Insightful classification of crystal structures using deep learning

Computational methods that automatically extract knowledge from data are critical for enabling data-driven materials science. A reliable identification of lattice symmetry is a crucial first step for materials characterization and analytics. Current methods require a user-specified threshold, and are unable to detect average symmetries for defective structures. Here, we propose a machine-learning-based approach to automatically classify structures by crystal symmetry. First, we represent crystals by calculating a diffraction image, then construct a deep-learning neural-network model for classification. Our approach is able to correctly classify a dataset comprising more than 100 000 simulated crystal structures, including heavily defective ones. The internal operations of the neural network are unraveled through attentive response maps, demonstrating that it uses the same landmarks a materials scientist would use, although never explicitly instructed to do so. Our study paves the way for crystal-structure recognition of - possibly noisy and incomplete - three-dimensional structural data in big-data materials science.Comment: Nature Communications, in press (2018

A Review of Wavelet Based Fingerprint Image Retrieval

A digital image is composed of pixels and information about brightness of image and RGB triples are used to encode color information. Image retrieval problem encountered when searching and retrieving images that is relevant to a user’s request from a database. In Content based image retrieval, input goes in the form of an image. In these images, different features are extracted and then the other images from database are retrieved accordingly. Biometric distinguishes the people by their physical or behavioral qualities. Fingerprints are viewed as a standout amongst the most solid for human distinguishment because of their uniqueness and ingenuity. To retrieve fingerprint images on the basis of their textural features,by using different wavelets. From the input fingerprint image, first of all center point area is selected and then its textural features are extracted and stored in database. When a query image comes then again its center point is selected and then its texture feature are extracted. Then these features are matched for similarity and then resultant image is displayed. DOI: 10.17762/ijritcc2321-8169.15026

New approaches in the chemometric analysis of infrared spectra of extra-virgin olive oils

The aim of this paper is to apply new chemometric approaches to obtain quantitative information from near and mid infrared spectra of Andalusian extra-virgin olive oils, using gas chromatography as a classical reference analytical technique. Estimations of the content in saturated, monounsaturated and polyunsaturated fatty acids are given using partial least squares regression from the near and mid infrared data matrices as well as their concatenated matrix. The different estimations are evaluated in terms of goodness of fit (calibration) and prediction (validation), as a function of the number of partial least squares factors in the regression model and the used matrix of data. Furthermore, the nature, systematic or random, of the prediction errors is studied by a decomposition of their mean squared error. Finally, procedures of cross-validation are implemented in order to generalize the previous results

Accelerating Pattern Recognition Algorithms On Parallel Computing Architectures

The move to more parallel computing architectures places more responsibility on the programmer to achieve greater performance. The programmer must now have a greater understanding of the underlying architecture and the inherent algorithmic parallelism. Using parallel computing architectures for exploiting algorithmic parallelism can be a complex task. This dissertation demonstrates various techniques for using parallel computing architectures to exploit algorithmic parallelism. Specifically, three pattern recognition (PR) approaches are examined for acceleration across multiple parallel computing architectures, namely field programmable gate arrays (FPGAs) and general purpose graphical processing units (GPGPUs). Phase-only filter correlation for fingerprint identification was studied as the first PR approach. This approach\u27s sensitivity to angular rotations, scaling, and missing data was surveyed. Additionally, a novel FPGA implementation of this algorithm was created using fixed point computations, deep pipelining, and four computation phases. Communication and computation were overlapped to efficiently process large fingerprint galleries. The FPGA implementation showed approximately a 47 times speedup over a central processing unit (CPU) implementation with negligible impact on precision. For the second PR approach, a spiking neural network (SNN) algorithm for a character recognition application was examined. A novel FPGA implementation of the approach was developed incorporating a scalable modular SNN processing element (PE) to efficiently perform neural computations. The modular SNN PE incorporated streaming memory, fixed point computation, and deep pipelining. This design showed speedups of approximately 3.3 and 8.5 times over CPU implementations for 624 and 9,264 sized neural networks, respectively. Results indicate that the PE design could scale to process larger sized networks easily. Finally for the third PR approach, cellular simultaneous recurrent networks (CSRNs) were investigated for GPGPU acceleration. Particularly, the applications of maze traversal and face recognition were studied. Novel GPGPU implementations were developed employing varying quantities of task-level, data-level, and instruction-level parallelism to achieve efficient runtime performance. Furthermore, the performance of the face recognition application was examined across a heterogeneous cluster of multi-core and GPGPU architectures. A combination of multi-core processors and GPGPUs achieved roughly a 996 times speedup over a single-core CPU implementation. From examining these PR approaches for acceleration, this dissertation presents useful techniques and insight applicable to other algorithms to improve performance when designing a parallel implementation

Investigating the quasi-liquid layer on ice surfaces: a comparison of order parameters

