Diachronic evolvement from tetra-icosahedral to quasi-hexagonal close-packed bimetal clusters.

Here we report a diachronic evolvement from tetra-icosahedral Au30Ag12(C[triple bond, length as m-dash]CR)24 to quasi-hcp (hexagonal close-packed) Au47Ag19(C[triple bond, length as m-dash]CR)32 via a one-step reduction, in which the size/structure conversion of the two clusters is not a typical Oswald growth process, but involves interface shrinking followed by core rearrangement and surface polymerization. Au30Ag12(C[triple bond, length as m-dash]CR)24 has an aesthetic Au18Ag8 kernel that is composed of four interpenetrating Au10Ag3 icosahedra, while Au47Ag19(C[triple bond, length as m-dash]CR)32 has a twisted Au19 core capped by a Au12Ag19 shell that are stacked in a layer-by-layer manner with a quasi-hcp pattern. The discovery of the two clusters not only provides further evidence for icosahedral clusters with longer excited-state lifetime compared to hcp-like clusters, but also discloses a double increase in catalytic reactivity for electrocatalytic oxidation of ethanol over quasi-hcp clusters in comparison with icosahedral clusters. This work provides the rationale for reversing the bottom-up growth process to remake bimetal clusters.

Broadband wavelength designable achromatic grating based on a cholesteric liquid crystal template.

Liquid crystal (LC) gratings have played important roles in light field control due to the advantages of being lightweight, low cost, having no moving parts, and low power consumption. However, the chromatic aberration limits the bandwidth of the LC device and affects the efficiency of the grating. To solve the chromatic aberration issue, a broadband wavelength designable achromatic grating is proposed. Different grating structures are integrated into a single-layer templated cholesteric liquid crystal (CLC) device, and the achromatic diffraction wavelength of the grating can be freely designed from the visible spectral region to the infrared range within the Bragg reflection band of the CLCs. The diffraction intensity of different orders can be changed with the electric field applied to meet the need for dynamic modulation. This grating shows suitable potential applications in optical communication and displays.

IFKMHC: Implicit Fuzzy K-Means Model for High-Dimensional Data Clustering.

The graph-information-based fuzzy clustering has shown promising results in various datasets. However, its performance is hindered when dealing with high-dimensional data due to challenges related to redundant information and sensitivity to the similarity matrix design. To address these limitations, this article proposes an implicit fuzzy k-means (FKMs) model that enhances graph-based fuzzy clustering for high-dimensional data. Instead of explicitly designing a similarity matrix, our approach leverages the fuzzy partition result obtained from the implicit FKMs model to generate an effective similarity matrix. We employ a projection-based technique to handle redundant information, eliminating the need for specific feature extraction methods. By formulating the fuzzy clustering model solely based on the similarity matrix derived from the membership matrix, we mitigate issues, such as dependence on initial values and random fluctuations in clustering results. This innovative approach significantly improves the competitiveness of graph-enhanced fuzzy clustering for high-dimensional data. We present an efficient iterative optimization algorithm for our model and demonstrate its effectiveness through theoretical analysis and experimental comparisons with other state-of-the-art methods, showcasing its superior performance.

A Survey on Deep Active Learning: Recent Advances and New Frontiers.

Active learning seeks to achieve strong performance with fewer training samples. It does this by iteratively asking an oracle to label newly selected samples in a human-in-the-loop manner. This technique has gained increasing popularity due to its broad applicability, yet its survey papers, especially for deep active learning (DAL), remain scarce. Therefore, we conduct an advanced and comprehensive survey on DAL. We first introduce reviewed paper collection and filtering. Second, we formally define the DAL task and summarize the most influential baselines and widely used datasets. Third, we systematically provide a taxonomy of DAL methods from five perspectives, including annotation types, query strategies, deep model architectures, learning paradigms, and training processes, and objectively analyze their strengths and weaknesses. Then, we comprehensively summarize the main applications of DAL in natural language processing (NLP), computer vision (CV), data mining (DM), and so on. Finally, we discuss challenges and perspectives after a detailed analysis of current studies. This work aims to serve as a useful and quick guide for researchers in overcoming difficulties in DAL. We hope that this survey will spur further progress in this burgeoning field.

