Ensemble Analysis Research Articles

Pathways-ensemble-analysis v1.1.0: an open-source library for systematic and robust analysis of pathway ensembles

Abstract. Ensembles of mitigation pathways, produced by multiple different models, are becoming increasingly influential as the world seeks to define climate goals and implement policy to meet them. In this context, a range of open-source codes has been developed to standardise and facilitate the systematic and robust analysis of mitigation pathways. We introduce a new open-source package, pathways-ensemble-analysis, which provides an object-oriented framework for the key steps in analysis, describing its structure and providing an illustrative example of its use. By following the suggested application steps of the tool, a user can conveniently perform a systematic and robust analysis of pathway ensembles. This tool is therefore a further step which can help the community in conducting best practices in pathway ensemble analysis.

PROMOTING ARCHITECTURAL HERITAGE THROUGH MUSEUM EDUCATION IN ARCHITECTURE AND DESIGN TRAINING

The paper represents a theoretical-practical study that highlights the role and necessity of museum education in the initial training of architecture and design students from the perspective of promoting national architectural heritage. The article presents basic concepts, as well as some researchers' views on architectural heritage and museum education for architecture and design students in formal and non-formal contexts, which can be integrated in various educational levels, relating to the five general contents of education. Likewise, concrete educational actions involving students in researching, restoring, valorizing, and promoting national architectural heritage are described through the example reflected in the Restoration Concept Project of Balioz Manor. This project implies an in-depth investigation of the issue and includes a set of scientific research methods (documentation, analysis of scientific sources, Strengths, Weaknesses, Opportunities, Threats (SWOT analysis) of urban areas, SWOT analysis of architectural ensembles, photography, surveys, case studies, comparison, and interpretation of results) that led to the achievement of pre-established objectives. In conclusion, focusing on students in the context of the initial training of architecture and design students by involving them in projects for revitalization, renovation, conservation, etc., of architectural heritage contributes to enhancing the performance of future specialists in their professional activities.

CParty: hierarchically constrained partition function of RNA pseudoknots.

Biologically relevant RNA secondary structures are routinely predicted by efficient dynamic programming algorithms that minimize their free energy. Starting from such algorithms, one can devise partition function algorithms, which enable stochastic perspectives on RNA structure ensembles. As the most prominent example, McCaskill's partition function algorithm is derived from pseudoknot-free energy minimization. While this algorithm became hugely successful for the analysis of pseudoknot-free RNA structure ensembles, as of yet there exists only one pseudoknotted partition function implementation, which covers only simple pseudoknots and comes with a borderline-prohibitive complexity of O(n5) in the RNA length n. Here, we develop a partition function algorithm corresponding to the hierarchical pseudoknot prediction of HFold, which performs exact optimization in a realistic pseudoknot energy model. In consequence, our algorithm CParty carries over HFold's advantages over classical pseudoknot prediction in characterizing the Boltzmann ensemble at equilibrium. Given an RNA sequence S and a pseudoknot-free structure G, CParty computes the partition function over all possibly pseudoknotted density-2 structures G∪G' of S that extend the fixed G by a disjoint pseudoknot-free structure G'. Thus, CParty follows the common hypothesis of hierarchical pseudoknot formation, where pseudoknots form as tertiary contacts only after a first pseudoknot-free "core" G and we call the computed partition function hierarchically constrained (by G). Like HFold, the dynamic programming algorithm CParty is very efficient, achieving the low complexity of the pseudoknot-free algorithm, i.e. cubic time and quadratic space. Finally, by computing pseudoknotted ensemble energies, we unveil kinetics features of a therapeutic target in SARS-CoV-2. CParty is available at https://github.com/HosnaJabbari/CParty.

