Virus machines, which develop models of computation inspired by biological processes and the spread of viruses among hosts, deviate from the traditional methods. These virus machines are recognized for their computational power (functioning as algorithms) and their ability to tackle computationally difficult problems. In this paper, we introduce a new extension of the matrix-based representation of virus machines. In this way, hosts, the number of viruses and the instructions to control virus transmission are represented as vectors and matrices, describing the computations of virus machines by linear algebra operations. We also use our matrix representation to show invariants, useful in the proofs, of such machines. In addition, an explicit example is shown to clarify the computation and invariants using the representation. That is, a virus machine that computes the discrete logarithm, which relies on the presumed intractability of cryptosystems such the digital signature algorithm.

The parallel simulation of Spiking Neural P systems is mainly based on a matrix representation, where the graph inherent to the neural model is encoded in an adjacency matrix. The simulation algorithm is based on a matrix-vector multiplication, which is an operation efficiently implemented on parallel devices. However, when the graph of a Spiking Neural P system is not fully connected, the adjacency matrix is sparse and hence, lots of computing resources are wasted in both time and memory domains. For this reason, two compression methods for the matrix representation were proposed in a previous work, but they were not implemented nor parallelized on a simulator. In this paper, they are implemented and parallelized on GPUs as part of a new Spiking Neural P system with delays simulator. Extensive experiments are conducted on high-end GPUs (RTX2080 and A100 80GB), and it is concluded that they outperform other solutions based on state-of-the-art GPU libraries when simulating Spiking Neural P systems.

The COVID-19 pandemic has led to an over-whelming volume of scientific publications as researchers strive to address the crisis. To alleviate information overload, the COVID-19 Open Research Dataset (CORD-19) was released to help in analyzing large amounts of data and facilitate faster response. Most existing tools based on CORD-19 use extractive summarizers, which suffer from poor coherence and readability. Thus more abstractive summarizers for COVID-19 are needed. Specifically, using state-of-the-art (SOTA) transformers has shown to be successful in summarizing biomedical datasets like arXiv and PubMed. In this study, we finetune two checkpoints of SOTA transformer PEGASUS-X on the CORD-19 dataset: PEGASUS-XBASE-CORD19 and PEGASUS-XBASE-arXiv-CORD19. Our results highlight the importance of finetuning summarizers on domain-specific datasets in the abstractive summarization of COVID-19 research: checkpoints finetuned on CORD-19 out-perform other existing checkpoints and transformers finetuned on more general research datasets (e.g., arXiv and PubMed). After stopword removal in evaluation, we observe that PEGASUS-XBASE-arXiv-CORD19 surpasses PEGASUS-XBASE-CORD19 by a small margin. Our checkpoints still fall behind earlier sequence-to-sequence models; however, this limitation may be due to our constrained GPU resources. Future works, with access to more resources, can further improve our checkpoints for COVID-19 research summarization.

Methods for optimizing and evolving spiking neural P systems (in short, SN P systems) have been previously developed with the use of a genetic algorithm framework. So far, these computations, both evolving and simulating, were done only sequentially. Due to the non-deterministic and parallel nature of SN P systems, it is natural to harness parallel processors in implementing its evolution and simulation. In this work, a parallel framework for the evolution of SN P Systems is presented. This is the result of extending our previous work by implementing it on a CUDA-enabled graphics processing unit and adapting CuSNP design in simulations. Using binary addition and binary subtraction with 3 different categories each as initial SN P systems, the GPU-based evolution runs up to 9x faster with respect to its CPU-based evolution counterparts. Overall, when considering the whole process, the GPU framework is up to 3 times faster than the CPU version.

Spiking Neural P system is a computing model inspired on how the neurons in a living being are interconnected and exchange information. As a model in embrane computing, it is a non-deterministic and massively-parallel system. The latter makes GPU a good candidate for accelerating the simulation of these models. A matrix representation for systems with and without delay have been previously designed, and algorithms for simulating them with deterministic systems was also developed. So far, non-determinism has been problematic for the design of parallel simulators. In this work, an algorithm for simulating non-deterministic spiking neural P system with delays is presented. In order to study how the simulations get accelerated on a GPU, this algorithm was implemented in CUDA and used to simulate non-uniform and uniform solutions to the Subset Sum problem as a case study. The analysis is completed with a comparison of time and space resources in the GPU of such simulations.