A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction

The Nonlinear autoregressive exogenous (NARX) model, which predicts the current value of a time series based upon its previous values as well as the current and past values of multiple driving (exogenous) series, has been studied for decades. Despite the fact that various NARX models have been developed, few of them can capture the long-term temporal dependencies appropriately and select the relevant driving series to make predictions. In this paper, we propose a dual-stage attention-based recurrent neural network (DA-RNN) to address these two issues. In the first stage, we introduce an input attention mechanism to adaptively extract relevant driving series (a.k.a., input features) at each time step by referring to the previous encoder hidden state. In the second stage, we use a temporal attention mechanism to select relevant encoder hidden states across all time steps. With this dual-stage attention scheme, our model can not only make predictions effectively, but can also be easily interpreted. Thorough empirical studies based upon the SML 2010 dataset and the NASDAQ 100 Stock dataset demonstrate that the DA-RNN can outperform state-of-the-art methods for time series prediction.Comment: International Joint Conference on Artificial Intelligence (IJCAI), 201

Serum level of A-kinase anchoring protein 1, negatively correlated with insulin resistance and body mass index, decreases slightly in patients with newly diagnosed T2DM

Introduction: At present, the number of people suffering from diabetes and obesity is increasing in China, and also all over the world. Researchers found that decreased expression of A-kinase anchoring protein 1 (AKAP1), which was thought to regulate the function and structure of mitochondria, might be related to these two diseases. However, as far as we know, there is no study about the changes of serum AKAP1 protein in these two diseases. Hence we conducted this experiment to study the relationship between serum levels of AKAP1 with T2DM and obesity. Material and methods: There were 261 subjects involved in the experiment, including 130 patients with newly diagnosed T2DM and 131 individuals with normal glucose tolerance (NGT). They were further divided into four groups as follows. Subjects with NGT and normal weight (NW) were assigned to the NGT+NW group, those with NGT but with overweight (OW) or obesity (OB) were assigned to the NGT+OW/OB group, and so on; the rest were divided into the T2DM+NW group and the T2DM+OW/OB group. Serum AKAP1 levels were tested by ELISA method and compared by T-test. Linear regression was applied to discuss independent factors of AKAP1. Multiple logistic regression was used to analyse the relationship between AKAP1 and the prevalence of T2DM. Results: Serum AKAP1 in the NGT+NW group was 1.74 ± 0.42 ng/mL, higher than that in the NGT+OW/OB group, at 1.59 ± 0.41 ng/mL (t = 2.114, p = 0.036), and the T2DM+OW/OB group, at 1.52 ± 0.36 ng/ml (t = 3.219, p = 0.002). A-kinase anchoring protein 1 in 130 subjects with T2DM was lower than that in subjects with NGT, 1.57 ± 0.35 ng/mL vs. 1.67 ± 0.42 ng/mL, t = 2.036, p = 0.043. Liner regression showed that insulin resistance (IR) and body mass index (BMI) were independent factors negatively related to AKAP1: b = –0.019 and –0.032, respectively. Compared to the highest tertile of AKAP1, the prevalence of T2DM was higher in the other two tertiles; OR was 2.207 (1.203, 4.050) and 2.051 (1.121, 3.753), respectively. Conclusions: Serum AKAP1 level decreases slightly in patients with T2DM and obesity. Subjects with lower leve1s of serum AKAP1 are susceptible to T2DM.

Extended application of random-walk shielding-potential viscosity model of metals in wide temperature region

The transport properties of matter have been widely investigated. In particular, shear viscosity over a wide parameter space is crucial for various applications, such as designing inertial confinement fusion (ICF) targets and determining the Rayleigh-Taylor instability. In this work, an extended random-walk shielding-potential viscosity model (RWSP-VM) [Phys. Rev. E 106, 014142] based on the statistics of random-walk ions and the Debye shielding effect is proposed to elevate the temperature limit of RWSP-VM in evaluating the shear viscosity of metals. In the extended model, we reconsider the collision diameter that is introduced by hard-sphere concept, hence, it is applicable in both warm and hot temperature regions (10^1-10^7 eV) rather than the warm temperature region (10^1-10^2 eV) in which RWSP-VM is applicable. The results of Be, Al, Fe, and U show that the extended model provides a systematic way to calculate the shear viscosity of arbitrary metals at the densities from about 0.1 to 10 times the normal density (the density at room temperature and 1 standard atmosphere). This work will help to develop viscosity model in wide region when combined with our previous low temperature viscosity model [AIP Adv. 11, 015043].Comment: 6 pages, 5 figure

