PREDICTION OF CORROSION RESISTANCE OF THE DEFORMED SEMI-FINISHED PRODUCTS OF STEEL 35ХГФ BASED ON DATA FROM EBSD-ANALYSIS

Коррозионная стойкость – важнейшая характеристика современных функциональных материалов. В данной работе анализируются возможности метода автоматического анализа картин дифракции обратнорассеянных электронов (EBSD) для прогнозирования коррозионной стойкости по отношению к углеводородам горячекатаных труб из стали 35ХГФ.Corrosion resistance – is the important feature of modern functional materials, in this work presented an analysis of capabilities of EBSD-method for prediction corrosion resistance steel towards hydrocarbons

Honest-but-curious nets: sensitive attributes of private inputs can be secretly coded into the classifiers' outputs

It is known that deep neural networks, trained for the classification of non-sensitive target attributes, can reveal sensitive attributes of their input data through internal representations extracted by the classifier. We take a step forward and show that deep classifiers can be trained to secretly encode a sensitive attribute of their input data into the classifier's outputs for the target attribute, at inference time. Our proposed attack works even if users have a full white-box view of the classifier, can keep all internal representations hidden, and only release the classifier's estimations for the target attribute. We introduce an information-theoretical formulation for such attacks and present efficient empirical implementations for training honest-but-curious (HBC) classifiers: classifiers that can be accurate in predicting their target attribute, but can also exploit their outputs to secretly encode a sensitive attribute. Our work highlights a vulnerability that can be exploited by malicious machine learning service providers to attack their user's privacy in several seemingly safe scenarios; such as encrypted inferences, computations at the edge, or private knowledge distillation. Experimental results on several attributes in two face-image datasets show that a semi-trusted server can train classifiers that are not only perfectly honest but also accurately curious. We conclude by showing the difficulties in distinguishing between standard and HBC classifiers, discussing challenges in defending against this vulnerability of deep classifiers, and enumerating related open directions for future studies

Stochastic 2D Well-Path Assessments for Naturally Fractured Carbonate Reservoirs

Summary Simple but geologically reasonable and calibrated 2D stochastic models are useful to quantify significant risks and uncertainties associated with alternative-development-well trajectories, particularly when statistical relationships can be established to help quantify those risks and uncertainties, and when the geologic features that create the risks and uncertainties are not adequately addressed within reservoir-flow models. Our example stochastic 2D model considered the naturally fractured depositional-slope region of an isolated carbonate buildup, and the model was populated with relevant features including distributions and geometric details of natural fractures, natural-fracture clustering, and intraformational slope clinoforms that define a mechanically layered sequence. The model was calibrated by use of well-production results and production-logging data so that it reproduced observed well results for cases where the lower sequence boundary does not occur above the oil/water contact (OWC), adding confidence that the model could be used to represent the statistical effect of various alternative trajectories for future wells. Experimental design (ED) was used to determine the significant uncertainties and well-path decisions. Heel and toe elevation and the number of clinoforms encountered by the well were the only significant variables for modeling the frequency of water production. For modeling the frequency of direct well communication to the gas cap, the same variables were significant, in addition to well direction, completion length, and fracture density. The amount of fracture clustering applied in the model was also significant. For our example case, changing the well-elevation profile was effective in managing gas or water risks; however, tradeoffs were evident—and quantified—in attempting to simultaneously address both risks. Minimizing drawdown was not an effective strategy because productivity was low and rarely resulted in economic water-free production if any open fracture connected the well with the aquifer.</jats:p