This is an extended version of our publication Learning state machines from data streams: A generic strategy and an improved heuristic, International Conference on Grammatical Inference (ICGI) 2023, Rabat, Morocco. It has been extended with a formal proof on PAC-bounds, and the discussion and analysis of a similar approach has been moved from the appendix and is now a full Section. State machine models are models that simulate the behavior of discrete event systems, capable of representing systems such as software systems, network interactions, and control systems, and have been researched extensively. The nature of most learning algorithms however is the assumption that all data be available at the beginning of the algorithm, and little research has been done in learning state machines from streaming data. In this paper, we want to close this gap further by presenting a generic method for learning state machines from data streams, as well as a merge heuristic that uses sketches to account for incomplete prefix trees. We implement our approach in an open-source state merging library and compare it with existing methods. We show the effectiveness of our approach with respect to run-time, memory consumption, and quality of results on a well known open dataset. Additionally, we provide a formal analysis of our algorithm, showing that it is capable of learning within the PAC framework, and show a theoretical improvement to increase run-time, without sacrificing correctness of the algorithm in larger sample sizes.
Home healthcare has become more and more central in the last decades, due to the advantages it can bring to both healthcare institutions and patients. Planning activities in this context, however, presents significant challenges related to route planning and mutual synchronization of caregivers.In this paper we propose a new compact model for the combined optimization of scheduling (of the activities) and routing (of the caregivers) characterized by fewer variables and constraints when compared with the models previously available in the literature. The new model is solved by a constraint programming solver and compared experimentally with the exact and metaheuristic approaches available in the literature on the common datasets adopted by the community. The results show that the new model provides improved lower bounds for the vast majority of the instances, while producing at the same time high quality heuristic solutions, comparable to those of tailored metaheuristics, for small/medium size instances.
Uttam U. Deshpande, Supriya Shanbhag, Ramesh Koti
et al.
Phone calls are strictly forbidden in certain locations due to the potential security threats. Mobile phones’ growing capabilities have also increased the risk of their misuse in places that are restricted, like manufacturing plants. Unauthorized mobile phone use in these environments can lead to significant safety hazards, operational disruptions, and security breaches. There is an urgent need to develop an intelligent system that can identify the presence of individuals as well as cellphone usage. We propose an advanced Artificial Intelligence and Computer Vision-based real-time cell phone detection system to detect mobile phone usage in restricted zones. Modern deep learning approaches, such as YOLOv8 for real-time object detection to accurately detect cell phone usage, are combined with dense layers of ResNet-50 to perform image classification tasks. We highlight the critical need for such detection systems in manufacturing settings and discuss the specific challenges encountered. To support this research, we have developed a custom dataset of 2,150 images, which features a diverse array of images with varying foreground and background elements to reflect real-world conditions. Our experimental results demonstrate that YOLOv8 achieves a Mean Average Precision (mAP50) of 49.5% at 0.5 IoU for cellphone detection tasks and an accuracy of 96.03% for prediction tasks. These findings underscore the effectiveness of our AI and CV-based system in detecting unauthorized mobile phone usage in restricted zones.
Urslla Uchechi Izuazu, Cosmas Ifeanyi Nwakanma, Dong-Seong Kim
et al.
Abstract Deep learning-based intrusion detection systems (DL-IDS) have proven effective in detecting cyber threats. However, their vulnerability to adversarial attacks and environmental noise, particularly in industrial settings, limits practical application. Current IDS models often assume ideal conditions, overlooking noise and adversarial manipulations, leading to degraded performance when deployed in real-world environments. Additionally, the black-box nature of DL model complicates decision-making, especially in industrial control systems (ICS) network, where understanding model behavior is crucial. This paper introduces the eXplainable Cyber-Threat Detection Framework (XC-TDF), a novel solution designed to overcome these challenges. XC-TDF enhances robustness against noise and adversarial attacks using regularization and adversarial training respectively, and also improves transparency through an eXplainable Artificial Intelligence (XAI) module. Simulation results demonstrate its effectiveness, showing resilience to perturbation by achieving commendable accuracy of 100% and 99.4% on the Wustl-IIoT2021 and Edge-IIoT datasets, respectively.
Eric Cady, Nicholas Bowman, Alexandra Z. Greenbaum
et al.
The Nancy Grace Roman Space Telescope (``Roman'') is a 2.4m space telescope scheduled for a 2026 launch. The Coronagraph Instrument (CGI) on Roman is a technology-demonstration instrument with a coronagraph and, for the first time in space, deformable mirrors and active wavefront control. This paper walks through the algorithmic and system-level architecture of the HOWFSC implementation for CGI, including the use of ground-in-the-loop (GITL) operations to support computationally-expensive operations, and reports on instrument performance measured during thermal vacuum testing in instrument integration and test. CGI achieved better than $5\times10^{-8}$ total raw contrast with two independent coronagraph architectures covering 3-9 and 6-20 $λ/D$ between them and a $360^{\circ}$ dark hole on each. The contrast limits appear to be driven by time available for testing, and do not appear to represent a floor in the achievable performance of CGI in flight.
