Random Set Research Articles

Kernel-based Sensitivity Analysis for (Excursion) Sets

In this article, we aim to perform sensitivity analysis of set-valued models and, in particular, to quantify the impact of uncertain inputs on feasible sets, which are key elements in solving a robust optimization problem under constraints. While most sensitivity analysis methods deal with scalar outputs, this article introduces a novel approach to perform sensitivity analysis with set-valued outputs. Our innovative methodology is designed for excursion sets, but is versatile enough to be applied to set-valued simulators, including those found in viability fields, or when working with maps like pollutant concentration maps or flood zone maps. We propose to use the Hilbert-Schmidt Independence Criterion (HSIC) with a kernel designed for set-valued outputs. After setting a probabilistic framework for random sets, a first contribution is the proof that this kernel is characteristic, an essential property in a kernel-based sensitivity analysis context. To measure the contribution of each input, we then propose to use HSIC-ANOVA indices. With these indices, we can identify which inputs should be neglected (screening) and we can rank the others according to their influence (ranking). The estimation of these indices is also adapted to the set-valued outputs. Finally, we test the proposed method on three test cases of excursion sets.

Transcriptomic point of departure determination: a comparison of distribution-based and gene set-based approaches

A key step in assessing the potential human and environmental health risks of industrial and agricultural chemicals is to determine the toxicity point of departure (POD), which is the highest dose level that causes no adverse effect. Transcriptomic POD (tPOD) values have been suggested to accurately estimate toxicity POD values. One step in the most common approach for tPOD determination involves mapping genes to annotated gene sets, a process that might lead to substantial information loss particularly in species with poor gene annotation. Alternatively, methods that calculate tPOD values directly from the distribution of individual gene POD values omit this mapping step. Using rat transcriptome data for 79 molecules obtained from Open TG-GATEs (Toxicogenomics Project Genomics Assisted Toxicity Evaluation System), the hypothesis was tested that methods based on the distribution of all individual gene POD values will give a similar tPOD value to that obtained via the gene set-based method. Gene set-based tPOD values using four different gene set structures were compared to tPOD values from five different individual gene distribution methods. Results revealed a high tPOD concordance for all methods tested, especially for molecules with at least 300 dose-responsive probesets: for 90% of those molecules, the tPOD values from all methods were within 4-fold of each other. In addition, random gene sets based upon the structure of biological knowledge-derived gene sets produced tPOD values with a median absolute fold change of 1.3–1.4 when compared to the original biological knowledge-derived gene set counterparts, suggesting that little biological information is used in the gene set-based tPOD generation approach. These findings indicate using individual gene distributions to calculate a tPOD is a viable and parsimonious alternative to using gene sets. Importantly, individual gene distribution-based tPOD methods do not require knowledge of biological organization and can be applied to any species including those with poorly annotated gene sets.

Radially transverse isotropic inclusions in isotropic elastic media: Local fields, neutral inclusions, effective elastic properties

Radially transverse isotropic inclusions in homogeneous isotropic elastic host media are considered. Mellin transform method is used for solution of the volume integral equation of the problem for an isolated inclusion subjected to a constant external stress (strain) field. The tensor structure of the solution is revealed with precision to three scalar functions of the radial coordinate, and the system of ordinary differential equations for these functions is derived. For multilayered radially transverse isotropic inclusions with constant elastic coefficients inside layers, explicit solution of these equations is obtained. An efficient numerical algorithm of solution for inclusions with an arbitrary number of the layers is proposed. Neutral inclusions that do not disturb homogeneous external fields applied to the medium are considered. It is shown that an inclusion with an isotropic core and radially transverse isotropic external layer can be weak neutral by appropriate choice of the layer elastic constants. The effective field method is used for determination of the effective elastic stiffness tensor of a homogeneous isotropic medium containing a random set of radially transverse isotropic inclusions.

An Optimal Multiquadric Variable Shape Parameter for Boundary Value Problems Using Particle Swarm Optimization

The multiquadric radial basis function method has been widely used to solve partial differential equations-based problems regarding its flexibility and meshfree characteristics. The accuracy and stability of this method are derived and based on the use of a free-shape parameter that sensibly controls the comportment of the technique. Significant improvements have already been reported and show that variable shape parameters conduct the method to handle problems with striking results compared to global-based techniques. Nevertheless, choosing a suitable set of shape parameters is still an open topic because of the complexity of the method when the number of collocation points increases. The current work proposes a variant particle swarm optimization based on local displacement with attractors to determine the multi-quadratic function's ``best'' optimal variable shape parameter in solving boundary value problems. Based on an initially random set of variable shape parameters, the proposed algorithm first performs and evaluates the errors between the expected exact solution and the approximate solution thoroughly. In the first stage, the particle swarm algorithm search for an optimal set of shape parameters that minimize the error and the conditioning number of the radial basis system matrix. In the second stage, the obtained optimal set of shape parameters is applied to solve the considered problem. In this way, when the number of collocation points increases, the first stage based on particle swarm optimization stabilizes the strategy. It proposes an ``acceptable'' set of shape parameters for the given problem. The proposed method is applied to a set of well-known boundary value problems in one and two-dimensional spaces and compared to other techniques published in the literature. The results show that the proposed method achieves more accurate solutions than recently proposed in the literature.

