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In this paper, we introduce the concept of S‐J‐ideals in both commutative and noncommutative rings. For a commutative ring R and a multiplicatively closed subset S, we show that many properties of J‐ideals apply to S‐J‐ideals and examine their characteristics in various ring constructions, such as homomorphic image rings, quotient rings, cartesian product rings, polynomial rings, power series rings, idealization rings, and amalgamation rings. In noncommutative rings, where S is an m‐system, we define right S‐J‐ideals. We demonstrate the equivalence of S‐J‐ideals and right S‐J‐ideals in commutative rings with identity and provide examples to illustrate the connections between right S‐prime ideals and J‐ideals. [ABSTRACT FROM AUTHOR]

Chimeric antigen receptor (CAR)-T cell therapy demonstrates substantial efficacy in various hematological malignancies. However, its application in solid tumors is still limited. Clinical studies report suboptimal outcomes such as reduced cytotoxicity of CAR-T cells and tumor evasion, underscoring the need to address the challenges of sliding cytotoxicity in CAR-T cells. Despite improvements from fourth and next-generation CAR-T cells, new challenges include systemic toxicity from continuously secreted proteins, low productivity, and elevated costs. Recent research targets genetic modifications to boost killing potential, metabolic interventions to hinder tumor progression, and diverse combination strategies to enhance CAR-T cell therapy. Efforts to reduce the duration and cost of CAR-T cell therapy include developing allogenic and in-vivo approaches, promising significant future advancements. Concurrently, innovative technologies and platforms enhance the potential of CAR-T cell therapy to overcome limitations in treating solid tumors. This review explores strategies to optimize CAR-T cell therapies for solid tumors, focusing on enhancing cytotoxicity and overcoming application restrictions. We summarize recent advances in T cell subset selection, CAR-T structural modifications, infiltration enhancement, genetic and metabolic interventions, production optimization, and the integration of novel technologies, presenting therapeutic approaches that could improve CAR-T cell therapy's efficacy and applicability in solid tumors. [ABSTRACT FROM AUTHOR]

The accuracy of Interferometric Synthetic Aperture Radar surface deformation solutions depends on the quality of the chosen interferogram subset. We present a method to select interferogram subsets based on unwrapping errors rather than temporal baseline thresholds. Using Sentinel‐1 interferograms over the Tulare Basin, California, we show that subtle phase noise can lead to up to 31.5 mm/yr line‐of‐sight (LOS) errors in short temporal baseline subset solutions, while decorrelation leads to a systematic underestimation of LOS rates (up to 92.3 mm/yr) in long temporal baseline subset solutions. Our new workflow better mitigates these noise sources at the same time. In the Eagle Ford region, Texas, our strategy better reconstructed up to 11.9 cm of cumulative LOS deformation between 2017 and 2021 over a ∼ ${\sim} $900 km2 $\mathrm{k}{\mathrm{m}}^{\mathrm{2}}$ region. This deformation feature can be linked to the total volume of produced oil and water through a linear relationship. Plain Language Summary: Surface deformation estimates derived from spaceborne imaging radar data are often impacted by weather conditions and surface vegetation changes. While it is common to select high‐quality data based on the time separation between two image passes, we designed a new data selection strategy based on measurable phase artifacts. We applied the new method to two vegetated regions that experienced land subsidence due to agricultural groundwater pumping (Tulare Basin, California) or oil and gas production (Eagle Ford region, Texas). In both cases, we show that surface deformation estimates can vary substantially depending on the choice of data subsets. Our strategy better mitigates different InSAR measurement noise terms at the same time. We showed that the observed subsidence in the Eagle Ford region is linearly proportional to the volume of produced oil and water from unconventional oil and gas resources exploitation. Furthermore, ongoing production activities have led to an increase in human‐induced earthquakes. Based on these findings, accurate surface deformation derived from InSAR data is now achievable in densely vegetated regions and can play an important role in future induced seismicity studies. Key Points: InSAR phase coherence does not always decrease with temporal baselinesChoosing interferograms based on phase unwrapping errors rather than temporal baselines better mitigates InSAR measurement noiseThe improved InSAR analysis strategy reveals up to 11.9 cm of LOS deformation signals related to oil and gas operations over the Eagle Ford [ABSTRACT FROM AUTHOR]

In this paper, for the sake of enhancing the security of wireless transmission, we proposed a novel system based on spatial and direction modulation (SDM) combined with generalized spatial modulation (GSM) which is aided by covert information mapping (CIM), termed as the CIM-GSDM system. In such a system, the legitimated user is equipped with distributed receivers so as to demodulate the conveyed signal by exploiting its indices while disturbing eavesdroppers for information security. More specifically, part of the information is modulated into the indices of the legitimated distributed receiver subsets with the aid of the mapped covert information and the interference matrix, while another part of the message is arranged by conventional amplitude-phase modulation. The proposed system can reap the benefits from both GSM and CIM to make eavesdropper suffer great mixture. Furthermore, the detection scheme and theoretical analysis of error performance are discussed as well. The simulation results exhibit that the bit error rate (BER) performance of legitimate user is much better than that of the eavesdropper while the proposed scheme improves the security compared to the original CIM-SDM system at the same spectral efficiency. [ABSTRACT FROM AUTHOR]

Let G = (V, E) be a connected graph. A subset S of V is called a γ-free set if there exists a γ-set D of G such that S ∩ D = ∅. If further the induced subgraph H = G[V − S] is connected, then S is called a cc-γ-free set of G. We use this concept to identify connected induced subgraphs H of a given graph G such that γ(H) ≤ γ(G). We also introduce the concept of γ-totally-free and γ-fixed sets and present several basic results on the corresponding parameters. [ABSTRACT FROM AUTHOR]

Multi-objective optimization algorithms are crucial for addressing real-world problems, particularly with regard to optimizing well control parameters, which are often computationally expensive due to their reliance on numerical simulations. Surrogate-assisted models help to reduce this computational burden, but their effectiveness depends on the quality of the surrogates, which can be affected by candidate dimension and noise. This study proposes a novel surrogate-assisted multi-objective optimization framework (MOO-SESA) that combines selective ensemble support-vector regression with NSGA-II. The framework's uniqueness lies in its adaptive selection of a diverse subset of surrogates, established prior to iteration, to enhance accuracy, robustness, and computational efficiency. To our knowledge, this is the first instance in which selective ensemble techniques with multi-objective optimization have been applied to reservoir well control problems. Through employing an ensemble strategy for improving the quality of the surrogate model, MOO-SESA demonstrated superior well control scenarios and faster convergence compared to traditional surrogate-assisted models when applied to the SPE10 and Egg reservoir models. [ABSTRACT FROM AUTHOR]

