Corrosion Inhibition of AISI 316L and Modified-AISI 630 Stainless Steel by the New Organic Inhibitor [(CH3)2N]3PSe in Chloride Media:Electrochemical and Physical Study

We evaluate the effect of the Tris-dimethylaminoselenophosphoramide (SeAP)on the corrosion inhibition of modified-AISI 630 and AISI 316L stainless steel (SS) in 3 wt. % NaCl. The electrochemical behaviors of tested SS samples are investigated before and after adding the Seep into the chloride media by potentiodynamic polarization technique. The adsorption of SeAP onto both SS surfaces is verified by global discharge optical emission spectroscopy (GDOES).  SeAP is found to be a good inhibitor for SS corrosion, especially when added at a concentration of 0.5 wt. %

Defensive Approximation: Securing CNNs using Approximate Computing

In the past few years, an increasing number of machine-learning and deep learning structures, such as Convolutional Neural Networks (CNNs), have been applied to solving a wide range of real-life problems. However, these architectures are vulnerable to adversarial attacks. In this paper, we propose for the first time to use hardware-supported approximate computing to improve the robustness of machine learning classifiers. We show that our approximate computing implementation achieves robustness across a wide range of attack scenarios. Specifically, for black-box and grey-box attack scenarios, we show that successful adversarial attacks against the exact classifier have poor transferability to the approximate implementation. Surprisingly, the robustness advantages also apply to white-box attacks where the attacker has access to the internal implementation of the approximate classifier. We explain some of the possible reasons for this robustness through analysis of the internal operation of the approximate implementation. Furthermore, our approximate computing model maintains the same level in terms of classification accuracy, does not require retraining, and reduces resource utilization and energy consumption of the CNN. We conducted extensive experiments on a set of strong adversarial attacks; We empirically show that the proposed implementation increases the robustness of a LeNet-5 and an Alexnet CNNs by up to 99% and 87%, respectively for strong grey-box adversarial attacks along with up to 67% saving in energy consumption due to the simpler nature of the approximate logic. We also show that a white-box attack requires a remarkably higher noise budget to fool the approximate classifier, causing an average of 4db degradation of the PSNR of the input image relative to the images that succeed in fooling the exact classifierComment: ACM International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS 2021

Defending with Errors: Approximate Computing for Robustness of Deep Neural Networks

Machine-learning architectures, such as Convolutional Neural Networks (CNNs) are vulnerable to adversarial attacks: inputs crafted carefully to force the system output to a wrong label. Since machine-learning is being deployed in safety-critical and security-sensitive domains, such attacks may have catastrophic security and safety consequences. In this paper, we propose for the first time to use hardware-supported approximate computing to improve the robustness of machine-learning classifiers. We show that successful adversarial attacks against the exact classifier have poor transferability to the approximate implementation. Surprisingly, the robustness advantages also apply to white-box attacks where the attacker has unrestricted access to the approximate classifier implementation: in this case, we show that substantially higher levels of adversarial noise are needed to produce adversarial examples. Furthermore, our approximate computing model maintains the same level in terms of classification accuracy, does not require retraining, and reduces resource utilization and energy consumption of the CNN. We conducted extensive experiments on a set of strong adversarial attacks; We empirically show that the proposed implementation increases the robustness of a LeNet-5, Alexnet and VGG-11 CNNs considerably with up to 50% by-product saving in energy consumption due to the simpler nature of the approximate logic.Comment: arXiv admin note: substantial text overlap with arXiv:2006.0770

Mortality from gastrointestinal congenital anomalies at 264 hospitals in 74 low-income, middle-income, and high-income countries: a multicentre, international, prospective cohort study

