On-chip spectrometers have the potential to offer dramatic size, weight, and power advantages over conventional benchtop instruments for many applications such as spectroscopic sensing, optical network performance monitoring, hyperspectral imaging, and radio-frequency spectrum analysis. Existing on-chip spectrometer designs, however, are limited in spectral channel count and signal-to-noise ratio. Here we demonstrate a transformative on-chip digital Fourier transform spectrometer that acquires high-resolution spectra via time-domain modulation of a reconfigurable Mach-Zehnder interferometer. The device, fabricated and packaged using industry-standard silicon photonics technology, claims the multiplex advantage to dramatically boost the signal-to-noise ratio and unprecedented scalability capable of addressing exponentially increasing numbers of spectral channels. We further explore and implement machine learning regularization techniques to spectrum reconstruction. Using an ‘elastic-D1’ regularized regression method that we develop, we achieved significant noise suppression for both broad (>600 GHz) and narrow (<25 GHz) spectral features, as well as spectral resolution enhancement beyond the classical Rayleigh criterion. On-chip spectrometers typically have limited spectral channels and low signal to noise ratios. Here the authors introduce a digital architecture that uses switches to change the interferometer path lengths, enabling exponentially more spectral channels per circuit element and lower noise by leveraging a machine learning reconstruction algorithm.
Intrusion detection systems (IDSs) are intrinsically linked to a comprehensive solution of cyberattacks prevention instruments. To achieve a higher detection rate, the ability to design an improved detection framework is sought after, particularly when utilizing ensemble learners. Designing an ensemble often lies in two main challenges such as the choice of available base classifiers and combiner methods. This paper performs an overview of how ensemble learners are exploited in IDSs by means of systematic mapping study. We collected and analyzed 124 prominent publications from the existing literature. The selected publications were then mapped into several categories such as years of publications, publication venues, datasets used, ensemble methods, and IDS techniques. Furthermore, this study reports and analyzes an empirical investigation of a new classifier ensemble approach, called stack of ensemble (SoE) for anomaly-based IDS. The SoE is an ensemble classifier that adopts parallel architecture to combine three individual ensemble learners such as random forest, gradient boosting machine, and extreme gradient boosting machine in a homogeneous manner. The performance significance among classification algorithms is statistically examined in terms of their Matthews correlation coefficients, accuracies, false positive rates, and area under ROC curve metrics. Our study fills the gap in current literature concerning an up-to-date systematic mapping study, not to mention an extensive empirical evaluation of the recent advances of
This paper presents a novel approach to dynamic pricing and distributed energy management in virtual power plant (VPP) networks using multi-agent reinforcement learning (MARL). As the energy landscape evolves towards greater decentralization and renewable integration, traditional optimization methods struggle to address the inherent complexities and uncertainties. Our proposed MARL framework enables adaptive, decentralized decision-making for both the distribution system operator and individual VPPs, optimizing economic efficiency while maintaining grid stability. We formulate the problem as a Markov decision process and develop a custom MARL algorithm that leverages actor-critic architectures and experience replay. Extensive simulations across diverse scenarios demonstrate that our approach consistently outperforms baseline methods, including Stackelberg game models and model predictive control, achieving an 18.73% reduction in costs and a 22.46% increase in VPP profits. The MARL framework shows particular strength in scenarios with high renewable energy penetration, where it improves system performance by 11.95% compared with traditional methods. Furthermore, our approach demonstrates superior adaptability to unexpected events and mis-predictions, highlighting its potential for real-world implementation.
Benjamin Monmege, Julie Parreaux, Pierre-Alain Reynier
Weighted Timed Games (WTG for short) are the most widely used model to describe controller synthesis problems involving real-time issues. Unfortunately, they are notoriously difficult, and undecidable in general. As a consequence, one-clock WTGs have attracted a lot of attention, especially because they are known to be decidable when only non-negative weights are allowed. However, when arbitrary weights are considered, despite several recent works, their decidability status was still unknown. In this paper, we solve this problem positively and show that the value function can be computed in exponential time (if weights are encoded in unary).