AbstractWith the rapid development of quantum computers, researchers have shown quantum advantages in physics-oriented problems. Quantum algorithms tackling computational biology problems are still lacking. In this article, we demonstrate the quantum advantage in analyzing CITE-seq data. CITE-seq, a single-cell technology, enables researchers to simultaneously measure expressions of RNA and surface protein detected by antibody-derived tags (ADTs) in the same cells. CITE-seq data hold tremendous potential for identifying ADTs associated with targeted genes and identifying cell types effectively. However, both tasks are challenging since the best subset of ADTs needs to be identified from enormous candidate subsets. To surmount the challenge, we develop a quantum algorithm named bisection Grover’s search (BGS) for the best subset selection of ADT markers in CITE-seq data. BGS takes advantage of quantum parallelism by integrating binary search and Grover’s algorithm to enable fast computation. Theoretical results are provided to show the privilege of BGS in the estimation error and computational complexity. The empirical performance of the BGS algorithm is demonstrated on both the IBM quantum computer and simulator. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work. [ABSTRACT FROM AUTHOR]

One of the primary issues that arises in statistical modeling pertains to the assessment of the relative importance of each variable in the model. A variety of techniques have been proposed to quantify variable importance for regression models. However, in the context of best subset selection, fewer satisfactory methods are available. With this motivation, we here develop a variable importance measure expressly for this setting. We investigate and illustrate the properties of this measure, introduce algorithms for the efficient computation of its values, and propose a procedure for calculating p-values based on its sampling distributions. We present multiple simulation studies to examine the properties of the proposed methods, along with an application to demonstrate their practical utility. [ABSTRACT FROM AUTHOR]

Feature subset selection problem is an NP hard problem and there is a need for computationally efficient algorithms that find near optimal feature subsets which improve the performance of a classifier. Two major challenges for feature subset selection are high-dimensional data, that is, data with a large number of features and large datasets. Scalability of the feature selection algorithms in terms of accuracy for high dimensional data and the time taken for large datasets are important issues. We propose a consensus clustering based approach to feature selection that addresses these issues. There exist many greedy feature ranking algorithms in the literature that are computationally efficient. Each algorithm assigns a different ranking order to the features. A consensus among these rankings may provide a feature ranking that performs well with respect to time as well as accuracy. The goal of this work is to propose efficient algorithms that work on small as well as large datasets. The contributions of this work include: i. A fast and scalable approach for feature selection Feature ranking based on consensus clustering(FRCC), has been designed using the available feature ranking algorithms from the literature. ii. A parallelizable version of FRCC, namely, Hybrid Feature Selection(HFS), is proposed to address the feature reduction in large datasets. The implementation results show that FRCC clearly outperforms many recent algorithms in the literature on small as well as large dimensional data sets. HFS has been implemented on datasets with lakhs of instances and dimensionality in hundreds and thousands. HFS proves to be very effective in terms of feature reduction and accuracy in comparison to the results obtained by recent algorithms in the literature. [ABSTRACT FROM AUTHOR]

Ongoing modern computational advancements continue to make it easier to collect increasingly large and complex datasets, which can often only be realistically analyzed using models defined by intractable likelihood functions. This Stats invited feature article introduces and provides an extensive simulation study of a new approximate Bayesian computation (ABC) framework for estimating the posterior distribution and the maximum likelihood estimate (MLE) of the parameters of models defined by intractable likelihoods, that unifies and extends previous ABC methods proposed separately. This framework, copulaABCdrf, aims to accurately estimate and describe the possibly skewed and high-dimensional posterior distribution by a novel multivariate copula-based meta-t distribution based on univariate marginal posterior distributions that can be accurately estimated by distribution random forests (drf), while performing automatic summary statistics (covariates) selection, based on robustly estimated copula dependence parameters. The copulaABCdrf framework also provides a novel multivariate mode estimator to perform MLE and posterior mode estimation and an optional step to perform model selection from a given set of models using posterior probabilities estimated by drf. The posterior distribution estimation accuracy of the ABC framework is illustrated and compared with previous standard ABC methods through several simulation studies involving low- and high-dimensional models with computable posterior distributions, which are either unimodal, skewed, or multimodal; and exponential random graph and mechanistic network models, each defined by an intractable likelihood from which it is costly to simulate large network datasets. This paper also proposes and studies a new solution to the simulation cost problem in ABC involving the posterior estimation of parameters from datasets simulated from the given model that are smaller compared to the potentially large size of the dataset being analyzed. This proposal is motivated by the fact that, for many models defined by intractable likelihoods, such as the network models when they are applied to analyze massive networks, the repeated simulation of large datasets (networks) for posterior-based parameter estimation can be too computationally costly and vastly slow down or prohibit the use of standard ABC methods. The copulaABCdrf framework and standard ABC methods are further illustrated through analyses of large real-life networks of sizes ranging between 28,000 and 65.6 million nodes (between 3 million and 1.8 billion edges), including a large multilayer network with weighted directed edges. The results of the simulation studies show that, in settings where the true posterior distribution is not highly multimodal, copulaABCdrf usually produced similar point estimates from the posterior distribution for low-dimensional parametric models as previous ABC methods, but the copula-based method can produce more accurate estimates from the posterior distribution for high-dimensional models, and, in both dimensionality cases, usually produced more accurate estimates of univariate marginal posterior distributions of parameters. Also, posterior estimation accuracy was usually improved when pre-selecting the important summary statistics using drf compared to ABC employing no pre-selection of the subset of important summaries. For all ABC methods studied, accurate estimation of a highly multimodal posterior distribution was challenging. In light of the results of all the simulation studies, this article concludes by discussing how the copulaABCdrf framework can be improved for future research. [ABSTRACT FROM AUTHOR]

