72 research outputs found
Inference of the size of nonlinear network systems from perceptible dynamics
No full textNetwork dynamical systems are ubiquitous in science and engineering. The most basic property of a network dynamical system is its size, which, for scalar dynamics, corresponds to the number of nodes. For linear network systems, recent studies have developed reliable tools for inferring the size of the system from perceptible dynamics (measurements of one or some of the network nodes) across multiple experiments. Here, we extend these tools to nonlinear network systems by putting forward a model-agnostic approach that combines clustering techniques, the use of detection matrices, and spectral analysis. The theoretical premise of the algorithm is that, under mild assumptions, the variation between the dynamics of some nodes across multiple measurements can be used to bound the variation between the dynamics of all nodes across the same measurements. By applying clustering techniques on perceptible dynamics, we identify nearby measurements, about which the variational dynamics are approximately linear and the use of the detection matrix is valid. From the spectrum of the detection matrix, we infer its rank, which corresponds to the size of the nonlinear network system. We demonstrate our approach via numerical experiments on different nonlinear network systems, including different types of hypergraphs. Whether nonlinearity comes from individual dynamics of the nodes or the interactions among them, it is rarely a feature that one can dismiss. Our work paves the way to infer the size of a nonlinear network system when governing equations are unknown and only limited data are accessible
Emergence of sublinear scaling of firearm ownership in the United States
No full textAbstract Recently, Succar and Porfiri (Nature Cities 1(3):216–224, 2024) reported sublinear scaling for firearm ownership in the United States. Their analysis hinted at a causal role of prevalence of homicides and firearm accessibility on firearm ownership, supporting self-protection as a driver of firearm ownership. In this study, we propose a microscopic, individual-level model to explain these macroscopic, city-level findings. In the model, individuals dwell in a city and buy a gun if they experience a violent interaction and know a dealer. We examine the model from a network science perspective and show the emergence of sublinear scaling with an exponent matching empirical observations. Beyond scaling, the model provides accurate predictions of city rankings in terms of firearm ownership, underscoring the explanatory power of the self-protection theory
Detecting hidden states in stochastic dynamical systems
No full textInferring the number of states of a stochastic system from partial measurements is a fundamental problem in physics, for which methodological tools remain scarce. It is sometimes difficult to distinguish the stochastic dynamical states from measurements, deceiving us into incorrect models and flawed understanding of natural phenomena. Here, we propose a model-free statistical framework, grounded in network and control theory, to estimate the number of states of a stochastic system from perceptible dynamics. The framework extends previous techniques for deterministic systems, based on the rank of ancillary matrices. We show applications of our approach to a variety of physics domains, such as statistical mechanics, biophysics, physical chemistry, and epidemiology
Corrigendum to “Interstitial ectopic pregnancy diagnosis by three-dimensional ultrasound and its laparoscopic management: A case report” [Int J Reprod BioMed 2019; 17: 945-950]
No full textThe publisher has been informed of an error that occurred on page 945 in which the Institute name must be changed to Royan instead of Rayan. On behalf of the author, the publisher wishes to apologize for this error. The online version of the article has been updated on 29 February 2020
Significance of Static Backgrounds for Video Object Detection
No full textVideo Object Detectors (VID) are used in various applications such as surveillance, inspection, etc. Often in these applications there exists a spatial area of interest and a static background. The static backgrounds remain constant throughout the video sequence in the training data establishing an undesirable correlation with the moving object during training. To hide static backgrounds in the video, masking is an option. We create multiple synthetic datasets and reveal that (i) VIDs detect moving objects better if the static background in the train and test set are similar or from the same distribution.(ii) VIDs drop in performance if the static background in the train and test are different. (iii) Adding more static backgrounds during training does not make VID robust to static background changes at test time. (iv) Masking or removing static backgrounds cannot prevent VIDs from learning correlations with static backgrounds. The experiments shed light on the usage of static backgrounds for detecting dynamic objects.Mechanical Engineering | Vehicle Engineering | Cognitive Robotic
Autonomous Guidance and Control for Precision Landing on Planetary Bodies: Convex Optimization Approach For Mars and Titan Case Studies
No full textAutonomously landing a spacecraft on the surface of a planetary body with a degree of precision in the order of meters is highly challenging. Over the course of time, the landing ellipse, defined as the region with a 99% likelihood of where a space vehicle will land, has improved steadily but currently still has dimensions in the order of kilometers. The first and single Martian spacecraft that has performed a guided atmospheric entry and utilized precision landing technologies is the Mars Science Laboratory (MSL). The MSL probe and its focal point, the Curiosity rover, has thus been the most advanced mission yet to have flown to Mars. Nonetheless, space missions to the Red Planet have thus-far never landed following fuel optimal paths. Similarly, dispersions for landing on Titan with current technologies expand to hundreds of kilometers. The only reference mission to Titan is the Huygens probe, which has not utilized