A new polynomial $$Q_G(x)$$ associated to graphs G is defined and studied. The main theorems represent $$Q_G(x)$$ as a quasi-specialization of the rank-generating polynomial $$S_G(x,y)$$ of Oxley and Whittle, J Comb Theory Ser B 59:210–244, 1993, [10] and show that $$Q_G$$ is likewise a generalized Tutte–Grothendieck invariant. The value $$Q_G(1)$$ gives the amplitude of acceptance for a class of quantum circuits with associated graphs G. This class, called stabilizer circuits or Clifford circuits, has long been known to have deterministic polynomial time simulation, so $$Q_G(1)$$ is polynomial time computable, given G as input. The specialization has $$y = -\sqrt{2}i$$ , which (along with its complex conjugate) is the only choice that invalidates formulas in a theorem by Noble, Comb. Probab. Comput 15:449–461, 2006, [9] classifying hard and easy real points of $$S_G$$ , so the complexity of other points $$Q_G(x)$$ is open. We reduce the base cases for $$S_G$$ by adjoining multiple kinds of isolated nodes and draw possible further implications of the connections between matroid theory and quantum computing developed by this work.
In this work, we have classified the frames of a broadcast soccer video into four classes, namely long shot, medium shot, close shot and logo frame. A two-stream deep neural network (DNN) model is proposed for the shot classification. Along with static image features, player attributes like count of the players in a frame, area, width and height of the players are used as features for the classification. The heterogeneous features are fed into the DNN model through a late fusion strategy. In addition to shot classification, we propose a model to detect replay within a soccer video. The logo frames are used to decide the temporal boundary of a replay segment. A majority class assignment strategy is employed to improve the accuracy of replay detection. The experimental results show that our method is at least 12% better than that of similar approaches.
Intents are Android’s intra- and inter-application communication mechanism. They specify an action to perform, with extra data, and are sent to a receiver component or broadcast to many components. Components, in the same or in a distinct app, receive the intent if they are available to perform the desired action. Hence, a sound static analyzer must be aware of information flows through intents. That can be achieved by considering intents as both source (when reading) and sink (when writing) of confidential data. But this is overly conservative if the intent stays inside the same app or if the set of apps installed on the device is known in advance. In such cases, a sound approximation of the flow of intents leads to a more precise analysis. This work describes SDLI, a novel static analyzer that, for each app, creates an XML summary file reporting a description of the tainted information in outwards intents and of the intents the app is available to serve. SDLI discovers confidential information leaks when two apps communicate, by matching their XML summaries, looking for tainted outwards intents of the first app that can be inwards intents of the second app. The tool is implemented inside Julia, an industrial static analyzer. On the DroidBench test cases, its shows a precision higher than 75%. On some popular apps from the Google Play marketplace, it spots inter-apps leaks of confidential data, hence showing its practical effectiveness.
Version vectors constitute an essential feature of distributed systems that enable the computing elements to keep track of causality between the events of the distributed systems. In this article, we study a variant named Bounded Version Vectors. We define the semantics of version vectors using the framework of Mazurkiewicz traces. We use these semantics along with the solution for the gossip problem to come up with a succinct bounded representation of version vectors in distributed environments where replicas communicate via pairwise synchronization.
In this paper, we have implemented an unsupervised approach for finding out the significant genes from microarray gene expression datasets. The proposed method is based on implements a quantum clustering approach to represent gene-expression data as equations and uses the procedure to search for the most probable set of clusters given the available data. The main contribution of this approach lies in the ability to take into account the essential features or genes using clustering. Here, we present a novel clustering approach that extends ideas from scale-space clustering and support-vector clustering. This clustering method is used as a feature selection method. Our approach is fundamentally based on the representation of datapoints or features in the Hilbert space, which is then represented by the Schrödinger equation, of which the probability function is a solution. This Schrödinger equation contains a potential function that is extended from the initial probability function.The minima of the potential values are then treated as cluster centres. The cluster centres thus stand out as representative genes. These genes are evaluated using classifiers, and their performance is recorded over various indices of classification. From the experiments, it is found that the classification performance of the reduced set is much better than the entire dataset.The only free-scale parameter, sigma, is then altered to obtain the highest accuracy, and the corresponding biological significance of the genes is noted.
