Reducing Latency of DAG-based Consensus in the Asynchronous Setting via the UTXO Model

DAG-based consensus has attracted significant interest due to its high throughput in asynchronous network settings. However, existing protocols such as DAG-rider (Keidar et al., PODC 2021) and ``Narwhal and Tusk'' (Danezis et al., Eurosys 2022) face two undesired practical issues: (1) high transaction latency and (2) high cost to verify transaction outcomes. To address (1), this work introduces a novel commit rule based on the Unspent Transaction Output (UTXO) Data Model, which allows a node to predict the transaction results before triggering the commitment. We propose a new consensus algorithm named ``Board and Clerk'', which reduces the transaction latency by half for roughly 50% of transactions. As the tolerance for faults escalates, more transactions can partake in this latency reduction. In addition, we also propose the Hyper-Block Model with two flexible proposing strategies to tackle (2): blocking and non-blocking. Using our proposed strategies, each node first predicts the transaction results if its proposal is committed and packs this result as a commitment in its proposal. The hyper-block packs the signature of the proposal and the outputs of the consensus layer together in order to prove the transaction results

A Diverse Domain Generative Adversarial Network for Style Transfer on Face Photographs.

The applications of style transfer on real time photographs are very trending now. This is used in various applications especially in social networking sites such as SnapChat and beauty cameras. A number of style transfer algorithms have been proposed but they are computationally expensive and generate artifacts in output image. Besides, most of research work only focuses on some traditional painting style transfer on real photographs. However, our work is unique as it considers diverse style domains to be transferred on real photographs by using one model. In this paper, we propose a Diverse Domain Generative Adversarial Network (DD-GAN) which performs fast diverse domain style translation on human face images. Our work is highly efficient and focused on applying different attractive and unique painting styles to human photographs while keeping the content preserved after translation. Moreover, we adopt a new loss function in our model and use PReLU activation function which improves and fastens the training procedure and helps in achieving high accuracy rates. Our loss function helps the proposed model in achieving better reconstructed images. The proposed model also occupies less memory space during training. We use various evaluation parameters to inspect the accuracy of our model. The experimental results demonstrate the effectiveness of our method as compared to state-of-the-art results

A Diverse Domain Generative Adversarial Network for Style Transfer on Face Photographs

The applications of style transfer on real time photographs are very trending now. This is used in various applications especially in social networking sites such as SnapChat and beauty cameras. A number of style transfer algorithms have been proposed but they are computationally expensive and generate artifacts in output image. Besides, most of research work only focuses on some traditional painting style transfer on real photographs. However, our work is unique as it considers diverse style domains to be transferred on real photographs by using one model. In this paper, we propose a Diverse Domain Generative Adversarial Network (DD-GAN) which performs fast diverse domain style translation on human face images. Our work is highly efficient and focused on applying different attractive and unique painting styles to human photographs while keeping the content preserved after translation. Moreover, we adopt a new loss function in our model and use PReLU activation function which improves and fastens the training procedure and helps in achieving high accuracy rates. Our loss function helps the proposed model in achieving better reconstructed images. The proposed model also occupies less memory space during training. We use various evaluation parameters to inspect the accuracy of our model. The experimental results demonstrate the effectiveness of our method as compared to state-of-the-art results

CBISI-LSTM Deep Learning Model for Short-term Cross-border Capital Flow Prediction

With the drastic fluctuation of the international financial market in recent years, the cross-border capital flow between Shanghai and Hong Kong has become increasingly active. The lack of effective and timely tracking monitoring and scientific management of cross-border capital flow in the capital market will seriously affect the overall financial security of China\u27s economy. This paper constructs the cross-border investor sentiment index CBISI based on principal component analysis and analyzes the impact of cross-border investor sentiment and cross-border capital flows by constructing the VAR model. In addition, CBISI is used as part of the input variable of LSTM to forecast the cross-border capital flow (NF). The findings of the study indicate that changes in cross-border investor sentiment will have a significant short-term impact on cross-border capital flows, and the addition of CBISI will improve the accuracy of cross-border flow estimates

Mechanistic insight on water dissociation on pristine low-index TiO2 surfaces from machine learning molecular dynamics simulations