Robust and Convergent Distributed Cooperative Localization With Labeled Bernoulli Random Finite Set

Robust and Convergent Distributed Cooperative Localization With Labeled Bernoulli Random Finite Set

Social influence and the choice of product upgrades: evidence from virtual product adoption in online games

PurposePrevious studies on social influence and virtual product adoption have mainly taken users’ purchase behavior as a dichotomous variable (i.e. purchasing or not). Given the prevalence of competing versions (basic vs upgraded) of a virtual product in online communities, this paper investigated the differences in the effect of social influence on users’ adoption of basic and upgraded choices of a virtual product. It also examined how the effect varies with users’ social status and user-level network density.Design/methodology/approachA natural experiment was conducted in an online game community. Two competing versions (basic vs upgraded) of a virtual product were provided for in-game purchase while a random set of users selected from 897,765 players received the notification of their friends’ adoption information. A competing-risk model was used to test the hypotheses.FindingsSocial influence exerts a stronger positive effect on users’ adoption of the upgraded virtual product than of the basic virtual product. Middle-status users have the greatest (least) susceptibility to social influence in adopting the upgraded (basic) virtual product than low- and high-status users. User’s network density enhances the effect of social influence on adoption of both virtual products, even more for the upgraded one.Originality/valueThis research contributes to the social influence and product adoption literature by disentangling the different effects of social influence on basic and upgraded versions of a virtual product. It also identifies the boundary conditions that social influence works for each version of the virtual product.

Developing and validating a clinlabomics-based machine-learning model for early detection of retinal detachment in patients with high myopia.

Retinal detachment (RD) is a vision-threatening disorder of significant severity. Individuals with high myopia (HM) face a 2 to 6 times higher risk of developing RD compared to non-myopes. The timely identification of high myopia-related retinal detachment (HMRD) is crucial for effective treatment and prevention of additional vision impairment. Consequently, our objective was to streamline and validate a machine-learning model based on clinical laboratory omics (clinlabomics) for the early detection of RD in HM patients. We extracted clinlabomics data from the electronic health records for 24,440 HM and 5607 HMRD between 2015 and 2022. Lasso regression analysis assessed fifty-nine variables, excluding collinear variables (variance inflation factor > 10). Four models based on random forest, gradient boosting machine (GBM), generalized linear model, and Deep Learning Model were trained for HMRD diagnosis and employed for internal validation. An external test of the models was done. Three random data sets were further processed to validate the performance of the diagnostic model. The primary outcomes were the area under the receiver operating characteristic curve (AUC) and the area under the precision-recall curve (AUCPR) to diagnose HMRD. Nine variables were selected by all models. Given the AUC and AUCPR values across the different sets, the GBM model was chosen as the final diagnostic model. The GBM model had an AUC of 0.8550 (95%CI = 0.8322-0.8967) and an AUCPR of 0.5584 (95%CI = 0.5250-0.5879) in the training set. The AUC and AUCPR in the internal validation were 0.8405 (95%CI = 0.8060-0.8966) and 0.5355 (95%CI = 0.4988-0.5732). During the external test evaluation, it reached an AUC of 0.7579 (95%CI = 0.7340-0.7840) and an AUCPR of 0.5587 (95%CI = 0.5345-0.5880). A similar discriminative capacity was observed in the three random data sets. The GBM model was well-calibrated across all the sets. The GBM-RD model was implemented into a web application that provides risk prediction for HM individuals. GBM algorithms based on nine features successfully predicted the diagnosis of RD in patients with HM, which will help ophthalmologists to establish a preliminary diagnosis and to improve diagnostic accuracy in the clinic.

Binary robustness of random attractors for 2D-Ginzburg–Landau equations with Wong–Zakai noise

Consider a non-autonomous 2D-Ginzburg–Landau equation driven by Wong–Zakai noise or white noise, respectively, we first show the existence of pullback random attractors, which are random compact attracting sets indexed by two parameters: the size of Wong–Zakai noise and the current time. We then establish the robustness of the attractors when both parameters are simultaneously convergent. An essential difficulty arises from the possible loss of the convergence of solutions and only part convergence of solutions is available, which is a new phenomenon for 2D-GL equation distinguishing with the 1D case. So, by using part joint-convergence, regularity, eventual local-compactness and recurrence, we establish a binary robustness theorem of pullback random attractors and apply it to the weakly dissipative stochastic equation.