Neural networks for abstraction and reasoning

For half a century, artificial intelligence research has attempted to reproduce the human qualities of abstraction and reasoning - creating computer systems that can learn new concepts from a minimal set of examples, in settings where humans find this easy. While specific neural networks are able to solve an impressive range of problems, broad generalisation to situations outside their training data has proved elusive. In this work, we look at several novel approaches for solving the ion & Reasoning Corpus (ARC). This is a dataset of abstract visual reasoning tasks introduced to test algorithms on broad generalization. Despite three international competitions with $100,000 in prizes, the best algorithms still fail to solve a majority of ARC tasks. The best solvers today rely on complex hand-crafted rules, without using machine learning at all. We revisit whether recent advances in neural networks allow progress on this task, or whether an entirely different class of models are required. First, we adapt the DreamCoder neurosymbolic reasoning solver to ARC. DreamCoder automatically writes programs in a bespoke domain-specific language to perform reasoning, using a neural network to mimic human intuition. We present the Perceptual ion and Reasoning Language (PeARL) language, which allows DreamCoder to solve ARC tasks, and propose a new recognition model that allows us to significantly improve on the previous best implementation. We also propose a new encoding and augmentation scheme that allows large language models (LLMs) to solve ARC tasks, and find that the largest models can solve some ARC tasks. LLMs are able to solve a different group of problems to state-of-the-art solvers, and provide an interesting way to complement other approaches. We perform an ensemble analysis, combining systems to achieve better results than any system alone and analysing individual strengths. However, it is sobering to see that approaches based on neural networks still lag behind existing hand-crafted solvers, and we suggest avenues for future improvements. Our findings with the ensemble model may indicate that a diversity of methods might be necessary to solve problems in ARC. Humans likely employ diverse strategies to solve ARC. Studies involving human participants to identify the strategies they employ to solve ARC could provide valuable insights for future AI approaches. Finally, we publish the arckit Python library to make future research on ARC easier.

Peak-Based Machine Learning for Plastic Type Classification in Time-of-Flight Secondary Ion Mass Spectrometry.

Time-of-flight secondary ion mass spectrometry (ToF-SIMS) measurement data and machine learning were used in this work to classify six different types of plastics. In order to take into account the characteristics of the measurement data, the local maxima of the measurement data were first examined in a preprocessing step. Several machine learning methods were then implemented to create a model that could successfully classify the plastics. To visualize the data distribution, we applied a dimensionality reduction method, namely, principal component analysis. Finally, to distinguish between the six types of plastics, we conducted an ensemble analysis using four tree-based algorithms: decision tree, random forest, gradient boosting, and LIGHTGBM. This approach can identify the feature importance of plastic samples and allow the inference of the chemical properties of each plastic type. In this way, ToF-SIMS data could be utilized to successfully classify plastics and enhance explainability.

Thermodynamic Langevin equations.

The physical significance of the stochastic processes associated to the generalized Gibbs ensembles is scrutinized here with special attention to the thermodynamic fluctuations of small systems. Differently from the so-called stochastic thermodynamics, which starts from stochastic versions of the first and second law of thermodynamics and associates thermodynamic quantities to microscopic variables, here we consider stochastic variability directly in the macroscopic variables. By recognizing the potential structure of the Gibbs ensembles, when expressed as a function of the potential entropy generation, we obtain exact nonlinear thermodynamic Langevin equations(TLEs) for macroscopic variables, with drift expressed in terms of entropic forces. The analysis of the canonical ensemble for an ideal monoatomic gas and the related TLEs show that introducing currents leads to nonequilibrium heat transfer conditions with interesting bounds on entropy production but with no obvious thermodynamic limit. For a colloidal particle under constant force, the TLEs for macroscopic variables are different from those for the microscopic position, typically used in stochastic thermodynamics; while TLEs are consistent with the fundamental equationobtained from the Hamiltonian, stochastic thermodynamics requires isothermal conditions and entropy proportional to position.

Optimizing SMS Spam Detection Using Machine Learning: A Comparative Analysis of Ensemble and Traditional Classifiers

