Online updating of active function cross-entropy clustering

Gaussian mixture models have many applications in density estimation and data clustering. However, the model does not adapt well to curved and strongly nonlinear data, since many Gaussian components are typically needed to appropriately fit the data that lie around the nonlinear manifold. To solve this problem, the active function cross-entropy clustering (afCEC) method was constructed. In this article, we present an online afCEC algorithm. Thanks to this modification, we obtain a method which is able to remove unnecessary clusters very fast and, consequently, we obtain lower computational complexity. Moreover, we obtain a better minimum (with a lower value of the cost function). The modification allows to process data streams

Indian and Oriental studies in a Euro-Indian perspective for the 21st century

Collegium Civitas; University of WarsawThe basic presumption is the need in Oriental studies to go much further than mere description of different civilisations. They should be compared with our own, and the question of whether the concepts evolved by those civilisations can help us better understand the reality in which we actually happen to live should be asked. For the adoption of this approach to the study of South Asia, it is suggested that European and Indian civilisations are ‘twins-unlike’. The paradox is intended since certain—so to say—general structural aspects of both civilisations are similar (geographical magnitude, variety of climate, size of population, and its anthropological, ethnic, linguistic and religious diversity), but as far as content is concerned they are of course very much unlike each other. The conclusion of our comparison is that Indian traditional civilisation is that of sustenance and containment while the European one is that of progress, development and expansion. Proper synergy of the two tendencies is postulated for sustainable development to be achieved

Evolutionary Multi-Agent Computing in Inverse Problems

The paper tackles the application of evolutionary multi-agent computing to solving inverse problems. High costs of fitness function call become a major difficulty when approachingthese problems with population-based heuristics, however evolutionary agent-based systems (EMAS)turn out to reduce the fitness function calls, which makes them a  possible weapon of choicefor them. The paper recalls the basics of EMAS and describes the considered problem (Step and Flash Imprint Lithography),later showing convincing results, that EMAS turns out to be more effective than classical evolutionary algorithm

Population Diversity in Ant-inspired Optimization Algorithms

Finding a balance between exploration and exploitation is very important in the case of metaheuristics optimization, especially in the systems leveraging population of individuals expressing (as in Evolutionary Algorithms, etc.) or constructing (as in Ant Colony Optimization) solutions. Premature convergence is a real problem and finding means of its automatic detection and counteracting are of great importance. Measuring diversity in Evolutionary Algorithms working in real-value search space is often computationally complex, but feasible while measuring diversity in combinatorial domain is practically impossible (cf. Closest String Problem). Nevertheless, we propose several practical and feasible diversity measurement techniques dedicated to Ant Colony Optimization algorithms, leveraging the fact that even though analysis of the search space is at least an NP problem, we can focus on the pheromone table, where the direct outcomes of the search are expressed and can be analyzed. Besides proposing the measurement techniques, we apply them to assess the diversity of several variants of ACO, and closely analyze their features for the classic ACO. The discussion of the results is the first step towards applying the proposed measurement techniques in auto-adaptation of the parameters affecting directly the exploitation and exploration features in ACO in the future

The 3020insC Allele of NOD2 Predisposes to Cancers of Multiple Organs

The NOD2 gene has been associated with susceptibility to Crohn's disease and individuals with Crohn's disease are at increased risk for cancer at a number of organ sites. We studied the association between the 3020insC allele of the NOD2 gene and cancer among 2604 cancer patients and 1910 controls from Poland. Patients were diagnosed with one of twelve types of cancer in the Szczecin region between 1994 and 2004. Significant associations were found for colon cancer (OR = 1.8; 95% CI 1.2 to 2.6), for lung cancer (OR = 1.7; 95% CI = 1.1 to 2.5) and for ovarian cancer (OR = 1.6; 95% CI 1.1 to 2.3). In addition, a significant association was found for early-onset laryngeal cancer (OR = 2.9; 95% CI 1.4 to 6.2) and for breast cancer in the presence of DCIS (OR = 2.1 95% CI = 1.2 to 3.6). The NOD2 3020insC allele is relatively common (in Poland 7.3% of individuals) and may be responsible for an important fraction of cancer cases. We estimate that the lifetime cancer risk among carriers of this allele is 30% higher than that of individuals with two wild-type alleles

