Enhanced CO₂ photoreduction to methane via Schottky Zn₃N₂/KPCN heterojunctions for sustainable energy applications.

The pressing necessity to mitigate climate change and decrease greenhouse gas emissions has driven the advancement of heterostructure-based photocatalysts for effective CO₂ reduction. This study introduces a novel heterojunction photocatalyst formed by integrating potassium-doped polymeric carbon nitride (KPCN) with metallic Zn₃N₂, synthesized via a microwave-assisted molten salt method. The resulting Schottky contact effectively suppresses the reverse diffusion of electrons, achieving spatial separation of photogenerated charges and prolonging their lifetime, which significantly enhances photocatalytic activity and efficiency.

Stabilizing and Accelerating Federated Learning on Heterogeneous Data With Partial Client Participation.

Federated learning (FL) commonly encourages the clients to perform multiple local updates before the global aggregation, thus avoiding frequent model exchanges and relieving the communication bottleneck between the server and clients. Though empirically effective, the negative impact of multiple local updates on the stability of FL is not thoroughly studied, which may result in a globally unstable and slow convergence. Based on sensitivity analysis, we define in this paper a local-update stability index for the general FL, as measured by the maximum inter-client model discrepancy after the multiple local updates that mainly stems from the data heterogeneity.

Combination of facial and nose features of Amur tigers to determine age.

We found that the area of black round or irregular-shaped spots on the tiger's nose increased with age, indicating a positive relationship between age and nose features. We used the deep learning model to train the facial and nose image features to identify the age of Amur tigers, using a combination of classification and prediction methods to achieve age determination with an accuracy of 87.81%.

Source-Free Domain Adaptation With Domain Generalized Pretraining for Face Anti-Spoofing.

Source-free domain adaptation (SFDA) shows the potential to improve the generalizability of deep learning-based face anti-spoofing (FAS) while preserving the privacy and security of sensitive human faces. However, existing SFDA methods are significantly degraded without accessing source data due to the inability to mitigate domain and identity bias in FAS. In this paper, we propose a novel Source-free Domain Adaptation framework for FAS (SDA-FAS) that systematically addresses the challenges of source model pre-training, source knowledge adaptation, and target data exploration under the source-free setting.

Variance Reduced Domain Randomization for Reinforcement Learning With Policy Gradient.

By introducing randomness on the environments, domain randomization (DR) imposes diversity to the policy training of deep reinforcement learning, and thus improves its capability of generalization. The randomization of environments, however, introduces another source of variability for the estimate of policy gradients, in addition to the already high variance incurred by trajectory sampling. Therefore, with standard state-dependent baselines, the policy gradient methods may still suffer high variance, causing a low sample efficiency during the training of DR.

3-D Point Cloud Attribute Compression With -Laplacian Embedding Graph Dictionary Learning.

3-D point clouds facilitate 3-D visual applications with detailed information of objects and scenes but bring about enormous challenges to design efficient compression technologies. The irregular signal statistics and high-order geometric structures of 3-D point clouds cannot be fully exploited by existing sparse representation and deep learning based point cloud attribute compression schemes and graph dictionary learning paradigms. In this paper, we propose a novel p-Laplacian embedding graph dictionary learning framework that jointly exploits the varying signal statistics and high-order geometric structures for 3-D point cloud attribute compression.

BNET: Batch Normalization With Enhanced Linear Transformation.

Batch normalization (BN) is a fundamental unit in modern deep neural networks. However, BN and its variants focus on normalization statistics but neglect the recovery step that uses linear transformation to improve the capacity of fitting complex data distributions. In this paper, we demonstrate that the recovery step can be improved by aggregating the neighborhood of each neuron rather than just considering a single neuron.

Surface, Interface and Structure Optimization of Metal-Organic Frameworks: Towards Efficient Resourceful Conversion of Industrial Waste Gases.