With the rapid rise of mobile communication, Short Message Service (SMS) has become an essential platform for transmitting information. However, the growing volume of unsolicited and harmful spam messages presents significant challenges for both users and mobile network operators. This study explores the effectiveness of various machine learning models, including Random Forest, Gradient Boosting, AdaBoost, Support Vector Machine (SVM), Logistic Regression, and an Ensemble Voting Classifier, in detecting SMS spam. A dataset containing 5,572 SMS messages, labeled as either spam or ham (legitimate), was used to evaluate these models. Hyperparameter tuning was performed on each model to optimize accuracy, and the models were assessed using metrics such as precision, recall, F1-score, and accuracy. The results indicated that the SVM and Ensemble Voting Classifier achieved the highest performance, with accuracies of 0.9857 and 0.9848, respectively. Both models demonstrated superior recall for spam messages, making them highly effective for real-world spam detection systems. While Random Forest, Gradient Boosting, and AdaBoost also performed well, their slightly lower recall for spam suggests that they may misclassify some spam as legitimate messages. The study highlights the effectiveness of machine learning models in addressing the SMS spam problem, particularly when using ensemble methods. Future research should focus on addressing class imbalance and exploring deep learning approaches to further enhance model performance. These findings offer valuable insights for developing more accurate and scalable SMS spam detection systems.

Seasonal amplification of subweekly temperature variability over extratropical Southern Hemisphere land masses

Temperature variability has substantial socioeconomic impacts through its association with the frequency and severity of heat extremes. Under anthropogenic influence, climate models project seasonally-dependent amplifications of near-surface temperature variability over some sectors of the Southern Hemisphere, and robust positive trends have already been observed in recent decades. Here we show that the amplification of subweekly temperature variability simulated by the multi-model ensemble mean of the sixth phase of the Coupled Model Intercomparison Project (CMIP6) over South Africa, Australia, and South America is often substantially smaller than in reanalyses in recent decades, reaching a similar amplification only at the end of the 21st century due to a weaker amplification of subweekly variance generation efficiency. Analysis of a large model ensemble indicates that this discrepancy may be due to internal climatic variability suggesting that the recent rapid amplification seen in reanalyses may slow down or even temporarily reverse in the near future.

Balancing the scales: assessing the impact of irrigation and pathogen burden on potato blackleg disease and soil microbial communities

BackgroundUnderstanding the interaction between environmental conditions, crop yields, and soil health is crucial for sustainable agriculture in a changing climate. Management practices to limit disease are a balancing act. For example, in potato production, dry conditions favour common scab (Streptomyces spp.) and wet conditions favour blackleg disease (Pectobacterium spp.). The exact mechanisms involved and how these link to changes in the soil microbiome are unclear. Our objectives were to test how irrigation management and bacterial pathogen load in potato seed stocks impact: (i) crop yields; (ii) disease development (blackleg or common scab); and (iii) soil microbial community dynamics.MethodsWe used stocks of seed potatoes with varying natural levels of Pectobacterium (Jelly [high load], Jelly [low load] and Estima [Zero – no Pectobacterium]). Stocks were grown under four irrigation regimes that differed in the timing and level of watering. The soil microbial communities were profiled using amplicon sequencing at 50% plant emergence and at harvest. Generalised linear latent variable models and an annotation-free mathematical framework approach (ensemble quotient analysis) were then used to show the interacting microbes with irrigation regime and Pectobacterium pathogen levels.ResultsIrrigation increased blackleg symptoms in the plots planted with stocks with low and high levels of Pectobacterium (22–34%) but not in the zero stock (2–6%). However, withholding irrigation increased common scab symptoms (2–5%) and reduced crop yields. Irrigation did not impact the composition of the soil microbiome, but planting stock with a high Pectobacterium burden resulted in an increased abundance of Planctomycetota, Anaerolinea and Acidobacteria species within the microbiome. Ensemble quotient analysis highlighted the Anaerolinea taxa were highly associated with high levels of Pectobacterium in the seed stock and blackleg symptoms in the field.ConclusionsWe conclude that planting seed stocks with a high Pectobacterium burden alters the abundance of specific microbial species within the soil microbiome and suggest that managing pathogen load in seed stocks could substantially affect soil communities, affecting crop health and productivity.BWvw4eepooU5poSUC2MUy8Video

Automated model-free analysis of cryo-EM volume ensembles with SIREn.

