Powder crystallography is the experimental science of determining the structure of molecules provided in crystalline-powder form,by analyzing their x-ray diffraction(XRD)*** many materials are readily available as cry...
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Powder crystallography is the experimental science of determining the structure of molecules provided in crystalline-powder form,by analyzing their x-ray diffraction(XRD)*** many materials are readily available as crystalline powder,powder crystallography is of growing usefulness to many ***,powder crystallography does not have an analytically known solution,and therefore the structural inference typically involves a laborious process of iterative design,structural refinement,and domain knowledge of skilled experts.A key obstacle to fully automating the inference process computationally has been formulating the problem in an end-to-end quantitative form that is suitable for machine learning,while capturing the ambiguities around molecule orientation,symmetries,and reconstruction *** we present an ML approach for structure determination from powder diffraction *** works by estimating the electron density in a unit cell using a variational coordinate-based deep neural *** demonstrate the approach on computed powder x-ray diffraction(PXRD),along with partial chemical composition information,as *** evaluated on theoretically simulated data for the cubic and trigonal crystal systems,the system achieves up to 93.4%average similarity(as measured by structural similarity index)with the ground truth on unseen materials,both with known and partially-known chemical composition information,showing great promise for successful structure solution even from degraded and incomplete input *** approach does not presuppose a crystalline structure and the approach are readily extended to other situations such as nanomaterials and textured samples,paving the way to reconstruction of yet unresolved nanostructures.
This paper introduces a new Database Transposition, Substitution and XORing Algorithm (DTSXA) based on using chaotic maps. It is based primarily on two well-known security properties: confusion and diffusion. A random...
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After the authors had successfully modelled and simulated the outdoor traffic of a warehouse with the aid of high-level Petri nets, they failed to do so for the combined in- and outbound traffic using the same techniq...
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IQ is one of the indicators that has always been of interest to psychiatrists, doctors and cognitivescience researchers. Since this index plays a key role in people's lives and also in the occurrence of brain abn...
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Resonant operation, exploiting high quality-factor planar inductors, has recently enabled gigahertz (GHz) applications for large-area electronics (LAE), providing a new technology platform for large-scale and flexible...
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This study investigates explainable machine learning algorithms for identifying depression from speech. Grounded in evidence from speech production that depression affects motor control and vowel generation, pre-train...
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The aim of our research is the enhancement of decision support methods grounded in statistical quality control. In our study we combine machine learning classifiers, and explanatory algorithms (XAI) with the Six Sigma...
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In this paper, we discuss an indexing method for solving the multiple string pattern matching problem, by which we are given a set of short pattern strings R = {r1,.., rl} and required to locate all those substrings o...
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Providing explanations based on user reviews in recommender systems (RS) may increase users' perception of transparency or effectiveness. However, little is known about how these explanations should be presented t...
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Goal misalignment, reward sparsity and difficult credit assignment are only a few of the many issues that make it difficult for deep reinforcement learning (RL) agents to learn optimal policies. Unfortunately, the bla...
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