Autoencoder-based Online Data Quality Monitoring for the CMS Electromagnetic Calorimeter

The online Data Quality Monitoring system (DQM) of the CMS electromagnetic calorimeter (ECAL) is a crucial operational tool that allows ECAL experts to quickly identify, localize, and diagnose a broad range of detector issues that would otherwise hinder physics-quality data taking. Although the existing ECAL DQM system has been continuously updated to respond to new problems, it remains one step behind newer and unforeseen issues. Using unsupervised deep learning, a real-time autoencoder-based anomaly detection system is developed that is able to detect ECAL anomalies unseen in past data. After accounting for spatial variations in the response of the ECAL and the temporal evolution of anomalies, the new system is able to efficiently detect anomalies while maintaining an estimated false discovery rate between $10^{-2}$ to $10^{-4}$, beating existing benchmarks by about two orders of magnitude. The real-world performance of the system is validated using anomalies found in 2018 and 2022 LHC collision data. Additionally, first results from deploying the autoencoder-based system in the CMS online DQM workflow for the ECAL barrel during Run 3 of the LHC are presented, showing its promising performance in detecting obscure issues that could have been missed in the existing DQM system.Comment: Submitted to the Proceedings of 21st International Workshop on Advanced Computing and Analysis Techniques in Physics Research ACAT 2022 conferenc

Searching for Intelligent Life in Gravitational Wave Signals Part I: Present Capabilities and Future Horizons

We show that the Laser Interferometer Gravitational Wave Observatory (LIGO) is a powerful instrument in the Search for Extraterrestrial Intelligence (SETI). LIGO's ability to detect gravitational waves (GWs) from astrophysical sources, such as binary black hole mergers, also provides the potential to detect extraterrestrial mega-technology, such as Rapid and/or Massive Accelerating spacecraft (RAMAcraft). We show that LIGO is sensitive to RAMAcraft of 1 Jupiter mass accelerating to a fraction of the speed of light (e.g. 30\%) from $10 - 100\,$kpc or a Moon mass from $1-10\,$pc. While existing SETI searches can probe on the order of ten-thousand stars for human-scale technology (e.g. radio waves), LIGO can probe all 10$^{11}$ stars in the Milky Way for RAMAcraft. Moreover, thanks to the $f^{-1}$ scaling of RAMAcraft signals, our sensitivity to these objects will increase as low-frequency detectors are developed and improved, allowing for the detection of smaller masses further from Earth. In particular, we find that DECIGO and the Big Bang Observer (BBO) will be about 100 times more sensitive than LIGO, increasing the search volume by 10$^{6}$, while LISA and Pulsar Timing Arrays (PTAs) may improve sensitivities to objects with long acceleration periods. In this paper, we calculate the waveforms for linearly-accelerating RAMAcraft in a form suitable for LIGO, Virgo, and KAGRA searches and provide the range for a variety of masses and accelerations. We expect that the current and upcoming GW detectors will soon become an excellent complement to the existing SETI efforts.Comment: 20 pages, 12 figures, submitted to MNRAS, comments welcom

Development and evaluation of a didactic tool with augmented reality for Quechua language learning in preschoolers

"It is important to preserve our cultural identity through the preservation of our mother tongue, contributing to its dissemination. Augmented reality (AR) is a great ally of education that provides efficiency, and productivity and increases the interest of students in their academic activities. An AR application was developed for learning Quechua in preschool children, thus improving their learning, satisfaction, and preference compared to traditional teaching. Previously, learning styles were identified for better coverage of the application; the design thinking methodology was applied for the development of the application, then the respective tests were conducted where it was obtained that the children's performance improved by 28.3% more compared to traditional teaching, with an average satisfaction of 89% of the classrooms, and 81% of students' preference. It was concluded that the proposed application considerably favors the written and audiovisual learning of the Quechua language in preschool students.

Changing Mathematical Paradigms at the University Level: Feedback from a Flipped Classroom at a Peruvian University

The university-level mathematics teaching adopted by many professors is still a traditional classroom, and many students’ perception of mathematics is that it is a complicated subject. The operationality of the flipped classroom proposal implemented at a university has a poten tial that can be used to change the perception that university students and teachers have towards the mathematics course, as well as to change the methodology of many teachers on how they teach their courses in the classroom. This research is the result of the implementa tion of the flipped classroom methodology in the basic mathematics course that is part of the professional careers of the engineering faculty of a Peruvian university. The aim of this study was to analyze the impact of applying the flipped classroom on academic results and atti tudes towards mathematics, with an experimental group of 227 students and a control group of 215 students. The academic results were measured at each of the stages indicated in the course syllabus, T1, partial exam, T2 and final exam; attitudes towards mathematics were also assessed at cognitive, procedural and affective levels at the end of the university semester. The Kolmogorov-Smirnov normality test was applied and yielded a value of p = 0.00, indicating that the grades obtained by the students did not follow a normal distribution. With the data obtained, the Mann-Whitney U test was performed, obtaining a p = 0.00 value (α = 0,052 tails). p < α makes us conclude that there are statistically significant differences between the scores of the experimental group compared to the control group. The results show a significant improvement in the academic performance and positive attitudes of students who took the course using the flipped classroom compared to those who did not use this methodolog

