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

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

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

Comparison of cerebrospinal fluid space between probable normal pressure hydrocephalus and Alzheimer’s disease

IntroductionIdiopathic normal pressure hydrocephalus (INPH) is a potentially reversible syndrome characterized by complex symptoms, difficulty in diagnosis and a lack of detailed clinical description, and it is difficult to distinguish from Alzheimer’s disease (AD). The objective of this study was to design a method for measuring the actual amount of hydrocephalus in patients with INPH and to evaluate INPH.MethodsAll subjects underwent a 3D T1-weighted MRI. Statistical parametric mapping 12 was used for preprocessing images, statistical analysis, and voxel-based morphometric gray matter (GM) volume, white matter (WM) volume, and cerebrospinal fluid (CSF) volume analysis. The demographic and clinical characteristics of the groups were compared using a t-test for continuous variables and a chi-square test for categorical variables. Pearson’s correlation analysis and Bonferroni’s statistic-corrected one-way ANOVA were used to determine the relationship among demographic variables. Receiver operating characteristic (ROC) curves were used to assess the accuracy of the callosal angle (CA), WM ratio, and CSF ratio in distinguishing probable INPH from AD.ResultsThe study included 42 patients with INPH, 32 patients with AD, and 24 healthy control subjects (HCs). There were no differences among the three groups in basic characteristics except for Mini-Mental State Examination (MMSE). There was a correlation between the intracranial CSF ratio and CA. The WM ratio and CSF ratio in patients with INPH and AD were statistically different. Furthermore, the combination of CA, WM ratio, and CSF ratio had a greater differential diagnostic value between INPH and AD patients than CA alone.ConclusionINPH can be accurately assessed by measuring intracranial CSF ratio, and the addition of WM ratio and CSF ratio significantly improved the differential diagnostic value of probable INPH from AD compared to CA alone

Multi-tissue integrative analysis of personal epigenomes

Evaluating the impact of genetic variants on transcriptional regulation is a central goal in biological science that has been constrained by reliance on a single reference genome. To address this, we constructed phased, diploid genomes for four cadaveric donors (using long-read sequencing) and systematically charted noncoding regulatory elements and transcriptional activity across more than 25 tissues from these donors. Integrative analysis revealed over a million variants with allele-specific activity, coordinated, locus-scale allelic imbalances, and structural variants impacting proximal chromatin structure. We relate the personal genome analysis to the ENCODE encyclopedia, annotating allele- and tissue-specific elements that are strongly enriched for variants impacting expression and disease phenotypes. These experimental and statistical approaches, and the corresponding EN-TEx resource, provide a framework for personalized functional genomics