CSTS: Exploring Class-Specific and Task-Shared Embedding Representation for Few-Shot Learning.

Few-shot learning (FSL) is a challenging yet promising technique that aims to discriminate objects based on a few labeled examples. Learning a high-quality feature representation is key with few-shot data, and many existing models attempt to extract general information from the sample or task levels. However, the common sample-level means of feature representation limits the models generalizability to different tasks, while task-level representation may lose class characteristics due to excessive information aggregation. In this article, we synchronize the class-specific and task-shared information from the class and task levels to obtain a better representation. Structure-based contrastive learning is introduced to obtain class-specific representations by increasing the interclass distance. A hierarchical class structure is constructed by clustering semantically similar classes using the idea of granular computing. When guided by a class structure, it is more difficult to distinguish samples in different classes that have similar characteristics than those with large interclass differences. To this end, structure-guided contrastive learning is introduced to study class-specific information. A hierarchical graph neural network is established to transfer task-shared information from coarse to fine. It hierarchically infers the target sample based on all samples in the task and yields a more general representation for FSL classification. Experiments on four benchmark datasets demonstrate the advantages of our model over several state-of-the-art models.

Incremental high average-utility itemset mining: survey and challenges.

The High Average Utility Itemset Mining (HAUIM) technique, a variation of High Utility Itemset Mining (HUIM), uses the average utility of the itemsets. Historically, most HAUIM algorithms were designed for static databases. However, practical applications like market basket analysis and business decision-making necessitate regular updates of the database with new transactions. As a result, researchers have developed incremental HAUIM (iHAUIM) algorithms to identify HAUIs in a dynamically updated database. Contrary to conventional methods that begin from scratch, the iHAUIM algorithm facilitates incremental changes and outputs, thereby reducing the cost of discovery. This paper provides a comprehensive review of the state-of-the-art iHAUIM algorithms, analyzing their unique characteristics and advantages. First, we explain the concept of iHAUIM, providing formulas and real-world examples for a more in-depth understanding. Subsequently, we categorize and discuss the key technologies used by varying types of iHAUIM algorithms, encompassing Apriori-based, Tree-based, and Utility-list-based techniques. Moreover, we conduct a critical analysis of each mining method's advantages and disadvantages. In conclusion, we explore potential future directions, research opportunities, and various extensions of the iHAUIM algorithm.

Higher ultra processed foods intake is associated with low muscle mass in young to middle-aged adults: a cross-sectional NHANES study.

Design: Ultra-processed foods (UPFs) have become a pressing global health concern, prompting investigations into their potential association with low muscle mass in adults. Methods: This cross-sectional study analyzed data from 10,255 adults aged 20-59 years who participated in the National Health and Nutritional Examination Survey (NHANES) during cycles spanning from 2011 to 2018. The primary outcome, low muscle mass, was assessed using the Foundation for the National Institutes of Health (FNIH) definition, employing restricted cubic splines and weighted multivariate regression for analysis. Sensitivity analysis incorporated three other prevalent definitions to explore optimal cut points for muscle quality in the context of sarcopenia. Results: The weighted prevalence of low muscle mass was 7.65%. Comparing the percentage of UPFs calories intake between individuals with normal and low muscle mass, the values were found to be similar (55.70 vs. 54.62%). Significantly linear associations were observed between UPFs consumption and low muscle mass (P for non-linear = 0.7915, P for total = 0.0117). Upon full adjustment for potential confounding factors, participants with the highest UPFs intake exhibited a 60% increased risk of low muscle mass (OR = 1.60, 95% CI: 1.13 to 2.26, P for trend = 0.003) and a decrease in ALM/BMI (ß = -0.0176, 95% CI: -0.0274 to -0.0077, P for trend = 0.003). Sensitivity analysis confirmed the consistency of these associations, except for the International Working Group on Sarcopenia (IWGS) definition, where the observed association between the highest quartiles of UPFs (%Kcal) and low muscle mass did not attain statistical significance (OR = 1.35, 95% CI: 0.97 to 1.87, P for trend = 0.082). Conclusion: Our study underscores a significant linear association between higher UPFs consumption and an elevated risk of low muscle mass in adults. These findings emphasize the potential adverse impact of UPFs on muscle health and emphasize the need to address UPFs consumption as a modifiable risk factor in the context of sarcopenia.