Cyrill Scheidegger, Malte Londschien, Peter Bühlmann
The linear instrumental variable (IV) model is widely used in observational studies, yet its validity hinges on strong assumptions. Classical specification tests such as the Sargan-Hansen J test are limited to overidentified settings and are therefore not applicable in the common just-identified case, where the number of instruments is equal to the number of endogenous variables. We propose a novel test for the well-specification of the linear IV model under the assumption that the structural error is mean independent of the instruments. This assumption enables specification testing even in the just-identified setting. Our approach uses the idea of residual prediction: if the two-stage least squares residuals can be predicted from the instruments better than chance, this indicates misspecification. The resulting test employs sample splitting and a user-chosen machine learning method, and we show asymptotic type I error control and consistency against a broad class of alternatives. We further show how the proposed testing principle can be adapted to settings with weak or many instruments via an Anderson-Rubin-type inversion, thereby substantially extending the applicability. The tests accommodate heteroskedasticity- and cluster-robust inference and are implemented in the R package RPIV and the ivmodels software package for Python.
Using open astronomical multifrequency databases, we constructed light curves and developed a comprehensive visualisation and sonification analysis for the blazars Mrk~501, Mrk~1501, Mrk~421, BL~Lacerta, AO~0235+164, 3C~66A, OJ~049, OJ~287, and PKS~J2134-0153. This study employed Musical Instrument Digital Interface (MIDI) and Parameter Mapping Sonification (PMSon) techniques to generate waveforms, spectrograms, and sonifications. These representations demonstrate that data visualisation and sonification are powerful tools for analysing astronomical objects like blazars, providing insights into their multifrequency variability. This work highlights how sonification and visualisation can aid in identifying potential patterns, power variations, regularities, and gaps in the data. This multimodal approach also underscores the importance of inclusivity in scientific communication, offering accessible methods for exploring the complex behaviour of blazars.
Alma Y. Alanis, Gustavo Munoz-Gomez, Nancy F. Ramirez
et al.
This work introduces an impulsive neural control algorithm designed to mitigate the spread of epidemic diseases. The objective of this paper is the development of a vaccination strategy based on a PIN-type impulsive controller based on an online-trained neural identifier to control the spread of infectious diseases under a complex network approach with time-varying connections where each node represents a population of individuals whose dynamics are defined by the MSEIR epidemiological model. Considering an unknown model of the system, a neural identifier is designed that provides a nonlinear model for the complex network trained through an extended Kalman filter algorithm. Simulation results are presented by applying the proposed control scheme for a complex network parameterized as infectious diseases.
Instruments are categorized into the 5 groups in the Sachs-Hornbostel system: idiophones, membranophones, aerophones, chordophones, and electrophones. It might be easy to tell the Sachs-Hornbostel group that an instrument belongs to. However, distinguishing single instrument sound can be hard in monophonic or polyphonic music pieces and it is an important subject for musicians. Using computer science models can help musicians to analyze songs easily and fasten the speed of finding the instrument that are wanted by music producers or composers. This work aims to compare different models on particular instruments (monophonic sound) recognition which is an important problem in the field of music information retrieval. Jupyter Notebook is included for easy reproducibility. Among the six models chosen in this research: k-nearest neighbors(kNN), Support Vector Machines(SVM), Gaussian Mixture Modeling(GMM), Artificial Neural Networks(ANN), Convolutional Neural Networks(CNN) and Recurrent Neural Networks(RNN), CNN is the most accurate model and SVM is the fastest model while CNN has the prospect of being improved because it can be adjusted manually.
In order to generalise semi-invariant Riemannian maps, Sahin first introduced the idea of “Generic Riemannian maps”. We extend the idea of generic Riemannian maps to the case in which the total manifold is a nearly Kaehler manifold. We study the integrability conditions for the horizontal distribution although vertical distribution is always integrable. We also study the geometry of foliations of two distributions and obtain the necessary and sufficient condition for generic Riemannian maps to be totally geodesic. Additionally, we study the generic Riemannian map with umbilical fibers.
D. R. Bhatta, Supram Hosuru Subramanya, Deependra Hamal
et al.
