An Automated Analyzer for Financial Security of Ethereum Smart Contracts

At present, millions of Ethereum smart contracts are created per year and attract financially motivated attackers. However, existing analyzers do not meet the need to precisely analyze the financial security of large numbers of contracts. In this paper, we propose and implement FASVERIF, an automated analyzer for fine-grained analysis of smart contracts' financial security. On the one hand, FASVERIF automatically generates models to be verified against security properties of smart contracts. On the other hand, our analyzer automatically generates the security properties, which is different from existing formal verifiers for smart contracts. As a result, FASVERIF can automatically process source code of smart contracts, and uses formal methods whenever possible to simultaneously maximize its accuracy. We evaluate FASVERIF on a vulnerabilities dataset by comparing it with other automatic tools. Our evaluation shows that FASVERIF greatly outperforms the representative tools using different technologies, with respect to accuracy and coverage of types of vulnerabilities

Die-sinking electrical discharge machining with oxygen-mixed water-in-oil emulsion working fluid

Abstract Water-in-oil emulsion has been proposed as the working fluid of die-sinking electrical discharge machining in our previous research. Compared with the traditional mineral oil-based working fluid, the water-in-oil emulsion was more environmentally benign and efficiency. This study presents a new method of die-sinking electrical discharge machining that uses oxygen-mixed water-in-oil emulsion as the working fluid. Experimental results showed that the material removal rate can be highly improved, and the relative electrode wear ratio can be significantly reduced by mixing oxygen into the water-in-oil emulsion. The recast layer was much thinner with this oxygen-assisted machining method

Mitigating the Alignment Tax of RLHF

LLMs acquire a wide range of abilities during pre-training, but aligning LLMs under Reinforcement Learning with Human Feedback (RLHF) can lead to forgetting, which is also known as the alignment tax. To empirically verify this hypothesis, we conducted experiments with existing RLHF algorithms using OpenLLaMA-3B, which revealed a pronounced alignment tax in NLP tasks. On the other hand, despite various techniques to mitigate forgetting, they are often at odds with the RLHF performance, leading to a trade-off between reward maximization and forgetting mitigation. In light of the above pressing issue in aligning LLMs, in this paper we explore model averaging, which interpolates between pre and post RLHF model weights, to achieve a more efficient reward-tax Pareto front. To understand its effectiveness, We offer theoretical insights into model averaging, revealing that it enhances performance Pareto front by increasing feature diversity on the layers where tasks share overlapped feature spaces. Empirical evidence corroborates our analysis by showing the benefits of averaging low-level transformer layers. Building on the analysis and the observation that averaging different layers of the transformer leads to significantly different reward-tax trade-offs, we propose Adaptive Model Averaging (AMA) to adaptively find various combination ratios of model layers. AMA seeks to maximize the alignment reward while incurring minimal alignment tax. Moreover, we validate AMA's performance across a range of RLHF algorithms over OpenLLaMA-3B and further extend our findings to Mistral-7B.Comment: 28 Page

Tip60 Suppresses Cholangiocarcinoma Proliferation and Metastasis via PI3k-AKT

Background/Aims: Aberrant expression of Tip60 is associated with progression in many cancers. However, the role of Tip60 in cancer progression remains contradictory. The aim of this study was to investigate the clinical significance, biological functions and underlying mechanisms of Tip60 deregulation in cholangiocarcinoma (CCA) for the first time. Methods: Quantitative real-time PCR (QRT-PCR), western blotting and immunohistochemistry staining (IHC) were carried out to measure Tip60 expression in CCA tissues and cell lines. Kaplan–Meier analysis and the log-rank test were used for survival analysis. In vitro, cell proliferation was evaluated by flow cytometry and CCK-8, colony formation, and EDU assays. Migration/ invasion was evaluated by trans-well assays. Phosphokinase array was used to confirm the dominant signal regulated by Tip60. Tumor growth and metastasis were demonstrated in vivo using a mouse model. Results: Tip60 was notably downregulated in CCA tissues, which was associated with greater tumor size, venous invasion, and TNM stage. Down-regulation of Tip60 was associated with tumor progression and poorer survival in CCA patients. In vitro and in vivo studies demonstrated that Tip60 suppressed growth and metastasis throughout the progression of CCA. We further identified the PI3K/AKT pathway as a dominant signal of Tip60 and suggested that Tip60 regulated CCA cell proliferation and metastasis via PT3K-AKT pathway. Pearson analysis revealed that PTEN was positively correlated with the Tip60 level in CCA tissues. Conclusion: Tip60, as a tumor suppressor in CCA via the PI3K/AKT pathway, might be a promising therapeutic target or prognostic marker for CCA

