Learning Two-Branch Neural Networks for Image-Text Matching Tasks

Image-language matching tasks have recently attracted a lot of attention in the computer vision field. These tasks include image-sentence matching, i.e., given an image query, retrieving relevant sentences and vice versa, and region-phrase matching or visual grounding, i.e., matching a phrase to relevant regions. This paper investigates two-branch neural networks for learning the similarity between these two data modalities. We propose two network structures that produce different output representations. The first one, referred to as an embedding network, learns an explicit shared latent embedding space with a maximum-margin ranking loss and novel neighborhood constraints. Compared to standard triplet sampling, we perform improved neighborhood sampling that takes neighborhood information into consideration while constructing mini-batches. The second network structure, referred to as a similarity network, fuses the two branches via element-wise product and is trained with regression loss to directly predict a similarity score. Extensive experiments show that our networks achieve high accuracies for phrase localization on the Flickr30K Entities dataset and for bi-directional image-sentence retrieval on Flickr30K and MSCOCO datasets.Comment: accepted version in TPAMI 201

Possible Luttinger liquid behavior of edge transport in monolayer transition metal dichalcogenide crystals.

In atomically-thin two-dimensional (2D) semiconductors, the nonuniformity in current flow due to its edge states may alter and even dictate the charge transport properties of the entire device. However, the influence of the edge states on electrical transport in 2D materials has not been sufficiently explored to date. Here, we systematically quantify the edge state contribution to electrical transport in monolayer MoS2/WSe2 field-effect transistors, revealing that the charge transport at low temperature is dominated by the edge conduction with the nonlinear behavior. The metallic edge states are revealed by scanning probe microscopy, scanning Kelvin probe force microscopy and first-principle calculations. Further analyses demonstrate that the edge-state dominated nonlinear transport shows a universal power-law scaling relationship with both temperature and bias voltage, which can be well explained by the 1D Luttinger liquid theory. These findings demonstrate the Luttinger liquid behavior in 2D materials and offer important insights into designing 2D electronics

A Multiobjective Robust Scheduling Optimization Mode for Multienergy Hybrid System Integrated by Wind Power, Solar Photovoltaic Power, and Pumped Storage Power

Wind power plant (WPP), photovoltaic generators (PV), cell-gas turbine (CGT), and pumped storage power station (PHSP) are integrated into multienergy hybrid system (MEHS). Firstly, this paper presents MEHS structure and constructs a scheduling model with the objective functions of maximum economic benefit and minimum power output fluctuation. Secondly, in order to relieve the uncertainty influence of WPP and PV on system, robust stochastic theory is introduced to describe uncertainty and propose a multiobjective stochastic scheduling optimization mode by transforming constraint conditions with uncertain variables. Finally, a 9.6 MW WPP, a 6.5 MW PV, three CGT units, and an upper reservoir with 10 MW·h equivalent capacity are chosen as simulation system. The results show MEHS system can achieve the best operation result by using the multienergy hybrid generation characteristic. PHSP could shave peak and fill valley of load curve by optimizing pumping storage and inflowing generating behaviors based on the load supply and demand status and the available power of WPP and PV. Robust coefficients can relieve the uncertainty of WPP and PV and provide flexible scheduling decision tools for decision-makers with different risk attitudes by setting different robust coefficients, which could maximize economic benefits and minimize operation risks at the same time

A Genetic Algorithm-based BP Neural Network Method for Operational Performance Assessment of ATC Sector

To assess operational performance of air traffic control sector, a multivariate detection index system consisting of 5 variables and 17 indicators is presented, which includes operational trafficability, operational complexity, operational safety, operational efficiency, and air traffic controller workload. An improved comprehensive evaluation method, is designed for the assessment by optimizing initial weights and thresholds of back propagation (BP) neural network using genetic algorithm. By empirical study conducted in one air traffic control sector, 400 sets of sample data are selected and divided into 350 sets for network training and 50 sets for network testing, and the architecture of genetic algorithm-based back propagation (GABP) neural network is established as a three-layer network with 17 nodes in input layer, 5 nodes in hidden layers, and 1 node in output layer. Further testing with both GABP and traditional BP neural network reveals that GABP neural network performs betterthan BP neural work in terms of mean error, mean square error and error probability, indicating that GABP neural network can assess operational performance of air traffic control sector with high accuracy and stable generalization ability. The multivariate detection index system and GABP neural network method in this paper can provide comprehensive, accurate, reliable and practical operational performance assessment of air traffic control sector, which enable the frontline of air traffic service provider to detect and evaluate operational performance of air traffic control sector in real time, and trigger an alarm when necessary.</p

