, such that the discriminant palmprint representation can be adaptively enhanced. Meanwhile, a low-rank reconstruction term integrating with the projection matrix learning is proposed, in such a manner that the robustness of the projection matrix can be improved. Particularly, since no extra structure capture term is imposed into the proposed model, the complexity of the model can be greatly reduced. Experimental results have proven the superiority of the proposed SSL_RMPR by achieving the best recognition performances on a number of real-world palmprint databases.

Revolutionizing Nitrofurantoin Delivery: Unraveling Challenges and Pioneering Solutions for Enhanced Efficacy in UTI Treatment

Abstract: Nitrofurantoin is an antimicrobial drug, highly effective in the treatment of critical or chronic bacterial infections of the urinary tract, and hence, it is the first line choice of drug for the treatment of urinary tract infections (UTI). Although the molecule is legacy in nature, there are many challenges in terms of drug product formulation and efficacy thereof. The authors are main-ly focused in this literature review on, but not limited to, understanding the molecule in terms of physico-chemical properties of the drug, pharmacokinetics and pharmacodynamics, approved and withdrawn formulations, challenges concerning drug formulation, the cause of drug shortage in the market, improvement areas in terms of formulation and its therapeutic effectiveness. The au-thors found during their widespread review that the major challenge in the existing conventional drug delivery system of nitrofurantoin is the fluctuation of plasma concentration owing to its var-iability in drug absorption. Further, they understood that the variability in absorption is due to in-herent variability in particle size distribution. Based on the findings, authors also explored the possibilities to deliver the drug in novel drug delivery systems such as nano self-emulsifying emulsions, nanoemulsions and multiple emulsions where the drug can be presented in soluble form and hence the variability in absorption and fluctuation in plasma concentration of drug can be avoided and described briefly the salient features of each drug delivery in this review.

Aquatic Angiosperms of India

Some of the salient features pertaining to the taxonomy of a few interesting aquatic angiosperms and their distribution, variability of fruits and seeds, their economic importance, floral biology, anatomy, embryology and cytology are presented here. The varied and diverse pattern of distribution of some Indian aquatic taxa is described with examples. Certain unique embryological features noticed in some aquatic plants are pointed out. Though the data on the cytplogy of Indian aquatic angiosperms is raher meagre, it is evident that from the evolutionary point of view, polyploidy has played an important role in the differentiation of races, varieties and species. The nomenclature of Nymphoides Hill and Necharmndra Planch., is discussed. Finally, the ample scope present for further work for a better understanding of these plants is emphasised.

Analyzing physical activity impact based on ubiquitous nonlinear dynamics and electroencephalography data.

Understanding complex systems is made easier with the tools provided by the theory of nonlinear dynamic systems. It provides novel ideas, algorithms, and techniques for signal processing, analysis, and classification. Presently, these ideas are being applied to the investigation of how physiological signals evolve. The study applies nonlinear dynamics theory to electroencephalogram (EEG) signals to better comprehend the range of alcoholic mental states. One of the main contributions of this paper is an algorithm for automatically distinguishing between sober and drunken EEG signals based on their salient features. The study utilized various entropy-based features, including ApEn, SampEn, Shannon and Renyi entropies, PE, TS, FE, WE, and KSE, to extract information from EEG signals. To identify the most relevant features, the study employed ranking methods like T-test, Wilcoxon, and Bhattacharyya, and trained SVM classifiers with the selected features. The Bhattacharyya ranking method was found to be the most effective in achieving high classification accuracy, sensitivity, and specificity. Classification accuracy of 95.89%, the sensitivity of 94.43%, and specificity of 96.67% are achieved by the SVM classifier with radial basis function (RBF) for polynomial Kernel using the Bhattacharyya ranking method. From the result, it is clear that the model serves as a cost-effective and accurate decision-support tool for doctors in diagnosing alcoholism and for rehabilitation centres to monitor the effectiveness of interventions aimed at mitigating or reversing brain damage caused by alcoholism.

Enhancing Out-of-distribution Generalization on Graphs via Causal Attention Learning