Industrial waste gas emissions from fossil fuel over-exploitation have aroused great attention in modern society. Recently, metal-organic frameworks (MOFs) have been developed in the capture and catalytic conversion of industrial exhaust gases such as SO , H S, NO , CO , CO, etc. Based on these resourceful conversion applications, in this review, we summarize the crucial role of the surface, interface, and structure optimization of MOFs for performance enhancement.

VERTICOX: Vertically Distributed Cox Proportional Hazards Model Using the Alternating Direction Method of Multipliers.

The Cox proportional hazards model is a popular semi-parametric model for survival analysis. In this paper, we aim at developing a federated algorithm for the Cox proportional hazards model over vertically partitioned data (i.e.

NCGNN: Node-Level Capsule Graph Neural Network for Semisupervised Classification.

Message passing has evolved as an effective tool for designing graph neural networks (GNNs). However, most existing methods for message passing simply sum or average all the neighboring features to update node representations. They are restricted by two problems: 1) lack of interpretability to identify node features significant to the prediction of GNNs and 2) feature overmixing that leads to the oversmoothing issue in capturing long-range dependencies and inability to handle graphs under heterophily or low homophily.

Seed the Views: Hierarchical Semantic Alignment for Contrastive Representation Learning.

Self-supervised learning based on instance discrimination has shown remarkable progress. In particular, contrastive learning, which regards each image as well as its augmentations as an individual class and tries to distinguish them from all other images, has been verified effective for representation learning. However, conventional contrastive learning does not model the relation between semantically similar samples explicitly.

Hybrid ISTA: Unfolding ISTA With Convergence Guarantees Using Free-Form Deep Neural Networks.

It is promising to solve linear inverse problems by unfolding iterative algorithms (e.g., iterative shrinkage thresholding algorithm (ISTA)) as deep neural networks (DNNs) with learnable parameters.

Automatic Representative Frame Selection and Intrathoracic Lymph Node Diagnosis With Endobronchial Ultrasound Elastography Videos.

Endobronchial ultrasound (EBUS) elastography videos have shown great potential to supplement intrathoracic lymph node diagnosis. However, it is laborious and subjective for the specialists to select the representative frames from the tedious videos and make a diagnosis, and there lacks a framework for automatic representative frame selection and diagnosis. To this end, we propose a novel deep learning framework that achieves reliable diagnosis by explicitly selecting sparse representative frames and guaranteeing the invariance of diagnostic results to the permutations of video frames.

From Micropores to Ultra-micropores inside Hard Carbon: Toward Enhanced Capacity in Room-/Low-Temperature Sodium-Ion Storage.

Hard-carbon anode dominated with ultra-micropores (< 0.5 nm) was synthesized for sodium-ion batteries via a molten diffusion-carbonization method. The ultra-micropores dominated carbon anode displays an enhanced capacity, which originates from the extra sodium-ion storage sites of the designed ultra-micropores.

Automatic Image Selection Model Based on Machine Learning for Endobronchial Ultrasound Strain Elastography Videos.

Background: Endoscopic ultrasound (EBUS) strain elastography can diagnose intrathoracic benign and malignant lymph nodes (LNs) by reflecting the relative stiffness of tissues. Due to strong subjectivity, it is difficult to give full play to the diagnostic efficiency of strain elastography. This study aims to use machine learning to automatically select high-quality and stable representative images from EBUS strain elastography videos.

3D-Assembled rutile TiO spheres with c-channels for efficient lithium-ion storage.

Three-dimensional (3D) TiO architectures have attracted significant attention recently as they can improve the electrochemical stability and realize the full potential of TiO-based anodes in lithium ion batteries. Here, flower-like rutile TiO spheres with radially assembled nanorods (c-channels) were fabricated via a simple hydrothermal method. The 3D radial architecture affords massive active sites to fortify the lithium storage.

Monodispersed Ruthenium Nanoparticles on Nitrogen-Doped Reduced Graphene Oxide for an Efficient Lithium-Oxygen Battery.

