We propose the use of an asymmetric dissimilarity measure in centroid-based clustering. The dissimilarity employed is the Alpha–Beta divergence (AB-divergence), which can be asymmetrized using its ...parameters. We compute the degree of asymmetry of the AB-divergence on the basis of the within-cluster variances. In this way, the proposed approach is able to flexibly model even clusters with significantly different variances. Consequently, this method overcomes one of the major drawbacks of the standard symmetric centroid-based clustering.
•We propose the use of asymmetric dissimilarity in centroid-based clustering.•We asymmetrize the AB-divergence on the basis of the within-cluster variances.•The proposed approach efficiently takes into consideration clusters with different variances.•Experiments were conducted on real and simulated data of high and low dimensionality.
It is known that recurrent neural networks may have difficulties remembering data over long time lags. To overcome this problem, we propose an extended architecture of recurrent neural networks, ...which is able to deal with long time lags between relevant input signals. A register of latches at the input layer of the network is applied to bypass irrelevant input information and to propagate relevant inputs. The latches are implemented with differentiable multiplexers, thus enabling the derivatives to be propagated through the network. The relevance of input vectors is learned concurrently with the weights of the network using a gradient-based algorithm.
Electronic computer circuits consisting of a large number of connected logic gates of the same type, such as NOR, can be easily fabricated and can implement any logic function. In contrast, designed ...genetic circuits must employ orthogonal information mediators owing to free diffusion within the cell. Combinatorial diversity and orthogonality can be provided by designable DNA- binding domains. Here, we employed the transcription activator-like repressors to optimize the construction of orthogonal functionally complete NOR gates to construct logic circuits. We used transient transfection to implement all 16 two-input logic functions from combinations of the same type of NOR gates within mammalian cells. Additionally, we present a genetic logic circuit where one input is used to select between an AND and OR function to process the data input using the same circuit. This demonstrates the potential of designable modular transcription factors for the construction of complex biological information-processing devices.
There is a large body of research devoted to identifying the complexity of structures in networks. In the context of network theory, a complex network is a graph with nontrivial topological ...features—features that do not occur in simple networks, such as lattices or random graphs, but often occur in graphs modeling real systems. The study of complex networks is a young and active area of scientific research inspired largely by the empirical study of real-world networks, such as computer networks and logistic transport networks. Transport is of great importance for the economic and cultural cooperation of any country with other countries, the strengthening and development of the economic management system, and in solving social and economic problems. Provision of the territory with a well-developed transport system is one of the factors for attracting population and production, serving as an important advantage for locating productive forces and providing an integration effect. In this paper, we introduce a new method for quantifying the complexity of a network based on presenting the nodes of the network in Cartesian coordinates, converting to polar coordinates, and calculating the fractal dimension using the ReScaled ranged (R/S) method. Our results suggest that this approach can be used to determine complexity for any type of network that has fixed nodes, and it presents an application of this method in the public transport system.
Fuelled by successes in Computer Go, Monte Carlo tree search (MCTS) has achieved widespread adoption within the games community. Its links to traditional reinforcement learning (RL) methods have been ...outlined in the past; however, the use of RL techniques within tree search has not been thoroughly studied yet. In this paper we re-examine in depth this close relation between the two fields; our goal is to improve the cross-awareness between the two communities. We show that a straightforward adaptation of RL semantics within tree search can lead to a wealth of new algorithms, for which the traditional MCTS is only one of the variants. We confirm that planning methods inspired by RL in conjunction with online search demonstrate encouraging results on several classic board games and in arcade video game competitions, where our algorithm recently ranked first. Our study promotes a unified view of learning, planning, and search.
Abstract
Background: Due to the importance of family medicine and a relative shortage of doctors in this discipline, it is important to know how the decision to choose a career in this field is made.
...Objective: Since this decision is closely linked to students' attitudes towards family medicine, we were interested in identifying those attitudes that predict intended career choice in family medicine.
Methods: A cross-sectional study was performed among 316 final-year medical students of the Ljubljana Medical Faculty in Slovenia. The students filled out a 164-item questionnaire, developed based on the European definition of family medicine and the EURACT Educational Agenda, using a seven-point Likert scale containing attitudes towards family medicine. The students also recorded their interest in family medicine on a five-point Likert scale. Attitudes were selected using a feature selection procedure with artificial neural networks that best differentiated between students who are likely and students who are unlikely to become family physicians.
Results: Thirty-one out of 164 attitudes predict a career in family medicine, with a classification accuracy of at least 85%. Predictors of intended career choice in family medicine are related to three categories: understanding of the discipline, working in a coherent health care system and person-centredness. The most important predictor is an appreciation of a long-term doctor-patient relationship.
Conclusion: Students whose intended career choice is family medicine differ from other students in having more positive attitudes towards family physicians' competences and towards characteristics of family medicine and primary care.
Monte Carlo algorithms are one of the three main reinforcement learning paradigms that are capable of efficiently solving control and decision problems in dynamic environments. Through sampling they ...shape the values of states in the search space. Based on these values they develop an exploration policy that is in turn used to guide the future direction of sampling. Studies confirm the convergence of this interleaving iterative approach to an optimal solution; however, when a learning agent lacks prior knowledge of the problem domain, the convergence rate may be extremely slow in case of an erroneous staring policy that causes far-from-optimal value estimates. In this paper we present a brief overview of Monte Carlo control algorithms in the scope of reinforcement learning and propose a method to improve the convergence by gradually forgetting early estimates. Our method keeps track of the state values with a moving average that gives a higher weight to the recent rewards and discounts the weight of the previous rewards, while assuming that the policy is improving over time. We apply it to the general on-policy Monte Carlo control algorithm and to the popular upper confidence bounds for trees algorithm in the Monte Carlo tree search framework. The evaluation on several decision problems confirms that our method regularly improves the convergence rate of both algorithms and in some cases also their final policy.
Objective. The European Academy of Teachers in General Practice / Family Medicine (EURACT) has developed an educational agenda, the key document for teaching family medicine in Europe. The aim of our ...study was to find out how final year medical students at the beginning of their family medicine clerkship understand the discipline of family medicine. Methods. The attitudes toward family medicine were paraphrased and developed into a 164-item questionnaire, which was administered to 335 final-year medical students at the beginning of their clerkship. Using combinatorial optimization with genetic algorithms we selected 30 items which yielded the highest Cronbach alpha reliability coefficient. Finally, we performed a factor analysis to find which dimensions of family medicine were recognised by the students and compared them with the domains defined in the EURACT definition. Results. The 30-item questionnaire had a Cronbach alpha reliability coefficient of 0.919. The differences between male and female students were not very significant (p=0.061). With the factor analysis we recognised seven factors, belonging to three out of six domains of the EURACT educational agenda: primary care management, personcenteredness and comprehensive approach. Conclusion. Final-year medical students at the beginning of their family medicine clerkship understand some of the dimensions of family medicine rather well, but they are not aware of some important competences of family doctors. There is a necessity to teach students about specific problem solving skills and the importance of balance between the health needs of an individual patient and the community.
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