Generating Infinite-Resolution Texture using GANs with Patch-by-Patch Paradigm

In this paper, we introduce a novel approach for generating texture images of infinite resolutions using Generative Adversarial Networks (GANs) based on a patch-by-patch paradigm. Existing texture synthesis techniques often rely on generating a large-scale texture using a one-forward pass to the generating model, this limits the scalability and flexibility of the generated images. In contrast, the proposed approach trains GANs models on a single texture image to generate relatively small patches that are locally correlated and can be seamlessly concatenated to form a larger image while using a constant GPU memory footprint. Our method learns the local texture structure and is able to generate arbitrary-size textures, while also maintaining coherence and diversity. The proposed method relies on local padding in the generator to ensure consistency between patches and utilizes spatial stochastic modulation to allow for local variations and diversity within the large-scale image. Experimental results demonstrate superior scalability compared to existing approaches while maintaining visual coherence of generated textures

Generation of non-stationary stochastic fields using Generative Adversarial Networks with limited training data

In the context of generating geological facies conditioned on observed data, samples corresponding to all possible conditions are not generally available in the training set and hence the generation of these realizations depends primary on the generalization capability of the trained generative model. The problem becomes more complex when applied on non-stationary fields. In this work, we investigate the problem of training Generative Adversarial Networks (GANs) models against a dataset of geological channelized patterns that has a few non-stationary spatial modes and examine the training and self-conditioning settings that improve the generalization capability at new spatial modes that were never seen in the given training set. The developed training method allowed for effective learning of the correlation between the spatial conditions (i.e. non-stationary maps) and the realizations implicitly without using additional loss terms or solving a costly optimization problem at the realization generation phase. Our models, trained on real and artificial datasets were able to generate geologically-plausible realizations beyond the training samples with a strong correlation with the target maps

Parental experience of potential adverse drug reactions related to their oral administration of antipyretic analgesics in children in Saudi Arabia.

Background: Oral antipyretic analgesic medicines are commonly used in children and have the potential for adverse drug reactions (ADRs). Objective: The aim of this study was to explore parental experiences of potential ADRs related to their oral administration of antipyretic analgesics in children in the Kingdom of Saudi Arabia. Methods: For this cross-sectional survey, a paper-based questionnaire, consent form and information sheet were handed out to 1000 parents who had administered an oral antipyretic analgesic medicine to their children during the previous 3 months. Data were entered and analyzed using SPSS version 21.0 (IBM-SPSS Inc, Armonk, NY). Simple descriptive and inferential statistics were used. Management and ethical approvals were attained. Results: During March to April 2017, 661 parents agreed to participate, giving a response rate of 66.1%. Of the surveyed sample, 208 parents had observed 1 or more potential ADRs (31.5%, n = 208 out of 661). Parents’ (n = 208) most commonly reported potential ADRs (n = 523) were loss of appetite (23%, n = 120 out of 523), stomachache (20.3%, n = 106 out of 523), abdominal colic (13%, n = 68 out of 523), and diarrhea (10.3%, n = 54 out of 523). Parents described severity of the ADRs as slight (71.8%, n = 342 out of 476), annoying to the child (7.9%, n = 85 to of 476), significant and affecting daily tasks (3.6%, n = 17 out of 476) and significant and led to the hospital (6.7%, n = 32 out of 476). Fever was the top-ranked reason for using antipyretic analgesic medicines (41.0%, n = 271 out of 661), followed by toothache (25.0%, n = 165 out of 661) and tonsillitis/laryngitis (24.7%, n = 163 out of 661). Among parents, 34.7% (n = 165 out of 476) did not seek medical attention when a potential ADR occurred, whereas 26.3% (n = 125 out of 476) of parents took their children to hospital clinics. Conclusions: Although the majority of parentally reported (but not proven) ADRs were mild, a number of significant ADRs were reported. Future research should consider whether there is a role for physicians and pharmacists in educating parents in Saudi Arabia, and perhaps more widely, about the optimal use of oral antipyretic and analgesic medicines in children. (Curr Ther Res Clin Exp. 2020; 81:XXX–XXX) © 2020 Elsevier HS Journals, Inc

