Word2Vec-based Personal Trait Computing from User-generated Text

Personal trait is a habitual pattern for measuring behavior, thoughts, and emotions. It varies over individuals and is relatively stable in different situations over time. The personal trait is of great significance since it can be used in many applications, such as recommendation system, chatbot, and human resource management. Personal traits are easily recognized through wearable devices, social media, and the like. Most of the existing studies focus on user profile, behavior and personality. Specially, user profile and behavior are a person’s manifestations that cannot accurately capture a person’s internal characters. Personality is generally calculated by Big Five, which is obscure for non-psychologists. Generally, specific personal traits are especially critical in many aspects, such as disease detection, individual understanding, etc. Therefore, measuring more specific personal traits is essential. Given this, this paper proposes a word2vec-based general method for personal traits computing, which mainly includes topic word extraction, personal trait matrix generation, and personal trait computing. Furthermore, a case study is conducted to verify the effectiveness of the proposed method, and further analysis is provided to validate the methods

Network slice selection in softwarization-based mobile networks

Recently, network slicing (NS) has been introduced as a key enabler to accommodate diversified services in network functions virtualization–enabled software‐defined mobile networks. Although there has been some research work on network slice deployment and configuration, how user equipments select the most appropriate network slice is still an essential yet challenging issue, as slice selection may substantially affect the resource utilization and user Quality of Service (QoS). In this paper, we investigate the optimal selection of end‐to‐end slices with the aim of improving network resources utilization while guaranteeing the QoS of users. We formulate the optimal slice selection problem as maximizing the users satisfaction degree and prove it is NP‐hard. We thus resort to genetic algorithm (GA) to find a suboptimal solution and develop a GA‐based heuristic algorithm. The effectiveness of our proposed NS selection algorithm is validated via simulation experiments

TransFusionOdom: Interpretable Transformer-based LiDAR-Inertial Fusion Odometry Estimation

Multi-modal fusion of sensors is a commonly used approach to enhance the performance of odometry estimation, which is also a fundamental module for mobile robots. However, the question of \textit{how to perform fusion among different modalities in a supervised sensor fusion odometry estimation task?} is still one of challenging issues remains. Some simple operations, such as element-wise summation and concatenation, are not capable of assigning adaptive attentional weights to incorporate different modalities efficiently, which make it difficult to achieve competitive odometry results. Recently, the Transformer architecture has shown potential for multi-modal fusion tasks, particularly in the domains of vision with language. In this work, we propose an end-to-end supervised Transformer-based LiDAR-Inertial fusion framework (namely TransFusionOdom) for odometry estimation. The multi-attention fusion module demonstrates different fusion approaches for homogeneous and heterogeneous modalities to address the overfitting problem that can arise from blindly increasing the complexity of the model. Additionally, to interpret the learning process of the Transformer-based multi-modal interactions, a general visualization approach is introduced to illustrate the interactions between modalities. Moreover, exhaustive ablation studies evaluate different multi-modal fusion strategies to verify the performance of the proposed fusion strategy. A synthetic multi-modal dataset is made public to validate the generalization ability of the proposed fusion strategy, which also works for other combinations of different modalities. The quantitative and qualitative odometry evaluations on the KITTI dataset verify the proposed TransFusionOdom could achieve superior performance compared with other related works.Comment: Submitted to IEEE Sensors Journal with some modifications. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Interfacing Nickel Nitride and Nickel Boosts Both Electrocatalytic Hydrogen Evolution and Oxidation Reactions

Electrocatalysts of the hydrogen evolution and oxidation reactions (HER and HOR) are of critical importance for the realization of future hydrogen economy. In order to make electrocatalysts economically competitive for large-scale applications, increasing attention has been devoted to developing noble metal-free HER and HOR electrocatalysts especially for alkaline electrolytes due to the promise of emerging hydroxide exchange membrane fuel cells. Herein, we report that interface engineering of Ni3N and Ni results in a unique Ni3N/Ni electrocatalyst which exhibits exceptional HER/HOR activities in aqueous electrolytes. A systematic electrochemical study was carried out to investigate the superior hydrogen electrochemistry catalyzed by Ni3N/Ni, including nearly zero overpotential of catalytic onset, robust long-term durability, unity Faradaic efficiency, and excellent CO tolerance. Density functional theory computations were performed to aid the understanding of the electrochemical results and suggested that the real active sites are located at the interface between Ni3N and Ni