Background Intensive care units (ICU) are essential healthcare facility for life threatening conditions. Bacterial contamination of objects/instruments in ICU is an important source of nosocomial infections. This study is aimed to determine the level of bacterial contamination of instruments/objects which are commonly touched by healthcare workers and frequently come in contact with the neonates. Methods This hospital based prospective study was conducted in neonatal intensive care unit (NICU) of Manipal Teaching Hospital, Pokhara, Nepal. A total of 146 samples collected from surfaces of incubators, radiant warmers, suction tips, ventilators, stethoscopes, door handles, weighing machines, mothers’ beds, phototherapy beds, laryngoscope, telephone sets, blood pressure machine, etc. formed the material of the study. Isolation, identification and antibiotic susceptibility of the bacterial isolates was performed by standard techniques. Blood culture isolates from NICU patients during the study period were compared with the environmental isolates. Results Out of 146 samples, bacterial growth was observed in 109. A total of 119 bacterial isolates were retrieved from 109 samples. Three common potential pathogens isolated were Escherichia coli (n = 27), Klebsiella species (n = 21) and Staphylococcus aureus (n = 18). Majority of E. coli and Klebsiella isolates were from incubators, suction tips and mothers’ beds. Majority of S. aureus isolates were cultured from radiant warmers. Among S. aureus isolates, 33.3% (6/18) were methicillin resistant. Majority of the bacterial isolates were susceptible to gentamicin and amikacin. Common potential pathogens isolated from blood culture of NICU patients were S. aureus and Klebsiella species . Conclusion High degree of bacterial contamination of objects/instruments in NICU was recorded. Isolation of potential pathogens like E. coli , Klebsiella species and S. aureus is a major threat of nosocomial infections. Blood culture data of NICU reflects possibility of nosocomial infections from contaminated sites. Gentamicin and amikacin may be used for empirical therapy in suspected cases of nosocomial infections in NICU.
Malaria adalah penyakit yang disebabkan oleh parasite bernama Plasmodium. Tercatat keseluruhan kasus malaria yang terjadi di Indonesia pada tahun 2019 adalah sebanyak 250.644 kasus. Dan kasus malaria tertinggi terjadi di provinsi Papua, yaitu sebesar 86% atau sebanyak 216.380 kasus. Di Provinsi Papua, penyakit malaria dialami oleh semua usia dan bulan-bulan terjadi peningkatan pasien penderita malaria juga sangat bervariasi. Hal ini mengakibatkan dinas Kesehatan mengalami kesulitan dalam mengelompokan jenis malaria berdasarkan usia pasien dan bulan-bulan kejadian. Sebenarnya sudah ada penelitian yang menjelaskan pengelompokan jenis-jenis malaria, namun belum dijelaskan secara terperinci masing-masing kelompok malaria seperti Malaria Tropika, Malaria Tertiana, Malaria Quartana, Malaria Ovale. Tujuan dari penelitian ini adalah, melakukan analisis cluster terhadap beberapa jenis malaria, usia dan bulan kejadian. Metode cluster yang digunakan dalam penelitian ini adalah metode Single Linkage dan K-Means. Selanjutnya kedua metode akan di evalusi menggunakan standar deviasi. Metode terbaik yang dapat digunakan untuk analisis cluster adalah metode yang memiliki nilai standar deviasi lebih kecil. Data yang digunakan dalam penelitian ini adalah data sekunder yang diperoleh dari Dinas Kesehatan Provinsi Papua. Hasil penelitian menunjukan bahwa, metode Single Linkage lebih akurat dibandingkan dengan K-Means. Dimana dari 50 pasien terdapat 47 pasien lebih dominan terkena penyakit malaria tertiana yaitu pada rentang usia remaja dan dewasa pada bulan juni. Sehingga diharapkan pemerintah Provinsi Papua dapat memberikan sosialisasi kepada masyarakat, khususnya mereka yang pada rentang usia remaja dan dewasa. Karena hampir 94% penyakit malaria tertiana di derita oleh mereka yang berusia remaja dan dewasa.
In this paper, we derive a novel method as a generalization over LCEs such as E2C. The method develops the idea of learning a locally linear state space by adding a multi-step prediction, thus allowing for more explicit control over the curvature. We show that the method outperforms E2C without drastic model changes which come with other works, such as PCC and P3C. We discuss the relation between E2C and the presented method and derive update equations. We provide empirical evidence, which suggests that by considering the multi-step prediction, our method – ms-E2C – allows learning much better latent state spaces in terms of curvature and next state predictability. Finally, we also discuss certain stability challenges we encounter with multi-step predictions and how to mitigate them.
This work deals with the design optimization of electrical machines under the consideration of manufacturing uncertainties. In order to efficiently quantify the uncertainty, blackbox machine learning methods are employed. A multi-objective optimization problem is formulated, maximizing simultaneously the reliability, i.e., the yield, and further performance objectives, e.g., the costs. A permanent magnet synchronous machine is modeled and simulated in commercial finite element simulation software. Four approaches for solving the multi-objective optimization problem are described and numerically compared, namely: epsilon-constraint scalarization, weighted sum scalarization, a multi-start weighted sum approach and a genetic algorithm.