Quantifying the effects of five rehabilitation training methods on the ability of elderly men to control bowel movements: a finite element analysis study

PurposeThe study aims to develop a finite element model of the pelvic floor and thighs of elderly men to quantitatively assess the impact of different pelvic floor muscle trainings and the urinary and defecation control ability.MethodsA finite element model of the pelvic floor and thighs of elderly men was constructed based on MRI and CT. Material properties of pelvic floor tissues were assigned through literature review, and the relative changes in waistline, retrovesical angle (RVA) and anorectad angulation (ARA) to quantitatively verify the effectiveness of the model. By changing the material properties of muscles, the study analyzed the muscle strengthening or impairment effects of the five types of rehabilitation training for four types of urination and defecation dysfunction. The changes in four outcome indicators, including the retrovesical angle, anorectad angulation, stress, and strain, were compared.ResultsThis study indicates that ARA and RVA approached their normal ranges as material properties changed, indicating an enhancement in the urinary and defecation control ability, particularly through targeted exercises for the levator ani muscle, external anal sphincter, and pelvic floor muscles. This study also emphasizes the effectiveness of personalized rehabilitation programs including biofeedback, exercise training, electrical stimulation, magnetic stimulation, and vibration training and advocates for providing optimized rehabilitation training methods for elderly patients.DiscussionBased on the results of computational biomechanics, this study provides foundational scientific insights and practical recommendations for rehabilitation training of the elderly’s urinary and defecation control ability, thereby improving their quality of life. In addition, this study also provides new perspectives and potential applications of finite element analysis in elderly men, particularly in evaluating and designing targeted rehabilitation training

COMET: NFT Price Prediction with Wallet Profiling

As the non-fungible token (NFT) market flourishes, price prediction emerges as a pivotal direction for investors gaining valuable insight to maximize returns. However, existing works suffer from a lack of practical definitions and standardized evaluations, limiting their practical application. Moreover, the influence of users' multi-behaviour transactions that are publicly accessible on NFT price is still not explored and exhibits challenges. In this paper, we address these gaps by presenting a practical and hierarchical problem definition. This approach unifies both collection-level and token-level task and evaluation methods, which cater to varied practical requirements of investors. To further understand the impact of user behaviours on the variation of NFT price, we propose a general wallet profiling framework and develop a COmmunity enhanced Multi-bEhavior Transaction graph model, named COMET. COMET profiles wallets with a comprehensive view and considers the impact of diverse relations and interactions within the NFT ecosystem on NFT price variations, thereby improving prediction performance. Extensive experiments conducted in our deployed system demonstrate the superiority of COMET, underscoring its potential in the insight toolkit for NFT investors.Comment: Accepted by KDD 2024 (ADS Track

Advances of antimicrobial dressings loaded with antimicrobial agents in infected wounds

Wound healing is a complex process that is critical for maintaining the barrier function of the skin. However, when a large quantity of microorganisms invade damaged skin for an extended period, they can cause local and systemic inflammatory responses. If left untreated, this condition may lead to chronic infected wounds. Infected wounds significantly escalate wound management costs worldwide and impose a substantial burden on patients and healthcare systems. Recent clinical trial results suggest that the utilization of effective antimicrobial wound dressing could represent the simplest and most cost-effective strategy for treating infected wounds, but there has hitherto been no comprehensive evaluation reported on the efficacy of antimicrobial wound dressings in promoting wound healing. Therefore, this review aims to systematically summarize the various types of antimicrobial wound dressings and the current research on antimicrobial agents, thereby providing new insights for the innovative treatment of infected wounds