User-defined Parameters Research Articles

Testing the sensitivity of the CAESAR-Lisflood landscape evolution model to grid cell size

Abstract. Landscape evolution models (LEMs) are useful for understanding how large-scale processes and perturbations influence the development of the surface of the Earth and other planets. With their increasing sophistication and improvements in computational power, they are finding greater uptake in analyses at finer spatial and temporal scales. For many LEMs, the land surface is represented by a grid of regularly spaced and sized grid cells, or pixels, referred to as a digital elevation model (DEM), yet despite the importance of the DEM to LEM studies, there has been little work to understand the influence of grid cell size (i.e. resolution) on model behaviour. This is despite the choice of grid cell size being arbitrary for many studies, with users needing to balance detail with computational efficiency. Using the Morris method (MM) for global sensitivity analysis, the sensitivity of the CAESAR-Lisflood LEM to the grid cell size is evaluated relative to a set of influential user-defined parameters, showing that it had a similar level of influence as a key hydrological parameter and the choice of sediment transport law. Outputs relating to discharge and sediment yields remained stable across different grid cell sizes until the cells became so large that the representation of the hydrological network degraded. Although total sediment yields remained steady when changing the grid cell sizes, closer analysis revealed that using a coarser grid resulted in it being built up from fewer yet more geomorphically active events, risking outputs that are “the right answer but for the wrong reasons”. These results are important considerations for modellers using LEMs and the methodologies detailed provide solutions to understanding the impacts of modelling choices on outputs.

Low-Complexity Adaptive Beamforming Algorithm With High Dimensional and Small Samples

A large-scale array (LSA) inevitably encounter scenarios with a small number of samples, and its beamformer suffers from high computational complexity. High computational complexity prevents the system from being used in practical online engineering applications. The complex vector of the beamformer weights can be expressed as the product of training snapshots and the signal steering vector, and a coefficient vector, since the optimal weight vector is a linear combination of basis vectors of the signal-plus-interference subspace. In this study, a new adaptive beamformer is developed on the basis of the minimum variance distortionless response (MVDR) criterion and kernel techniques. The new beamformer only needs to compute the inversion of a low-dimensional Gram matrix instead of the high-dimensional sample covariance matrix, which significantly reduces the calculation cost. Moreover, an efficient loading parameter calculation method (only related to the received matrix and not required user-defined parameters) is derived, which can adaptively suppress the mismatches of the ill-conditioned Gram matrix. Furthermore, a fast version of the new beamformer is formulated for the LSA under the scanning mode. Simulation results demonstrate that the new beamformer achieves better performance and a lower computation load than existing algorithms for a small number of samples. Especially, insufficient samples and high computational complexity problems are more frequently aroused in the space-time broadband array signal processing. Interestingly, the new techniques can be successfully extended to wideband array signal processing and yield satisfactory beam pattern shapes.

A fast [formula omitted] time hybrid clustering algorithm using the circumference proximity based merging technique for diversified datasets

Clustering has been widely employed for extracting intrinsic groups because of its low reliance on domain knowledge. Though several clustering techniques have been developed in the literature, the majority of them become inefficient due to their dependence on user-defined parameters or inability to cluster multi-scale datasets. To handle these issues, several hybrid clustering algorithms which combine the advantages of partitional, hierarchical, and graph-based clustering techniques have been developed. Hybrid clustering algorithms first partition the data into smaller clusters and then merge them into genuine clusters. However, most of them incur a quadratic time complexity which limits their application for large datasets. The proposed work presents a two-step fast hybrid clustering algorithm based on partitions and efficient merging to reduce the computational cost by maintaining the clustering quality. In the first step, the dispersion of the data is considered to produce balanced partitions. Then graph circumference proximity-based merging technique is proposed to merge the sub-clusters. The overall computational complexity of the algorithm is O(NlgN) where N is the number of data points, ignoring the dimension. To the best of our knowledge, this is the fastest known graph neighborhood-based hybrid clustering algorithm. Experimental results on various diversified datasets exhibit a significant improvement in the running time as well as cluster quality and robustness against noise.

