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Identifying protein-protein interactions (PPIs) represents a challenging research problem in computational biology. Even though machine learning methods have significantly advanced the research in this field, current approaches struggle to accurately predict interactions for previously unseen proteins. Supervised autoencoders, which are neural networks trained to simultaneously predict labels and reconstruct their inputs, have been proved to offer generalization guarantees in the linear case. Moreover, the addition of a reconstruction branch was empirically shown to improve the performance of standard neural networks classifiers on several tasks. In this paper, we introduce a two-stage sequence-based PPI prediction method based on supervised autoencoders. The proposed approach consists of initially training a denoising autoencoder on protein sequences, followed by a supervised training stage in which the model learns to both predict whether two proteins interact and to reconstruct the two proteins in the pair. An experimental analysis was performed on two public PPI data sets containing testing pairs formed using both seen and unseen protein sequences. The results show that our approach surpasses, on the two data sets, multiple machine learning classifiers proposed in the literature. [ABSTRACT FROM AUTHOR]

Biological, social and economical systems expose enormous levels of complexity, and studying situations of cooperation of conflict encompassing such systems is of particular interest. The N-Player Iterated Prisoner's Dilemma (NIPD) is a general game-theoretic model that captures realistic scenarios of cooperation and conflict. This paper studies the emergence of cooperation in the NIPD, using methods borrowed from the field of reinforcement learning. Such methods attracted immense amounts of attention over the last decade, and have shown promising results in robotics, game-playing, multi-agent systems, etc. However, it is well-known that plain reinforcement learning applied in the NIPD will converge to the Nash equilibrium of the game, which is defection. Therefore, additional mechanisms are needed to foster the development and upkeep of cooperation. This work considers different interconnection topologies for players of the NIPD, and uses dynamic rewiring (players can severe some connections and form others) as a mechanism to encourage cooperation. In such scenarios, the initial interconnection topology is also a crucial aspect. Thus, conducted experiments are concerned with the NIPD over dynamic complex networks, where players can rewire edges in order to position themselves in more cooperative settings. Our results show that cooperation can emerge in such settings. [ABSTRACT FROM AUTHOR]

The neural networks (NN) remain as black boxes, albeit their quite successful stories everywhere. It is mainly because they provide only the complex structure of the underlying network with a huge validation data set whenever their serendipities reveal themselves. In this paper, we propose the statistical NN learning model related to the concept of universal Turing computer for regression predictive model. Based on this model, we define 'statistically successful NN (SSNN) learning.' This is mainly done by calculating the well-known Cramer–Rao lower bound for the averaged square error (ASE) of NN learning. Using such formal definition, we propose an ASE-based NN learning (ANL) algorithm. The ANL algorithm not only implements the Cramer–Rao lower bound successfully but also presents an effective way to figure out a complicated geometry of ASE over hyper-parameter space for NN. This enables the ANL to be free of huge validation data set. Simple numerical simulation and real data analysis are done to evaluate performance of the ANL and present how to implement it. [ABSTRACT FROM AUTHOR]

The paper describes a solution to utterance style transfer within the speaker's identity and emotional tone exchange while maintaining the utterance's content. Using deep generative neural networks, we developed two models that differ in introducing information related to the speaker's identity and an additional variable representing the expected emotional category. The embedding of emotions is taken from the convolutional network that was trained to classify emotional categories. Siamese network is responsible for learning the content embeddings. The models can perform the style transfer between any two speakers with a satisfactory result. This assessment is based on a survey that considers the degree of content retention, the quality of transferring voice features related to identity, and the degree of converting emotional features into a desirable category. [ABSTRACT FROM AUTHOR]

The paper focuses on simulating an artificial life in which neural networks (recurrent (RNN) and Long Short Term Memory (LSTM) networks) control prey and predator agents. The research goal was to check whether a simple genetic algorithm evolves the LSTM based controller that competes with the classic RNN controller in the real world. We also examined the impact of audio communication within a given species on the survival of agents. Our experiments evidenced the LSTM network results were slightly worse than the RNN controller. We also showed that prey agents developed herd behavior in response to predator pressure. They learned to form herds that allowed them to resist predator attacks. It was also possible to observe the prey agent's cooperation in searching for food when the plants formed clusters. [ABSTRACT FROM AUTHOR]

