Application of process algebraic verification and reduction techniques to SystemC designs

SystemC is an IEEE standard system-level language used in hardware/software codesign and has been widely adopted in the industry. This paper describes a formal approach to verifying SystemC designs by providing a mapping to the process algebra mCRL2. Our mapping formalizes both the simulation semantics as well as exhaustive state-space exploration of SystemC designs. By exploiting the existing reduction techniques of mCRL2 and also its model-checking tools, we efficiently locate the race conditions in a system and resolve them. A tool is implemented to automatically perform the proposed mapping. This mapping and the implemented tool enabled us to exploit process-algebraic verification techniques to analyze a number of case-studies, including the formal analysis of a single-cycle and a pipelined MIPS processor specified in SystemC.

Orthogonally Regularized Deep Networks For Image Super-resolution

Deep learning methods, in particular trained Convolutional Neural Networks (CNNs) have recently been shown to produce compelling state-of-the-art results for single image Super-Resolution (SR). Invariably, a CNN is learned to map the low resolution (LR) image to its corresponding high resolution (HR) version in the spatial domain. Aiming for faster inference and more efficient solutions than solving the SR problem in the spatial domain, we propose a novel network structure for learning the SR mapping function in an image transform domain, specifically the Discrete Cosine Transform (DCT). As a first contribution, we show that DCT can be integrated into the network structure as a Convolutional DCT (CDCT) layer. We further extend the network to allow the CDCT layer to become trainable (i.e. optimizable). Because this layer represents an image transform, we enforce pairwise orthogonality constraints on the individual basis functions/filters. This Orthogonally Regularized Deep SR network (ORDSR) simplifies the SR task by taking advantage of image transform domain while adapting the design of transform basis to the training image set

DFDL: Discriminative Feature-oriented Dictionary Learning for Histopathological Image Classification

In histopathological image analysis, feature extraction for classification is a challenging task due to the diversity of histology features suitable for each problem as well as presence of rich geometrical structure. In this paper, we propose an automatic feature discovery framework for extracting discriminative class-specific features and present a low-complexity method for classification and disease grading in histopathology. Essentially, our Discriminative Feature-oriented Dictionary Learning (DFDL) method learns class-specific features which are suitable for representing samples from the same class while are poorly capable of representing samples from other classes. Experiments on three challenging real-world image databases: 1) histopathological images of intraductal breast lesions, 2) mammalian lung images provided by the Animal Diagnostics Lab (ADL) at Pennsylvania State University, and 3) brain tumor images from The Cancer Genome Atlas (TCGA) database, show the significance of DFDL model in a variety problems over state-of-the-art methodsComment: Accepted to IEEE International Symposium on Biomedical Imaging (ISBI), 201

Multi-task Image Classification via Collaborative, Hierarchical Spike-and-Slab Priors

Promising results have been achieved in image classification problems by exploiting the discriminative power of sparse representations for classification (SRC). Recently, it has been shown that the use of \emph{class-specific} spike-and-slab priors in conjunction with the class-specific dictionaries from SRC is particularly effective in low training scenarios. As a logical extension, we build on this framework for multitask scenarios, wherein multiple representations of the same physical phenomena are available. We experimentally demonstrate the benefits of mining joint information from different camera views for multi-view face recognition.Comment: Accepted to International Conference in Image Processing (ICIP) 201

Structural Analysis of Medical Tourists' Feedback toward Medical Centers Behavior for Evaluating the Health Tourism Barriers in Kashan City

Introduction: Health tourism as an economic development strategy in countries where medical tourists spend more than others to improve their health has increased the income and gross national product. In this regard, Kashan city, with its numerous medical centers with international licenses, has a high potential in developing medical tourism at the national and regional levels. Therefore, the present study has structural analyzed the feedback of health tourists regarding the behavior of medical centers in order to evaluate the obstacles of medical tourism in the hospitals of Kashan city.Data and Method: This research is an appelied study in terms of practical purpose and is a survey and analytical study in terms of method and type. The data collection tool is a researcher-made questionnaire with 5 indicators and 61 variables, which was completed by 308 medical tourists in Kashan medical centers after confirming validity and reliability with Cronbach's alpha 0.807. Data analysis was done through t-test, Tukey's and structural equation modeling.Results: Results showed that the usage of famous doctors with a t-statistic of 106.59 is the best factor in the reason for choosing a medical center. The variable of disease diagnosis and therapy methods with a t-statistic of 84.50 is the best factor in the satisfaction from services, and the variable of patient companions' access to comfort facilities with a t-statistic of 119.26 is the best factor in the satisfaction from facilities. The variable of reaching the health center through land routes with a t-statistic of 36.38 is the best factor in the access to hospital. Also, the indicators of the reasons for choosing center, satisfaction from services and awareness methods have the highest effect with factor loadings of 0.99, 0.94 and 0.7, respectively. In return, indicators of satisfaction from facilities and access methods have the lowest effect with factor loadings of 0.24 and 0.34 respectively.Conclusion: As a result, it indicates that the presence of experienced doctors with a brilliant treatment record plays a positive role in attracting medical tourists. Also, it can be stated that the comfort facilities of medical centers can play a role in their acceptability from the view point of health tourists as much as medical services. Therefore, for the development of medical tourism in Kashan city, strengthening the facilities of health tourism and transportation infrastructures in the region is the first priority of planning to remove obstacles

DyNetKAT: An Algebra of Dynamic Networks

We introduce a formal language for specifying dynamic updates for Software Defined Networks. Our language builds upon Network Kleene Algebra with Tests (NetKAT) and adds constructs for synchronisations and multi-packet behaviour to capture the interaction between the control- and data-plane in dynamic updates. We provide a sound and ground-complete axiomatisation of our language. We exploit the equational theory to provide an efficient reasoning method about safety properties for dynamic networks. We implement our equational theory in DyNetiKAT -- a tool prototype, based on the Maude Rewriting Logic and the NetKAT tool, and apply it to a case study. We show that we can analyse the case study for networks with hundreds of switches using our initial tool prototype