An agent-based model to simulate SARS-CoV-2 contamination of surfaces and meat cuts in processing plants

At the beginning of the COVID-19 pandemic, several contamination clusters were reported in food-processing plants in France and several countries worldwide. Therefore, a need arose to better understand viral transmission in such occupational environments from multiple perspectives: the protection of workers in hotspots of viral circulation; the prevention of supply disruption due to the closure of plants; and the prevention of cluster expansion due to exports of food products contaminated by the virus to other locations. This paper outlines a simulation-based approach (using agent-based models) to study the effects of measures taken to prevent the contamination of workers, surfaces, and food products. The model includes user-defined parameters to integrate characteristics relating to SARS-CoV-2 (variant of concern to be considered, symptom onset…), food-processing plants (dimensions, ventilation…), and other sociodemographic transmission factors based on laboratory experiments as well as industrial and epidemiological investigations. Simulations were performed for a typical meat-processing plant in different scenarios for illustration purposes. The results suggested that increasing the mask-wearing ratio led to great reductions in the probability of observing clusters of more than 25 infections. In the case of clusters, masks being worn by all workers limited the presence of contamination (defined as levels of at least 5 log10 viral RNA copies) on meat cuts at less than 0.05 % and maintained the production capacity of the plant at optimal levels. Increasing the average distance between two workers from less than 1 m to more than 2 m decreased the cluster-occurrence probability by up to 15 % as well as contamination of food products during cluster situations. The developed approach can open up several perspectives in terms of potential communication-support tools for the agri-food sector and further reuses or adaptations for other hazards and occupational environments.

Tailored machine learning models for functional RNA detection in genome-wide screens.

The insilico prediction of non-coding and protein-coding genetic loci has received considerable attention in comparative genomics aiming in particular at the identification of properties of nucleotide sequences that are informative of their biological role in the cell. We present here a software framework for the alignment-based training, evaluation and application of machine learning models with user-defined parameters. Instead of focusing on the one-size-fits-all approach of pervasive insilico annotation pipelines, we offer a framework for the structured generation and evaluation of models based on arbitrary features and input data, focusing on stable and explainable results. Furthermore, we showcase the usage of our software package in a full-genome screen of Drosophila melanogaster and evaluate our results against the well-known but much less flexible program RNAz.

Adaptive Event-Triggered Control for Nonlinear Systems With Asymmetric State Constraints: A Prescribed-Time Approach

Finite/Fixed-time control yields a promising tool to optimize a system&#x0027;s settling time, but lacks the ability to separately define the settling time and the convergence domain (known as <i>practically prescribed-time stability</i>, PPTS). We provide a sufficient condition for PPTS based on a new piecewise exponential function, which decouples the settling time and convergence domain into separately user-defined parameters. We propose an adaptive event-triggered prescribed-time control scheme for nonlinear systems with asymmetric output constraints, using an exponential-type barrier Lyapunov function. We show that this PPTS control scheme can guarantee tracking error convergence performance, while restricting the output state according to the prescribed asymmetric constraints. Compared with traditional finite/fixed-time control, the proposed methodology yields separately user-defined settling time and convergence domain without the prior information on disturbance. Moreover, asymmetric state constraints can be handled in the control structure through bias state transformation, which offers an intuitive analysis technique for general constraint issues. Simulation and experiment results on a heterogeneous teleoperation system demonstrate the merits of the proposed control scheme.

Biologically Inspired Self-Organizing Computational Model to Mimic Infant Learning