Background: In recent years, significant morbidity and mortality in patients with severe inflammatory bowel disease (IBD) and cytomegalovirus (CMV) have drawn considerable attention to the status of CMV infection in the intestinal mucosa of IBD patients and its role in disease progression. However, there is currently no high-throughput sequencing data for ulcerative colitis patients with CMV infection (CMV + UC), and the immune microenvironment in CMV + UC patients have yet to be explored. Method: The xCell algorithm was used for evaluate the immune microenvironment of CMV + UC patients. Then, WGCNA analysis was explored to obtain the co-expression modules between abnormal immune cells and gene level or protein level. Next, three machine learning approach include Random Forest, SVM-rfe, and Lasso were used to filter candidate biomarkers. Finally, Best Subset Selection algorithms was performed to construct the diagnostic model. Results: In this study, we performed transcriptomic and proteomic sequencing on CMV + UC patients to establish a comprehensive immune microenvironment profile and found 11 specific abnormal immune cells in CMV + UC group. After using multi-omics integration algorithms, we identified seven co-expression gene modules and five co-expression protein modules. Subsequently, we utilized various machine learning algorithms to identify key biomarkers with diagnostic efficacy and constructed an early diagnostic model. We identified a total of eight biomarkers (PPP1R12B, CIRBP, CSNK2A2, DNAJB11, PIK3R4, RRBP1, STX5, TMEM214) that play crucial roles in the immune microenvironment of CMV + UC and exhibit superior diagnostic performance for CMV + UC. Conclusion: This 8 biomarkers model offers a new paradigm for the diagnosis and treatment of IBD patients post-CMV infection. Further research into this model will be significant for understanding the changes in the host immune microenvironment following CMV infection. [ABSTRACT FROM AUTHOR]

One key aspect of the human experience is our ongoing stream of thoughts. These thoughts can be broadly categorized into various dimensions, which are associated with different impacts on mood, well-being, and productivity. While the past literature has often identified eye movements associated with a specific thought dimension (task-relatedness) during experimental tasks, few studies have determined if these various thought dimensions can be classified by oculomotor activity during naturalistic tasks. Employing thought sampling, eye tracking, and machine learning, we assessed the classification of nine thought dimensions (task-relatedness, freely moving, stickiness, goal-directedness, internal–external orientation, self-orientation, others orientation, visual modality, and auditory modality) across seven multi-day recordings of seven participants during self-selected computer tasks. Our analyses were based on a total of 1715 thought probes across 63 h of recordings. Automated binary-class classification of the thought dimensions was based on statistical features extracted from eye movement measures, including fixation and saccades. These features all served as input into a random forest (RF) classifier, which was then improved with particle swarm optimization (PSO)-based selection of the best subset of features for classifier performance. The mean Matthews correlation coefficient (MCC) values from the PSO-based RF classifier across the thought dimensions ranged from 0.25 to 0.54, indicating above-chance level performance in all nine thought dimensions across participants and improved performance compared to the RF classifier without feature selection. Our findings highlight the potential of machine learning approaches combined with eye movement measures for the real-time prediction of naturalistic ongoing thoughts, particularly in ecologically valid contexts. [ABSTRACT FROM AUTHOR]

Machine learning (ML) has been extensively applied in various disciplines. However, not much attention has been paid to data heterogeneity in databases and number of samples used to train ML models in hydrology. In this study, we addressed these issues and their impacts on the accuracy and reliability of ML models in the estimation of saturated hydraulic conductivity, Ks. We selected 17,990 soil samples from the USKSAT database and created random subsets N = 2,000, 4,000, 6,000, 8,000, 10,000, 12,000, 14,000, 16,000, and 17,990, 80% of which were used for training. The random subset selection was repeated 50 times. The extreme gradient boosting (XGBoost) algorithm was used to estimate Ks from other soil properties, such as bulk density, soil depth, texture, and organic content. For each subset, we conducted the learning curve analysis on the training and cross‐validation data sets. Results showed that for all training sample sizes the number of samples was not enough for the training and cross‐validation curves to reach a plateau. We also applied the concept of representative elementary volume by plotting the average coefficient of determination, R2, and root mean square log‐transformed error, RMSLE, against the training sample size. For the testing data set, as the number of training sample size increased from 1,600 to 14,392 the average R2 value increased from 0.74 to 0.90, while the average RMSLE value decreased from 1.08 to 0.69. Either the learning curve or representative sample size analysis is required to investigate whether the number of samples is enough or not. Key Points: Learning curves were applied to address effects of data heterogeneity and number of samples on machine learning‐based model estimationsConcept of representative elementary volume was used to determine the representative sample size in machine learningThe number of samples was not enough for the training and cross‐validation curves to reach a plateau [ABSTRACT FROM AUTHOR]

A μ-way (v,k,t) trade T of volume m consists of μ pairwise disjoint collections Ti,...,Tμ, each of m blocks of size k such that for every t-subset of a v-set V, the number of blocks containing this t-subset is the same in each Ti for 1 ≤ i ≤ μ. If any t-subset of the v-set V occurs at most once in each Ti for 1 ≤ i ≤ μ, then T is called a μ-way (v, k, t) Steiner trade. In 2016, it was proved that there exists a 3-way (v, k, 2) Steiner trade of volume m when 12(k - 1) ≤ m for each k. Here we improve the lower bound to 8(k - 1) for even k, by using a recursive construction. [ABSTRACT FROM AUTHOR]

In this paper, a subset-optimized eight-dimensional trellis-coded quadrature amplitude modulation (SO-8DTCM-16QAM) format for higher-order constellations in high-speed optical communications is proposed. This scheme increases the number of subsets of base 2D constellation divisions. On this basis, it is further optimized by using 2D subsets for Cartesian product combinations to obtain 4D subsets and eliminate the combinations with small Euclidean distances. Finally, the 4D subsets are utilized to construct interrelated 8D subsets for trellis coding modulation and signal transmission. The proposed scheme can effectively reduce the decoding complexity and outperforms the conventional scheme at a high signal-to-noise ratio (SNR). Simulation verification of the proposed scheme is carried out, and the results show that the SO-8DTCM-16QAM achieves signal-to-noise ratio (SNR) gains of 1.60 dB, 1.56 dB, 1.51 dB, and 1.33 dB, respectively, compared with the conventional 8D-16QAM signals when BTB and 5/20/30 km optical signal transmission are performed. The SO-8DTCM-16QAM also achieves an SNR gain of 1.86 dB, 1.75 dB, and 1.22 dB at a net transmission rate of 14/21/28 GBaud. In addition, the SO-8DTCM-16/32/64QAM achieves an SNR gain of 1.27 dB, 0.80 dB, and 1.24 dB, respectively, when compared with the unoptimized 8DTCM-16/32/64QAM. Meanwhile, the proposed eight-subset SO-8DTCM-QAM scheme reduces the complexity of the decoding computation in the subset selection part and the constellation point selection part by 93.75% and 50%, respectively, compared with the unoptimized eight-subset and four-subset 8DTCM-QAM schemes. It can be seen that the proposed scheme simultaneously optimizes the transmission performance and complexity of high-speed optical communication systems and has practical application value. [ABSTRACT FROM AUTHOR]