While Kepler was still working in Graz during 1598, some letters to his mentor Michael Maestlin demonstrate his interest in astronomical clocks and machines. The first letter, dated January 6, 1598 contains a detailed description of a machine. In the second letter, written between June 1 and 11, 1598, Kepler starts with a brief review of clocks and machines of his time, then goes on to describe the requirements necessary for a useful mechanical instrument, based on the latest information of the day. In the Epitome Astronomiae Copernicanae (1618) he reiterates the importance and utility of astronomical and horological machines to divulgate the Copernican model of the Cosmo, to inform and assist scientists in their celestial calculations and hypotheses, even during periods of poor visibility in the night sky. I will present a translation of the design by Kepler and a hypothetical three-dimensional virtual reconstruction of his machine. This project reveals the ongoing research by Kepler and his understanding of the cinematic of planets, still bound to the homocentric spheres concept while the idea of orbita was maturing. At the same time the project of Kepler reveals a reasoning on a clear description of retrograde motion of planets, fully developed later in his Astronomia Nova. His machine demonstrates the Copernican concept of the Sun and its planets as a unique system. He also wants to show how the planet moves from the viewpoint of an Earth based observer. He shows how to solve the basic mechanical problem of moving all the planets simultaneously with just one driving mechanism, which was impossible to accomplish with the Aristotelian theory of homocentric spheres.
The victorious spirit, which predominates in all the provinces of the United States of
America, penetrates the scale of values in rise concerning the real G.D.P. The symbiosis of
the progresses witnessed by the American nation along of the time, as a result of the
management organized in “American smart style”, have reflection in the values regarding
the real G.D.P. of the United States of America. The aim of this original research pursues to
display the permanent increase regarding the real G.D.P. of the United States of America,
between 2021-2030
Deep neural networks have achieved almost human-level results in various tasks and have become popular in the broad artificial intelligence domains. Uncertainty estimation is an on-demand task caused by the black-box point estimation behavior of deep learning. The deep ensemble provides increased accuracy and estimated uncertainty; however, linearly increasing the size makes the deep ensemble unfeasible for memory-intensive tasks. To address this problem, we used model pruning and quantization with a deep ensemble and analyzed the effect in the context of uncertainty metrics. We empirically showed that the ensemble members’ disagreement increases with pruning, making models sparser by zeroing irrelevant parameters. Increased disagreement im-plies increased uncertainty, which helps in making more robust predictions. Accordingly, an energy-efficient compressed deep ensemble is appropriate for memory-intensive and uncertainty-aware tasks.
Artificial neural networks are means which are, among several other approaches, effectively usable for modelling and control of non-linear dynamic systems. In case of modelling systems input and output signals are a-priori known, supervised learning methods can be used. But in case of controller design of dynamic systems the required (optimal) controller output is a-priori unknown, supervised learning cannot be used. In such case we only can define some criterion function, which represents the required control performance of the closed-loop system. We present a neuro-evolution design for control of a continuous-time controller of non-linear dynamic systems. The controller is represented by an MLP-type artificial neural network. The learning algorithm of the neural network is based on an evolutionary approach with genetic algorithm. An integral-type performance index representing control quality, which is based on closed-loop simulation, is minimised. The results are demonstrated on selected experiments with controller reference value changes as well as with noisy system outputs.
Tanmoy Chattopadhyay, Sven Herrmann, Barry Burke
et al.
We present an evaluation of a novel on-chip charge detector, called the Single electron Sensitive Read Out (SiSeRO), for charge-coupled device (CCD) image sensor applications. It uses a p-MOSFET transistor at the output stage with a depleted internal gate beneath the p-MOSFET. Charge transferred to the internal gate modulates the source-drain current of the transistor. We have developed a drain current readout module to characterize the detector. The prototype sensor achieves a charge/current conversion gain of 700 pA per electron, an equivalent noise charge (ENC) of 15 electrons (e-) root mean square (RMS), and a full width half maximum (FWHM) of 230 eV at 5.9 keV. In this paper, we discuss the SiSeRO working principle, the readout module developed at Stanford, and the first characterization test results of the SiSeRO prototypes. While at present only a proof-of-concept experiment, in the near future we plan to use next generation sensors with improved noise performance and an enhanced readout module. In particular, we are developing a readout module enabling Repetitive Non-Destructive Readout (RNDR) of the charge, which can in principle yield sub-electron ENC performance. With these developments, we eventually plan to build a matrix of SiSeRO amplifiers to develop an active pixel sensor with an on-chip ASIC-based readout system. Such a system, with fast readout speeds and sub-electron noise, could be effectively utilized in scientific applications requiring fast and low-noise spectro-imagers.