Recent technological advancements have fostered human–robot coexistence in work and residential environments. The assistive robot must exhibit humane behavior and consistent care to become an integral part of the human habitat. Furthermore, the robot requires an adaptive unsupervised learning model to explore unfamiliar conditions and collaborate seamlessly. This paper introduces variants of the growing hierarchical self-organizing map (GHSOM)-based computational models for assistive robots, which constructs knowledge from unsupervised exploration-based learning. Traditional self-organizing map (SOM) algorithms have shortcomings, including finite neuron structure, user-defined parameters, and non-hierarchical adaptive architecture. The proposed models overcome these limitations and dynamically grow to form problem-dependent hierarchical feature clusters, thereby allowing associative learning and symbol grounding. Infants can learn from their surroundings through exploration and experience, developing new neuronal connections as they learn. They can also apply their prior knowledge to solve unfamiliar problems. With infant-like emergent behavior, the presented models can operate on different problems without modifications, producing new patterns not present in the input vectors and allowing interactive result visualization. The proposed models are applied to the color, handwritten digits clustering, finger identification, and image classification problems to evaluate their adaptiveness and infant-like knowledge building. The results show that the proposed models are the preferred generalized models for assistive robots.

Aroma Characterization of Roasted Meat and Meat Substitutes Using Gas Chromatography-Mass Spectrometry with Simultaneous Selective Detection and a Dedicated Software Tool, AromaMS.

The development of healthier and more sustainable food products, such as plant-based meat substitutes (PBMSs), have received significant interest in recent years. A thorough understanding of the aroma composition can support efforts to improve the sensory properties of PBMS products and promote their consumer acceptability. Here, we developed an integrated hardware and software approach for aroma analysis of roasted food based on simultaneous analysis with three complementary detectors. Following the standard procedure of aroma headspace sampling and separation using solid-phase microextraction-gas chromatography, the column flow was split into three channels for the following detectors for the selective detection of nitrogen and sulfur (N/S)-containing compounds: an electron ionization-mass spectrometry for identification through a library search, a nitrogen-phosphorous detector, and a flame-photometric detector (FPD)/pulsed-FPD. Integration of results from the different types of detectors was achieved using a software tool, called AromaMS, developed in-house for data processing. As stipulated by the user, AromaMS performed either non-targeted screening for all volatile organic compounds (VOCs) or selective screening for N/S-containing VOCs that play a major role in the aroma experience. User-defined parameters for library matching and the retention index were applied to further eliminate false identifications. This new approach was successfully applied for comparative analysis of roasted meat and PBMS samples.

Regret and Cumulative Constraint Violation Analysis for Distributed Online Constrained Convex Optimization

This paper considers the distributed online convex optimization problem with time-varying constraints over a network of agents. This is a sequential decision making problem with two sequences of arbitrarily varying convex loss and constraint functions. At each round, each agent selects a decision from the decision set, and then only a portion of the loss function and a coordinate block of the constraint function at this round are privately revealed to this agent. The goal of the network is to minimize the network-wide loss accumulated over time. Two distributed online algorithms with full-information and bandit feedback are proposed. Both dynamic and static network regret bounds are analyzed for the proposed algorithms, and network cumulative constraint violation is used to measure constraint violation, which excludes the situation that strictly feasible constraints can compensate the effects of violated constraints. In particular, we show that the proposed algorithms achieve $\mathcal{O}(T^{\max\{\kappa,1-\kappa\}})$ static network regret and $\mathcal{O}(T^{1-\kappa/2})$ network cumulative constraint violation, where $T$ is the time horizon and $\kappa\in(0,1)$ is a user-defined trade-off parameter. Moreover, if the loss functions are strongly convex, then the static network regret bound can be reduced to $\mathcal{O}(T^{\kappa})$. Finally, numerical simulations are provided to illustrate the effectiveness of the theoretical results.

Coverage and Lifetime Optimization by Self-Optimizing Sensor Networks.

