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

Does calcium diffusional global feedback leads to slow light adaptation in Drosophila photoreceptors? - A 3D biophysical modelling approach

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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

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

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Ripple Excitation-Based Adaptive Sensorless Control of IPMSM in Full Speed Range

This paper proposes an adaptive sensorless finite-control-set model predictive control (FCS-MPC) method for the interior permanent magnet synchronous motor (IPMSM). The method is feasible in the full speed range from zero speed to the flux-weakening region above rated speed, without the method transition between the low and the high speeds. A ripple excitation-based position estimation method is proposed to extract the rotor-saliency-based position information from the inherent voltage ripples of the FCS-MPC. Therefore, the additional voltage margin consumption, the special sampling timing, or the interference in the fundamental control are all avoided. Furthermore, to alleviate the parameter dependency, an adaptive predictive model with the online estimation of the lumped voltage terms and the equivalent dynamic susceptances is proposed. In this way, the main machine parameters and the unmodeled disturbances in the d-q voltage equations of the IPMSM are all considered. The position deviation due to the cross-coupling effect is also analyzed, and a compensation method is proposed. Finally, the effectiveness of the proposed method is validated by the experimental results and the comparison with existing methods