Increased postoperative complications after laparoscopic gastrectomy in patients with preserved ratio impaired spirometry.

BACKGROUND: Preserved ratio impaired spirometry (PRISm), defined as decreased forced expiratory volume in the first second in the setting of normal ratio, is associated with an increased risk of respiratory disease and systemic comorbidities. Unlike severe obstructive pulmonary disease, little is known about the impact of PRISm on short-term outcomes in patients undergoing laparoscopic gastrectomy (LG) and its association with small airway dysfunction (SAD). METHODS: This study enrolled 830 patients who underwent preoperative spirometry and LG between January 2021 and August 2023. Of these, 228 patients were excluded. Participants were categorized into 3 groups based on their baseline lung function, and postoperative outcomes were subsequently analyzed. Potential associations between postoperative outcomes and various clinical variables were examined using univariate and multivariate analyses. RESULTS: PRISm was identified in 16.6% of the patients, whereas SAD was present in 20.4%. The incidence of postoperative pulmonary complications (PPCs) was notably higher in the SAD group (20.3% vs 9.8%, P = .002) and the PRISm group (28.0% vs 9.8%, P < .001) than the normal group. Among the 3 groups, pneumonia was the most frequently observed PPC. Multivariate analysis revealed that both SAD (odds ratio [OR], 2.34; 95% CI, 1.30-4.22; P = .005) and PRISm (OR, 3.26; 95% CI, 1.80-5.90; P < .001) independently constituted significant risk factors associated with the occurrence of PPCs. Univariate analysis showed that female was a possible risk factor for PPCs in PRISm group. CONCLUSION: Our study showed that PRISm and SAD were associated with the increased PPCs in patients undergoing LG for gastric cancer.

Recent Approaches for Cleaving the C─C Bond During Ethanol Electro-Oxidation Reaction.

Direct ethanol fuel cells (DEFCs) play an indispensable role in the cyclic utilization of carbon resources due to its high volumetric energy density, high efficiency, and environmental benign character. However, owing to the chemically stable carbon-carbon (C─C) bond of ethanol, its incomplete electrooxidation at the anode severely inhibits the energy and power density output of DEFCs. The efficiency of C─C bond cleaving on the state-of-the-art Pt or Pd catalysts is reported as low as 7.5%. Recently, tremendous efforts are devoted to this field, and some effective strategies are put forward to facilitate the cleavage of the C─C bond. It is the right time to summarize the major breakthroughs in ethanol electrooxidation reaction. In this review, some optimization strategies including constructing core-shell nanostructure with alloying effect, doping other metal atoms in Pt and Pd catalysts, engineering composite catalyst with interface synergism, introducing cascade catalytic sites, and so on, are systematically summarized. In addition, the catalytic mechanism as well as the correlations between the catalyst structure and catalytic efficiency are further discussed. Finally, the prevailing limitations and feasible improvement directions for ethanol electrooxidation are proposed.

A Statistical Physics Perspective: Understanding the Causality Behind Convolutional Neural Network Adversarial Vulnerability.

The adversarial vulnerability of convolutional neural networks (CNNs) refers to the performance degradation of CNNs under adversarial attacks, leading to incorrect decisions. However, the causes of adversarial vulnerability in CNNs remain unknown. To address this issue, we propose a unique cross-scale analytical approach from a statistical physics perspective. It reveals that the huge amount of nonlinear effects inherent in CNNs is the fundamental cause for the formation and evolution of system vulnerability. Vulnerability is spontaneously formed on the macroscopic level after the symmetry of the system is broken through the nonlinear interaction between microscopic state order parameters. We develop a cascade failure algorithm, visualizing how micro perturbations on neurons' activation can cascade and influence macro decision paths. Our empirical results demonstrate the interplay between microlevel activation maps and macrolevel decision-making and provide a statistical physics perspective to understand the causality behind CNN vulnerability. Our work will help subsequent research to improve the adversarial robustness of CNNs.