Convolutional Neural Networks with Transfer Learning for Pneumonia Detection

"Pneumonia is a type of acute respiratory infection caused by microbes, and viruses that affect the lungs. Pneumonia is the leading cause of infant mortality in the world, accounting for 81% of deaths in children under five years of age. There are approximately 1.2 million cases of pneumonia in children under five years of age and 180 000 died in 2016. Early detection of pneumonia can help reduce mortality rates. Therefore, this paper presents four convolutional neural network (CNN) models to detect pneumonia from chest X-ray images. CNNs were trained to classify X-ray images into two types: normal and pneumonia, using several convolutional layers. The four models used in this work are pre-trained: VGG16, VGG19, ResNet50, and InceptionV3. The measures that were used for the evaluation of the results are Accuracy, recall, and F1-Score. The models were trained and validated with the dataset. The results showed that the Inceptionv3 model achieved the best performance with 72.9% accuracy, recall 93.7%, and F1-Score 82%. This indicates that CNN models are suitable for detecting pneumonia with high accuracy.

Contributions of the 5G Network with Respect to Decent Work and Economic Growth (Sustainable Development Goal 8): A Systematic Review of the Literature

Decent work and economic growth are fundamental elements for the sustainable development of a society, with Sustainable Development Goal 8 (SDG8) being one of the key objectives of the United Nations’ 2030 Agenda. The 5G network has great potential to contribute significantly to the achievement of SDG8, offering faster and more reliable connectivity, which opens up new possibilities for innovation, operational efficiency, and job creation. The present study aimed to investigate the role of 5G technologies concerning decent work and economic growth (SDG8). As part of the method, 265 articles extracted from main databases such as Scopus, IEEExplore, and ScienceDirect were analyzed using the PRISMA methodology, resulting in 74 relevant articles after applying the inclusion and exclusion criteria. As a result, a greater contribution to the use of the 5G network was identified in sectors such as manufacturing, health, and transportation, generating greater economic growth and job creation. It was also found that the technological applications with the greatest contributions are “Internet of Things” and “Artificial intelligence”. Finally, it was concluded that the results of this review are useful for future research on technologies that support 5G networks, contributing to economic growth and equitable and sustainable decent work in a wide range of sectors and rural areas

Neural Control of Sensory Acquisition: The Vestibulo-Ocular Reflex

We present a new hypothesis that the cerebellum plays a key role in actively controlling the acquisition of sensory infonnation by the nervous system. In this paper we explore this idea by examining the function of a simple cerebellar-related behavior, the vestibula-ocular reflex or VOR, in which eye movements are generated to minimize image slip on the retina during rapid head movements. Considering this system from the point of view of statistical estimation theory, our results suggest that the transfer function of the VOR, often regarded as a static or slowly modifiable feature of the system, should actually be continuously and rapidly changed during head movements. We further suggest that these changes are under the direct control of the cerebellar cortex and propose experiments to test this hypothesis

WormBase - Annotating many nematode genomes

WormBase (www.wormbase.org) has been serving the scientific community for over 11 years as the central repository for genomic and genetic information for the soil nematode Caenorhabditis elegans. The resource has evolved from its beginnings as a database housing the genomic sequence and genetic and physical maps of a single species, and now represents the breadth and diversity of nematode research, currently serving genome sequence and annotation for around 20 nematodes. In this article, we focus on WormBase’s role of genome sequence annotation, describing how we annotate and integrate data from a growing collection of nematode species and strains. We also review our approaches to sequence curation, and discuss the impact on annotation quality of large functional genomics projects such as modENCODE

Text prediction recurrent neural networks using long shortterm memory-dropout

"Unit short-term memory (LSTM) is a type of recurrent neural network (RNN) whose sequence-based models are being used in text generation and/or prediction tasks, question answering, and classification systems due to their ability to learn long-term dependencies. The present research integrates the LSTM network and dropout technique to generate a text from a corpus as input, a model is developed to find the best way to extract the words from the context. For training the model, the poem ""La Ciudad y los perros"" which is composed of 128,600 words is used as input data. The poem was divided into two data sets, 38.88% for training and the remaining 61.12% for testing the model. The proposed model was tested in two variants: word importance and context. The results were evaluated in terms of the semantic proximity of the generated text to the given context.