Lithium-oxygen batteries with ultrahigh energy densities have drawn considerable attention as next-generation energy storage devices. However, their practical applications are challenged by sluggish reaction kinetics aimed at the formation/decomposition of discharge products on battery cathodes. Developing effective catalysts and understanding the fundamental catalytic mechanism are vital to improve the electrochemical performance of lithium-oxygen batteries.

General Bitwidth Assignment for Efficient Deep Convolutional Neural Network Quantization.

Model quantization is essential to deploy deep convolutional neural networks (DCNNs) on resource-constrained devices. In this article, we propose a general bitwidth assignment algorithm based on theoretical analysis for efficient layerwise weight and activation quantization of DCNNs. The proposed algorithm develops a prediction model to explicitly estimate the loss of classification accuracy led by weight quantization with a geometrical approach.

Efficient photocatalytic hydrogen peroxide generation coupled with selective benzylamine oxidation over defective ZrS nanobelts.

Photocatalytic hydrogen peroxide (HO) generation represents a promising approach for artificial photosynthesis. However, the sluggish half-reaction of water oxidation significantly limits the efficiency of HO generation. Here, a benzylamine oxidation with more favorable thermodynamics is employed as the half-reaction to couple with HO generation in water by using defective zirconium trisulfide (ZrS) nanobelts as a photocatalyst.

iPool-Information-Based Pooling in Hierarchical Graph Neural Networks.

With the advent of data science, the analysis of network or graph data has become a very timely research problem. A variety of recent works have been proposed to generalize neural networks to graphs, either from a spectral graph theory or a spatial perspective. The majority of these works, however, focus on adapting the convolution operator to graph representation.

Evoking ordered vacancies in metallic nanostructures toward a vacated Barlow packing for high-performance hydrogen evolution.

Metallic nanostructures are commonly densely packed into a few packing variants with slightly different atomic packing factors. The structural aspects and physicochemical properties related with the vacancies in such nanostructures are rarely explored because of lack of an effective way to control the introduction of vacancy sites. Highly voided metallic nanostructures with ordered vacancies are however energetically high lying and very difficult to synthesize.

Partially-Connected Neural Architecture Search for Reduced Computational Redundancy.

Differentiable architecture search (DARTS) enables effective neural architecture search (NAS) using gradient descent, but suffers from high memory and computational costs. In this paper, we propose a novel approach, namely Partially-Connected DARTS (PC-DARTS), to achieve efficient and stable neural architecture search by reducing the channel and spatial redundancies of the super-network. In the channel level, partial channel connection is presented to randomly sample a small subset of channels for operation selection to accelerate the search process and suppress the over-fitting of the super-network.

Deep learning with convex probe endobronchial ultrasound multimodal imaging: A validated tool for automated intrathoracic lymph nodes diagnosis.

Background And Objectives: Along with the rapid improvement of imaging technology, convex probe endobronchial ultrasound (CP-EBUS) sonographic features play an increasingly important role in the diagnosis of intrathoracic lymph nodes (LNs). Conventional qualitative and quantitative methods for EBUS multimodal imaging are time-consuming and rely heavily on the experience of endoscopists. With the development of deep-learning (DL) models, there is great promise in the diagnostic field of medical imaging.

Big Data Privacy in Biomedical Research.

Biomedical research often involves studying patient data that contain personal information. Inappropriate use of these data might lead to leakage of sensitive information, which can put patient privacy at risk. The problem of preserving patient privacy has received increasing attentions in the era of big data.

Gas-Phase Photoelectrocatalytic Oxidation of NO TiO Nanorod Array/FTO Photoanodes.

Most photoelectrocatalytic (PEC) reactions are performed in the liquid phase for convenient electron transfer in an electrolyte solution. Herein, a novel PEC reactor involving a tandem combination of TiO nanorod array/fluorine-doped tin oxide (TiO-NR/FTO) working electrodes and an electrochemical auxiliary cell was constructed to drive the highly efficient PEC oxidation of indoor gas (NO). With the aid of a low bias voltage (0.