A METHOD FOR MITIGATING UNDERFITTING ISSUE IN TIME SERIES MODEL USING REGRESSORS

The present disclosure focuses on mitigating underfitting issue in time series model using regressors. The present disclosure replaces the large parameter with a new structure. The new structure may include a few layers of smaller models. Specifically, the “smaller” model may be defined as the number of parameters for the model may be smaller but can handle all the features. Thus, each smaller model is trained with all the data points. The smaller models may generate intermediate predictions which may be fed as the input to the next smaller model present in the next layer. As a result, the DPP value of each model in the structure is substantially higher and hence the underfitting issue may be efficiently resolved

Genuine full characterization of partially coherence beam

For partially coherent light fields with random fluctuations, the intensity distributions and statistics have been proven to be more propagation robust compared with coherent light. However, its full potential in practical applications has not been realized due to the lack of four-dimensional optical field measurement. Here, a general modal decomposition method of partially coherent light field is proposed and demonstrated. The decomposed random modes can be used to, but not limited to, reconstruct average intensity, cross spectral density and orthogonal decomposition properties of the partially coherent light fields. Due to its versatility and flexibility, this method provides a powerful tool to further reveal light field invariant or retrieve embedded information after propagation through complex media. The Gaussian-shell-model beam and partially coherent Gaussian array are used as examples to demonstrate the reconstruction and even prediction of second-order statistical characteristics. This method is expected to pave the way for applications of partially coherent light in optical imaging, optical encryption and anti-turblence optical communication

An Automated Vulnerability Detection Framework for Smart Contracts

With the increase of the adoption of blockchain technology in providing decentralized solutions to various problems, smart contracts have become more popular to the point that billions of US Dollars are currently exchanged every day through such technology. Meanwhile, various vulnerabilities in smart contracts have been exploited by attackers to steal cryptocurrencies worth millions of dollars. The automatic detection of smart contract vulnerabilities therefore is an essential research problem. Existing solutions to this problem particularly rely on human experts to define features or different rules to detect vulnerabilities. However, this often causes many vulnerabilities to be ignored, and they are inefficient in detecting new vulnerabilities. In this study, to overcome such challenges, we propose a framework to automatically detect vulnerabilities in smart contracts on the blockchain. More specifically, first, we utilize novel feature vector generation techniques from bytecode of smart contract since the source code of smart contracts are rarely available in public. Next, the collected vectors are fed into our novel metric learning-based deep neural network(DNN) to get the detection result. We conduct comprehensive experiments on large-scale benchmarks, and the quantitative results demonstrate the effectiveness and efficiency of our approach

L-shaped association of serum calcium with all-cause and CVD mortality in the US adults: A population-based prospective cohort study

BackgroundCalcium is involved in many biological processes, but the impact of serum calcium levels on long-term mortality in general populations has been rarely investigated.MethodsThis prospective cohort study analyzed data from the National Health and Nutrition Examination Survey (1999–2018). All-cause mortality, cardiovascular disease (CVD) mortality, and cancer mortality were obtained through linkage to the National Death Index. Survey-weighted multivariate Cox regression was performed to compute hazard ratios (HRs) and 95% confidential intervals (CIs) for the associations of calcium levels with risks of mortality. Restricted cubic spline analyses were performed to examine the non-linear association of calcium levels with all-cause and disease-specific mortality.ResultsA total of 51,042 individuals were included in the current study. During an average of 9.7 years of follow-up, 7,592 all-cause deaths were identified, including 2,391 CVD deaths and 1,641 cancer deaths. Compared with participants in the first quartile (Q1) of serum calcium level [≤2.299 mmol/L], the risk of all-cause mortality was lower for participants in the second quartile (Q2) [2.300–2.349 mmol/L], the third quartile (Q3) [2.350–2.424 mmol/L] and the fourth quartile (Q4) [≥2.425 mmol/L] with multivariable-adjusted HRs of 0.81 (95% CI, 0.74–0.88), 0.78 (95% CI, 0.71–0.86), and 0.80 (95% CI, 0.73, 0.88). Similar associations were observed for CVD mortality, with HRs of 0.82 (95% CI, 0.71–0.95), 0.87 (95% CI, 0.74–1.02), and 0.83 (95% CI, 0.72, 0.97) in Q2–Q4 quartile. Furthermore, the L-shaped non-linear associations were detected for serum calcium with the risk of all-cause mortality. Below the median of 2.350 mmol/L, per 0.1 mmol/L higher serum calcium was associated with a 24% lower risk of all-cause mortality (HR: 0.76, 95% CI, 0.70–0.83), however, no significant changes were observed when serum calcium was above the median. Similar L-shaped associations were detected for serum calcium with the risk of CVD mortality with a 25% reduction in the risk of CVD death per 0.1 mmol/L higher serum calcium below the median (HR: 0.75, 95% CI, 0.65–0.86).ConclusionL-shaped associations of serum calcium with all-cause and CVD mortality were observed in US adults, and hypocalcemia was associated with a higher risk of all-cause mortality and CVD mortality