precision landing technologies nor optimal path planning. Besides, the goal of the Cassini-Huygens mission was to maximize descent time to augment scientific data retrieval of Titan's atmosphere. As part of the NASA Space Exploration Technology Directorate, a parafoil is proposed for landing on Titan due to its cost effectiveness, ease of deployment, relatively low mass compared to the prospective payload and capabilities of precise autonomous delivery. While considering all phases of Entry, Descent and Landing (EDL) and all elements of Guidance, Navigation and Control (GNC), the central focus of the research was put on the (powered and parafoil) terminal descent phase. The research core concerns a convex optimization programming approach to guarantee soft-landing. The algorithm has been verified based on the extensive Mars powered descent guidance literature. As part of the research conducted at NASA/JPL/Caltech, the algorithm has been extended to become compatible with landing a parafoil on Saturn's moon Titan. Throughout the discussion a distinction is made between lossless and successive convexification for optimal guidance. Both types have been simulated to either compute fuel optimal paths for powered Mars landing or pull-power optimal paths for parafoil Titan landing. The soft-landing is guaranteed while adhering to imposed mission constraints. By using the full capability of the spacecraft unprecedented precision may be achieved. This will enable engineers and scientists to reach the most alluring places on planetary bodies, thereby providing humanity a deeper understanding of the Universe.JPL Visiting Student Research ProgramAerospace Engineerin
Measuring the quality of publicly available synthetic IDS datasets: A comparative study
No full textYear after year, the amount of network intrusions and costs associated to them rises. Research in this area is, therefore, of high importance and provides valuable insight in how to prevent or counteract intrusions. Machine learning algorithms seem to be a promising answer for automated network intrusion detection, as their results often reach upwards of 99\%+ on datasets. Yet even with these results, the problem does not seem to be solved as the same models do not reach similar scores on real live network traffic. This indicates a problem with the datasets. In this work, we explored six recent network intrusion datasets and measured the quality of them from a design perspective and through a practical binary classification approach. Furthermore, we explored the need for complex classification models on these datasets, as research has shifted more towards using black-box models as opposed to white-box models. Through a literature study on the quality metrics of a dataset, we found there is a general lack of agreement amongst researchers regarding what makes a dataset good and realistic. We also found areas in which datasets are often lacking in and provide concrete advice on how to upgrade the quality of these datasets. For our practical classification approach, we built a general classification pipeline using a Random Forest. Three feature sets were tested, two of which form an ablation study to measure the effects of trigrams on the classification score. On all datasets the general classification model reaches good classification results (>90\%+) and for three datasets even reached state-of-the-art results. The ablation study yielded a positive effect of using trigrams for classification on the datasets. Our white-box approach performed on par or better than most black-box techniques. We conclude that black-box models are unnecessary in this problem context and that the techniques should shift back to white-box approaches. Next, we attempted to link the quality of dataset methodologies to the difficulty of obtaining state-of-the-art classification results. Apart from the complexity of the attack vector and benign traffic variety, we found no further properties that define this relationship. Finally, this work started with the assumption that real live network traffic is more complex and therefore more difficult to classify well on than most available datasets. Newer datasets do show improvements over older datasets and our classification results corroborated the validity of the assumption.Computer Science | Cyber Securit
Convex Optimization Guidance for Precision Landing on Titan
No full textPrecision landing is an anticipated technology for future interplanetary missions. Autonomous spacecraft Entry, Descent and Landing (EDL) on the surface of a planetary body with a degree of precision in the order of meters is highly challenging. In this paper, a successive convexification guidance algorithm is utilized to simulate autonomous precision landing sequences on Saturn’s moon Titan. Due to its unique geophysical features, studying the science of matter within Titan’s atmosphere and beneath its surface is one of NASA’s most important planetary science objectives. As part of the Space Exploration Technology Directorate, a parafoil is proposed for landing on Titan due to its cost effectiveness, ease of deployment, low mass compared to the prospective payload and capabilities of precise autonomous delivery. This paper focuses on path optimization and guidance law development for high-fidelity dynamics parafoil tuning in the dense and adverse wind atmosphere of Titan, defined as a nonlinear and nonconvex optimal control problem. The powerful successive convexification method is used to solve the problem accordingly. The algorithm is designed such that the converged solution adheres to the nonlinear dynamics and kinematics in accordance with the original formulation, while respecting the state and control constraints. The six-degree-of-freedom (6DoF) simulations results show that this robust method is suitable for autonomous interplanetary applications.Virtual/online event due to COVID-19Astrodynamics & Space Mission
Non-Commutative Probability for the Spectral Analysis of Simplicial Complexes
No full textFree probability theory, invented by Voiculescu, and greatly expanded by Speicher, is a young and active area of research with numerous applications in pure and applied mathematics.