Water adsorption and dissociation processes on pristine low-index TiO$_{2}$ interfaces are important but poorly understood outside the well-studied anatase (101) and rutile (110). To understand these, we construct three sets of machine learning potentials that are simultaneously applicable to various TiO$_{2}$ surfaces, based on three density-functional-theory approximations. Here we show the water dissociation free energies on seven pristine TiO$_{2}$ surfaces, and predict that anatase (100), anatase (110), rutile (001), and rutile (011) favor water dissociation, anatase (101) and rutile (100) have mostly molecular adsorption, while the simulations of rutile (110) sensitively depend on the slab thickness and molecular adsorption is preferred with thick slabs. Moreover, using an automated algorithm, we reveal that these surfaces follow different types of atomistic mechanisms for proton transfer and water dissociation: one-step, two-step, or both. These mechanisms can be rationalized based on the arrangements of water molecules on the different surfaces. Our finding thus demonstrates that the different pristine TiO$_{2}$ surfaces react with water in distinct ways, and cannot be represented using just the low-energy anatase (101) and rutile (110) surfaces

VirulentHunter : deep learning-based virulence factor predictor illuminates pathogenicity in diverse microbial contexts

Virulence factors (VFs) are critical determinants of bacterial pathogenicity, but current homology-based identification methods often miss novel or divergent VFs, and many machine learning approaches neglect functional classification. Here, we present VirulentHunter, a novel deep learning framework that enable simultaneous VF identification and classification directly from protein sequences by leveraging the crucial step of fine-tuning pretrained protein language model. We curate a comprehensive VF database by integrating diverse public resources and expanding VF category annotations. Our benchmarking results demonstrate that VirulentHunter outperforms existing methods, particularly in identifying VFs lacking detectable homologs. Additionally, strain-level analysis using VirulentHunter highlights distinct pathogenicity profiles between Mycobacterium tuberculosis and Mycobacterium avium, revealing enrichment in VFs related to adherence, effector delivery systems, and immune modulation in M. tuberculosis, compared to biofilm formation and motility in M. avium. Furthermore, metagenomic profiling of gut microbiota from inflammatory bowel disease patient reveals a depletion of VFs associated with immune homeostasis. These results underscore the versatility of VirulentHunter as a powerful tool for VF analysis across diverse applications. To facilitate broader accessibility, we provide a freely accessible web service for VF prediction (http://www.unimd.org/VirulentHunter), accommodating protein sequences, genomes, and metagenomic data

A review on optimization of V2V charging services in intelligent transportation environment

In recent years, the electric vehicle (EV) industry has seen vigorous growth. However, deficiencies in charging infrastructure layout, structure and operation have become obstacles to further market expansion. Thus, relevant policies have listed the Internet of vehicles (IoV), vehicle-grid bidirectional interaction, distributed energy storage, and other charging facilities alongside cutting-edge technological innovations in the smart energy sector as key development priorities. Since traditional grid to vehicle (G2V) charging model struggles to meet the large-scale parallel charging demands of EVs under limited charging infrastructure conditions, the existing structure of energy consumption needs to adapt to the dynamic changes in demand. Against this backdrop, the concept of vehicle to vehicle (V2V) charging has been proposed to alleviate the limitations of G2V charging mode in terms of time and space domains, exploiting the potential of smart EVs as mobile distributed energy storage units. This facilitates flexible energy supply, offering a new approach to optimizing EV charging services and supporting the development of future intelligent transportation system (ITS). Focusing on the optimization direction centered around V2V charging, the relevant researches in recent years were reviewed. Firstly, the charging services in the intelligent transportation scenarios were classified, and an overview of the V2V charging mode was provided. Then, the proposed V2V charging management schemes from a technical emphasis perspective were categorized, and the optimization strategies were elaborated in detail. Finally, the development prospects of V2V charging in ITS were exlored, and open research topics for future studies were discussed

Navigating Hurdles:A Review of the Obstacles Facing the Development of the Pandemic Treaty

INTRODUCTION: The emergence of the COVID-19 pandemic has served as a call for enhanced global cooperation and a more robust pandemic preparedness and response framework. As a result of this pressing demand, dialogues were initiated to establish a pandemic treaty designed to foster a synchronized global strategy for addressing forthcoming health emergencies. In this review, we discussed the main obstacles to this treaty.RESULTS: Among several challenges facing the pandemic treaty, we highlighted (1) global cooperation and political will, (2) equity in access to resources and treatments, (3) sustainable financing, (4) compliance and enforcement mechanisms, (5) sovereignty concerns, and (6) data sharing and transparency.CONCLUSION: Navigating the hurdles facing the development of the pandemic treaty requires concerted efforts, diplomatic finesse, and a shared commitment to global solidarity. Addressing challenges in global cooperation, equitable access, transparency, compliance, financing, and sovereignty is essential for forging a comprehensive and effective framework for pandemic preparedness and response on the global stage.</p