Ice surfaces are characterized by pre-melted quasi-liquid layers (QLLs), which mediate both crystal growth processes and interactions with external agents. Understanding QLLs at the molecular level is necessary to unravel the mechanisms of ice crystal formation. Computational studies of the QLLs heavily rely on the accuracy of the methods employed for identifying the local molecular environment and arrangements, discriminating between solid-like and liquid-like water molecules. Here we compare the results obtained using different order parameters to characterize the QLLs on hexagonal ice (Ih) and cubic ice (Ic) model surfaces investigated with molecular dynamics (MD) simulations in a range of temperatures. For the classification task, in addition to the traditional Steinhardt order parameters in different flavours, we select an entropy fingerprint and a deep learning neural network approach (DeepIce), which are conceptually different methodologies. We find that all the analysis methods give qualitatively similar trends for the behaviours of the QLLs on ice surfaces with temperature, with some subtle differences in the classification sensitivity limited to the solid-liquid interface. The thickness of QLLs on the ice surface increases gradually as the temperature increases. The trends of the QLL size and of the values of the order parameters as a function of temperature for the different facets may be linked to surface growth rates which, in turn, affect crystal morphologies at lower vapour pressure. The choice of the order parameter can be therefore informed by computational convenience except in cases where a very accurate determination of the liquid-solid interface is important

[[alternative]]Design of a Fingerprint Classification System

計畫編號：NSC94-2213-E032-020研究期間：200508~200607研究經費：536,000[[abstract]]在生物辨識研究的範疇裡，指紋可以算是目前最廣為應用的生物特徵之一，也因為指紋被如此廣泛應用及建檔，指紋資料庫規模日益龐大，因此，為了減少在龐大的指紋資料庫中搜尋的時間，以減少系統運算量，我們通常會先將指紋作一初步的分類。指紋分類可視為指紋辨識的粗略比對程序，用以剔除差異性過大的樣本，在過去的研究文獻中，已提出許多種有效的指紋分類的方法，這些方法各有其優缺點，其中奇異點的搜尋易受雜訊影響，而且許多演算法速度上表現的並不傑出。本研究計畫中擬設計出一個有效且快速的分類方法。在本系統中，我們計畫直接透過在自動指紋辨識系統(AFIS)中細化後的影像擷取指紋方向的資訊以免除重複的影像強化步驟來提昇整體系統效能，並設計一個快速的區域中心搜尋演算法，找出我們所感興趣的區域中心，接著我們試圖分析中心周圍的方向資訊，找出每一類型指紋所代表的方向特徵，再將其特徵擷取下來作為分類依據，而且這些特徵將來也可以作為身份辨識的延伸資訊。最後我們擬以此研究領域中被廣為使用的NIST-4資料庫來訓練、測試我們的系統。[[sponsorship]]行政院國家科學委員

An Isolated Water Droplet in the Aqueous Solution of a Supramolecular Tetrahedral Cage

Water under nanoconfinement at ambient conditions has exhibited low-dimensional ice formation and liquid-solid phase transitions, but with structural and dynamical signatures which map onto known regions of waters phase diagram. Using THz absorption spectroscopy and ab initio molecular dynamics, we have investigated the ambient water confined in a supramolecular tetrahedral assembly, and determined that a distinct network of 9-10 water molecules is present within the nanocavity of the host. The low-frequency absorption spectrum and theoretical analysis of the water in the $Ga_4$$L_6$$^{-12}$ host demonstrate that the structure and dynamics of the encapsulated droplet is distinct from any known phase of water. A further inference is that the release of the highly unusual encapsulated water droplet creates a strong thermodynamic driver for the high affinity binding of guests in aqueous solution for the $Ga_4$$L_6$$^{-12}$ supramolecular construct

Applying Fourier-Transform Infrared Spectroscopy and Self-Organizing Maps for Forensic Classification of White-Copy Papers

White-copy A4 paper is an important kind of substrate for preparation of most formal as well as informal documents. It often encountered as questioned document in cases such as falsification, embezzlement or forgery. By comparing the questioned piece, (e.g. of a contract) against the rest deemed authentic, forgery indicator could be derived from an inconsistent chemical composition.  However, classification and even differentiation of white copy paper have been difficult due to highly similar physical properties and chemical composition. Self-organizing map (SOM) has been proven useful in many published works as a good tool for clustering and classification of samples, especially when involving high-dimensional data. In this preliminary paper, we explore the feasibility of SOM in classifying white copy paper for forensic purposes. A total of 150 infrared spectra were collected from three varieties of white paper using Attenuated Total Reflectance Fourier-transform infrared (ATR-FTIR) spectroscopy. Each IR spectrum composed of over thousands of wavenumbers (i.e. input variables) and resembles chemical fingerprint for the sample. Comparative performance between raw wavenumbers and its reduced form (i.e. principal components, PCs) in SOM modeling also conducted. Results showed that SOM built with PCs is much efficient than built with raw wavenumbers, with the classification accuracy of over 90% is obtained with external validation test. This study shows that SOM coupled with ATR-FTIR spectroscopy could be a potential non-destructive approach for forensic paper analysis