Programmable Photoswitchable Microcapsules Enable Precise and Tailored Drug Delivery from Microfluidics.

The development of precision personalized medicine poses a significant need for the next generation of advanced diagnostic and therapeutic technologies, and one of the key challenges is the development of highly time-, space-, and dose-controllable drug delivery systems that respond to the complex physiopathology of patient populations. In response to this challenge, an increasing number of stimuli-responsive smart materials are integrated into biomaterial systems for precise targeted drug delivery. Among them, responsive microcapsules prepared by droplet microfluidics have received much attention. In this study, we present a UV-visible light cycling mediated photoswitchable microcapsule (PMC) with dynamic permeability-switching capability for precise and tailored drug release. The PMCs were fabricated using a programmable pulsed aerodynamic printing (PPAP) technique, encapsulating an aqueous core containing magnetic nanoparticles and the drug doxorubicin (DOX) within a poly(lactic-co-glycolic acid) (PLGA) composite shell modified by PEG-b-PSPA. Selective irradiation of PMCs with ultraviolet (UV) or visible light (Vis) allows for high-precision time-, space-, and dose-controlled release of the therapeutic agent. An experimentally validated theoretical model was developed to describe the drug release pattern, holding promise for future customized programmable drug release applications. The therapeutic efficacy and value of patternable cancer cell treatment activated by UV radiation is demonstrated by our experimental results. After in vitro transcatheter arterial chemoembolization (TACE), PMCs can be removed by external magnetic fields to mitigate potential side effects. Our findings demonstrate that PMCs have the potential to integrate embolization, on-demand drug delivery, magnetic actuation, and imaging properties, highlighting their immense potential for tailored drug delivery and embolic therapy.

Solute-particle separation in microfluidics enhanced by symmetrical convection.

The utilization of microfluidic technology for miniaturized and efficient particle sorting holds significant importance in fields such as biology, chemistry, and healthcare. Passive separation methods, achieved by modifying the geometric shapes of microchannels, enable gentle and straightforward enrichment and separation of particles. Building upon previous discussions regarding the effects of column arrays on fluid flow and particle separation within microchips, we introduced a column array structure into an H-shaped microfluidic chip. It was observed that this structure enhanced mass transfer between two fluids while simultaneously intercepting particles within one fluid, satisfying the requirements for particle interception. This enhancement was primarily achieved by transforming the originally single-mode diffusion-based mass transfer into dual-mode diffusion-convection mass transfer. By further optimizing the column array, it was possible to meet the basic requirements of mass transfer and particle interception with fewer microcolumns, thereby reducing device pressure drop and facilitating the realization of parallel and high-throughput microfluidic devices. These findings have enhanced the potential application of microfluidic systems in clinical and chemical engineering domains.

Cervical Cancer Classification From Pap Smear Images Using Deep Convolutional Neural Network Models.

As one of the most common female cancers, cervical cancer often develops years after a prolonged and reversible pre-cancerous stage. Traditional classification algorithms used for detection of cervical cancer often require cell segmentation and feature extraction techniques, while convolutional neural network (CNN) models demand a large dataset to mitigate over-fitting and poor generalization problems. To this end, this study aims to develop deep learning models for automated cervical cancer detection that do not rely on segmentation methods or custom features. Due to limited data availability, transfer learning was employed with pre-trained CNN models to directly operate on Pap smear images for a seven-class classification task. Thorough evaluation and comparison of 13 pre-trained deep CNN models were performed using the publicly available Herlev dataset and the Keras package in Google Collaboratory. In terms of accuracy and performance, DenseNet-201 is the best-performing model. The pre-trained CNN models studied in this paper produced good experimental results and required little computing time.

Two-Stage Self-Supervised Contrastive Learning Aided Transformer for Real-Time Medical Image Segmentation.