In graph classification, attention- and pooling-based graph neural networks (GNNs) predominate to extract salient features from the input graph and support the prediction. They mostly follow the paradigm of “learning to attend,” which maximizes the mutual information between the attended graph and the ground-truth label. However, this paradigm causes GNN classifiers to indiscriminately absorb all statistical correlations between input features and labels in the training data without distinguishing the causal and noncausal effects of features. Rather than emphasizing causal features, the attended graphs tend to rely on noncausal features as shortcuts to predictions. These shortcut features may easily change outside the training distribution, thereby leading to poor generalization for GNN classifiers. In this article, we take a causal view on GNN modeling. Under our causal assumption, the shortcut feature serves as a confounder between the causal feature and prediction. It misleads the classifier into learning spurious correlations that facilitate prediction in in-distribution (ID) test evaluation while causing significant performance drop in out-of-distribution (OOD) test data. To address this issue, we employ the backdoor adjustment from causal theory—combining each causal feature with various shortcut features, to identify causal patterns and mitigate the confounding effect. Specifically, we employ attention modules to estimate the causal and shortcut features of the input graph. Then, a memory bank collects the estimated shortcut features, enhancing the diversity of shortcut features for combination. Simultaneously, we apply the prototype strategy to improve the consistency of intra-class causal features. We term our method as CAL+, which can promote stable relationships between causal estimation and prediction, regardless of distribution changes. Extensive experiments on synthetic and real-world OOD benchmarks demonstrate our method’s effectiveness in improving OOD generalization. Our codes are released at https://github.com/shuyao-wang/CAL-plus .

Unlocking the Potential of Oxidative Asymmetric Catalysis with Continuous Flow Electrochemistry.

In the field of catalytic asymmetric synthesis, the less-treated path lies in oxidative catalytic asymmetric transformations. The hurdles of pinpointing the appropriate chemical oxidants and addressing their compatibility issues with catalysts and functionalities present significant challenges. Organic electrochemistry, employing traceless electrons for redox reactions, is underscored as a promising solution. However, the commonly used electrolysis in batch cells introduces its own set of challenges, hindering the advancement of electrochemical asymmetric catalysis. Here we introduce a microfluidic electrochemistry platform with single-pass continuous flow reactors that exhibits a wide-ranging applicability to various oxidative asymmetric catalytic transformations. This is exemplified through the sulfenylation of 1,3-dicarbonyls, dehydrogenative C-C coupling, and dehydrogenative alkene annulation processes. The unique properties of microfluidic electrochemical reactors not only eliminate the need for chemical oxidants but also enhance reaction efficiency and reduce the use of additives and electrolytes. These salient features of microfluidic electrochemistry expedite the discovery and development of oxidative asymmetric transformations. In addition, the continuous production facilitated by parallel single-pass reactors ensures straightforward reaction upscaling, removing the necessity for reoptimization across various scales, as evidenced by direct translation from milligram screening to hectogram asymmetric synthesis.

Subscription Pricing for Free Delivery Services

Many firms that offer home delivery of products purchased online now provide consumers the option of subscribing to a membership plan for free delivery of limited or unlimited number of orders instead of customers paying delivery charges for each order. This paper seeks to understand the benefits to firms and consumers of introducing such free-delivery subscription (FDS) plans to complement their traditional pay-for-delivery (PFD) option, characterize the firm's optimal FDS subscription pricing strategy, and study the effects of product, consumer, and market characteristics on the subscription fees and firm's profits. We develop and analyze an economic model that incorporates salient features of product delivery settings that prior models of subscription pricing do not fully capture. The features include simultaneous availability of PFD and FDS options, lift in a consumer's demand when she switches from PFD to FDS, the possibility of attracting new consumers when the firm introduces FDS, and order batching by customers due to per-order delivery charges and free-delivery order allowances. Our utility-based approach to model consumers’ purchasing choices incorporates consumer heterogeneity both in terms of their utility for the product and their shopping preference under the PFD option. For homogeneous consumers, we show that introducing a FDS plan raises the firm's profit, establish the optimality of offering unlimited free-delivery allowance, and analyze comparative statics. With multiple customer types, our analysis of two subscription pricing strategies for FDS plans—a single “Universal” plan and multiple “Tiered” plans with varying subscription fees and free-delivery limits—leads to several interesting insights on delivery pricing strategies, as well as drivers and sensitivity of the optimal policy, profits, and number of subscribers. We supplement this analysis with extensive computational experiments that reveal a substantial increase in profits when a firm adds one or more FDS plans to its PFD option even though the optimal subscription fee(s) only covers part of the firm's actual shipping costs. These results also permit us to gauge the influence and understand the trends in the profitability of the Universal and Tiered plans as various parameters change.

Cutaneous Hydrophilic Polymer Embolism: An Important and Overlooked Clinical Entity.

Hydrophilic polymer embolism from vascular medical devices is an underrecognized clinical entity that can cause deleterious end-organ ischemia and culminate in mortality. This is concerning as we are in the era where minimally invasive procedures are commonplace. Diagnosis is often made retrospectively after obtaining histopathological tissue samples showing endoluminal, cerebriform, amorphous, anucleate, basophilic, nonrefractile, nonpolarizable foreign body material. We detail 2 more cases of cutaneous hydrophilic polymer embolism to underscore its salient clinicopathological features and increase awareness of this important iatrogenic entity.