The aim of this study was to investigate the impact of eBPF technology on the performance of network solutions in Kubernetes clusters. Two configurations were compared: a traditional iptables-based setup and eBPF based solution via the Cilium networking plugin. Performance tests were conducted, measuring throughput, latency, CPU usage, and memory consumption under unloaded and loaded conditions. The results indicate that the traditional configuration achieved higher throughput and lower latency in unloaded scenarios. However, under load, the eBPF-enabled cluster demonstrated advantages, including reduced CPU and memory usage and slightly improved latency. This study highlights the potential of eBPF as an efficient technology for Kubernetes environments, particularly in scenarios demanding high performance and resource efficiency.
Information technology, Electronic computers. Computer science
Johandri Vosloo, Kenneth R. Uren, George van Schoor
The Tennessee Eastman process serves as a benchmark system for the evaluation of fault diagnosis techniques. Current simulator implementations are available in FORTRAN and in a C-mex S-function in MATLAB. The C-mex file is a conversion of the FORTRAN code to C for implementation in MATLAB. Both implementations have the limitation that not all the variables and parameters are directly accessible. Hence, a complete and open Tennessee Eastman process simulator was developed in Simulink to allow for total access to all parameters and variables and better Simulink integration. This implementation will give researchers more freedom towards the design of control and fault diagnosis techniques.
Sabrina Kletz, Klaus Schoeffmann, Jenny Benois-Pineau
et al.
Recorded videos from surgeries have become an increasingly important information source for the field of medical endoscopy, since the recorded footage shows every single detail of the surgery. However, while video recording is straightforward these days, automatic content indexing - the basis for content-based search in a medical video archive - is still a great challenge due to the very special video content. In this work, we investigate segmentation and recognition of surgical instruments in videos recorded from laparoscopic gynecology. More precisely, we evaluate the achievable performance of segmenting surgical instruments from their background by using a region-based fully convolutional network for instance-aware (1) instrument segmentation as well as (2) instrument recognition. While the first part addresses only binary segmentation of instances (i.e., distinguishing between instrument or background) we also investigate multi-class instrument recognition (i.e., identifying the type of instrument). Our evaluation results show that even with a moderately low number of training examples, we are able to localize and segment instrument regions with a pretty high accuracy. However, the results also reveal that determining the particular instrument is still very challenging, due to the inherently high similarity of surgical instruments.
Surgical skills are associated with clinical outcomes. To improve surgical skills and thereby reduce adverse outcomes, continuous surgical training and feedback is required. Currently, assessment of surgical skills is a manual and time-consuming process which is prone to subjective interpretation. This study aims to automate surgical skill assessment in laparoscopic cholecystectomy videos using machine learning algorithms. To address this, a three-stage machine learning method is proposed: first, a Convolutional Neural Network was trained to identify and localize surgical instruments. Second, motion features were extracted from the detected instrument localizations throughout time. Third, a linear regression model was trained based on the extracted motion features to predict surgical skills. This three-stage modeling approach achieved an accuracy of 87 ± 0.2% in distinguishing good versus poor surgical skill. While the technique cannot reliably quantify the degree of surgical skill yet it represents an important advance towards automation of surgical skill assessment.
Heart disease is recognized as one of the leading factors of death rate worldwide. Biomedical instruments and various systems in hospitals have massive quantities of clinical data. Therefore, understanding the data related to heart disease is very important to improve prediction accuracy. This article has conducted an experimental evaluation of the performance of models created using classification algorithms and relevant features selected using various feature selection approaches. For results of the exploratory analysis, ten feature selection techniques, i.e., ANOVA, Chi-square, mutual information, ReliefF, forward feature selection, backward feature selection, exhaustive feature selection, recursive feature elimination, Lasso regression, and Ridge regression, and six classification approaches, i.e., decision tree, random forest, support vector machine, K-nearest neighbor, logistic regression, and Gaussian naive Bayes, have been applied to Cleveland heart disease dataset. The feature subset selected by the backward feature selection technique has achieved the highest classification accuracy of 88.52%, precision of 91.30%, sensitivity of 80.76%, and f-measure of 85.71% with the decision tree classifier.