Cryogenic electron microscopy (cryo-EM) has the potential to capture snapshots of proteins in motion and generate hypotheses linking conformational states to biological function. This potential has been increasingly realized by the advent of machine learning models that allow 100s-1,000s of 3D density maps to be generated from a single dataset. How to identify distinct structural states within these volume ensembles and quantify their relative occupancies remain open questions. Here, we present an approach to inferring variable regions directly from a volume ensemble based on the statistical co-occupancy of voxels, as well as a 3D-convolutional neural network that predicts binarization thresholds for volumes in an unbiased and automated manner. We show that these tools recapitulate known heterogeneity in a variety of simulated and real cryo-EM datasets, and highlight how integrating these tools with existing data processing pipelines enables improved particle curation and the construction of quantitative conformational landscapes.

Vertical Displacements and Sea‐Level Changes in Eastern North America Driven by Glacial Isostatic Adjustment: An Ensemble Modeling Approach

AbstractGlacial isostatic adjustment (GIA) describes the response of the solid Earth, oceans, and gravitational field to the spatio‐temporal evolution of ice sheets during a glacial cycle. Present‐day vertical displacements and sea‐level changes vary throughout eastern North America in response to the melting of the Laurentide Ice Sheet following the Last Glacial Maximum. We use the open‐source software SELEN4.0 (a SealEveL EquatioN solver) to investigate the influence of GIA on vertical land motions and sea‐level changes in eastern North America. Further, we evaluate the uncertainties associated with the lithospheric thickness and viscosity structure using an ensemble modeling approach (129,956 total simulations). We identify the best‐fitting rheological profiles by comparing modeled vertical displacements to vertical velocity rates derived from Global Positioning System (GPS). We find a general pattern of subsidence (causing accelerated relative sea‐level rise) in the eastern United States region and uplift (causing relative sea‐level fall) in the eastern Canada region consistent with previous studies for two tested ice sheet models (ICE‐6G(VM5a) and ICE‐7G(VM7)). Overall, we find lower rates of modeled vertical displacement using ICE‐6G(VM5a) compared with ICE‐7G(VM7), which produces lower residuals when compared with the GPS‐derived vertical velocity rates. Our ensemble analysis identifies adjustments to the nominal VM5a and VM7 viscosity models that improve fits to the GPS‐imaged vertical velocity rates throughout eastern North America and on the North American Atlantic Coast. The differences in our best‐fitting models for inland versus coastal regions highlight the importance of exploring lateral viscosity variations for GIA modeling throughout North America and elsewhere.

Investigating the Impact of Maternal Characteristics and Smoking on Birth Weight: An Ensemble Regression Analysis

Investigating the Impact of Maternal Characteristics and Smoking on Birth Weight: An Ensemble Regression Analysis

Analysis of an Infectious Disease Vaccination Prediction System Based on the MF-Conv LSTM Model

Infectious diseases can seriously threaten people's life safety and have a serious impact on social stability. Therefore, it should improve society’s stability under infectious diseases and ensure the safety of people's lives. A personnel flow feature extraction model based on Multi-Feature Convolutional Long Short-Term Memory (MF-Conv LSTM) is designed based on the characteristics of human daily activity behavior. This can optimize the accuracy of transmission simulation prediction for infectious disease vaccination. When using multi-feature ensemble analysis to extract human daily activity features as input for infectious disease simulation and prediction models, the learner's prediction score for the recurrent infectious diseases reached 0.8705. When using multi-feature ensemble analysis, the predicted scores did not exceed 0.85. The designed infectious disease vaccine transmission prediction model can accurately simulate the infectious behavior of infectious diseases. This provides direction for developing strategies to disrupt the infectious diseases’ spread. This reduces the infectious diseases’ harm to people's personal safety and improves social stability during the spread of large-scale infectious diseases.

Synthesis and analysis of an ensemble of SAR signals

Due to the development of digital technologies for receiving and transmitting radio location signals, the development of modern radars with a synthesized aperture has made it possible to widely use an ensemble of probing signals with periodic sequences of identical pulses. The manipulation of probing pulses in transmission opens up opportunities to reduce the level of interference of ambiguity in range, expand the capture bands, improve azimuth resolution, and reduce interference from the background surrounding the target. However, the literature currently does not sufficiently address the issues of synthesis and analysis of ensembles of probing signals for a synthetic aperture radar (RSA) placed on board an unmanned aerial vehicle. Several variants of the implementation of ensembles of probing signals in the RSA are considered, mathematical modeling and experimental testing are carried out on the basis of the RSA operating in the VHF frequency range, placed on board an unmanned aerial vehicle (UAV). The conducted modeling allows us to draw the following conclusions. The best characteristics are possessed by an ensemble of signals encoded by Huffman codes, where M-sequences were used to "scatter" the roots. In second place is an ensemble of signals encoded by composite M-sequences. However, with an increase in the base of probing signals (N>64), the best results are shown by an ensemble of signals based on composite M-sequences.

