Probabilistic Approach to Structural Change Prediction in Evolving Social Networks

We propose a predictive model of structural changes in elementary subgraphs of social network based on Mixture of Markov Chains. The model is trained and verified on a dataset from a large corporate social network analyzed in short, one day-long time windows, and reveals distinctive patterns of evolution of connections on the level of local network topology. We argue that the network investigated in such short timescales is highly dynamic and therefore immune to classic methods of link prediction and structural analysis, and show that in the case of complex networks, the dynamic subgraph mining may lead to better prediction accuracy. The experiments were carried out on the logs from the Wroclaw University of Technology mail server

Multifunctions determined by integrable functions

Integral properties of multifunctions determined by vector valued functions are presented. Such multifunctions quite often serve as examples and counterexamples. In particular it can be observed that the properties of being integrable in the sense of Bochner, McShane or Birkhoff can be transferred to the generated multifunction while Henstock integrability does not guarantee i

Bond breaking with auxiliary-field quantum Monte Carlo

Bond stretching mimics different levels of electron correlation and provides a challenging testbed for approximate many-body computational methods. Using the recently developed phaseless auxiliary-field quantum Monte Carlo (AF QMC) method, we examine bond stretching in the well-studied molecules BH and N$_2$, and in the H$_{50}$ chain. To control the sign/phase problem, the phaseless AF QMC method constrains the paths in the auxiliary-field path integrals with an approximate phase condition that depends on a trial wave function. With single Slater determinants from unrestricted Hartree-Fock (UHF) as trial wave function, the phaseless AF QMC method generally gives better overall accuracy and a more uniform behavior than the coupled cluster CCSD(T) method in mapping the potential-energy curve. In both BH and N$_2$, we also study the use of multiple-determinant trial wave functions from multi-configuration self-consistent-field (MCSCF) calculations. The increase in computational cost versus the gain in statistical and systematic accuracy are examined. With such trial wave functions, excellent results are obtained across the entire region between equilibrium and the dissociation limit.Comment: 8 pages, 3 figures and 3 tables. Submitted to JC

Next challenges for adaptive learning systems

Learning from evolving streaming data has become a 'hot' research topic in the last decade and many adaptive learning algorithms have been developed. This research was stimulated by rapidly growing amounts of industrial, transactional, sensor and other business data that arrives in real time and needs to be mined in real time. Under such circumstances, constant manual adjustment of models is in-efficient and with increasing amounts of data is becoming infeasible. Nevertheless, adaptive learning models are still rarely employed in business applications in practice. In the light of rapidly growing structurally rich 'big data', new generation of parallel computing solutions and cloud computing services as well as recent advances in portable computing devices, this article aims to identify the current key research directions to be taken to bring the adaptive learning closer to application needs. We identify six forthcoming challenges in designing and building adaptive learning (pre-diction) systems: making adaptive systems scalable, dealing with realistic data, improving usability and trust, integrat-ing expert knowledge, taking into account various application needs, and moving from adaptive algorithms towards adaptive tools. Those challenges are critical for the evolving stream settings, as the process of model building needs to be fully automated and continuous.</jats:p

Stability Studies of a Mixture of Paracetamol and Ascorbic Acid, Prepared Extempore, at Elevated Temperature and Humidity Conditions

Purpose: To determine the effect of the temperature of water used for the preparation of paracetamol and ascorbic acid mixture on its stability, as well as to assess the influence of humidity on the stability of single components and their mixtures.Methods: The stability of the mixtures in aqueous medium was evaluated with the aid of UV–Vis spectrophotometer interfaced with a computer. Spectral analysis was adapted to monitor changes in the aqueous medium of a commercial paracetamol and ascorbic acid mixture, an extemporaneously prepared mixture of paracetamol and ascorbic acid, and the individual preparations of paracetamol and ascorbic acid.Results: The degradation rate was lower in commercial preparation (6.80 × 10-3 min-1), compared to that of the extemporaneously prepared ascorbic acid/paracetamol mixture (2.30 × 10-2 min-1). The decomposition of the commercial product in aqueous medium was 3.38 times slower than that of the extemporaneously prepared mixture. Ascorbic acid, tested under the same conditions as the commercial product, was unstable in aqueous solutions, with a degradation rate of 1.17×10-2 min-1. Ascorbic acid, dissolved in water, degraded completely within 4 h at room temperature, whereas paracetamol remained stable under the same conditions for 11 days.Conclusion: The individual drugs in their original form retained their stability for 72 h, but some of the mixtures, in particular, the extemporaneously prepared ones showed more rapid degradation. Extemporaneous preparation of paracetamol/ascorbic acid liquid mixtures should not be encouraged

Influencia de los tensides en la liberación de las sustancias medicinales de los geles hidrófilos: influencia del polisorbato 20 y polisorbato 80 en la liberación del hidrocortisona de los geles hidrófilos

El proceso de liberación de hidrocortisona de los hidrogeles con la adición del 1% y del 3% del polisorbato 20o polisorbato 80, en la presencia de propilenglicol - 1,2 o PEG 200, tiene dos fases. Durante la primera fase lasvelocidades de liberación son más altas, comparando con la segunda fase. La segunda fase de liberación correspondea la cinética de primer orden. Los periodos de semiliberación en el transcurso de esta fase oscilan entre15,67 y 23,50

NetSim: The framework for complex network generator

Networks are everywhere and their many types, including social networks, the Internet, food webs etc., have been studied for the last few decades. However, in real-world networks, it's hard to find examples that can be easily comparable, i.e. have the same density or even number of nodes and edges. We propose a flexible and extensible NetSim framework to understand how properties in different types of networks change with varying number of edges and vertices. Our approach enables to simulate three classical network models (random, small-world and scale-free) with easily adjustable model parameters and network size. To be able to compare different networks, for a single experimental setup we kept the number of edges and vertices fixed across the models. To understand how they change depending on the number of nodes and edges we ran over 30,000 simulations and analysed different network characteristics that cannot be derived analytically. Two of the main findings from the analysis are that the average shortest path does not change with the density of the scale-free network but changes for small-world and random networks; the apparent difference in mean betweenness centrality of the scale-free network compared with random and small-world networks

On the LrL^r-differentiability of Two Lusin Classes and a Full Descriptive Characterization of the HKrHK_r-integral

It is proved that any function of a Lusin-type class, the class of $ACG_r$-functions, is differentiable almost everywhere in the sense of a derivative defined in the space~$L^r$, $1\le r<\infty$. This leads to obtaining a full descriptive characterization of a Henstock-Kurzweil-type integral, the $HK_r$-integral, which serves to recover functions from their $L^r$-derivatives. The class $ACG_r$ is compared with the classical Lusin class $ACG$ and it is shown that a continuous $ACG$-function can fail to be $L^r$-differentiable almost everywhere