The availability of large, high-quality annotated datasets in the medical domain poses a substantial challenge in segmentation tasks. To mitigate the reliance on annotated training data, self-supervised pre-training strategies have emerged, particularly employing contrastive learning methods on dense pixel-level representations. In this work, we proposed to capitalize on intrinsic anatomical similarities within medical image data and develop a semantic segmentation framework through a self-supervised fusion network, where the availability of annotated volumes is limited. In a unified training phase, we combine segmentation loss with contrastive loss, enhancing the distinction between significant anatomical regions that adhere to the available annotations. To further improve the segmentation performance, we introduce an efficient parallel transformer module that leverages Multiview multiscale feature fusion and depth-wise features. The proposed transformer architecture, based on multiple encoders, is trained in a self-supervised manner using contrastive loss. Initially, the transformer is trained using an unlabeled dataset. We then fine-tune one encoder using data from the first stage and another encoder using a small set of annotated segmentation masks. These encoder features are subsequently concatenated for the purpose of brain tumor segmentation. The multiencoder-based transformer model yields significantly better outcomes across three medical image segmentation tasks. We validated our proposed solution by fusing images across diverse medical image segmentation challenge datasets, demonstrating its efficacy by outperforming state-of-the-art methodologies.

Research on robust fault-tolerant control of the controllable suspension based on knowledge-data fusion driven.

For the robust fault-tolerant control of the controllable suspension system, a control strategy driven by knowledge-data fusion is proposed. Firstly, the boundary fuzziness between perturbation type uncertainty and gain type fault is analyzed, and then a data-driven method is introduced to avoid the state estimation of system uncertainty and fault. The proximal policy optimization algorithm in reinforcement learning is selected to construct a "data control law", to deal with uncertainty and fault. On the other hand, based on the classical sky-hook control, the "knowledge control law" for system performance optimization is designed, taking into account the nonlinear and non-stationary characteristics of the system. Furthermore, the dependency between robust fault tolerance and performance optimization control is revealed, and the two control laws are fused by numerical multiplication, to realize the performance matching optimization control of robust fault tolerance of controllable suspension system driven by knowledge-data fusion. Finally, the effectiveness and feasibility of the proposed method are verified by the simulation and real-time experiment of non-stationary excitation and near-stationary excitation under the combination of uncertainty and fault.

SASAN: Shape-Adaptive Set Abstraction Network for Point-Voxel 3D Object Detection.

Point-voxel 3D object detectors have achieved impressive performance in complex traffic scenes. However, they utilize the 3D sparse convolution (spconv) layers with fixed receptive fields, such as voxel-based detectors, and inherit the fixed sphere radius from point-based methods for generating the features of keypoints, which make them weak in adaptively modeling various geometrical deformations and sizes of real objects. To tackle this issue, we propose a shape-adaptive set abstraction network (SASAN) for point-voxel 3D object detection. First, the proposal and offset generation module is adopted to learn the coordinates and confidences of 3D proposals and shape-adaptive offsets of the certain number of offset points for each voxel. Meanwhile, an extra offset supervision task is employed to guide the learning of shifting values of offset points, aiming at motivating the predicted offsets to preferably adapt to the various shapes of objects. Then, the shape-adaptive set abstraction module is proposed to extract multiscale keypoints features by grouping the neighboring offset points' features, as well as features learned from adjacent raw points and the 2-D bird-view map. Finally, the region of interest (RoI)-grid proposal refinement module is used to aggregate the keypoints features for further proposal refinement and confidence prediction. Extensive experiments on the competitive KITTI 3D detection benchmark demonstrate that the proposed SASAN gains superior performance as compared with state-of-the-art methods.

GBRS: A Unified Granular-Ball Learning Model of Pawlak Rough Set and Neighborhood Rough Set.

Pawlak rough set (PRS) and neighborhood rough set (NRS) are the two most common rough set theoretical models. Although the PRS can use equivalence classes to represent knowledge, it is unable to process continuous data. On the other hand, NRSs, which can process continuous data, rather lose the ability of using equivalence classes to represent knowledge. To remedy this deficit, this article presents a granular-ball rough set (GBRS) based on the granular-ball computing combining the robustness and the adaptability of the granular-ball computing. The GBRS can simultaneously represent both the PRS and the NRS, enabling it not only to be able to deal with continuous data and to use equivalence classes for knowledge representation as well. In addition, we propose an implementation algorithm of the GBRS by introducing the positive region of GBRS into the PRS framework. The experimental results on benchmark datasets demonstrate that the learning accuracy of the GBRS has been significantly improved compared with the PRS and the traditional NRS. The GBRS also outperforms nine popular or the state-of-the-art feature selection methods. We have open-sourced all the source codes of this article at http://www.cquptshuyinxia.com/GBRS.html, https://github.com/syxiaa/GBRS.