We present a full Bayesian analysis of multiplicative double seasonal autoregressive (DSAR) models in a unified way, considering identification (best subset selection), estimation, and prediction problems. We assume that the DSAR model errors are normally distributed and introduce latent variables for the model lags, and then we embed the DSAR model in a hierarchical Bayes normal mixture structure. By employing the Bernoulli prior for each latent variable and the mixture normal and inverse gamma priors for the DSAR model coefficients and variance, respectively, we derive the full conditional posterior and predictive distributions in closed form. Using these derived conditional posterior and predictive distributions, we present the full Bayesian analysis of DSAR models by proposing the Gibbs sampling algorithm to approximate the posterior and predictive distributions and provide multi-step-ahead predictions. We evaluate the efficiency of the proposed full Bayesian analysis of DSAR models using an extensive simulation study, and we then apply our work to several real-world hourly electricity load time series datasets in 16 European countries. [ABSTRACT FROM AUTHOR]

Data science is an interdisciplinary domain that has vast applications. One among them is the bioinformatics domain. Bioinformatics is an essential field of science that incorporates computational, statistical and mathematical aspects for various experimental studies. Microarray experiments are vital in identifying the genetic cause of acute diseases. But the massive volume of data generated from microarray experiments makes analysis tedious and time-consuming for researchers. Hence, an efficient method for selecting relevant gene features is essential. This article proposes a new model for autism gene subset selection termed optimized reliefF (OReliefF). The proposed OReliefF employs a new fuzzy multi-verse optimizer (FMVO) to optimize the selection of neighbours. The filter-based feature selection model obtains the gene subset with high classification accuracy and a low error rate. Benchmark datasets of National Center for Biotechnology Information, USA with accession numbers GSE25507, GSE26415 and GSE2704 are used for implementation. The experimental findings show that the proposed autism gene selection model outperformed the state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

Rising temperatures due to global warming necessitate immediate evaluation of heatwave patterns in Peninsular Malaysia (PM). For this purpose, this study utilized a locally developed heatwave index and a gridded daily maximum temperature (Tmax) dataset from ERA5 (1950–2022). During validation, the ERA5 dataset accurately represented the spatial pattern of Level 1 heatwaves, showing widespread occurrence. Historically, Level 1 heatwaves prevailed at 63.0%, followed by Level 2 at 27.7%, concentrated in northwestern states and the enclave between the Tahan and Titiwangsa mountain ranges. During very strong El Niño events in 1982/83, 1997/98, and 2015/16, Level 2 heatwave distributions were 10.4%, 26.8%, and 15.0%, respectively. For future projection, the model ensemble was created by selecting top-performing Global Climate Models (GCMs) using Kling-Gupta efficiency (KGE), ranked re-aggregation with compromise programming index (CPI), and GCM subset selection via Fisher-Jenks. The linear scaling bias-corrected GCMs (BC-GCMs), NorESM2-LM, ACCESS-CM2, MPI-ESM1-2-LR, ACCESS-ESM1-5, and FGOALS-g3, were found to exhibit better performance, and then ensemble. March to May show the highest increase in all scenarios, ranging from 3.3 °C to 4.4 °C for Level 1 heatwaves and 4.1 °C to 10.7 °C for Level 2 heatwaves. In the near future, SSP5-8.5 projects up to a 40.5% spatial increase for Level 1 heatwaves and a 2.3% increase for Level 2 heatwaves, affecting 97.1% and 57.2% of the area, respectively. In the far future, under SSP2-4.5 and SSP5-8.5, Tmax is projected to rise rapidly (1.5–4.5 °C) in the northern, western, and central regions, with increasing population exposure anticipated in the northern and western regions. [ABSTRACT FROM AUTHOR]

Variable selection involves selecting truly important predictors from p-dimensional multivariate functional predictors in functional predictive models. In this paper, a variable selection method is designed for scalar-on-function predictions entangled with nonlinear joint associations among scalar response and multiple functional predictors. First, a nonparametric functional nonlinear conditional correlation coefficient, namely, the FunNCC coefficient, is proposed to measure complex dependencies, including the nonmonotonic marginal dependence, along with the conditional associations of redundancy, complement, and interaction. Then, a model-free feature ordering and selection method is designed, where the FunNCC is utilized to rank relevance, enabling the selection of a subset of predictors with the strongest joint dependence. Since this method allows for quantitatively evaluating the contributions of predictors in explaining responses, it achieves moderate model interpretability. Finally, extensive simulation studies and two real-data cases involving air pollution regression and hand gesture recognition are conducted to evaluate the finite sample performance of the proposed method, and the results show that the proposed FunNCC and variable selection methods outperform state-of-the-art baselines. [ABSTRACT FROM AUTHOR]

We develop a three-part approach to Verb Sense Disambiguation (VSD) in German. After considering a set of lexical resources and corpora, we arrive at a statistically motivated selection of a subset of verbs and their senses from GermaNet. This sub-inventory is then used to disambiguate the occurrences of the corresponding verbs in a corpus resulting from the union of TüBa-D/Z, Salsa, and E-VALBU. The corpus annotated in this way is called TGVCorp. It is used in the third part of the paper for training a classifier for VSD and for its comparative evaluation with a state-of-the-art approach in this research area, namely EWISER. Our simple classifier outperforms the transformer-based approach on the same data in both accuracy and speed in German but not in English and we discuss possible reasons. [ABSTRACT FROM AUTHOR]