Implementational verification of advantage of the ray-scene intersection computing algorithm by packet tracing over the naive algorithm

Tematem pracy jest porównanie wydajności działających w środowisku GPU implementacji dwóch bezstosowych algorytmów wyznaczania przecięcia sceny i promieni śledzenia, to jest: algorytmu śledzenia pakietów oraz algorytmu naiwnego, który, abstrahując od wykorzystywanych struktur danych, stanowi prosty, wielowątkowy odpowiednik algorytmu wyznaczania przecięcia sceny i promienia śledzenia działającego w środowisku CPU.Poprzez analizę wyników działania poszczególnych algorytmów dla zbioru 25 modeli testowych o skomplikowanej i zróżnicowanej geometrii, w pracy zostanie wykazane, że technika wykorzystująca śledzenie pakietów ma na ogół istotną przewagę nad techniką opartą na śledzeniu pojedynczych promieni.The topic of the thesis focuses on efficiency comparison of the GPU implementations of two stackless ray-scene intersection computing algorithms, i.e. the packet tracing algorithm and the naive one, that, leaving the used data structures aside, poses the simple, multi-threaded counterpart to the ray-scene intersection computing algorithm running on the CPU.Through the analysis of results of the algorithms for the set of 25 test models with the complicated and diversified geometry, in this paper it will be shown, that the technique involving the packet tracing in general outperforms the another one based on tracing of the single rays

Active function Cross-Entropy clustering

Gaussian Mixture Models (GMM) have many applications in density estimation and data clustering. However, the models do not adapt well to curved and strongly nonlinear data, since many Gaussian components are typically needed to appropriately fit the data that lie around the nonlinear manifold. To solve this problem we constructed the Active Function Cross-Entropy Clustering (afCEC) method, which uses Gaussians in curvilinear coordinate systems. The method has a few advantages in relation to GMM: it enables easy adaptation to clustering of complicated data sets along with a predefined family of functions and does not need external methods to determine the number of clusters, as it automatically (on-line) reduces the number of groups. Experiments on synthetic data, Chinese characters, data from UCI repository and wind turbine monitoring systems show that the proposed nonlinear model typically obtains better results than the classical methods

A novel approach to type-reduction and design of interval type-2 fuzzy logic systems

Fuzzy logic systems, unlike black-box models, are known as transparent artificial intelligence systems that have explainable rules of reasoning. Type 2 fuzzy systems extend the field of application to tasks that require the introduction of uncertainty in the rules, e.g. for handling corrupted data. Most practical implementations use interval type-2 sets and process interval membership grades. The key role in the design of type-2 interval fuzzy logic systems is played by the type-2 inference defuzzification method. In type-2 systems this generally takes place in two steps: type-reduction first, then standard defuzzification. The only precise type-reduction method is the iterative method known as Karnik-Mendel (KM) algorithm with its enhancement modifications. The known non-iterative methods deliver only an approximation of the boundaries of a type-reduced set and, in special cases, they diminish the profits that result from the use of type-2 fuzzy logic systems. In this paper, we propose a novel type-reduction method based on a smooth approximation of maximum/minimum, and we call this method a smooth type-reduction. Replacing the iterative KM algorithm by the smooth type-reduction, we obtain a structure of an adaptive interval type-2 fuzzy logic which is non-iterative and as close to an approximation of the KM algorithm as we like

Evolutionary algorithm for selecting dynamic signatures partitioning approach

In the verification of identity, the aim is to increase effectiveness and reduce involvement of verified users. A good compromise between these issues is ensured by dynamic signature verification. The dynamic signature is represented by signals describing the position of the stylus in time. They can be used to determine the velocity or acceleration signal. Values of these signals can be analyzed, interpreted, selected, and compared. In this paper, we propose an approach that: (a) uses an evolutionary algorithm to create signature partitions in the time and velocity domains; (b) selects the most characteristic partitions in terms of matching with reference signatures; and (c) works individually for each user, eliminating the need of using skilled forgeries. The proposed approach was tested using Biosecure DS2 database which is a part of the DeepSignDB, a database with genuine dynamic signatures. Our simulations confirmed the correctness of the adopted assumptions