Quantum Hidden Markov Models based on Transition Operation Matrices

In this work, we extend the idea of Quantum Markov chains [S. Gudder. Quantum Markov chains. J. Math. Phys., 49(7), 2008] in order to propose Quantum Hidden Markov Models (QHMMs). For that, we use the notions of Transition Operation Matrices (TOM) and Vector States, which are an extension of classical stochastic matrices and probability distributions. Our main result is the Mealy QHMM formulation and proofs of algorithms needed for application of this model: Forward for general case and Vitterbi for a restricted class of QHMMs.Comment: 19 pages, 2 figure

It is not real until it feels real : testing a new method for simulation of eyewitness experience with virtual reality technology and equipment

Laboratory research in the psychology of witness testimony is often criticized for its lack of ecological validity, including the use of unrealistic artifcial stimuli to test memory performance. The purpose of our study is to present a method that can provide an intermediary between laboratory research and feld studies or naturalistic experiments that are difcult to control and administer. It uses Video-360° technology and virtual reality (VR) equipment, which cuts subjects of from external stimuli and gives them control over the visual feld. This can potentially increase the realism of the eyewitness's experience. To test the method, we conducted an experiment comparing the immersion efect, emotional response, and memory performance between subjects who watched a video presenting a mock crime on a head-mounted display (VR goggles; n=57) and a screen (n=50). The results suggest that, compared to those who watched the video on a screen, the VR group had a deeper sense of immersion, that is, of being part of the scene presented. At the same time, they were not distracted or cognitively overloaded by the more complex virtual environment, and remembered just as much detail about the crime as those viewing it on the screen. Additionally, we noted signifcant diferences between subjects in ratings of emotions felt during the video. This may suggest that the two formats evoke diferent types of discrete emotions. Overall, the results confrm the usefulness of the proposed method in witness research

Effective Training of Deep Convolutional Neural Networks for Hyperspectral Image Classification through Artificial Labeling

Hyperspectral imaging is a rich source of data, allowing for a multitude of effective applications. However, such imaging remains challenging because of large data dimension and, typically, a small pool of available training examples. While deep learning approaches have been shown to be successful in providing effective classification solutions, especially for high dimensional problems, unfortunately they work best with a lot of labelled examples available. The transfer learning approach can be used to alleviate the second requirement for a particular dataset: first the network is pre-trained on some dataset with large amount of training labels available, then the actual dataset is used to fine-tune the network. This strategy is not straightforward to apply with hyperspectral images, as it is often the case that only one particular image of some type or characteristic is available. In this paper, we propose and investigate a simple and effective strategy of transfer learning that uses unsupervised pre-training step without label information. This approach can be applied to many of the hyperspectral classification problems. The performed experiments show that it is very effective at improving the classification accuracy without being restricted to a particular image type or neural network architecture. The experiments were carried out on several deep neural network architectures and various sizes of labeled training sets. The greatest improvement in overall accuracy on the Indian Pines and Pavia University datasets is over 21 and 13 percentage points, respectively. An additional advantage of the proposed approach is the unsupervised nature of the pre-training step, which can be done immediately after image acquisition, without the need of the potentially costly expert’s time

Blood Stain Classification with Hyperspectral Imaging and Deep Neural Networks

In recent years, growing interest in deep learning neural networks has raised a question on how they can be used for effective processing of high-dimensional datasets produced by hyperspectral imaging (HSI). HSI, traditionally viewed as being within the scope of remote sensing, is used in non-invasive substance classification. One of the areas of potential application is forensic science, where substance classification on the scenes is important. An example problem from that area—blood stain classification—is a case study for the evaluation of methods that process hyperspectral data. To investigate the deep learning classification performance for this problem we have performed experiments on a dataset which has not been previously tested using this kind of model. This dataset consists of several images with blood and blood-like substances like ketchup, tomato concentrate, artificial blood, etc. To test both the classic approach to hyperspectral classification and a more realistic application-oriented scenario, we have prepared two different sets of experiments. In the first one, Hyperspectral Transductive Classification (HTC), both a training and a test set come from the same image. In the second one, Hyperspectral Inductive Classification (HIC), a test set is derived from a different image, which is more challenging for classifiers but more useful from the point of view of forensic investigators. We conducted the study using several architectures like 1D, 2D and 3D convolutional neural networks (CNN), a recurrent neural network (RNN) and a multilayer perceptron (MLP). The performance of the models was compared with baseline results of Support Vector Machine (SVM). We have also presented a model evaluation method based on t-SNE and confusion matrix analysis that allows us to detect and eliminate some cases of model undertraining. Our results show that in the transductive case, all models, including the MLP and the SVM, have comparative performance, with no clear advantage of deep learning models. The Overall Accuracy range across all models is 98–100% for the easier image set, and 74–94% for the more difficult one. However, in a more challenging inductive case, selected deep learning architectures offer a significant advantage; their best Overall Accuracy is in the range of 57–71%, improving the baseline set by the non-deep models by up to 9 percentage points. We have presented a detailed analysis of results and a discussion, including a summary of conclusions for each tested architecture. An analysis of per-class errors shows that the score for each class is highly model-dependent. Considering this and the fact that the best performing models come from two different architecture families (3D CNN and RNN), our results suggest that tailoring the deep neural network architecture to hyperspectral data is still an open problem

Spatial metaphors of psychological time : the study of imprisoned men

Psychological time is a complex concept. We examined how male inmates (N = 32) experience selected aspects of time. The study focused on the experience of the present and how it is related to the past and the future. To explore how time is experienced, we referred to spatial metaphors that conceptualize time in simpler categories based on spatial relations. Inmates tend to experience the present as brief moments, and when defining it – and other dimensions of time – they use a limited vocabulary. They tend to have an egocentric perception of time, in their estimations focusing more on the life time than the history time. Inmates seem to be primarily present-oriented, and they think about the present and the future frequently. At the same time, they do not perceive the interrelations between the three dimensions of time. It is discussed that the temporal dispositions of the inmates can be influenced by specific conditions of prison isolation, in which time serves as a method of discipline