Cellular Automata Applications in Shortest Path Problem

Cellular Automata (CAs) are computational models that can capture the essential features of systems in which global behavior emerges from the collective effect of simple components, which interact locally. During the last decades, CAs have been extensively used for mimicking several natural processes and systems to find fine solutions in many complex hard to solve computer science and engineering problems. Among them, the shortest path problem is one of the most pronounced and highly studied problems that scientists have been trying to tackle by using a plethora of methodologies and even unconventional approaches. The proposed solutions are mainly justified by their ability to provide a correct solution in a better time complexity than the renowned Dijkstra's algorithm. Although there is a wide variety regarding the algorithmic complexity of the algorithms suggested, spanning from simplistic graph traversal algorithms to complex nature inspired and bio-mimicking algorithms, in this chapter we focus on the successful application of CAs to shortest path problem as found in various diverse disciplines like computer science, swarm robotics, computer networks, decision science and biomimicking of biological organisms' behaviour. In particular, an introduction on the first CA-based algorithm tackling the shortest path problem is provided in detail. After the short presentation of shortest path algorithms arriving from the relaxization of the CAs principles, the application of the CA-based shortest path definition on the coordinated motion of swarm robotics is also introduced. Moreover, the CA based application of shortest path finding in computer networks is presented in brief. Finally, a CA that models exactly the behavior of a biological organism, namely the Physarum's behavior, finding the minimum-length path between two points in a labyrinth is given.Comment: To appear in the book: Adamatzky, A (Ed.) Shortest path solvers. From software to wetware. Springer, 201

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

<span style="font-size:21.5pt;mso-bidi-font-size:14.5pt; line-height:115%;font-family:"Times New Roman","serif";mso-fareast-font-family: "Times New Roman";mso-ansi-language:EN-US;mso-fareast-language:EN-US; mso-bidi-language:AR-SA">Regeneration of plantlets in <i><span style="font-size:21.0pt;mso-bidi-font-size:14.0pt;line-height:115%;font-family: "Times New Roman","serif";mso-fareast-font-family:"Times New Roman";mso-ansi-language: EN-US;mso-fareast-language:EN-US;mso-bidi-language:AR-SA">Sapindus trifoiatus </span></i><span style="font-size:21.5pt;mso-bidi-font-size:14.5pt;line-height:115%;font-family: "Times New Roman","serif";mso-fareast-font-family:"Times New Roman";mso-ansi-language: EN-US;mso-fareast-language:EN-US;mso-bidi-language:AR-SA">L.</span></span>

1288-1292<span style="font-size: 15.5pt;mso-bidi-font-size:8.5pt;font-family:" times="" new="" roman","serif""="">Continuous production of healthy plantlets of Sapindus trifoliatus L. was achieved via somatic embryos from long term cultures of an embryogenic mass (EM). A highly embryogenic culture of S. trifoliatus L. was obtained by recurrent embryogenesis from somatic embryos cultured on Murashige and Skoog's (MS) medium supplemented with kinetin (2.3 μM) and benzyladenine (8.8 μM). The cultures could be maintained without reduction of embryogenic competence for more than 20 months by subculture at 4 week intervals. About 90% mature somatic embryos on transfer to basal MS medium. germinated to plant lets, of which more than 70% survived when transferred to a sand and soil mixture in green house. </span

Long Short-Term Memory Deep-Filter in Remote Photoplethysmography

International audienceRemote photoplethysmography (rPPG) is a recent technique for estimating heart rate by analyzing subtle skin color variations using regular cameras. As multiple noise sources can pollute the estimated signal, post-processing techniques, such as bandpass filtering, are generally used. However, it is often possible to see alterations in the filtered signal that have not been suppressed, although an experienced eye can easily identify them. From this observation, we propose in this work to use an LSTM network to filter the rPPG signal. The network is able to learn the characteristic shape of the rPPG signal and especially its temporal structure, which is not possible with the usual signal processingbased filtering methods. The results of this study, obtained on a public database, have demonstrated that the proposed deep-learning-based filtering method outperforms the regular post-processing ones in terms of signal quality and accuracy of heart rate estimation

Effect of periodic gas-puffs on drift-tearing modes in ADITYA/ADITYA-U tokamak discharges

The effect of a periodic train of short gas-puff pulses on the rotation frequency and amplitude of drift-tearing modes has been studied in ADITYA/ADITYA-U tokamak. The short gas puffs, injecting approximately similar to 10(17)-10(18) molecules of fuel gas (hydrogen) at one toroidal location, are found to concomitantly decrease the drift-tearing mode rotation frequency and the mode amplitude during the period of injection and then recover back to its initial values when the gas pulse is over. This leads to a periodic modulation of the rotation frequency and amplitude of the drift-tearing modes that is correlated with the periodicity of the gas pulse injection. The underlying mechanism for this change in the mode characteristic appears to be related to gas puff induced change in the radial profile of the plasma pressure in the edge region that brings about a reduction in the diamagnetic drift frequency. Detailed experimental measurements and BOUT++ code simulations support such a reduction in diamagnetic drift frequency. Our results reveal a close interaction between the edge dynamics and core MHD phenomena in a tokamak that could help us better understand the rotation dynamics and amplitude pulsations of magnetic islands