Enhancing SCF with Privacy-Preserving and Splitting-Enabled E-Bills on Blockchain

Electronic Bill (E-Bill) is a rucial negotiable instrument in the form of data messages, relying on the Electronic Bill System (EB System). Blockchain technology offers inherent data sharing capabilities, so it is increasingly being adopted by small and medium-sized enterprises (SMEs) in the supply chain to build EB systems. However, the blockchain-based E-Bill still face significant challenges: the E-Bill is difficult to split, like non-fungible tokens (NFTs), and sensitive information such as amounts always be exposed on the blockchain. Therefore, to address these issues, we propose a novel data structure called Reverse-HashTree for Re-storing transactions in blockchain. In addition, we employ a variant of the Paillier public-key cryptosystem to ensure transaction validity without decryption, thus preserving privacy. Building upon these innovations, we designed BillChain, an EB system that enhances supply chain finance by providing privacy-preserving and splitting-enabled E-Bills on the blockchain. This work offers a comprehensive and innovative solution to the challenges faced by E-Bills applied in blockchain in the context of supply chain finance

Anisotropic carrier mobility of distorted Dirac cones: theory and application

We have theoretically investigated the intrinsic carrier mobility in semimetals with distorted Dirac cones under both longitudinal and transverse acoustic phonon scattering. An analytic formula for the carrier mobility was obtained. It shows that tilting significantly reduces the mobility. The theory was then applied to 8B-Pmmn borophene and borophane (fully hydrogenated borophene), both of which have tilted Dirac cones. The predicted carrier mobilities in 8B-Pmmn borophene at room temperature are both higher than that in graphene. For borophane, despite its superhigh Fermi velocity, the carrier mobility is lower than that in 8B-Pmmn owing to its smaller elastic constant under shear strain.Comment: 24 pages, 5 figures, 1 tabl

Exploring the Limits of ChatGPT for Query or Aspect-based Text Summarization

Text summarization has been a crucial problem in natural language processing (NLP) for several decades. It aims to condense lengthy documents into shorter versions while retaining the most critical information. Various methods have been proposed for text summarization, including extractive and abstractive summarization. The emergence of large language models (LLMs) like GPT3 and ChatGPT has recently created significant interest in using these models for text summarization tasks. Recent studies \cite{goyal2022news, zhang2023benchmarking} have shown that LLMs-generated news summaries are already on par with humans. However, the performance of LLMs for more practical applications like aspect or query-based summaries is underexplored. To fill this gap, we conducted an evaluation of ChatGPT's performance on four widely used benchmark datasets, encompassing diverse summaries from Reddit posts, news articles, dialogue meetings, and stories. Our experiments reveal that ChatGPT's performance is comparable to traditional fine-tuning methods in terms of Rouge scores. Moreover, we highlight some unique differences between ChatGPT-generated summaries and human references, providing valuable insights into the superpower of ChatGPT for diverse text summarization tasks. Our findings call for new directions in this area, and we plan to conduct further research to systematically examine the characteristics of ChatGPT-generated summaries through extensive human evaluation.Comment: Work in progres

DyExplainer: Explainable Dynamic Graph Neural Networks

Graph Neural Networks (GNNs) resurge as a trending research subject owing to their impressive ability to capture representations from graph-structured data. However, the black-box nature of GNNs presents a significant challenge in terms of comprehending and trusting these models, thereby limiting their practical applications in mission-critical scenarios. Although there has been substantial progress in the field of explaining GNNs in recent years, the majority of these studies are centered on static graphs, leaving the explanation of dynamic GNNs largely unexplored. Dynamic GNNs, with their ever-evolving graph structures, pose a unique challenge and require additional efforts to effectively capture temporal dependencies and structural relationships. To address this challenge, we present DyExplainer, a novel approach to explaining dynamic GNNs on the fly. DyExplainer trains a dynamic GNN backbone to extract representations of the graph at each snapshot, while simultaneously exploring structural relationships and temporal dependencies through a sparse attention technique. To preserve the desired properties of the explanation, such as structural consistency and temporal continuity, we augment our approach with contrastive learning techniques to provide priori-guided regularization. To model longer-term temporal dependencies, we develop a buffer-based live-updating scheme for training. The results of our extensive experiments on various datasets demonstrate the superiority of DyExplainer, not only providing faithful explainability of the model predictions but also significantly improving the model prediction accuracy, as evidenced in the link prediction task.Comment: 9 page