Answer Set Programming Modulo `Space-Time'

We present ASP Modulo `Space-Time', a declarative representational and computational framework to perform commonsense reasoning about regions with both spatial and temporal components. Supported are capabilities for mixed qualitative-quantitative reasoning, consistency checking, and inferring compositions of space-time relations; these capabilities combine and synergise for applications in a range of AI application areas where the processing and interpretation of spatio-temporal data is crucial. The framework and resulting system is the only general KR-based method for declaratively reasoning about the dynamics of `space-time' regions as first-class objects. We present an empirical evaluation (with scalability and robustness results), and include diverse application examples involving interpretation and control tasks

Size-Aware Hypergraph Motifs

Complex systems frequently exhibit multi-way, rather than pairwise, interactions. These group interactions cannot be faithfully modeled as collections of pairwise interactions using graphs, and instead require hypergraphs. However, methods that analyze hypergraphs directly, rather than via lossy graph reductions, remain limited. Hypergraph motif mining holds promise in this regard, as motif patterns serve as building blocks for larger group interactions which are inexpressible by graphs. Recent work has focused on categorizing and counting hypergraph motifs based on the existence of nodes in hyperedge intersection regions. Here, we argue that the relative sizes of hyperedge intersections within motifs contain varied and valuable information. We propose a suite of efficient algorithms for finding triplets of hyperedges based on optimizing the sizes of these intersection patterns. This formulation uncovers interesting local patterns of interaction, finding hyperedge triplets that either (1) are the least correlated with each other, (2) have the highest pairwise but not groupwise correlation, or (3) are the most correlated with each other. We formalize this as a combinatorial optimization problem and design efficient algorithms based on filtering hyperedges. Our experimental evaluation shows that the resulting hyperedge triplets yield insightful information on real-world hypergraphs. Our approach is also orders of magnitude faster than a naive baseline implementation

A symmetry for vanishing cosmological constant

Two different realizations of a symmetry principle that impose a zero cosmological constant in an extra-dimensional set-up are studied. The symmetry is identified by multiplication of the metric by minus one. In the first realization of the symmetry this is provided by a symmetry transformation that multiplies the coordinates by the imaginary number i. In the second realization this is accomplished by a symmetry transformation that multiplies the metric tensor by minus one. In both realizations of the symmetry the requirement of the invariance of the gravitational action under the symmetry selects out the dimensions given by D = 2(2n+1), n=0,1,2,... and forbids a bulk cosmological constant. Another attractive aspect of the symmetry is that it seems to be more promising for quantization when compared to the usual scale symmetry. The second realization of the symmetry is more attractive in that it is posible to make a possible brane cosmological constant zero in a simple way by using the same symmetry, and the symmetry may be identified by reflection symmetry in extra dimensions.Comment: Talk in the conference IRGAC 2006, 2nd International Conference on Quantum Theories and Renormalization Group in Gravity and Cosmology, Barcelon

Modeling Stable Matching Problems with Answer Set Programming

The Stable Marriage Problem (SMP) is a well-known matching problem first introduced and solved by Gale and Shapley (1962). Several variants and extensions to this problem have since been investigated to cover a wider set of applications. Each time a new variant is considered, however, a new algorithm needs to be developed and implemented. As an alternative, in this paper we propose an encoding of the SMP using Answer Set Programming (ASP). Our encoding can easily be extended and adapted to the needs of specific applications. As an illustration we show how stable matchings can be found when individuals may designate unacceptable partners and ties between preferences are allowed. Subsequently, we show how our ASP based encoding naturally allows us to select specific stable matchings which are optimal according to a given criterion. Each time, we can rely on generic and efficient off-the-shelf answer set solvers to find (optimal) stable matchings.Comment: 26 page

Graphene oxide integrated sensor for electrochemical monitoring ofmitomycin C–DNA interaction

WOS: 000302308600025PubMed ID: 22439135We present a graphene oxide (GO) integrated disposable electrochemical sensor for the enhanced detection of nucleic acids and the sensitive monitoring of the surface-confined interactions between the anticancer drug mitomycin C (MC) and DNA. Interfacial interactions between immobilized calf thymus double-stranded (dsDNA) and anticancer drug MC were investigated using differential pulse voltammetry (DPV) and electrochemical impedance spectroscopy (EIS) techniques. Based on three repetitive voltammetric measurements of 120 mu g mL(-1) DNA immobilized on GO-modified electrodes, the RSD % (n = 3) was calculated as 10.47% and the detection limit (DL) for dsDNA was found to be 9.06 mu g mL(-1). EIS studies revealed that the binding of the drug MC to dsDNA leads to a gradual decrease of its negative charge. As a consequence of this interaction, the negative redox species were allowed to approach the electrode, and thus increase the charge transfer kinetics. On the other hand, DPV studies exploited the decrease of the guanine signal due to drug binding as the basis for specifically probing the biointeraction process between MC and dsDNA.Royal Society through Joint Project Scheme [1212R0168]; Turkish Academy of Sciences (TUBA)Turkish Academy of SciencesThis work was supported by the Royal Society through Joint Project Scheme (Project No. 1212R0168). A.E. acknowledges the Turkish Academy of Sciences (TUBA) as an Associate member for its partial support. Authors would like to thank Dr. M. McMullan for the assistance on the synthesis of graphene oxide