In recent years, Artificial Intelligence has been used to assist healthcare professionals in detecting and diagnosing neurodegenerative diseases. In this study, we propose a methodology to analyze functional Magnetic Resonance Imaging signals and perform classification between Parkinson's disease patients and healthy participants using Machine Learning algorithms. In addition, the proposed approach provides insights into the brain regions affected by the disease. The functional Magnetic Resonance Imaging from the PPMI and 1000-FCP datasets were pre-processed to extract time series from 200 brain regions per participant, resulting in 11,600 features. Causal Forest and Wrapper Feature Subset Selection algorithms were used for dimensionality reduction, resulting in a subset of features based on their heterogeneity and association with the disease. We utilized Logistic Regression and XGBoost algorithms to perform PD detection, achieving 97.6% accuracy, 97.5% F1 score, 97.9% precision, and 97.7%recall by analyzing sets with fewer than 300 features in a population including men and women. Finally, Multiple Correspondence Analysis was employed to visualize the relationships between brain regions and each group (women with Parkinson, female controls, men with Parkinson, male controls). Associations between the Unified Parkinson's Disease Rating Scale questionnaire results and affected brain regions in different groups were also obtained to show another use case of the methodology. This work proposes a methodology to (1) classify patients and controls with Machine Learning and Causal Forest algorithm and (2) visualize associations between brain regions and groups, providing high-accuracy classification and enhanced interpretability of the correlation between specific brain regions and the disease across different groups. [ABSTRACT FROM AUTHOR]

The Machine Learning (ML) models are prone to a curse of dimensionality. The dataset with a greater number of features involves more computational cost and it may lead to low performance in the context of prediction accuracy. Therefore, in this research work we have predicted diabetes with more accuracy by using a smaller number of features. The heuristic methods Sequential Forward Selection (SFS), Sequential Backward Selection (SBS) and metaheuristic evolutionary methods — Whale Optimization Algorithm (WOA) and Genetic Algorithm (GA) are used for performing feature subset selection. The Gini index is also used as a filter evaluator. The performance of the feature subsets is analyzed by applying three different types of ML models, Random Forest (RF), Multi-Layer Perceptron (MLP) and K-Nearest Neighbor (KNN). We have predicted type-2 diabetes with an accuracy of 96.82%. Also, we have reduced the number of features up to 67.44% i.e., identified 32.56% most relevant features. [ABSTRACT FROM AUTHOR]

There is a fundamental subsets–partitions duality that runs through the exact sciences. In more concrete terms, it is the duality between elements of a subset and the distinctions of a partition. In more abstract terms, it is the reverse-the-arrows of category theory that provides a major architectonic of mathematics. The paper first develops the duality between the Boolean logic of subsets and the logic of partitions. Then, probability theory and information theory (as based on logical entropy) are shown to start with the quantitative versions of subsets and partitions. Some basic universal mapping properties in the category of S e t s are developed that precede the abstract duality of category theory. But by far the main application is to the clarification and interpretation of quantum mechanics. Since classical mechanics illustrates the Boolean worldview of full distinctness, it is natural that quantum mechanics would be based on the indefiniteness of its characteristic superposition states, which is modeled at the set level by partitions (or equivalence relations). This approach to interpreting quantum mechanics is not a jury-rigged or ad hoc attempt at the interpretation of quantum mechanics but is a natural application of the fundamental duality running throughout the exact sciences. [ABSTRACT FROM AUTHOR]

Abstract The accuracy of Interferometric Synthetic Aperture Radar surface deformation solutions depends on the quality of the chosen interferogram subset. We present a method to select interferogram subsets based on unwrapping errors rather than temporal baseline thresholds. Using Sentinel‐1 interferograms over the Tulare Basin, California, we show that subtle phase noise can lead to up to 31.5 mm/yr line‐of‐sight (LOS) errors in short temporal baseline subset solutions, while decorrelation leads to a systematic underestimation of LOS rates (up to 92.3 mm/yr) in long temporal baseline subset solutions. Our new workflow better mitigates these noise sources at the same time. In the Eagle Ford region, Texas, our strategy better reconstructed up to 11.9 cm of cumulative LOS deformation between 2017 and 2021 over a ∼900 km2 region. This deformation feature can be linked to the total volume of produced oil and water through a linear relationship.

Machine learning models are revolutionizing our approaches to discovering and designing bioactive peptides. These models often need protein structure awareness, as they heavily rely on sequential data. The models excel at identifying sequences of a particular biological nature or activity, but they frequently fail to comprehend their intricate mechanism(s) of action. To solve two problems at once, we studied the mechanisms of action and structural landscape of antimicrobial peptides as (i) membrane-disrupting peptides, (ii) membrane-penetrating peptides, and (iii) protein-binding peptides. By analyzing critical features such as dipeptides and physicochemical descriptors, we developed models with high accuracy (86–88%) in predicting these categories. However, our initial models (1.0 and 2.0) exhibited a bias towards α-helical and coiled structures, influencing predictions. To address this structural bias, we implemented subset selection and data reduction strategies. The former gave three structure-specific models for peptides likely to fold into α-helices (models 1.1 and 2.1), coils (1.3 and 2.3), or mixed structures (1.4 and 2.4). The latter depleted over-represented structures, leading to structure-agnostic predictors 1.5 and 2.5. Additionally, our research highlights the sensitivity of important features to different structure classes across models. [ABSTRACT FROM AUTHOR]

Typically, in high dimensional data sets, many covariates are not significantly associated with a response. Moreover, those covariates are highly correlated, leading to a multicollinearity problem. Hence, the model is sparse since the coefficient of most covariates are likely to be zero. The classical frequentist or likelihood-based variable selection via any criterion such as Bayesian Information Criteria (BIC) and Akaike Information Criteria (AIC) or a stepwise subset selection becomes infeasible when the number of variables are large. An alternative solution is a Bayesian variable selection. In this study, we used a variable selection via a Bayesian variable selection and the least absolute shrinkage and selection operator (LASSO) method in the logistic regression model. Moreover, those methods were expanded to be applied to real datasets. [ABSTRACT FROM AUTHOR]