A phase-transition model of reprocessible thermadapt shape memory polymer

A new type of thermadapt shape memory polymer (SMP) has not only the processability of thermoplastic SMP, but also the excellent shape fixation of thermosetting SMP. To enhance the application of this thermadapt SMP within industrial sectors, a comprehensive constitutive model based on phase transition is being proposed as an indicative descriptor of the semi-crystalline thermadapt SMP’s salient features, predominantly related to its two-way shape memory effect (SME) and thermal reprocessability. The concept of cooling elongation is also introduced in this model for modeling the two-way SME during the crystallization process. The molecular mechanism of chain-packing has been studied and used to establish phenomenological formulas. In addition, to systematically assess the temperature-time dependence of the crystallization process, the Avrami equation is improved by incorporating the distribution of polymer chain segments. This strategy provides a detailed investigation into the evolving pattern of the crystallization process in response to various temperature and time conditions. Compared with the experimental results, it is found that our model can well capture mechanical behavior in multiple shape memory cycles, including the two-way SME and reshaping process caused by bond exchange reaction. Furthermore, the potential application of SMP in smart mandrels is explored because the cooling elongation feature is able to endow it with self-adaptive expansion ability.

Centrifugal Forces Impacting Urbanization in the Eastern Mediterranean during Roman and Early Islamic Times

My goal with this essay is to make the existence of a distinctive Levantine cultural paradigm a lens through which to examine long-term patterns of urbanization and cultural change in the Eastern Mediterranean—focusing especially on present-day Israel, Jordan and the Palestinian territories. Inspired by the agenda and approach of global history, the essay is an attempt to highlight a number of salient features of societal formation processes in this region that set them apart from such processes in the heartlands of ancient Egypt and Mesopotamia. The paradigm holds that societal formation dynamics in the Levant have been dominated more by centrifugal than by centripetal forces, thus predisposing local social order in the region towards greater local agency, resiliency, polycentrism, heterarchical social structure and societal complexity less conspicuously reflected in grand monumentality. The implications of this hypothesis for understanding urban resilience in the Late Roman and Early Islamic periods in the Eastern Mediterranean will be explored drawing, in particular, on previous research on the “cities of the Decapolis” and on findings of archaeological excavations at Tall Hisban and the wider Madaba Plains region in Jordan.

“We Pau ?”: Culturally Responsive Language Assessment of Pidgin (Hawaiʻi Creole)–Speaking Children

Purpose: The purpose of this tutorial is to introduce speech-language pathologists to the salient characteristic of the creole language of Hawaiʻi. This language variety is called Hawaiʻi Creole by linguists, but speakers of the language variety generally call it Pidgin. Conclusions: This tutorial reviews the available literature on this understudied language variety to provide a brief overview of Pidgin's historical development and social context and its clinically salient linguistic features. Suggestions for culturally and linguistically responsive assessment of Pidgin-speaking children as well as suggestions for further reading on Pidgin are also provided.

Design, prototyping, and optimization of a handheld refrigeration probe system for dental pulp testing

Refrigeration technology is widely used in medical applications such as dental pulp testing, therapeutic hypothermia, and cryosurgery. There is a growing trend to replace conventional cryogen-based approaches with thermoelectric cooler-based thermal management systems. This transition is driven by the notable advantages of thermoelectric (TE) coolers, which overcome the limitations of cryogens while providing a safe, flexible, reusable, stable, and environmentally friendly refrigeration temperature. Within this transition, achieving a −30 °C refrigeration temperature, which is important for pulp testing, within the confines of a handheld space remains challenging. This study successfully addressed this challenge by introducing a high-performance heat dissipation component based on the principle of conjugate heat transfer (CHT). A handheld refrigeration probe system for dental pulp testing was developed. The prototype has the salient features of achieving a −35 °C temperature with a duration time of 4 min and a response time of 30 s within a cylindrical space of ∅ 35 mm × 150 mm. Furthermore, by geometrical optimization of the TE module and topology optimization of the heat dissipation design, the lowest temperature of the proposed probe can reach −44 °C. Overall, this study demonstrates the capabilities of TE and CHT in refrigerating and highlights the potential of this combined technology to improve the dental pulp testing in a handheld, stable, and ecological manner.

QEMCGAN: Quantized Evolutionary Gradient Aware Multiobjective Cyclic GAN for Medical Image Translation.