Summary Background Congenital anomalies are the fifth leading cause of mortality in children younger than 5 years globally. Many gastrointestinal congenital anomalies are fatal without timely access to neonatal surgical care, but few studies have been done on these conditions in low-income and middle-income countries (LMICs). We compared outcomes of the seven most common gastrointestinal congenital anomalies in low-income, middle-income, and high-income countries globally, and identified factors associated with mortality. Methods We did a multicentre, international prospective cohort study of patients younger than 16 years, presenting to hospital for the first time with oesophageal atresia, congenital diaphragmatic hernia, intestinal atresia, gastroschisis, exomphalos, anorectal malformation, and Hirschsprung’s disease. Recruitment was of consecutive patients for a minimum of 1 month between October, 2018, and April, 2019. We collected data on patient demographics, clinical status, interventions, and outcomes using the REDCap platform. Patients were followed up for 30 days after primary intervention, or 30 days after admission if they did not receive an intervention. The primary outcome was all-cause, in-hospital mortality for all conditions combined and each condition individually, stratified by country income status. We did a complete case analysis. Findings We included 3849 patients with 3975 study conditions (560 with oesophageal atresia, 448 with congenital diaphragmatic hernia, 681 with intestinal atresia, 453 with gastroschisis, 325 with exomphalos, 991 with anorectal malformation, and 517 with Hirschsprung’s disease) from 264 hospitals (89 in high-income countries, 166 in middleincome countries, and nine in low-income countries) in 74 countries. Of the 3849 patients, 2231 (58·0%) were male. Median gestational age at birth was 38 weeks (IQR 36–39) and median bodyweight at presentation was 2·8 kg (2·3–3·3). Mortality among all patients was 37 (39·8%) of 93 in low-income countries, 583 (20·4%) of 2860 in middle-income countries, and 50 (5·6%) of 896 in high-income countries (p<0·0001 between all country income groups). Gastroschisis had the greatest difference in mortality between country income strata (nine [90·0%] of ten in lowincome countries, 97 [31·9%] of 304 in middle-income countries, and two [1·4%] of 139 in high-income countries; p≤0·0001 between all country income groups). Factors significantly associated with higher mortality for all patients combined included country income status (low-income vs high-income countries, risk ratio 2·78 [95% CI 1·88–4·11], p<0·0001; middle-income vs high-income countries, 2·11 [1·59–2·79], p<0·0001), sepsis at presentation (1·20 [1·04–1·40], p=0·016), higher American Society of Anesthesiologists (ASA) score at primary intervention (ASA 4–5 vs ASA 1–2, 1·82 [1·40–2·35], p<0·0001; ASA 3 vs ASA 1–2, 1·58, [1·30–1·92], p<0·0001]), surgical safety checklist not used (1·39 [1·02–1·90], p=0·035), and ventilation or parenteral nutrition unavailable when needed (ventilation 1·96, [1·41–2·71], p=0·0001; parenteral nutrition 1·35, [1·05–1·74], p=0·018). Administration of parenteral nutrition (0·61, [0·47–0·79], p=0·0002) and use of a peripherally inserted central catheter (0·65 [0·50–0·86], p=0·0024) or percutaneous central line (0·69 [0·48–1·00], p=0·049) were associated with lower mortality. Interpretation Unacceptable differences in mortality exist for gastrointestinal congenital anomalies between lowincome, middle-income, and high-income countries. Improving access to quality neonatal surgical care in LMICs will be vital to achieve Sustainable Development Goal 3.2 of ending preventable deaths in neonates and children younger than 5 years by 2030

Inverse spin crossover in fluorinated Fe(1,10-phenanthroline) 2 (NCS) 2 adsorbed on Cu (001) surface

Density functional theory (DFT) including van der Waals weak interaction in conjunction with the so called rotational invariant DFT+U, where is the Hubbard interaction of the iron site, is used to show that the fluorinated spin crossover Fe(phen)(NCS) molecule whether in the gas phase or adsorbed on Cu(001) surface switches from the original low spin state to the high spin state. The calculated minimal energy path by means of both the nudged elastic band method and the constrained minimization method is found to be smaller for the fluorinated molecule. Using Bader electron density analysis and a point charge model, this inversion of the spin crossover is explained in terms of electron doping of the Fe-octahedron cage which led to an increase of the Fe–N bond lengths and the distortion of the Fe(II) octahedron. Consequently, the ligand-field splitting is drastically reduced, making the high-spin ground state more stable than the low-spin state. The calculated scanning tunneling microscopy (STM) images in the Tersoff–Hamann approximation show a clear distinction between the fluorinated and the unfluorinated molecule. This theoretical prediction is awaiting future STM experimental confirmation

Hardware support for trustworthy machine learning: a survey

Machine Learning (ML) are used in an increasing number of applications as they continue to deliver state-of-the-art performance across many areas including computer vision natural language processing (NLP), robotics, autonomous driving, and healthcare. While rapid progress in all aspects of ML development and deployment is occurring, there is a rising concern about the trustworthiness of these models, especially from security and privacy perspectives. Several attacks that jeopardize ML models’ integrity (e.g. adversarial attacks) and confidentiality (e.g. membership inference attacks) have been investigated in the literature. This, in return, triggered substantial work to protect ML models and advance their trustworthiness. Defenses generally act on the input data, the objective function, or the network structure to mitigate adversarial effects. However, these proposed defenses require substantial changes to the architecture, retraining procedure, or incorporate additional input data processing overheads. In addition, often these proposed defenses require high power and computational requirements, which make them challenging to deploy in embedded systems and Edge devices. Towards addressing the need for robust ML at acceptable overheads, recent works have investigated hardware-emanated solutions to enhance ML security and privacy. In this paper, we summarize recent works in the area of hardware support for trustworthy ML. In addition, we provide guidelines for future research in the area by identifying open problems that need to be addressed