In order to overcome the limitations of traditional stochastic models for smartphones, we introduce a double-difference code pseudorange residual (DDPR)-dependent stochastic model based on an optimal satellite subset, with the goal of mitigating the constraints imposed by the quality of GNSS observations in smartphones on the accuracy and reliability of phone-based GNSS positioning. In our methodology, the satellite selection process involved considering the integrated carrier-to-noise density ratio (C/N0) index of both the reference station and the smartphone, enabling us to construct a satellite subset characterized by superior observation quality. Furthermore, by leveraging the optimal subset of satellites and incorporating the C/N0-dependent stochastic model, we could determine the approximate location of the terminal through pseudorange differential positioning. Subsequently, we estimated the DDPRs for all satellites and utilized these values as prior information to build a stochastic model of the observations. Our findings indicate that in occluded environments, the DDPR-dependent stochastic model significantly enhances positioning accuracy for both the Huawei Mate40 and P40 terminals compared to the C/N0-dependent model. Numerically, the improvements in the north (N), east (E), and up (U) directions were approximately 30%, 32%, and 34% for the Mate40, and 26%, 33%, and 24% for the P40 terminal. This suggests that the proposed DDPR-dependent stochastic model effectively identifies and mitigates large gross error signals caused by multipath and non-line-of-sight (NLOS) signals, thereby assigning lower weights to these problematic observations and ultimately enhancing positioning accuracy. Moreover, the weighting method involves minimal computations and is straightforward to implement, making it particularly suitable for GNSS positioning applications on smartphones in complex urban environments. [ABSTRACT FROM AUTHOR]

The interpretation of complex biological datasets requires the identification of representative variables that describe the data without critical information loss. This is particularly important in the analysis of large phenotypic datasets (phenomics). Here we introduce Multi-Attribute Subset Selection (MASS), an algorithm which separates a matrix of phenotypes (e.g., yield across microbial species and environmental conditions) into predictor and response sets of conditions. Using mixed integer linear programming, MASS expresses the response conditions as a linear combination of the predictor conditions, while simultaneously searching for the optimally descriptive set of predictors. We apply the algorithm to three microbial datasets and identify environmental conditions that predict phenotypes under other conditions, providing biologically interpretable axes for strain discrimination. MASS could be used to reduce the number of experiments needed to identify species or to map their metabolic capabilities. The generality of the algorithm allows addressing subset selection problems in areas beyond biology. Multi-Attribute Subset Selection is an algorithm for identifying the most descriptive growth conditions in microbial phenotyping experiments. This streamlines high-throughput phenotyping efforts and helps to reveal biological insights. [ABSTRACT FROM AUTHOR]

Background: In recent years, researchers have made significant strides in understanding the heterogeneity of breast cancer and its various subtypes. However, the wealth of genomic and proteomic data available today necessitates efficient frameworks, instruments, and computational tools for meaningful analysis. Despite its success as a prognostic tool, the PAM50 gene signature's reliance on many genes presents challenges in terms of cost and complexity. Consequently, there is a need for more efficient methods to classify breast cancer subtypes using a reduced gene set accurately. Results: This study explores the potential of achieving precise breast cancer subtype categorization using a reduced gene set derived from the PAM50 gene signature. By employing a "Few-Shot Genes Selection" method, we randomly select smaller subsets from PAM50 and evaluate their performance using metrics and a linear model, specifically the Support Vector Machine (SVM) classifier. In addition, we aim to assess whether a more compact gene set can maintain performance while simplifying the classification process. Our findings demonstrate that certain reduced gene subsets can perform comparable or superior to the full PAM50 gene signature. Conclusions: The identified gene subsets, with 36 genes, have the potential to contribute to the development of more cost-effective and streamlined diagnostic tools in breast cancer research and clinical settings. [ABSTRACT FROM AUTHOR]

Bayesian inference typically focuses upon two issues. The first is estimating the parameters of some model from data, and the second is quantifying the evidence for alternative hypotheses—formulated as alternative models. This paper focuses upon a third issue. Our interest is in the selection of data—either through sampling subsets of data from a large dataset or through optimising experimental design—based upon the models we have of how those data are generated. Optimising data-selection ensures we can achieve good inference with fewer data, saving on computational and experimental costs. This paper aims to unpack the principles of active sampling of data by drawing from neurobiological research on animal exploration and from the theory of optimal experimental design. We offer an overview of the salient points from these fields and illustrate their application in simple toy examples, ranging from function approximation with basis sets to inference about processes that evolve over time. Finally, we consider how this approach to data selection could be applied to the design of (Bayes-adaptive) clinical trials. [ABSTRACT FROM AUTHOR]

We consider the stochastic contextual bandit problem under the high dimensional linear model. We focus on the case where the action space is finite and random, with each action associated with a randomly generated contextual covariate. This setting finds essential applications such as personalized recommendations, online advertisements, and personalized medicine. However, it is very challenging to balance the exploration and exploitation tradeoff. We modify the LinUCB algorithm in doubly growing epochs and estimate the parameter using the best subset selection method, which is easy to implement in practice. This approach achieves O (s T) regret with high probability, which is nearly independent of the "ambient" regression model dimension d. We further attain a sharper O (s T) regret by using the SupLinUCB framework and match the minimax lower bound of the low-dimensional linear stochastic bandit problem. Finally, we conduct extensive numerical experiments to empirically demonstrate our algorithms' applicability and robustness. for this article are available online. [ABSTRACT FROM AUTHOR]

Using a safe subset of C++ is a promising direction for increasing the safety of the programming language while maintaining its performance and productivity. In this paper, we examine how close existing C/C++ code is to conforming to a safe subset of C++. We examine the rules presented in existing safe C/C++ standards and safe C/C++ subsets. We analyze the code characteristics of 5.8 million code samples from the Exebench benchmark suite, two C/C++ benchmark suites, and five modern C++ applications using a static analysis tool. We find that raw pointers, unsafe casts, and unsafe library functions are used in both C/C++ code at large and in modern C++ applications. In general, C/C++ code at large does not differ much from modern C++ code, and continued work will be required to transition from existing C/C++ code to a safe subset of C++. [ABSTRACT FROM AUTHOR]

We study the problem of solvability of the equation ϕ (x) = ∫ Ω g (w) ϕ (f (x , ω)) P (d ω) + F (x) , where P is a probability measure on a σ-algebra of subsets of Ω, assuming Hölder continuity of F on the range of f. [ABSTRACT FROM AUTHOR]

In this paper we obtain some refinements and reverses of Callebaut's inequality for isotonic functionals via a result of Young's inequality due to Cartwright and Field. [ABSTRACT FROM AUTHOR]

A subset of cycles comprising a permutation σ in the symmetric group Sn, n ∈ N, is called a divisor of σ. Then the partial sums over divisors with sizes up to un, 0 ≤ u ≤ 1, of values of a nonnegative multiplicative function on a random permutation define a stochastic process with nondecreasing trajectories. When normalized the latter is just a random distribution function supported by the unit interval. We establish that its expectations under various weighted probability measures defined on the subsets of Sn are quasihypergeometric distribution functions. Their limits as n → ∞ cover the class of two-parameter beta distributions. It is shown that, under appropriate conditions, the convergence rate is of the negative power of n order. That opens a new possibility to model the beta distributions using divisors of permutations. [ABSTRACT FROM AUTHOR]