The static and fatigue analysis of plates made of 2D woven roving composites with holes is conducted. The parametrization of convex holes is proposed. The experimental results of the specimens without holes and with different shapes of notches are discussed. The experiments and the appropriate procedures are carried out with the aid of ASTM codes. The fatigue behavior is considered with the use of the low cycle fatigue method. The analysis is supplemented by numerical finite element modeling. The present work is an extension of the results discussed in the literature. The damage of plates with holes subjected to tension always occurs at the tip of the holes, i.e., (x = a, b = 0), both for static and fatigue failure. The originality and the novelty of this approach are described by the failure’s dependence on two parameters: n and the ratio of the a/b ratio characterizing the hole geometry. The fuzzy approach is employed to reduce the amount of experimental data.
Robert Jarolim, Astrid M. Veronig, Werner Pötzi
et al.
The constant improvement of astronomical instrumentation provides the foundation for scientific discoveries. In general, these improvements have only implications forward in time, while previous observations do not benefit from this trend. Here we provide a general deep learning method that translates between image domains of different instruments (Instrument-To-Instrument translation; ITI). We demonstrate that the available data sets can directly profit from the most recent instrumental improvements, by applying our method to five different applications of ground- and space-based solar observations. We obtain 1) solar full-disk observations with unprecedented spatial resolution, 2) a homogeneous data series of 24 years of space-based observations of the solar EUV corona and magnetic field, 3) real-time mitigation of atmospheric degradations in ground-based observations, 4) a uniform series of ground-based H$α$ observations starting from 1973, 5) magnetic field estimates from the solar far-side based on EUV imagery. The direct comparison to simultaneous high-quality observations shows that our method produces images that are perceptually similar and match the reference image distribution.
Federico Rossi, Robert C. Anderson, Saptarshi Bandyopadhyay
et al.
In this paper, we assess the scientific promise and technology feasibility of distributed instruments for planetary science. A distributed instrument is an instrument designed to collect spatially and temporally correlated data from multiple networked, geographically distributed point sensors. Distributed instruments are ubiquitous in Earth science, where they are routinely employed for weather and climate science, seismic studies and resource prospecting, and detection of industrial emissions. However, to date, their adoption in planetary surface science has been minimal. It is natural to ask whether this lack of adoption is driven by low potential to address high-priority questions in planetary science; immature technology; or both. To address this question, we survey high-priority planetary science questions that are uniquely well-suited to distributed instruments. We identify four areas of research where distributed instruments hold promise to unlock answers that are largely inaccessible to monolithic sensors, namely, weather and climate studies of Mars; localization of seismic events on rocky and icy bodies; localization of trace gas emissions, primarily on Mars; and magnetometry studies of internal composition. Next, we survey enabling technologies for distributed sensors and assess their maturity. We identify sensor placement (including descent and landing on planetary surfaces), power, and instrument autonomy as three key areas requiring further investment to enable future distributed instruments. Overall, this work shows that distributed instruments hold great promise for planetary science, and paves the way for follow-on studies of future distributed instruments for Solar System in-situ science.
Harry J. Greatorex, Ryan O. Milligan, Ingolf E. Dammasch
Despite the energetic significance of Lyman-alpha (Lyα; 1216Å) emission from solar flares, regular observations of flare related Lyα have been relatively scarce until recently. Advances in instrumental capabilities and a shift in focus over previous Solar Cycles mean it is now routinely possible to take regular co-observations of Lyα emission in solar flares. Thus, it is valuable to examine how the instruments selected for flare observations may influence the conclusions drawn from the analysis of their unique measurements. Here, we examine three M-class flares each observed in Lyα by GOES-14/EUVS-E, GOES-15/EUVS-E, or GOES-16/EXIS-EUVS-B, and at least one other instrument from PROBA2/LYRA, MAVEN/EUVM, ASO-S/LST-SDI, and SDO/EVE-MEGS-P. For each flare, the relative and excess flux, contrast, total energy, and timings of the Lyα emission were compared between instruments. It was found that while the discrepancies in measurements of the relative flux between instruments may be considered minimal, the calculated contrasts, excess fluxes, and energetics may differ significantly - in some cases up to a factor of five. This may have a notable impact on multi instrument investigations of the variable Lyα emission in solar flares and estimates of the contribution of Lyα to the radiated energy budget of the chromosphere. The findings presented in this study will act as a guide for the interpretation of observations of flare-related Lyα from upcoming instruments during future Solar Cycles and inform conclusions drawn from multi-instrument studies.