GLM-130B: An Open Bilingual Pre-trained Model

We introduce GLM-130B, a bilingual (English and Chinese) pre-trained language model with 130 billion parameters. It is an attempt to open-source a 100B-scale model at least as good as GPT-3 (davinci) and unveil how models of such a scale can be successfully pre-trained. Over the course of this effort, we face numerous unexpected technical and engineering challenges, particularly on loss spikes and divergence. In this paper, we introduce the training process of GLM-130B including its design choices, training strategies for both efficiency and stability, and engineering efforts. The resultant GLM-130B model offers significant outperformance over GPT-3 175B (davinci) on a wide range of popular English benchmarks while the performance advantage is not observed in OPT-175B and BLOOM-176B. It also consistently and significantly outperforms ERNIE TITAN 3.0 260B -- the largest Chinese language model -- across related benchmarks. Finally, we leverage a unique scaling property of GLM-130B to reach INT4 quantization without post training, with almost no performance loss, making it the first among 100B-scale models and more importantly, allowing its effective inference on 4$\times$RTX 3090 (24G) or 8$\times$RTX 2080 Ti (11G) GPUs, the most affordable GPUs required for using 100B-scale models. The GLM-130B model weights are publicly accessible and its code, training logs, related toolkit, and lessons learned are open-sourced at \url{https://github.com/THUDM/GLM-130B/}.Comment: Accepted to ICLR 202

The COVID-19 vaccines: recent development, challenges and prospects

The highly infectious coronavirus disease 2019 (COVID-19) associated with the pathogenic severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread to become a global pandemic. At present, the world is relying mainly on containment and hygiene-related measures, as well as repurposed drugs to control the outbreak. The development of COVID-19 vaccines is crucial for the world to return to pre-pandemic normalcy, and a collective global effort has been invested into protection against SARS-CoV-2. As of March 2021, thirteen vaccines have been approved for application whilst over 90 vaccine candidates are under clinical trials. This review focuses on the development of COVID-19 vaccines and highlights the efficacy and vaccination reactions of the authorised vaccines. The mechanisms, storage, and dosage specification of vaccine candidates at the advanced stage of development are also critically reviewed together with considerations for potential challenges. Whilst the development of a vaccine is, in general, in its infancy, current progress is promising. However, the world population will have to continue to adapt to the “new normal” and practice social distancing and hygienic measures, at least until effective vaccines are available to the general public

15-Deoxy- γ

Objective. 15-Deoxy-Δ12,14-prostaglandin J2 (15d-PGJ2) reduces inflammation and has been identified as an anti-inflammatory prostaglandin in numerous animal models. In this study, we investigated both effects of 15d-PGJ2 and its protection mechanism in concanavalin A- (ConA-) induced autoimmune hepatitis in mice. Materials and Methods. In vivo, Balb/C mice were injected with ConA (25 mg/kg) to induce acute autoimmune hepatitis, and 15d-PGJ2 (10 μg or 25 μg) was administered 1 h before the ConA injection. The histological grade, proinflammatory cytokine levels, and NF-κB and PPARγ activity were determined 6, 12, and 24 h after the ConA injection. In vitro, LO2 cells and RAW264.7 cells were pretreated with 15d-PGJ2 (2 μM) 1 h before the stimulation with ConA (30 μg/mL). The NF-κB and PPARγ activity were determined 30 min after the ConA administration. Results. Pretreatment with 15d-PGJ2 reduced the pathological effects of ConA-induced autoimmune hepatitis and significantly reduced the levels of cytokines after injection. 15d-PGJ2 activated PPARγ, blocked the degradation of IκBα, and inhibited the translocation of NF-κB into the nucleus. Conclusion. These results indicate that 15d-PGJ2 protects against ConA-induced autoimmune hepatitis by reducing proinflammatory cytokines. This reduction in inflammation may correlate with the activation of PPARγ and the reduction in NF-κB activity