Many intrusion detection algorithms that use optimization have been developed and are commonly used to detect intrusions. The process of selecting features and the parameters of the classifier are essential parts of how well an intrusion detection system works. This paper provides a detailed explanation and discussion of an improved intrusion detection method for multiclass classification. The proposed solution uses a combination of the modified teaching–learning-based optimization (MTLBO) algorithm, the modified JAYA (MJAYA) algorithm, and a support vector machine (SVM). MTLBO is used with supervised machine learning (ML) to select subsets of features. Selection of the fewest features possible without impairing the accuracy of the results in feature subset selection (FSS) is a multiobjective optimization issue. This paper presents MTLBO as a mechanism and investigates its algorithm-specific, parameter-free idea. This study used the modified JAYA (MJAYA) algorithm to optimize the C and gamma parameters of the support vector machine (SVM) classifier. When the proposed MTLBO-MJAYA-SVM algorithm was compared with the original TLBO and JAYA algorithms on a well-known intrusion detection dataset, it was found to outperform them significantly. [ABSTRACT FROM AUTHOR]

Adverse Outcome Pathway (AOP) is a useful tool to glean mode of action (MOE) of a chemical. However, in order to use it for the purpose of risk assessment, an AOP needs to be quantified using in vitro or in vivo data. Majority of quantitative AOPs developed so far, were for single exposure to progressively higher doses. Limited attempts were made to include time in the modeling. Here as a proof-of concept, we developed a hypothetical AOP, and quantified it using a virtual dataset for six repeated exposures using a Bayesian Network Analysis (BN) framework. The virtual data was generated using realistic assumptions. Effects of each exposure were analyzed separately using a static BN model and analyzed in combination using a dynamic BN (DBN) model. Our work shows that the DBN model can be used to calculate the probability of adverse outcome when other upstream KEs were observed earlier. These probabilities can help in identification of early indicators of AO. In addition, we also developed a data driven AOP pruning technique using a lasso-based subset selection, and show that the causal structure of AOP is itself dynamic and changes over time. This proof-of-concept study revealed the possibility for expanding the applicability of the AOP framework to incorporate biological dynamism in toxicity appearance by repeated insults. [ABSTRACT FROM AUTHOR]

Selecting a system or a subset of systems that have the best performance among many alternative systems is an important area of research. All efforts are paid to do this selection with a high probability of correct selection P (C S). In this paper, we compare the performance of two popular procedures used to select the best systems, namely, the method of Kim and Nelson () which is considered as a ranking and selection procedure, and the Optimal Computing Budget Allocation ( ℬ) procedure. Both of these procedures determine the number of samples for each system that are needed to achieve the maximum P (C S). Recently, the ℬ has been developed in order to be able to approximate the value of P (C S) , so both procedures approximate the value of P (C S). To do the comparison, we implement the two methods on two examples; one is a generic example and the other one involves a management of inventory model. The two examples assure that the ℬ method always outperforms the method. [ABSTRACT FROM AUTHOR]

In the era of big data, the demand for multivariate time series prediction has surged, drawing increased attention to feature selection and neural networks in machine learning. However, certain feature selection methods neglect the alignment between actual data sample differences and clustering results, while neural networks lack automatic parameter adjustment in response to changing target features. This paper presents the MBSSA-Bi-AESN model, a Bi-directional Adaptive Echo State Network that utilizes the modified salp swarm algorithm (MBSSA) and feature selection to address the limitations of manually set parameters. Initial feature subset selection involves assigning weights based on the consistency of clustering results with differences. Subsequently, the four critical parameters in the Bi-AESN model are optimized using MBSSA. The optimized Bi-AESN model and selected feature subset are then integrated for simultaneous model learning and optimal feature subset selection. Experimental analysis on eight datasets demonstrates the superior prediction accuracy of the MBSSA-Bi-AESN model compared to benchmark models, underscoring its feasibility, validity, and universality. [ABSTRACT FROM AUTHOR]

The purpose of this research is to introduce an approach to assist the diagnosis of Parkinson's disease (PD) by classifying functional near-infrared spectroscopy (fNIRS) studies as PD positive or negative. fNIRS is a noninvasive optical signal modality that conveys the brain's hemodynamic response, specifically changes in blood oxygenation in the cerebral cortex; and its potential as a tool to assist PD detection deserves to be explored since it is non-invasive and cost-effective as opposed to other neuroimaging modalities. Besides the integration of fNIRS and machine learning, a contribution of this work is that various approaches were implemented and tested to find the implementation that achieves the highest performance. All the implementations used a logistic regression model for classification. A set of 792 temporal and spectral features were extracted from each participant's fNIRS study. In the two best performing implementations, an ensemble of feature-ranking techniques was used to select a reduced feature subset, which was subsequently reduced with a genetic algorithm. Achieving optimal detection performance, our approach reached 100 % accuracy, precision, and recall, with an F1 score and area under the curve (AUC) of 1, using 14 features. This significantly advances PD diagnosis, highlighting the potential of integrating fNIRS and machine learning for non-invasive PD detection. [ABSTRACT FROM AUTHOR]

In the near future, a much larger scale of low earth orbit (LEO) satellites will be deployed, and satellite selection is an effective way to overcome challenges to processing capability and channel limitation of receivers caused by superabundant satellites in view. However, existing algorithms do not take into account the fact that a large-scale LEO constellation satellite selection subset can be closer to the theoretical optimal configuration than the traditional brute force search and recursive algorithm computational overhead is too large for end devices. In this study, the solution of the minimum optimization problem of the GDOP (Geometric Dilution of Precision) under the condition of limited elevation angle is first analyzed to determine the optimal ratio configuration scheme of top and bottom satellites under the optimal geometric satellite positioning problem. Subsequently, a fast satellite selection algorithm is proposed, which selects a subset of satellites that meet the different numbers of observation satellites and is close to the optimal geometric configuration by geometric method. The comparative simulation shows that, in the 1586 LEO constellation system, the proposed algorithm has an average GDOP of 1.52 when the satellite subset size is 10 and 0.95 when the subset size is 30. Compared with the recursive, the Rotating Partition algorithm and the Quasi-optimal algorithm, the proposed algorithm achieves better accuracy when its size is not close to the total number of observable satellites. [ABSTRACT FROM AUTHOR]