This Master thesis is a comprehensive study of a specific result in the recent preprint by C. Vargas, in which Vargas presents a survey of applications of non-commutative and free probability to topological data analysis.
The relevant result from the preprint reveals a new interpretation of Betti numbers for simplicial complexes in terms of distributions in an operator-valued probability space.
This thesis is mostly an exposition of the areas of free probability and algebraic topology; here, we do not present cutting-edge research in either free probability or algebraic topology. The author did a literature review for both fields and presents here the results in a comprehensive way along with detailed proofs and motivating examples that one may not find in a research paper. We believe that this thesis would help researchers to quickly grasp the main ideas and tools in both fields, and we hope it will help to advance the research in both areas and to develop applications in related areas
Algorithmes basés sur les k-mères pour la métagénomique orale ancienne : outils pour l'élimination et l'évaluation de la contamination en paléométagénomique
No full textLa paléométagénomique est l'étude du matériel génétique ancien à l'aide du séquençage métagénomique, un processus qui implique la caractérisation de l'ADN de tous les organismes d'un échantillon. Par matériel génétique ancien, nous entendons l'ADN provenant d'une source non vivante et présentant des signes de dégradation moléculaire. Le tartre dentaire s'est révélé être une source exceptionnellement riche d'ADN ancien et a été utilisé pour étudier l'évolution du microbiome buccal, ainsi que la santé bucco-dentaire et l'alimentation de l'homme. Malgré la mise en place de protocoles de laboratoire rigoureux pour le contrôle de la contamination de l'ADN ancien, les échantillons d'ADN ancien court sont encore très sensibles à la contamination par des sources environnementales, ce qui peut modifier radicalement la composition microbienne et conduire à des conclusions erronées après les analyses en aval. Cette thèse propose deux algorithmes qui s'appuient sur les k-mers (sous-séquences d'ADN) pour relever deux défis importants dans le domaine de la paléométagénomique : l'évaluation de la contamination via le suivi des sources microbiennes et l'élimination de la contamination au niveau des lectures. La première tâche a donné lieu à une publication en première auteure et à un logiciel ouvert appelé decOM, tandis que la seconde a également été publiée en tant qu'article du première auteure accompagné d'un logiciel ouvert appelé aKmerBroom. Les deux méthodes ont été testées sur des données métagénomiques orales anciennes, mais leur utilité peut être étendue à des échantillons qui ne proviennent pas de sources orales anciennes. Dans l'ensemble, cette thèse a prouvé que les algorithmes basés sur k-mer ont un immense potentiel pour l'élimination de la contamination et l'évaluation de la contamination des métagénomes, car ils tirent parti de la richesse des informations métagénomiques qui ont été séquencées et mises à la disposition du public au fil des ans.Palaeometagenomics is the study of ancient genetic material by using metage- nomic sequencing, a process that entails the characterisation of the DNA from all the organisms in a sample. By ancient genetic material we refer to the DNA that comes from a non-living source and that shows signs of molecular degradation. Dental calculus has proven to be an exceptionally rich source of ancient DNA (aDNA) and it has been used to investigate the evolution of the oral microbiome, as well as human oral health and diet. Despite the establishment of rigorous laboratory protocols for aDNA contamination control, aDNA samples are still highly susceptible to contamination from environmental sources, which can drastically alter the microbial composition and lead to erroneous conclusions after downstream analyses. This dissertation proposes two algorithms that rely on k-mers (sub-sequences of DNA) to address two relevant challenges in the field of palaeometagenomics: contamination assessment via Microbial Source Tracking and contamination removal at the read level. The former task resulted in a first-author publication and an open-software called decOM, while the latter has also been published as a first-author paper accompanied by an open-software called aKmerBroom. Both methods were tested on ancient oral metagenomic data, yet their utility can be extended to samples that do not originate from ancient oral sources. Overall, this thesis has proven that k-mer-based algorithms have an immense potential for contamination removal and contamination assessment of metagenomes, as they leverage the wealth of metagenomic information that has been sequenced and made publicly available throughout the years
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