Structure Space of Model Proteins --A Principle Component Analysis

We study the space of all compact structures on a two-dimensional square lattice of size $N=6\times6$. Each structure is mapped onto a vector in $N$-dimensions according to a hydrophobic model. Previous work has shown that the designabilities of structures are closely related to the distribution of the structure vectors in the $N$-dimensional space, with highly designable structures predominantly found in low density regions. We use principal component analysis to probe and characterize the distribution of structure vectors, and find a non-uniform density with a single peak. Interestingly, the principal axes of this peak are almost aligned with Fourier eigenvectors, and the corresponding Fourier eigenvalues go to zero continuously at the wave-number for alternating patterns ($q=\pi$). These observations provide a stepping stone for an analytic description of the distribution of structural points, and open the possibility of estimating designabilities of realistic structures by simply Fourier transforming the hydrophobicities of the corresponding sequences.Comment: 14 pages, 12 figures, Conclusion has been modifie

Unsupervised Learning of Frustrated Classical Spin Models I: Principle Component Analysis

This work aims at the goal whether the artificial intelligence can recognize phase transition without the prior human knowledge. If this becomes successful, it can be applied to, for instance, analyze data from quantum simulation of unsolved physical models. Toward this goal, we first need to apply the machine learning algorithm to well-understood models and see whether the outputs are consistent with our prior knowledge, which serves as the benchmark of this approach. In this work, we feed the compute with data generated by the classical Monte Carlo simulation for the XY model in frustrated triangular and union jack lattices, which has two order parameters and exhibits two phase transitions. We show that the outputs of the principle component analysis agree very well with our understanding of different orders in different phases, and the temperature dependences of the major components detect the nature and the locations of the phase transitions. Our work offers promise for using machine learning techniques to study sophisticated statistical models, and our results can be further improved by using principle component analysis with kernel tricks and the neural network method.Comment: 8 pages, 11 figure

Spectral Properties of H-Reflex Recordings After an Acute Bout of Whole-Body Vibration

Although research supports the use of whole-body vibration (WBV) to improve neuromuscular performance, the mechanisms for these improvements remain unclear. The purpose of this study was to identify the effect ofWBV on the spectral properties of electrically evoked H-reflex recordings in the soleus (SOL) muscle. The H-reflex recordings were measured in the SOL muscle of 20 participants before and after a bout of WBV. The H-reflexes were evoked every 15 seconds for 150 seconds after WBV. A wavelet procedure was used to extract spectral data, which were then quantified with a principle components analysis. Resultant principle component scores were used for statistical analysis. The analysis extracted 1 principle component associated with the intensity of the myoelectric spectra and 1 principle component associated with the frequency. The scores of the principle component that were related to the myoelectric intensity were smaller at 30 and 60 milliseconds after WBV than before WBV. The WBV transiently decreased the intensity of myoelectric spectra during electrically evoked contractions, but it did not influence the frequency of the spectra. The decrease in intensity likely indicates a smaller electrically evoked muscle twitch response, whereas the lack of change in frequency would indicate a similar recruitment pattern of motor units before and after WBV

Vision-based hand gesture interaction using particle filter, principle component analysis and transition network

Vision-based human-computer interaction is becoming important nowadays. It offers natural interaction with computers and frees users from mechanical interaction devices, which is favourable especially for wearable computers. This paper presents a human-computer interaction system based on a conventional webcam and hand gesture recognition. This interaction system works in real time and enables users to control a computer cursor with hand motions and gestures instead of a mouse. Five hand gestures are designed on behalf of five mouse operations: moving, left click, left-double click, right click and no-action. An algorithm based on Particle Filter is used for tracking the hand position. PCA-based feature selection is used for recognizing the hand gestures. A transition network is also employed for improving the accuracy and reliability of the interaction system. This interaction system shows good performance in the recognition and interaction test

Removal of Spectro-Polarimetric Fringes by 2D Pattern Recognition

We present a pattern-recognition based approach to the problem of removal of polarized fringes from spectro-polarimetric data. We demonstrate that 2D Principal Component Analysis can be trained on a given spectro-polarimetric map in order to identify and isolate fringe structures from the spectra. This allows us in principle to reconstruct the data without the fringe component, providing an effective and clean solution to the problem. The results presented in this paper point in the direction of revising the way that science and calibration data should be planned for a typical spectro-polarimetric observing run.Comment: ApJ, in pres