Interfacing Nickel Nitride and Nickel Boosts Both Electrocatalytic Hydrogen Evolution and Oxidation Reactions

Electrocatalysts of the hydrogen evolution and oxidation reactions (HER and HOR) are of critical importance for the realization of future hydrogen economy. In order to make electrocatalysts economically competitive for large-scale applications, increasing attention has been devoted to developing noble metal-free HER and HOR electrocatalysts especially for alkaline electrolytes due to the promise of emerging hydroxide exchange membrane fuel cells. Herein, we report that interface engineering of Ni3N and Ni results in a unique Ni3N/Ni electrocatalyst which exhibits exceptional HER/HOR activities in aqueous electrolytes. A systematic electrochemical study was carried out to investigate the superior hydrogen electrochemistry catalyzed by Ni3N/Ni, including nearly zero overpotential of catalytic onset, robust long-term durability, unity Faradaic efficiency, and excellent CO tolerance. Density functional theory computations were performed to aid the understanding of the electrochemical results and suggested that the real active sites are located at the interface between Ni3N and Ni

DA-TransUNet: Integrating Spatial and Channel Dual Attention with Transformer U-Net for Medical Image Segmentation

Accurate medical image segmentation is critical for disease quantification and treatment evaluation. While traditional Unet architectures and their transformer-integrated variants excel in automated segmentation tasks. However, they lack the ability to harness the intrinsic position and channel features of image. Existing models also struggle with parameter efficiency and computational complexity, often due to the extensive use of Transformers. To address these issues, this study proposes a novel deep medical image segmentation framework, called DA-TransUNet, aiming to integrate the Transformer and dual attention block(DA-Block) into the traditional U-shaped architecture. Unlike earlier transformer-based U-net models, DA-TransUNet utilizes Transformers and DA-Block to integrate not only global and local features, but also image-specific positional and channel features, improving the performance of medical image segmentation. By incorporating a DA-Block at the embedding layer and within each skip connection layer, we substantially enhance feature extraction capabilities and improve the efficiency of the encoder-decoder structure. DA-TransUNet demonstrates superior performance in medical image segmentation tasks, consistently outperforming state-of-the-art techniques across multiple datasets. In summary, DA-TransUNet offers a significant advancement in medical image segmentation, providing an effective and powerful alternative to existing techniques. Our architecture stands out for its ability to improve segmentation accuracy, thereby advancing the field of automated medical image diagnostics. The codes and parameters of our model will be publicly available at https://github.com/SUN-1024/DA-TransUnet

Electrolyzer Design for Flexible Decoupled Water Splitting and Organic Upgrading with Electron Reservoirs

The Bigger Picture Electrocatalytic water splitting is a green approach to producing clean H2 fuel, especially when it is driven by renewable energy sources. Conventional water electrolysis always produces H2 and O2 simultaneously under corrosive acidic or alkaline conditions with large voltage inputs, posing safety concerns of H2/O2 mixing. Therefore, it is desirable to develop a new electrolyzer design for decoupled water splitting in an eco-friendly neutral solution with small voltage inputs to enable separated H2 and O2 evolution. Herein, we report (ferrocenylmethyl)trimethylammonium chloride and Na4[Fe(CN)6] as proton-independent electron reservoirs for achieving separated H2 and O2 evolution in near-neutral solution driven by electricity or solar cells under sunlight irradiation. Na4[Fe(CN)6] can also integrate H2 evolution with organic oxidation to yield H2 and high-value organic products. This work offers promising economic and safety advantages for sustainable H2 production and organic transformation

TOWN IS/N TRANSITION

Master'sMASTER OF ARCHITECTURE (M.ARCH

Time Reversal Reconstruction Algorithm Based on PSO Optimized SVM Interpolation for Photoacoustic Imaging

Photoacoustic imaging is an innovative imaging technique to image biomedical tissues. The time reversal reconstruction algorithm in which a numerical model of the acoustic forward problem is run backwards in time is widely used. In the paper, a time reversal reconstruction algorithm based on particle swarm optimization (PSO) optimized support vector machine (SVM) interpolation method is proposed for photoacoustics imaging. Numerical results show that the reconstructed images of the proposed algorithm are more accurate than those of the nearest neighbor interpolation, linear interpolation, and cubic convolution interpolation based time reversal algorithm, which can provide higher imaging quality by using significantly fewer measurement positions or scanning times