Market analyzers use different parameters as features in the market data to analyze the market trends. The feature's values act as a signal to market fluctuations. Many studies have examined these features to predict market movement more effectively. However, the method to minimize the uncertainties associated with the features is not available in the literature. This exploratory study introduces the uncertainty optimization based feature selection method for stock marketing. We introduce a notion of certainty region of the feature as the set of feature values, which signify particular happening with certainty. We use rough set theory to find the feature's certainty region and uncertainty region and measure each feature's significance. The feature whose certainty region is the maximum is the most significant in the feature space. Hence we group the features by minimizing the uncertainty region of the most informative features to get feature subsets for feature selection. We propose an algorithm based on uncertainty optimization to find subsets of the feature set for effectiveness and performance enhancement in the feature selection. We obtain the decision rules with comprehensive coverage and excellent support using the selected features. The accuracy of classification using the chosen parameters is up to 85.91%, which is higher than 79.54% of the complete feature set. The study provides an uncertainty optimization model for more efficient market movement prediction. [ABSTRACT FROM AUTHOR]

A continuous operator T between two Banach lattices E and F is called almost order-weakly compact, whenever for each almost order bounded subset A of E, T(A) is a relatively weakly compact subset of F. We show that the positive operator T from E into a Dedekind complete Banach lattice F is almost order-weakly compact iff T(xn)... 0 in F for each disjoint almost order bounded sequence {xn} in E. In this manuscript, we study some properties of this class of operators and its relationships with the others known classes of operators. [ABSTRACT FROM AUTHOR]

In this manuscript, we introduce LGc-fuzzy Euclidean topological space in which L denotes a completely distributive lattice with a countable subset dense in it. We use the structure of LG-fuzzy topological space (X, T), which X is an L-fuzzy subset of the crisp set M and T: LMX → L, is an L-gradation of openness on X to define the fundamental concepts of LG-fuzzy analysis such as LG-locally compactness and LG-paracompactness and prove several theorems. In consequence, we show that any second countable Hausdorff LG-fuzzy topological space that is LG-locally compact is LG-paracompact. Also from any given metric ρ on a crisp set M and L-fuzzy subset X of it, we construct an Lgradation of openness Tρ on X and obtain LG-fuzzy topological metric space (X,Tρ). Finally, we prove an interesting theorem: Every LG-fuzzy topological metric space, is LG-paracompact. [ABSTRACT FROM AUTHOR]

Clonotypic αβ T cell responses to cargoes presented by major histocompatibility complex (MHC), MR1, or CD1 proteins underpin adaptive immunity. Those responses are mostly mediated by complementarity-determining region 3 motifs created by quasi-random T cell receptor (TCR) gene rearrangements, with diversity being highest for TCRγδ. Nonetheless, TCRγδ also displays nonclonotypic innate responsiveness following engagement of germline-encoded Vγ-specific residues by butyrophilin (BTN) or BTN-like (BTNL) proteins that uniquely mediate γδ T cell subset selection. We now report that nonclonotypic TCR engagement likewise induces distinct phenotypes in TCRαβ+ cells. Specifically, antibodies to germline-encoded human TCRVβ motifs consistently activated naïve or memory T cells toward core states distinct from those induced by anti-CD3 or superantigens and from others commonly reported. Those states combined selective proliferation and effector function with activation-induced inhibitory receptors and memory differentiation. Thus, nonclonotypic TCRVβ targeting broadens our perspectives on human T cell response modes and might offer ways to induce clinically beneficial phenotypes in defined T cell subsets. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the mean Landsberg curvature of two subclasses of (α, β)-metrics. We prove that these subclasses of (α, β)-metrics with vanishing mean Landsberg curvature have vanishing S-curvature. Using it, we prove that these Finsler metrics are weakly Landsbergian if and only if they are Berwaldian. [ABSTRACT FROM AUTHOR]

In the following text we show if D is Khalimsky line (resp. Khalimsky plane, Khalimsky circle, Khalimsky sphere), then for topological space X we show the collection of all quasicontinuous maps from D to X has cardinality card(X)N0. [ABSTRACT FROM AUTHOR]

We consider selecting the top-m alternatives from a finite number of alternatives via Monte Carlo simulation. Under a Bayesian framework, we formulate the sampling decision as a stochastic dynamic programming problem and develop a sequential sampling policy that maximizes a value function approximation one-step look ahead. To show the asymptotic optimality of the proposed procedure, the asymptotically optimal sampling ratios that optimize the large deviations rate of the probability of false selection for selecting the top-m alternatives have been rigorously defined. The proposed sampling policy is not only proved to be consistent but also achieve the asymptotically optimal sampling ratios. Numerical experiments demonstrate superiority of the proposed allocation procedure over existing ones. History: Accepted by Bruno Tuffin, Area Editor for Simulation. Funding: This work was supported by the National Natural Science Foundation of China [Grants 72250065, 72293582, 72022001, and 71901003], and the National Science Foundation [Grant DMS-2053489], the major project of the National Natural Science Foundation of China [Grant 72293582], and the China Scholarship Council [Grant CSC202206010152]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2021.0333) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2021.0333). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR]

We introduce a generalized additive model for location, scale, and shape (GAMLSS) next of kin aiming at distribution-free and parsimonious regression modeling for arbitrary outcomes. We replace the strict parametric distribution formulating such a model by a transformation function, which in turn is estimated from data. Doing so not only makes the model distribution-free but also allows to limit the number of linear or smooth model terms to a pair of location-scale predictor functions. We derive the likelihood for continuous, discrete, and randomly censored observations, along with corresponding score functions. A plethora of existing algorithms is leveraged for model estimation, including constrained maximum-likelihood, the original GAMLSS algorithm, and transformation trees. Parameter interpretability in the resulting models is closely connected to model selection. We propose the application of a novel best subset selection procedure to achieve especially simple ways of interpretation. All techniques are motivated and illustrated by a collection of applications from different domains, including crossing and partial proportional hazards, complex count regression, nonlinear ordinal regression, and growth curves. All analyses are reproducible with the help of the tram add-on package to the R system for statistical computing and graphics. [ABSTRACT FROM AUTHOR]