Joseph C. Shields, Jason Chu, Albert Conrad
et al.
The Large Binocular Telescope, with its expansive collecting area, angular resolving power, and advanced optical design, provides a robust platform for development and operation of advanced instrumentation for astronomical research. The LBT currently hosts a mature suite of instruments for spectroscopy and imaging at optical through mid-infrared wavelengths, supported by sophisticated adaptive optics systems. This contribution summarizes the current state of instrumentation, including upgrades to existing instruments and commissioning of second generation instruments now in progress. The LBT is soliciting proposals for next generation instrument concepts, with participation open to consortium members and others interested in participation in the Observatory.
Unlike the healthy cells in the breast tissue, cancerous breast cells are unwelcome and have strange properties. In both sexes, this will quickly expand and infiltrate adjacent tissue, leading to the formation of a tumour. Using the Intelligent-Breast Abnormality Detection (I-BAD) framework, many breast cancer parameters are evaluated in this article. It has already been shown that some indicators may be used for early detection of breast cancer. There is also discussion of the instruments and strategies that facilitate the monitoring of the selected breast health metrics. Classification methods that use machine learning to store and analyse data are also discussed. The suggested I-BAD framework’s process is then visually shown in clean drawings.
Option pricing has become a popular topic in the fields of finance and mathematics with the rapid development of stock and option markets. Now, more and more academics, financial companies and investors are attracted to study and do research about it. The theory of option pricing can also be used to price financial instruments with the similar structure to options and contribute to risk control and management. The Black-Scholes model is the basic and famous method applied for different options pricing with modifications and adjustments, and the results can be solved by some traditional numerical methods such as the binomial model, finite difference method, Monte Carlo method and so on. Machine learning has risen recently and begins to replace some complex work in traditional methods with the evolution of computers and computing power. How to use machine learning methods to predict the option price is a problem worthy to be solved. In this research, using the antithetic Monte Carlo method generates the prices of the up-and-out barrier options without rebate based on the Black-Scholes model. The generated dataset is divided into a training set and a test set for support vector regression, random forest, adaptive boosting and artificial neural networks. We compare the fitting and performance of all machine learning methods and find that random forest and artificial neural network methods fit better than others with fewer errors in predictions.
A relevant problem in combinatorial mathematics is the problem of counting independent sets of a graph <i>G</i>, denoted by <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>i</mi><mo>(</mo><mi>G</mi><mo>)</mo></mrow></semantics></math></inline-formula>. This problem has many applications in combinatorics, physics, chemistry and computer science. For example, in statistical physics, the computation of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>i</mi><mo>(</mo><mi>G</mi><mo>)</mo></mrow></semantics></math></inline-formula> has been useful in studying the behavior of the particles of a gas on a space modeled by a grid structure. Regarding hard counting problems, the computation of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>i</mi><mo>(</mo><mi>G</mi><mo>)</mo></mrow></semantics></math></inline-formula> for a graph <i>G</i> has been key to determining the frontier between efficient counting and intractable counting procedures. In this article, a novel algorithm for counting independent sets on grid-like structures is presented. We propose a novel algorithm for the computation of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>i</mi><mo>(</mo><msub><mi>G</mi><mrow><mi>m</mi><mo>,</mo><mi>n</mi></mrow></msub><mo>)</mo></mrow></semantics></math></inline-formula> for a grid graph with <i>m</i> rows and <i>n</i> columns based on the ‘Branch and Bound’ design technique. The splitting rule in our proposal is based on the well-known vertex reduction rule. The vertex in any subgraph from <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>G</mi><mrow><mi>m</mi><mo>,</mo><mi>n</mi></mrow></msub></semantics></math></inline-formula>, which is to be selected for the reduction rule, must have four internal incident faces. The ramification process is used to build a computation tree. Our proposal consists of decomposing the initial grid graph until outerplanar graphs are obtained as the ‘basic subgrids’ associated with the leave nodes of the computation tree. The resulting time-complexity of our proposal is inferior to the time-complexity of other classic methods, such as the transfer matrix method.