Electrochemically Induced Mesomorphism Switching in a Chlorpromazine Hydrochloride Lyotropic Liquid Crystal

The discovery of electrochemical switching of the Lα phase of chlorpromazine hydrochloride in water is reported. The phase is characterized using polarizing microscopy, X-ray scattering, rheological measurements, and microelectrode voltammetry. Fast, heterogeneous oxidation of the lyotropic liquid crystal is shown to cause a phase change resulting from the disordering of the structural order in a stepwise process. The underlying molecular dynamics is considered to be a cooperative effect of both increasing electrostatic interactions and an unfolding of the monomers from "butterfly"-shaped in the reduced form to planar in the oxidized form

Local Padding in Patch-Based GANs for Seamless Infinite-Sized Texture Synthesis

Texture models based on Generative Adversarial Networks (GANs) use zero-padding to implicitly encode positional information of the image features. However, when extending the spatial input to generate images at large sizes, zero-padding can often lead to degradation of quality due to the incorrect positional information at the center of the image and limit the diversity within the generated images. In this paper, we propose a novel approach for generating stochastic texture images at large arbitrary sizes using GANs model that is based on patch-by-patch generation. Instead of zero-padding, the model uses \textit{local padding} in the generator that shares border features between the generated patches; providing positional context and ensuring consistency at the boundaries. The proposed models are trainable on a single texture image and have a constant GPU scalability with respect to the output image size, and hence can generate images of infinite sizes. We show in the experiments that our method has a significant advancement beyond existing texture models in terms of the quality and diversity of the generated textures. Furthermore, the implementation of local padding in the state-of-the-art super-resolution models effectively eliminates tiling artifacts enabling large-scale super-resolution. Our code is available athttps://github.com/ai4netzero/Infinite_Texture_GANs

Generating unrepresented proportions of geological facies using Generative Adversarial Networks

In this work, we investigate the capacity of Generative Adversarial Networks (GANs) in interpolating and extrapolating facies proportions in a geological dataset. The new generated realizations with unrepresented (aka. missing) proportions are assumed to belong to the same original data distribution. Specifically, we design a conditional GANs model that can drive the generated facies toward new proportions not found in the training set. The presented study includes an investigation of various training settings and model architectures. In addition, we devised new conditioning routines for an improved generation of the missing samples. The presented numerical experiments on images of binary and multiple facies showed good geological consistency as well as strong correlation with the target conditions

General public's perspectives of issues relating to misuse of medicines: a cross-sectional survey in Jeddah, Saudi Arabia.

Background Misuse of prescription medicines is a global issue potentially resulting in severe consequences including adverse drug reactions, dependence, tolerance, increased healthcare utility and mortality. Objective To assess the public's perspectives of issues relating to medicines misuse. Method A survey of members of the public ( ≥ 18 years) attending medication safety awareness campaigns in Jeddah, Saudi Arabia. The questionnaire comprised: issues relating to misuse of prescription medicines; medicines used without being prescribed by a physician; and suggestions to reduce misuse. Potential participants were approached opportunistically during the campaigns, with those agreeing to participate administered the questionnaire and responses recorded electronically. Results Of the 511 respondents, 59 (11.5%) did not always have their prescription medicines prescribed by a physician, and 196 (38.4%) were uncertain. Commonly cited medicines obtained from sources other than a physician were analgesics (n = 375, 73.2%), antibiotics (n = 57, 11.2%), antipyretics (n = 33, 6.5%) and narcotics (n = 4, 0.8%). More than half (n = 282, 55.2%) claimed to know someone who had misused medicines, some with serious consequences including hospitalization (n = 96, 34.0%) and death (n = 14, 5.0%). Conclusion This general public survey has identified that issues of misuses of medicines in Jeddah, Saudi Arabia persist and may compromise safety and effectiveness of care