Aflatoxin contamination level estimation in food using reflectance multispectral imaging based system

This study presents a non-invasive, non-destructive, and rapid method of classifying and estimating aflatoxin levels in food samples using a multispectral imaging. A cost effective, mass deployable in-house built multispectral imaging system, suitable for onsite testing and operating in range from 365 nm to 940 nm, is utilized to capture the multispectral images of the aflatoxin contaminated bread samples. Spatial and spectral corrections are performed to mitigate the imperfections of the multispectral imaging system, followed by image preprocessing steps to mitigate the effect of random noise. Linear discriminant analysis is used to reduce the dimensionality of the feature space. Ensemble classification analysis utilizing classical machine learning classifiers yielded an accuracy of 90 % when classifying samples according to aflatoxin contamination levels. A functional relationship between the Bhattacharyya distance of noncontaminated samples to contaminated samples and aflatoxin contamination levels was modeled as a first order polynomial with an R2 value of 0.9723.

Skillful Seasonal Prediction of Global Onshore Wind Resources in SIDRI-ESS V1.0

The seasonal variation in wind resources has a great impact on wind energy generation, affecting the maintenance planning, operational strategies, and economic benefits of wind farms. Therefore, effective seasonal prediction of wind resources is crucial for the wind power industry. This study evaluates the seasonal prediction skill for global onshore wind resources using the SIDRI-ESS V1.0 dynamic prediction system. High prediction skill for 10 m wind speed (ws10m) is observed mainly in six regions: southern North America, northern South America, western and eastern Europe, and South and East Asia. These regions already have a substantial wind power industry or possess rich wind resources and will need wind power industry deployment in the future. Prediction skill is the highest at a 1-month lead time for most regions and decays as the lead time increases. The highest skill emerges in East Asia, with a temporal correlation coefficient (TCC) reaching 0.7, and persists with a 1-month to 5-month lead time. However, the highest skill for southern North America is at a 6-month lead time. Additionally, ensemble prediction effectively reduces uncertainty, such that a multi-member ensemble mean always matches or even exceeds the individual ensemble member with the best performance. Ensemble size analysis shows that increasing the number of ensemble members generally enhances the prediction skill, with 24 members being sufficient for most regions and lead times. However, further increasing the ensemble size is essential to improve the prediction skill at a 6-month lead time. Meanwhile, we also indicate that ws10m can be used as a proxy in evaluating seasonal prediction of wind resources over most regions, while direct seasonal prediction of wind power density is more effective for northern South America. The high seasonal prediction skill of SIDRI-ESS V1.0 highlights its potential for providing valuable seasonal climate prediction services to the wind power industry.

Inclusion Depth for Contour Ensembles.

Ensembles of contours arise in various applications like simulation, computer-aided design, and semantic segmentation. Uncovering ensemble patterns and analyzing individual members is a challenging task that suffers from clutter. Ensemble statistical summarization can alleviate this issue by permitting analyzing ensembles' distributional components like the mean and median, confidence intervals, and outliers. Contour boxplots, powered by Contour Band Depth (CBD), are a popular non-parametric ensemble summarization method that benefits from CBD's generality, robustness, and theoretical properties. In this work, we introduce Inclusion Depth (ID), a new notion of contour depth with three defining characteristics. First, ID is a generalization of functional Half-Region Depth, which offers several theoretical guarantees. Second, ID relies on a simple principle: the inside/outside relationships between contours. This facilitates implementing ID and understanding its results. Third, the computational complexity of ID scales quadratically in the number of members of the ensemble, improving CBD's cubic complexity. This also in practice speeds up the computation enabling the use of ID for exploring large contour ensembles or in contexts requiring multiple depth evaluations like clustering. In a series of experiments on synthetic data and case studies with meteorological and segmentation data, we evaluate ID's performance and demonstrate its capabilities for the visual analysis of contour ensembles.

