Reaction Systems (RSs) are a successful natural computing framework inspired by chemical reaction networks. A RS consists of a set of entities and a set of reactions. Entities can enable or inhibit each reaction, and are produced by reactions or provided by the environment. In a previous paper, we defined an original labelled transition system (LTS) semantics for RSs in the structural operational semantics (SOS) style. This approach has several advantages: (i) it provides a formal specification of the RS dynamics that enables the reuse of many formal analysis techniques and favors the implementation of tools, and (ii) it facilitates the definition of extensions of the RS framework by simply modifying some of the SOS rules in a modular way. In this paper, we demonstrate the extensibility of the framework by defining two quantitative variants of RSs: with reaction delays/durations, and with concentration levels. We provide a prototype logic programming implementation and apply our tool to a RS model of Th cells differentiation in the immune system.
Freeform optical systems include lens or mirror surfaces with shapes that have no axis of revolution in order to reach better performance in off-axis designs such as Head-Mounted Displays (HMD). They have an increased number of design parameters compared to rotationally symmetrical optical systems which makes their optimization more difficult. In this paper we presented results of several studies where various optimization techniques were used to optimize a freeform HMD prism with two mirrors. It was demonstrated that algorithms which performed well on traditional systems were not capable of obtaining high-quality solutions for the system under investigation. This paper presents a new parallel asynchronous memetic algorithm designed for optimizing freeform optical systems. The algorithm incorporates local search techniques into an asynchronous parallel computing procedure thus helping to speed-up the convergence to a high-quality solution. The results of a comparative study, presented in this paper along with the algorithm description, demonstrate that the proposed method is not only capable of obtaining a good solution but is also comparable with high-end commercial optical software packages.
We propose an analogy-based data augmentation approach for sentiment and style transfer named Analogy-Based Controllable Data Augmentation (ABCD). The object of data augmentation is to expand the number of sentences based on a limited amount of available data. We are given two unpaired corpora with different styles. In data augmentation, we retain the original text style while changing words to generate new sentences. We first train a self-attention-based convolutional neural network to compute the distribution of the contribution of each word to style in a given sentence. We call the words with high style contribution style-characteristic words. By substituting content words and style-characteristic words separately, we generate two new sentences. We use an analogy between the original sentence and these two additional sentences to generate another sentence. The results show that our proposed approach decrease perplexity by about 4 points and outperforms baselines on three transfer datasets.
This paper describes in details an innovative technique of injection of a global (or generally large-scale) quality measure into a deep neural network (DNN) in order to compensate for the tendency of DNNs to found the resulting classification virtually from a superposition of local neighbourhood transformations and projections. We used a state probability-like feature as the global quality measure and injected it into a DNN-based classifier deployed in a specific task of determining which parts of a web page are of certain interest for further processing by NLP techniques. Our goal was to decompose web sites of various internet discussion forums to useful content, i.e. the posts of users, and useless content, i.e. forum graphics, menus, banners, advertisements, etc.
Selection hyper-heuristics have been used successfully to solve hard optimization problems. These techniques choose a heuristic or a group of heuristics to create a solution and/or improve it. In a prior study, we proposed an approach that changes the heuristic set from which the hyper-heuristic is allowed to choose dynamically and that led to improving the performance of the hyper-heuristic. Previously, we manually designed the proposed approach which involved challenging design decisions and parameter tuning. In this study, we automate the design of the previously proposed approach using grammatical evolution to reduce human involvement in the design process. The proposed automated approach is evaluated on the domains of the CHeSC challenge. It is found that the automated design reduces the design time remarkably and performs as good as the manual design.