Generative adversarial networks (GANs) have become popular in medical imaging because of their remarkable performance and ability to translate images across different domains. However, GANs face several issues in image-to-image translation, including training instability, lack of diversity, and mode collapse. These issues become even more complex when using cyclic GANs. Additionally, collecting paired images required for GANs may be costly, especially in the medical domain. Cyclic GANs are a favorable choice for addressing this issue, as they can convert cross-domain images. However, no pre-existing technique or algorithm is comprehensive enough to handle diverse datasets and applications. To address these issues, we propose a novel Quantized Evolutionary Gradient Aware Multiobjective Cyclic GAN (QEMCGAN) that employs evolutionary computation, multiobjective optimization, and an intelligent selection scheme. We use simulated annealing and Pareto ranking selection using three fitness criteria to address local optima stagnation. Additionally, we use model quantization because of its suitability for low-cost IoT-based applications. Extensive trials reveal that EMCGAN and QEMCGAN produces more visually realistic images than other approaches while preserving both background information and salient features. In addition, QEMCGAN performs on par with the baseline approach even when the model size is halved, making it more efficient.

Working condition recognition of fused magnesium furnace based on stochastic configuration networks and reinforcement learning

Automatic and accurate recognition of abnormal working conditions of fused magnesia furnace is of great significance to the safe and reliable production of fused magnesia. Aiming at the defects of manual judgment of abnormal working conditions in the production process of fused magnesium furnace and the existing recognition method of abnormal working conditions based on machine learning, this paper proposes a working condition recognition model for fused magnesium furnace based on stochastic configuration networks and reinforcement learning. Firstly, a hybrid data augmentation method of generative and non-generative is used to obtain high-quality sample data with salient features. Secondly, based on ResNeXt, multi-scale local features are extracted under the condition of limited parameter quantity by controlling the cardinality. Combining a mixed model that enjoys the benefit of both self-attention and convolution (ACmix) and bidirectional feature pyramid network (BiFPN), the extracted local feature maps of different scales are cross-scale fused and focused, and more differentiated detailed feature information of the region of interest is retained. Thirdly, based on Transformer, the working condition recognition network of fused magnesium furnace is constructed to improve the global correlation between adjacent local features in the spatial dimension. The fused features are sent to stochastic configuration networks to establish a classification criterion for working condition recognition of fused magnesium furnace with generalization ability. Finally, reinforcement learning is used to evaluate the credibility of uncertain recognition results of samples in real time, and a self-optimizing adjustment action strategy at the Transformer encoding layer is defined. Build a library of Transformer models with different encoding layers, which is adapt to the different feature extraction requirements of multi-modal working samples. The experimental results show that the method in this paper has better recognition performance and generalization ability than other algorithms.

Cooperative Control of Multiagent Systems: A Quantization Feedback-Based Event-Triggered Approach.

This article addresses the synchronization tracking problem for high-order uncertain nonlinear multiagent systems via intermittent feedback under a directed graph. By resorting to a novel storer-based triggering transmission strategy in the state channels, we propose an event-triggered neuroadaptive control method with quantitative state feedback that exhibits several salient features: 1) avoiding continuous control updates by making the parameter estimations updated intermittently at the trigger instants; 2) resulting in lower-frequency triggering transmissions by using one event detector to monitor the triggering condition such that each agent only needs to broadcast information at its own trigger times; and 3) saving communication and computation resources by designing the intermittent updating of neural network weights using a dual-phase technique during the triggering period. Besides, it is shown that the proposed scheme is capable of steering the tracking/disagreement errors into an adjustable neighborhood close to the origin, and the existence of a strictly positive dwell time is proved to circumvent Zeno behavior. Both theoretical analysis and numerical simulation authenticate and validate the efficiency of the proposed protocols.

Investigating students’ meaning-making of multiple visual representations of epigenetics at different levels of biological organisation

ABSTRACT The aim of this study is to investigate students’ meaning–making of multiple visual representations of epigenetics at different levels of biological organisation, and to discern what visual aspects of the multiple visual representations might influence students’ reasoning. Adopting an exploratory approach, we analysed how students made meaning of visually communicated epigenetics phenomena while pointing at and reasoning about the multiple visual representations as part of semi-structured focus groups. We investigated students’ meaning-making of the multiple visual representations by analysing their indications through physical pointing and accompanying verbal utterances. The analysis revealed meaning-making and the nature of linking between levels of organisation in four distinct patterns, namely intra horizontal linking, inter horizontal linking, one level vertical linking and two level vertical linking. In addition, five different visual characteristics of the multiple visual representations emerged as influencing students’ reasoning while linking between different organisation levels: multiple visual representations, salient visual features, analogous visual features, familiar visual elements, and textual adjuncts. The study shows that multiple visual representations at different levels of organisation can support students’ meaning-making of epigenetics, indicating that this way of communicating can be transferable to other biological domains. Potential implications for future research and teaching practice are provided.