Understanding the initial conditions contributing to the rapid intensification of typhoons through ensemble sensitivity analysis

While steady improvements have been achieved for the track forecasts of typhoons, there has been a lack of improvement for intensity forecasts. One challenge for intensity forecasts is to capture the rapid intensification (RI), whose nonlinear characteristics impose great difficulties for numerical models. The ensemble sensitivity analysis (ESA) method is used here to analyze the initial conditions that contribute to typhoon intensity forecasts, especially with RI. Six RI processes from five typhoons (Chaba, Haima, Meranti, Sarika, and Songda) in 2016, are applied with ESA, which also gives a composite initial condition that favors subsequent RI. Results from individual cases have generally similar patterns of ESA, but with different magnitudes, when various cumulus parameterization schemes are applied. To draw the initial conditions with statistical significance, sample-mean azimuthal components of ESA are obtained. Results of the composite sensitivity show that typhoons that experience RI in 24 h favor enhanced primary circulation from low to high levels, intensified secondary circulation with increased radial inflow at lower levels and increased radial outflow at upper levels, a prominent warm core at around 300 hPa, and increased humidity at low levels. As the forecast lead time increases, the patterns of ESA are retained, while the sensitivity magnitudes decay. Given the general and quantitative composite sensitivity along with associated uncertainties for different cumulus parameterization schemes, appropriate sampling of the composite sensitivity in numerical models could be beneficial to capturing the RI and improving the forecasting of typhoon intensity.摘要针对台风强度特别是快速增强 (RI) 的预报难点, 本文对2016年5个台风的6次RI过程应用集合敏感性分析 (ESA), 以分析有利于RI的初始条件. 对于不同的个例和积云参数化方案, ESA获得的有利于台风RI的集合敏感性相似但量级存在差异. 复合敏感性揭示了经历RI的台风所需的初始条件, 其有更强的主环流, 更显著的暖心及增加的低层湿度. ESA估计的集合敏感性随预报时长增加而减弱. 通过采样复合敏感性可有望改进集合初始条件及台风强度预报.

Enhancing Agricultural Sustainability through Precision Fertilizer Recommendations: A Machine Learning Approach with Comparative Ensemble Analysis

Enhancing Agricultural Sustainability through Precision Fertilizer Recommendations: A Machine Learning Approach with Comparative Ensemble Analysis

Telomerase RNA structural heterogeneity in living human cells detected by DMS-MaPseq.

Telomerase is a specialized reverse transcriptase that uses an intrinsic RNA subunit as the template for telomeric DNA synthesis. Biogenesis of human telomerase requires its RNA subunit (hTR) to fold into a multi-domain architecture that includes the template-containing pseudoknot (t/PK) and the three-way junction (CR4/5). These two hTR domains bind the telomerase reverse transcriptase (hTERT) protein and are thus essential for telomerase catalytic activity. Here, we probe the structure of hTR in living cells using dimethyl sulfate mutational profiling with sequencing (DMS-MaPseq) and ensemble deconvolution analysis. Unexpectedly, approximately 15% of the steady state population of hTR has a CR4/5 conformation lacking features thought to be required for hTERT binding. The proportion of hTR CR4/5 that is folded into the primary functional conformation does not require hTERT expression and the fraction of hTR that assumes a misfolded CR4/5 domain is not refolded by overexpression of its hTERT binding partner. This result suggests a functional role for an RNA folding cofactor other than hTERT during telomerase biogenesis. Mutagenesis demonstrates that stabilization of the alternative CR4/5 conformation is detrimental to telomerase assembly and activity. Moreover, the alternative CR4/5 conformation is not found in telomerase RNP complexes purified from cells via an epitope tag on hTERT, supporting the hypothesis that only the major CR4/5 conformer is active. We propose that this misfolded portion of the cellular hTR pool is either slowly refolded or degraded. Thus, kinetic traps for RNA folding that have been so well-studied in vitro may also present barriers for assembly of ribonucleoprotein complexes in vivo.