Zero Cosmological Constant and Nonzero Dark Energy from Holographic Principle

It is shown that the first law of thermodynamics and the holographic principle applied to an arbitrary large cosmic causal horizon naturally demand the zero cosmological constant and non-zero dynamical dark energy in the form of the holographic dark energy. Semiclassical analysis shows that the holographic dark energy has a parameter $d=1$ and an equation of state comparable to current observational data, if the entropy of the horizon saturates the Bekenstein-Hawking bound. This result indicates that quantum field theory should be modified at large scale to explain dark energy. The relations among dark energy, quantum vacuum energy and entropic gravity are also discussed.Comment: Revtex 7 pages 2 fig

Applicability of the Long Chain Diol Index (LDI) as a Sea Surface Temperature Proxy in the Arabian Sea

The long-chain diol index (LDI) is a relatively new proxy for sea surface temperature (SST) which has been rarely applied in upwelling regions. Here, we evaluated its application by comparison with other SST records obtained by commonly used proxies, that is, the Mg/Ca ratio of the planktonic foraminifera species Globigerinoides ruber and the alkenone paleothermometer U-37(K '). We focused on the last glacial-interglacial transition of four different sedimentary archives from the western and northern Arabian Sea, which are currently under the influence of monsoon-induced upwelling and the associated development of an oxygen minimum zone. The UK ' 37 ${{\mathrm{U}}{\mathrm{K}\prime }}_{37}$ and Mg/Ca-G.ruber SST records revealed an increase of 0.6-3.4 degrees C from the Last Glacial Maximum to the late Holocene with somewhat higher amplitude in the northern part of the Arabian Sea than compared to the western part. In contrast, the LDI SSTs did not reveal major changes during the last glacial-interglacial transition which was followed by a decreasing trend during the Holocene. The LGM versus the Holocene LDI SSTs ranged between -0.2 and -2.7 degrees C. Particularly at one record, offshore Oman, the SST decrease during the Holocene was high in amplitude, suggesting a potential cold bias, possibly related to changes in upwelling intensity. This indicates that care has to be taken when applying the LDI for annual mean SST reconstruction in upwelling regions

The effect of social media communication on consumer perceptions of brands

Researchers and brand managers have limited understanding of the effects social media communication has on how consumers perceive brands. We investigated 504 Facebook users in order to observe the impact of firm-created and user-generated social media communication on brand equity, brand attitude and purchase intention by using a standardized online survey throughout Poland. To test the conceptual model, we analyzed 60 brands across three different industries: non-alcoholic beverages, clothing and mobile network operators. When analyzing the data, we applied the structural equation modeling technique to both investigate the interplay of firm-created and user-generated social media communication and examine industry-specific differences. The results of the empirical studies showed that user-generated social media communication had a positive influence on both brand equity and brand attitude, whereas firm-created social media communication affected only brand attitude. Both brand equity and brand attitude were shown to have a positive influence on purchase intention. In addition, we assessed measurement invariance using a multi-group structural modeling equation. The findings revealed that the proposed measurement model was invariant across the researched industries. However, structural path differences were detected across the models

Defining Image Memorability using the Visual Memory Schema

Memorability of an image is a characteristic determined by the human observers’ ability to remember images they have seen. Yet recent work on image memorability defines it as an intrinsic property that can be obtained independent of the observer. The current study aims to enhance our understanding and prediction of image memorability, improving upon existing approaches by incorporating the properties of cumulative human annotations. We propose a new concept called the Visual Memory Schema (VMS) referring to an organization of image components human observers share when encoding and recognizing images. The concept of VMS is operationalised by asking human observers to define memorable regions of images they were asked to remember during an episodic memory test. We then statistically assess the consistency of VMSs across observers for either correctly or incorrectly recognised images. The associations of the VMSs with eye fixations and saliency are analysed separately as well. Lastly, we adapt various deep learning architectures for the reconstruction and prediction of memorable regions in images and analyse the results when using transfer learning at the outputs of different convolutional network layers

Statistical Model of Shape Moments with Active Contour Evolution for Shape Detection and Segmentation

This paper describes a novel method for shape representation and robust image segmentation. The proposed method combines two well known methodologies, namely, statistical shape models and active contours implemented in level set framework. The shape detection is achieved by maximizing a posterior function that consists of a prior shape probability model and image likelihood function conditioned on shapes. The statistical shape model is built as a result of a learning process based on nonparametric probability estimation in a PCA reduced feature space formed by the Legendre moments of training silhouette images. A greedy strategy is applied to optimize the proposed cost function by iteratively evolving an implicit active contour in the image space and subsequent constrained optimization of the evolved shape in the reduced shape feature space. Experimental results presented in the paper demonstrate that the proposed method, contrary to many other active contour segmentation methods, is highly resilient to severe random and structural noise that could be present in the data