MeaeQ: Mount Model Extraction Attacks with Efficient Queries

We study model extraction attacks in natural language processing (NLP) where attackers aim to steal victim models by repeatedly querying the open Application Programming Interfaces (APIs). Recent works focus on limited-query budget settings and adopt random sampling or active learning-based sampling strategies on publicly available, unannotated data sources. However, these methods often result in selected queries that lack task relevance and data diversity, leading to limited success in achieving satisfactory results with low query costs. In this paper, we propose MeaeQ (Model extraction attack with efficient Queries), a straightforward yet effective method to address these issues. Specifically, we initially utilize a zero-shot sequence inference classifier, combined with API service information, to filter task-relevant data from a public text corpus instead of a problem domain-specific dataset. Furthermore, we employ a clustering-based data reduction technique to obtain representative data as queries for the attack. Extensive experiments conducted on four benchmark datasets demonstrate that MeaeQ achieves higher functional similarity to the victim model than baselines while requiring fewer queries. Our code is available at https://github.com/C-W-D/MeaeQ.Comment: Accepted by EMNLP 2023 main conferenc

Multi-Modal Face Stylization with a Generative Prior

In this work, we introduce a new approach for artistic face stylization. Despite existing methods achieving impressive results in this task, there is still room for improvement in generating high-quality stylized faces with diverse styles and accurate facial reconstruction. Our proposed framework, MMFS, supports multi-modal face stylization by leveraging the strengths of StyleGAN and integrates it into an encoder-decoder architecture. Specifically, we use the mid-resolution and high-resolution layers of StyleGAN as the decoder to generate high-quality faces, while aligning its low-resolution layer with the encoder to extract and preserve input facial details. We also introduce a two-stage training strategy, where we train the encoder in the first stage to align the feature maps with StyleGAN and enable a faithful reconstruction of input faces. In the second stage, the entire network is fine-tuned with artistic data for stylized face generation. To enable the fine-tuned model to be applied in zero-shot and one-shot stylization tasks, we train an additional mapping network from the large-scale Contrastive-Language-Image-Pre-training (CLIP) space to a latent $w+$ space of fine-tuned StyleGAN. Qualitative and quantitative experiments show that our framework achieves superior face stylization performance in both one-shot and zero-shot stylization tasks, outperforming state-of-the-art methods by a large margin

High Performance Indium-Doped ZnO Gas Sensor

Gas sensors for ethanol and acetone based on ZnO nanobelts with doping element indium were fabricated. Excellent sensitivity accompanied with short response time (10 s) and recovery time (23 s) to 150 ppm ethanol is obtained. For In-doped sensors, a minimum concentration of 37.5 ppm at 275°C in acetone was observed with an average sensitivity of 714.4, which is 7 times larger than that of the pure sensors and much larger than that reported response (16) of Co-doped ZnO nanofibers to acetone. These results indicate that doping elements can improve gas sensitivity, which is associated with oxygen space and valence ions. In-doped ZnO nanobelts exhibit higher sensitivity to acetone than that to ethanol. These results indicate that doped ZnO nanobelts can successfully distinguish acetone and ethanol, which can be put into various practical applications

Obesity and clinical outcomes in COVID-19 patients without comorbidities, a post-hoc analysis from ORCHID trial

ObjectiveLarge body of studies described individuals with obesity experiencing a worse prognosis in COVID-19. However, the effects of obesity on the prognosis of COVID-19 in patients without comorbidities have not been studied. Therefore, the current study aimed to provide evidence of the relationship between obesity and clinical outcomes in COVID-19 patients without comorbidities.MethodsA total of 116 hospitalized COVID-19 patients without comorbidities from the ORCHID study (Patients with COVID-19 from the Outcomes Related to COVID-19 Treated with Hydroxychloroquine among Inpatients with Symptomatic Disease) were included. Obesity is defined as a BMI of ≥30 kg/m2. A Cox regression analysis was used to estimate the hazard ratio (HR) for discharge and death after 28 days.ResultsThe percentage of obesity in COVID-19 patients without comorbidities was 54.3% (63/116). Discharge at 28 days occurred in 56/63 (84.2%) obese and 51/53 (92.2%) non-obese COVID-19 patients without comorbidities. Four (3.4%) COVID-19 patients without any comorbidities died within 28 days, among whom 2/63 (3.2%) were obese and 2/53 (3.8%) were non-obese. Multivariate Cox regression analyses showed that obesity was independently associated with a decreased rate of 28-day discharge (adjusted HR: 0.55, 95% CI: 0.35–0.83) but was not significantly associated with 28-day death (adjusted HR: 0.94, 95% CI: 0.18–7.06) in COVID-19 patients without any comorbidities.ConclusionsObesity was independently linked to prolonged hospital length of stay in COVID-19 without any comorbidity. Larger prospective trials are required to assess the role of obesity in COVID-19 related deaths

Graphic Template Establishment and Productivity Evaluation Model of Post-Fracturing Based on the Fluctuation Pattern of G-Function Curve