We propose an approach to self-optimizing wireless sensor networks (WSNs) which are able to find, in a fully distributed way, a solution to a coverage and lifetime optimization problem. The proposed approach is based on three components: (a) a multi-agent, social-like interpreted system, where the modeling of agents, discrete space, and time is provided by a 2-dimensional second-order cellular automata, (b) the interaction between agents is described in terms of the spatial prisoner's dilemma game, and (c) a local evolutionary mechanism of competition between agents exists. Nodes of a WSN graph created for a given deployment of WSN in the monitored area are considered agents of a multi-agent system that collectively make decisions to turn on or turn off their batteries. Agents are controlled by cellular automata (CA)-based players participating in a variant of the spatial prisoner's dilemma iterated game. We propose for players participating in this game a local payoff function that incorporates issues of area coverage and sensors energy spending. Rewards obtained by agent players depend not only on their personal decisions but also on their neighbor's decisions. Agents act in such a way to maximize their own rewards, which results in achieving by them a solution corresponding to the Nash equilibrium point. We show that the system is self-optimizing, i.e., can optimize in a distributed way global criteria related to WSN and not known for agents, provide a balance between requested coverage and spending energy, and result in expanding the WSN lifetime. The solutions proposed by the multi-agent system fulfill the Pareto optimality principles, and the desired quality of solutions can be controlled by user-defined parameters. The proposed approach is validated by a number of experimental results.

FACAM: A Fast and Accurate Clustering Analysis Method for Protein Complex Quantification in Single Molecule Localization Microscopy

Single molecule localization microscopy (SMLM) enables the analysis and quantification of protein complexes at the nanoscale. Using clustering analysis methods, quantitative information about protein complexes (for example, the size, density, number, and the distribution of nearest neighbors) can be extracted from coordinate-based SMLM data. However, since a final super-resolution image in SMLM is usually reconstructed from point clouds that contain millions of localizations, current popular clustering methods are not fast enough to enable daily quantification on such a big dataset. Here, we provide a fast and accurate clustering analysis method called FACAM, which is modified from the Alpha Shape method (a point dataset analysis method used in many fields). By taking advantage of parallel computation, FACAM is able to process millions of localizations in less than an hour, which is at least 10 times faster than the popular DBSCAN method. Furthermore, FACAM adaptively determines the segmentation threshold, and thus overcomes the problem of user-defined parameters. Using simulation and experimental datasets, we verified the advantages of FACAM over other reported clustering methods (including Ripley’s H, DBSCAN, and ClusterViSu).

Optimal sensor placement methodology of triaxial accelerometers using combined metaheuristic algorithms for structural health monitoring applications

Structural health monitoring applications present a valuable opportunity for cities to improve their infrastructures environmental life cycle, reduce operational maintenance cost and enhance their resiliency through early damage detection capabilities. The dilemma with structural health monitoring resides in balancing between the number of sensors to be installed and the amount of data that needs to be transferred, stored, and managed. These technical complications directly impact the cost efficiency of such solutions. Thus, limiting the wide range deployment of such technology. Henceforth, Optimal Sensor Placement (OSP) methodologies offers the possibility to optimize the sensor configuration that will ensure a proper and cost-efficient monitoring system to obtain the maximum modal information related to any infrastructure type.In this study, a novel “Modal Assurance Criterion (MAC)”-based methodology with the aim of the optimal sensor placement is presented using a 410 m high rise structure. The Modal Kinetic Energy (MKE) and a MAC-based objective function are combined in the suggested methodology. The optimization problem is solved using a new hybrid metaheuristic algorithm that combines Teaching–Learning-Based Optimization (TLBO), Artificial Bee Colonies (ABC), and Stochastic Paint Optimizer (SPO). With no user-defined parameters, the combination of algorithms exhibits up to 86% better fitness, avoids local optimums due to higher accuracy, and shows promising results for cost calculation optimization because it requires 50% to 70% fewer iterations compared to six evolutionary algorithms that are usually used.