Free-Standing Hierarchically Sandwich-Type Tungsten Disulfide Nanotubes/Graphene Anode for Lithium-Ion Batteries

Transition metal dichalcogenides (TMD), analogue of graphene, could form various dimensionalities. Similar to carbon, one dimensional (1D) nanotube of TMD materials has wide application in hydrogen storage，Li-ion batteries and supercapacitors due to their unique structure and properties. Here we demonstrate the feasibility of tungsten disulfide nanotubes (WS2-NTs)/graphene (GS) sandwich-type architecture as anode for lithium-ion batteries for the first time. The graphene based hierarchical architecture plays vital roles in achieving fast electron/ion transfer, thus leading to good electrochemical performance. When evaluated as anode, WS2-NTs /GS hybrid could maintain a capacity of 318.6 mA/g over 500 cycles at a current density of 1A/g. Besides, the hybrid anode does not require any additional polymetric binder, conductive additives or a separate metal current-collector. The relatively high density of this hybrid is beneficial for high capacity per unit volume. Those characteristics make it a potential anode material for light and high performance lithium-ion batteries

Search for dark matter produced in association with bottom or top quarks in √s = 13 TeV pp collisions with the ATLAS detector

A search for weakly interacting massive particle dark matter produced in association with bottom or top quarks is presented. Final states containing third-generation quarks and miss- ing transverse momentum are considered. The analysis uses 36.1 fb−1 of proton–proton collision data recorded by the ATLAS experiment at √s = 13 TeV in 2015 and 2016. No significant excess of events above the estimated backgrounds is observed. The results are in- terpreted in the framework of simplified models of spin-0 dark-matter mediators. For colour- neutral spin-0 mediators produced in association with top quarks and decaying into a pair of dark-matter particles, mediator masses below 50 GeV are excluded assuming a dark-matter candidate mass of 1 GeV and unitary couplings. For scalar and pseudoscalar mediators produced in association with bottom quarks, the search sets limits on the production cross- section of 300 times the predicted rate for mediators with masses between 10 and 50 GeV and assuming a dark-matter mass of 1 GeV and unitary coupling. Constraints on colour- charged scalar simplified models are also presented. Assuming a dark-matter particle mass of 35 GeV, mediator particles with mass below 1.1 TeV are excluded for couplings yielding a dark-matter relic density consistent with measurements

Spatiotemporal Monitoring of a Grassland Ecosystem and Its Net Primary Production Using Google Earth Engine: A Case Study of Inner Mongolia from 2000 to 2020

Grassland ecosystems are a significant part of the global ecosystem and support the livelihoods of millions of people. The Inner Mongolia grassland is the largest temperate grassland in the world, and an important ecological barrier for China, but due to human activities and climate change it has been faced with an ecological crisis in recent years. In this study, a modified Carnegie-Ames-Stanford approach (CASA) model based on the Google Earth Engine platform was used to determine the net primary production (NPP) in the Inner Mongolia grassland from 2000 to 2020. The results show that the average annual NPP of the Inner Mongolia grassland is 278.63 g C/m2, and 83.22% of the total area has shown an increasing trend during the study period. We also analyzed the impact of land-use/cover change (LUCC) and climatic factors on NPP. We found that: (1) the total area of grassland increased from 2000 to 2010 and then decreased from 2010 to 2020. During the whole study period, although the grassland area increased slightly by 4728.69 km2 because of LUCC, the overall effect of LUCC on grassland NPP was negative, with a loss of 17.63 Tg C compared to an increase of 16.38 Tg C. (2) The main meteorological factor affecting the NPP of the Inner Mongolia grassland is precipitation, followed by sunshine duration and temperature. About 97.06% of the grassland shows a positive correlation between NPP and precipitation. (3) The results for NPP and its changing trends are not completely consistent in the long- and short-term study periods. Considering the instability of grassland growth, it is necessary to take the periodic variation of precipitation into account when studying NPP. These results could provide basic information for policy formulation and scientific research into the ecological environment management of grassland areas in the future