In the process of fracturing construction in shale gas reservoirs, microseism is a common means and effective method to evaluate the fracturing effect, but it is not suitable for large-scale and large batches due to its high applicability conditions and cost. Therefore, a concise, fast and low-cost post-fracturing effect evaluation method is needed to evaluate the complexity of fractures formed by fracturing in shale gas reservoirs. In this paper, based on the basic theory of the G-function, the free variable μ was introduced to correct the filter loss coefficient with the participation of natural fractures, and thousands of G-function curves were plotted with fracturing data from hundreds of gas wells in the southern Fuling shale gas field in China. Through the feature analysis of the curve morphology, a G-function graphic template conforming to the block was established, which contains four types and eight morphologies. Four characteristic parameters in the G-function curve were selected to establish a productivity evaluation model for shale gas wells. Based on the validation results, it can be seen that the G-function graphic template and productivity evaluation model proposed by the author had a good correlation with the post-fracturing productivity of shale gas wells, which provided a fast, economical and accurate method for the post-fracturing effect evaluation and productivity evaluation of shale gas wells and can effectively provide feedback on the field fracturing effect and guide subsequent fracturing construction

Experimental Study on SiO₂ Nanoparticles-Assisted Alpha-Olefin Sulfonate Sodium (AOS) and Hydrolyzed Polyacrylamide (HPAM) Synergistically Enhanced Oil Recovery

The purpose of this study is to investigate the use of SiO2 nanoparticles in assisting with surfactants and polymers for tertiary oil recovery, with the aim of enhancing oil recovery. The article characterizes the performance of SiO2 nanoparticles, including particle size, dispersion stability, and zeta potential, evaluates the synergistic effects of nanoparticles with alpha-olefin sulfonate sodium (AOS) surfactants and hydrolyzed polyacrylamide (HPAM) on reducing interfacial tension and altering wettability, and conducts core flooding experiments in rock cores with varying permeabilities. The findings demonstrate that the particle size decreased from 191 nm to 125 nm upon the addition of SiO2 nanoparticles to AOS surfactant, but increased to 389 nm upon the addition of SiO2 nanoparticles to HPAM. The dispersibility experiment showed that the SiO2 nanoparticle solution did not precipitate over 10 days. After adding 0.05% SiO2 nanoparticles to AOS surfactant, the zeta potential was −40.2 mV, while adding 0.05% SiO2 nanoparticles to 0.1% HPAM resulted in a decrease in the zeta potential to −25.03. The addition of SiO2 nanoparticles to AOS surfactant further reduced the IFT value to 0.19 mN/m, altering the rock wettability from oil-wet to strongly water-wet, with the contact angle decreasing from 110° to 18°. In low-permeability rock core oil displacement experiments, the use of AOS surfactants and HPAM for enhanced oil recovery increased the recovery rate by 24.5% over water flooding. The recovery rate increased by 21.6% over water flooding in low-permeability rock core experiments after SiO2 nanoparticles were added and surfactants and polymers were utilized for oil displacement. This is because the nanoparticles blocked small pore throats, resulting in increased resistance and hindered free fluid flow. The main causes of this plugging are mutual interference and mechanical entrapment, which cause the pressure differential to rise quickly. In high-permeability rock core oil displacement experiments, the use of AOS surfactants and HPAM for oil recovery increased the recovery rate by 34.6% over water flooding. Additionally, the recovery rate increased by 39.4% over water flooding with the addition of SiO2 nanoparticles and the use of AOS surfactants and HPAM for oil displacement. Because SiO2 nanoparticles create wedge-shaped structures inside highly permeable rock cores, they create structural separation pressure, which drives crude oil forward and aids in diffusion. This results in a comparatively small increase in pressure differential. Simultaneously, the nanoparticles change the rock surfaces’ wettability, which lowers the amount of crude oil that adsorbs and improves oil recovery

Preparation and Performance Evaluation of a Self-Crosslinking Emulsion-Type Fracturing Fluid for Quasi-Dry CO₂ Fracturing

Quasi-dry CO2 fracturing technology is a new CO2 fracturing technology that combines liquid CO2 fracturing (dry CO2 fracturing) and water-based fracturing. It uses a liquid CO2 system containing a small amount of water-based fracturing fluid to carry sand, and it is characterized by sand blending at normal pressure, convenient preparation, the integrated application of resistance reduction and sand carrying, and no dedicated closed sand blender requirement. We developed a self-crosslinking emulsion-type water-based fracturing fluid (ZJL-1), which contained ionic bonds, hydrogen bonds, van der Waals forces, and hydrophobic associations, for quasi-dry CO2 fracturing, and the comprehensive properties of the ZJL-1 fracturing fluid were evaluated. The results showed that the ZJL-1 fracturing fluid had obvious viscoelastic characteristics, a heat loss rate of less than 10% at 200 °C, a good thermal stability, sufficient rheology under high temperature and high shear conditions, and a good thermal stability. The resistance reduction rate reached 70%, which demonstrates a good resistance reduction performance. Compared with conventional guar fracturing fluid, ZJL-1 can carry more sand and has a lower core damage rate. The on-site use of quasi-dry fracturing showed that optimizing the mixing ratio of liquid CO2 fracturing fluid and ZJL-1 fracturing fluid effectively enhanced oil and gas recovery. This can be used to optimize quasi-dry fracturing and can be used as a reference