MGRW-Transformer: Multigranularity Random Walk Transformer Model for Interpretable Learning.

Deep-learning models have been widely used in image recognition tasks due to their strong feature-learning ability. However, most of the current deep-learning models are "black box" systems that lack a semantic explanation of how they reached their conclusions. This makes it difficult to apply these methods to complex medical image recognition tasks. The vision transformer (ViT) model is the most commonly used deep-learning model with a self-attention mechanism that shows the region of influence as compared to traditional convolutional networks. Thus, ViT offers greater interpretability. However, medical images often contain lesions of variable size in different locations, which makes it difficult for a deep-learning model with a self-attention module to reach correct and explainable conclusions. We propose a multigranularity random walk transformer (MGRW-Transformer) model guided by an attention mechanism to find the regions that influence the recognition task. Our method divides the image into multiple subimage blocks and transfers them to the ViT module for classification. Simultaneously, the attention matrix output from the multiattention layer is fused with the multigranularity random walk module. Within the multigranularity random walk module, the segmented image blocks are used as nodes to construct an undirected graph using the attention node as a starting node and guiding the coarse-grained random walk. We appropriately divide the coarse blocks into finer ones to manage the computational cost and combine the results based on the importance of the discovered features. The result is that the model offers a semantic interpretation of the input image, a visualization of the interpretation, and insight into how the decision was reached. Experimental results show that our method improves classification performance with medical images while presenting an understandable interpretation for use by medical professionals.

Spectral Cross-Domain Neural Network With Soft-Adaptive Threshold Spectral Enhancement.

Electrocardiography (ECG) signals can be considered as multivariable time series (TS). The state-of-the-art ECG data classification approaches, based on either feature engineering or deep learning techniques, treat separately spectral and time domains in machine learning systems. No spectral-time domain communication mechanism inside the classifier model can be found in current approaches, leading to difficulties in identifying complex ECG forms. In this article, we proposed a novel deep learning model named spectral cross-domain neural network (SCDNN) with a new block called soft-adaptive threshold spectral enhancement (SATSE), to simultaneously reveal the key information embedded in spectral and time domains inside the neural network. More precisely, the domain-cross information is captured by a general convolutional neural network (CNN) backbone, and different information sources are merged by a self-adaptive mechanism to mine the connection between time and spectral domains. In SATSE, the knowledge from time and spectral domains is extracted via the fast Fourier transformation (FFT) with soft trainable thresholds in modified sigmoid functions. The proposed SCDNN is tested with several classification tasks implemented on the public ECG databases PTB-XL and CPSC2018. SCDNN outperforms the state-of-the-art approaches with a low computational cost regarding a variety of metrics in all classification tasks on both databases, by finding appropriate domains from the infinite spectral mapping. The convergence of the trainable thresholds in the spectral domain is also numerically investigated in this article. The robust performance of SCDNN provides a new perspective to exploit knowledge across deep learning models from time and spectral domains. The code repository can be found: https://github.com/DL-WG/SCDNN-TS.

Live Imaging of 3D Hanging Drop Arrays through Manipulation of Light-Responsive Pyroelectric Slippery Surface and Chip Adhesion.

Three-dimensional (3D) hanging drop cell culture is widely used in organoid culture because of its lack of selection pressure and rapid cell aggregation. However, current hanging drop technology has limitations, such as a dependence on complex microfluidic transport channels or specific capillary force templates for drop formation, which leads to unchangeable drop features. These methods also hinder live imaging because of space and complexity constraints. Here, we have developed a hanging drop construction method and created a flexible 3D hanging drop construction platform composed of a manipulation module and an adhesion module. Their harmonious operation allows for the easy construction of hanging drops of varying sizes, types, and patterns. Our platform produces a cell hanging drop chip with small sizes and clear fields of view, thereby making it compatible with live imaging. This platform has great potential for personalized medicine, cancer and drug discovery, tissue engineering, and stem cell research.