The analysis of the transitions that a protein conformation can undergo is an important step towards achieving a thorough understanding of protein functions. In this context, learning informative and transferable low-dimensional representations of proteins conformations is of great importance for developing robust computational approaches that can model proteins dynamical systems. In this paper we perform an experimental evaluation of the ability of the latent space representations learned by a Variational Autoencoder (VAE) trained on trajectories of proteins belonging to a superfamily to characterize an unseen protein from the same superfamily. In our study, we compare the latent space encodings obtained using two representations for the protein conformations and evaluate the impact of the latent space dimensionality on the embeddings' quality. The obtained results outline that the VAE's latent space can capture structural information characterizing a certain protein superfamily and, consequently, generalize to a different testing protein from that superfamily. [ABSTRACT FROM AUTHOR]

Predicting weather, and particularly severe weather, is an important challenge both for meteorological and machine learning researchers. The complexity and difficulty of the problem is mainly due to the chaotic character of the atmosphere and the implicit large set of meteorological information (radar, satellite or ground meteorological observations) which have to be analyzed by meteorologists. Thus, understanding the relationships between various meteorological parameters extracted from radar observations may be useful for providing additional comprehension about severe weather development and would help to identify situations when severe weather can occur. Self-organizing maps are being explored as an unsupervised classification model for detecting patterns in radar data which are relevant in predicting short-term weather changes. Experiments are performed on real radar data provided by the Romanian National Meteorological Administration. With the main goal of analyzing how the values for the weather radar products are evolving between consecutive radar scans, we empirically show that in general there is a slow change in the values over time, except for the situations when certain severe phenomena occur. The study conducted in this paper is aimed to provide a better insight regarding how the values of weather radar products are evolving in time both in calm and severe weather conditions, with the broader goal of using these findings for weather nowcasting. [ABSTRACT FROM AUTHOR]

Within the software testing domain, determining the order in which classes are tested in integration testing is an important problem called class integration test order identification (CITO). This problem is useful in integration testing , as it contributes to reducing the time needed for testing a software system and refers to the process of identifying an optimal order in which the application classes should be combined and tested as a group. This paper introduces a novel approach based on reinforcement learning for class integration test order optimization in the context of integration testing . The experimental evaluation is performed on four synthetic examples and on six existing software systems often used in the literature for this problem. The results obtained are analyzed and compared to similar related work from the literature, highlighting the potential of the current proposal. The proposed reinforcement learning based approach outperforms most methods existing in the software engineering literature for optimizing the test order for class-based integration. Moreover, it is general, and can be easily extended for optimizing the order in which software components should be tested during component integration testing of a software system. [ABSTRACT FROM AUTHOR]

Online data mining techniques are used to uncover relevant patterns in complex data which are dynamic by nature and thus continuously extended with real-time arriving data streams. Relational association rules (RARs), a data analysis and mining concept, extend the classical association rules so as to capture different relations between the attributes characterizing the data. This paper introduces a new Incremental Relational Association Rule Mining (IRARM) approach with the aim of progressively adapting the interesting relational association rules identified in a data set, when it is enlarged with new instances. We have experimentally evaluated IRARM on publicly available data sets. The reduction in mining time when using IRARM against mining from scratch emphasizes its efficiency in adapting the rules to real-time data extension. [ABSTRACT FROM AUTHOR]

Proteins are complex macromolecules which contribute to maintaining cellular environments and thus have fundamental roles in biological processes of living organisms. Understanding the conformational transitions of proteins represents an important stage towards comprehending protein function and would help to identify situations when mutations can occur. In this paper we use clustering as an unsupervised classification method in order to study the relevance of the residues’ relative solvent accessibility (RSA) values to analyze protein internal transitions. With the main goal of studying the evolution of RSA values between conformational transitions, we experimentally show that RSA values are slowly modifying as the protein undergoes conformational changes. The study conducted in this paper is aimed to provide a better apprehension of how proteins’ conformational transitions are evolving in time, with the broader goal of better understanding protein internal dynamics. [ABSTRACT FROM AUTHOR]

In this study, we present Swift Linked Data Miner, an interruptible algorithm that can directly mine an online Linked Data source (e.g., a SPARQL endpoint) for OWL 2 EL class expressions to extend an ontology with new SubClassOf: axioms. The algorithm works by downloading only a small part of the Linked Data source at a time, building a smart index in the memory and swiftly iterating over the index to mine axioms. We propose a transformation function from mined axioms to RDF Data Shapes. We show, by means of a crowdsourcing experiment, that most of the axioms mined by Swift Linked Data Miner are correct and can be added to an ontology. We provide a ready to use Protégé plugin implementing the algorithm, to support ontology engineers in their daily modeling work. [ABSTRACT FROM AUTHOR]