Sub-aperture SAR imaging with uncertainty quantification

In the problem of spotlight mode airborne synthetic aperture radar (SAR) image formation, it is well-known that data collected over a wide azimuthal angle violate the isotropic scattering property typically assumed. Many techniques have been proposed to account for this issue, including both full-aperture and sub-aperture methods based on filtering, regularized least squares, and Bayesian methods. A full-aperture method that uses a hierarchical Bayesian prior to incorporate appropriate speckle modeling and reduction was recently introduced to produce samples of the posterior density rather than a single image estimate. This uncertainty quantification information is more robust as it can generate a variety of statistics for the scene. As proposed, the method was not well-suited for large problems, however, as the sampling was inefficient. Moreover, the method was not explicitly designed to mitigate the effects of the faulty isotropic scattering assumption. In this work we therefore propose a new sub-aperture SAR imaging method that uses a sparse Bayesian learning-type algorithm to more efficiently produce approximate posterior densities for each sub-aperture window. These estimates may be useful in and of themselves, or when of interest, the statistics from these distributions can be combined to form a composite image. Furthermore, unlike the often-employed -regularized least squares methods, no user-defined parameters are required. Application-specific adjustments are made to reduce the typically burdensome runtime and storage requirements so that appropriately large images can be generated. Finally, this paper focuses on incorporating these techniques into SAR image formation process, that is, for the problem starting with SAR phase history data, so that no additional processing errors are incurred. The advantage over existing SAR image formation methods are clearly presented with numerical experiments using real-world data.

Imaging in lossy media

We study the effects of absorption in the medium on synthetic aperture imaging. We model absorption using the loss tangent, which is the imaginary part of the relative dielectric permittivity, and study two cases: (i) the loss tangent is known and (ii) the loss tangent is unknown. When the loss tangent is known and used in Kirchhoff migration (KM), we find that images of targets are range-shifted by approximately a central wavelength so that their predicted locations are closer to the synthetic aperture than they actually are. In contrast, we find that when the medium is unknown, the KM image does not exhibit this range-shift. Hence, we determine that it is better to not make use of any knowledge of the absorption when imaging. Using a recently developed transformation of KM images, which we call reciprocal-KM (rKM), we achieve tunably high-resolution images of targets through adjusting the value of a user-defined parameter ε. When rKM is applied to an imaging region containing two targets, we find that their predicted locations shift, especially in range, but within a fraction of central wavelength of their true locations.

A survey on opportunistic routing protocols in the Internet of Underwater Things

Compared with traditional ocean monitoring technologies, such as cable seabed observation networks, the Internet of Underwater Things (IoUT) can provide real-time underwater environment monitoring and improve efficiency of data collection. Data collection is one of the most essential functions of IoUT, while it is very challenging to achieve efficient data transmission in IoUT. Opportunistic routing is considered to have great advantages and potential in mitigating the drawbacks of underwater communication and improving the performance of data transmission. Therefore, in this paper, the opportunistic routing protocols for IoUT are reviewed and summarized. Taking advantage of the broadcast characteristic of wireless underwater communication, opportunistic routing selects a group of suitable neighbors as potential forwarders instead of selecting only one or a few specific next-hop nodes as in conventional routing, thus improving the success rate of data packet delivery. Critical to routing performance is which nodes participate in data forwarding as forwarders. The nodes act as forwarders are determined by user-defined routing parameters, which are referred to as route metrics in this paper. First, route metrics of opportunistic routing are analyzed and a comprehensive classification of opportunistic routing protocols is discussed based on the route metrics. Then, opportunistic routing protocols for IoUT are further investigated and compared in terms of the adopted communication technique, data delivery ratio, transmission delay efficiency, energy efficiency, buffer efficiency, reliability, etc. Based on the comprehensive classification and comparison, existing problems and future research issues regarding opportunistic routing in IoUT are finally presented.

DeltaShield: Information Theory for Human- Trafficking Detection

Given a million escort advertisements, how can we spot near-duplicates? Such micro-clusters of ads are usually signals of human trafficking (HT). How can we summarize them to convince law enforcement to act? Spotting micro-clusters of near-duplicate documents is useful in multiple, additional settings, including spam-bot detection in Twitter ads, plagiarism, and more. We present InfoShield , which makes the following contributions: practical , being scalable and effective on real data; parameter-free and principled , requiring no user-defined parameters; interpretable , finding a document to be the cluster representative, highlighting all the common phrases, and automatically detecting “slots” (i.e., phrases that differ in every document); and generalizable , beating or matching domain-specific methods in Twitter bot detection and HT detection, respectively, as well as being language independent. Interpretability is particularly important for the anti-HT domain, where law enforcement must visually inspect ads. Our experiments on real data show that InfoShield correctly identifies Twitter bots with an F1 score over 90% and detects HT ads with 84% precision. Moreover, it is scalable, requiring about 8 hours for 4 million documents on a stock laptop. Our incremental version, DeltaShield , allows for fast, incremental updates, with minor loss of accuracy.

Online Adaptive Algorithm for Constraint Energy Minimizing Generalized Multiscale Discontinuous Galerkin Method

In this research, we propose an online basis enrichment strategy within the framework of a recently developed constraint energy minimizing generalized multiscale discontinuous Galerkin method. Combining the technique of oversampling, one makes use of the information of the current residuals to adaptively construct basis functions in the online stage to reduce the error of multiscale approximation. A complete analysis of the method is presented, which shows the proposed online enrichment leads to a fast convergence from multiscale approximation to the fine-scale solution. The error reduction can be made sufficiently large by suitably selecting oversampling regions and the number of oversampling layers. Further, the convergence rate of the enrichment algorithm depends on a factor of exponential decay regarding the number of oversampling layers and a user-defined parameter. Numerical results are provided to demonstrate the effectiveness and efficiency of the proposed online adaptive algorithm.

Optimizing hierarchical tree dissection parameters using historic epidemiologic data as 'ground truth'.

Hierarchical clustering of pathogen genotypes is widely used to complement epidemiologic investigations of outbreaks. Investigators must dissect trees to obtain genetic partitions that provide epidemiologists with meaningful information. Statistical approaches to tree dissection often require a user-defined parameter to predict the optimal partition number and augmenting this parameter can drastically impact resultant partition memberships. Here, we demonstrate how to optimize a given tree dissection parameter to maximize accuracy irrespective of the tree dissection method used. We hierarchically clustered 1,873 genotypes of the foodborne pathogen Cyclospora spp., including 587 possessing links to historic outbreaks. We dissected the resulting tree using a statistical method requiring users to select the value of a 'stringency parameter' (s), with a recommended value of 95% to 99.5%. We dissected this hierarchical tree across s-values from 94% to 99.5% (at increments of 0.25%), to identify a value that maximized partitioning accuracy, defined as the degree to which genetic partitions conform to known epidemiologic groupings. We show that s-values of 96.5% and 96.75% yield the highest accuracy (> 99.9%) when clustering Cyclospora sp. isolates with known epidemiologic linkages. In practice, the optimized s-value will generate robust genetic partitions comprising isolates likely derived from a common food source, even when the epidemiologic grouping is not known prior to genetic clustering. While the s-value is specific to the tree dissection method used here, the optimization approach described could be applied to any parameter/method used to dissect hierarchical trees.

Distributed Predefined-Time Optimization for Second-Order Systems under Detail-Balanced Graphs

This paper studies the problem of distributed predefined-time optimization for leaderless consensus of second-order multi-agent systems under a class of weighted digraphs. The proposed framework has two main steps. In the first step, the agents communicate to perform a consensus-based distributed predefined-time optimization and to generate a constant optimal output reference for each agent. In the second step, each agent tracks its corresponding optimal output reference, using a sliding-mode controller to reach the global optimum in a predefined time, even under matched disturbances. The proposed algorithm relies explicitly on user-defined constant parameters. Numerical simulations are performed to validate the efficacy of the algorithm.

Initialization-free distributed prescribed-time consensus based algorithm for economic dispatch problem over directed network

To minimize the overall generation cost under the restrictions of supply and demand balance and individual generator capacity, the economic dispatch problem (EDP) over directed communication network is considered in this research. This article develops a distributed consensus-based algorithm to solve the EDP that can uniformly pre-assign the settling time. Different from the work with finite-time/fixed-time control, the proposed one allows the settling time to be a user-defined parameter independent of the initial states and other parameters. Moreover, the algorithm does not require initialization and allows the online changes of participating nodes and load demand, it can handle dynamic changes with a pre-arranged convergence time according to the need of the application. In addition, only a dual variable is exchanged with neighboring nodes, and no private information of the node is disclosed. Finally, the simulation results verify the effectiveness of our algorithm.