Aggregation formulation for on‐site multidepot vehicle scheduling scenario

AbstractThe multidepot vehicle scheduling problem (MDVSP) is a fundamental public transport challenge. To address the large‐scale model and inherent solution symmetry associated with the traditional trip‐to‐trip connection‐based approach for MDVSP, a new trip‐to‐route (T2R) connection‐based approach is proposed. Considering real‐world problem characteristics with numerous trips sharing common origin–destination stations and travel times on one route, this approach aggregates same vehicle possible trip sequences into a T2R connection. Two time‐space network aggregation (TSNA) flow formulation versions, route pair‐based TSNA and station pair‐based TSNA, were constructed. Furthermore, TSNA equivalence under any given decomposition strategy, including first‐in‐first‐out, with the multicommodity network flow (MCNF) model was demonstrated. Given the favorable separable TSNA structure, an alternating direction method of multipliers (ADMM)‐based procedure is proposed to decompose the MDVSP into multiple subproblems that can be linearized and readily solved using commercial solvers. The quality of the solutions was assessed using lower bounds obtained from the Lagrangian relaxation problem. The effectiveness and superiority of the proposed MDVSP models and algorithms were subsequently confirmed using random data sets and real‐world instances.

Weighted Stacking of Radio Images Affected by Noise and Interfering Signals

We implement an algorithm based on the weighted stacking of astronomical images that can combine different observations of the same region of the sky removing the interfering signals. We develop a C++ code that takes as input a set of spectral cubes and computes the local weights of the intensity for each pixel of every channel. The weights are calculated as the inverse variance of the nearby pixels and are used to compute the weighted merge of the input files. Astronomical sources, present in all cubes, are preserved by the weighted average. However, interfering signals, present in specific cubes and a certain frequency range, are down-weighted in the average and removed from the output spectral cube. We present the results obtained by analyzing simulated spectral cubes containing astronomical sources, noise, and a random set of interferences of different intensities and spectral occupations. The performance of the algorithm is evaluated by comparing the output result with the input sky-model image. Finally, we present the results obtained by applying the method to a set of real data consisting of observations of the Andromeda galaxy, Messier 31 (M31), at 6.6 GHz obtained with the Sardinia Radio Telescope.

A Network-Group Target State and Network Topology Estimation Method Based on Signals Containing Delay, Doppler and Address

Network-group targets are a set of objectives that adhere to a shared communication protocol, perform common tasks, and exhibit relatively coordinated movements. Typically, network-group targets emit radar and communication signals. However, they often employ a silent mode to evade our perception. Despite this, they continue to exchange data through their communication networks. By intercepting the communication signals of these targets, this paper proposes a method for estimating the state and network topology of network-group targets based on random finite set (RFS) theory. This method is based on the modeling of network-group targets using a labeled multi-Bernoulli (LMB) RFS. In state estimation, the usual method involves extracting the localization parameters from the signals in the first step and use these parameters for target tracking in the second step. This study focused on estimating the kinematic states of network-group targets using communication signals containing delay and Doppler information received by multiple mobile sensor platforms. The proposed method considers the coherency between frequency-hopping pulses in the signals, resulting in an improved estimation performance. In addition, considering that network-group targets require network access for information exchange, graph theory concepts were utilized to estimate the network topology of network-group targets by address measurement. The simulation results validated the effectiveness of the proposed method and demonstrated its superior performance.

IJCM_258A: Perceptions regarding mid-day meal programme among parents of government school children in rural field practice area of a government medical college a cross sectional study.

Background: Mid - Day Meal Scheme (MDMS) renamed as PM POSHAN in 2021 is aiding 11.80 crore children studying in 11.20 lakhs schools across the country making it the largest school feeding programme in the world. This study was intended to know the opinion of parents of children going to government schools regarding mid - day meal. Objectives: 1. To study the perceptions of parents regarding Mid -Day Meal Scheme. 2. To identify the barriers for non-consumption of Mid - day meals. Methodology: Study Design Cross - sectional study Sampling Systematic Random Sampling Study setting “Rural Field Practice Area of a Government medical college. Study Period November - December 2023 Inclusion criteria Parents and Guardians who gave written informed consent. Exclusion criteria Parents who could not respond due to mental problems. Ethical Prior ethical approval was taken from Institutional Ethics Committee. Data collection and analysis data collection was done using pre-designed semi structured questionnaire and analyzed using Excel and SPSS software. Results: Mean age of the respondents (parent) was 33±7SD. Majority (72.9%) of the respondents were mothers. 59.4% of parents perceived that quality of MDM was good. It was found that overall satisfaction among parents regarding MDM was 65%. More than two thirds (67.5%) of parents felt that MDM has improved their childs school performance. Conclusion: Most of the parents were satisfied about the quality of mid - day meal and few parents agreed that MDM has encouraged parents to send their children to school. Most of the parents agreed that MDM is beneficial and should be continued

Measuring External Rotation of the Fibula and Fibular Length in Bilateral Computed Tomography Scans: How Reliable Is This Method?

During ankle fracture surgery, goals include accurate reduction and fixation of the fibula regarding rotation and fibular length. Bilateral postoperative computed tomography (CT) can be performed to assess fibular rotation using the talar dome angle, and fibular length. The aim of this study was to compare side-to-side differences of the fibular rotation and fibular length using bilateral CT scans of uninjured ankles. Retrospective. Single center, Level I Academic Trauma Center. Patients with bilateral CT scans of uninjured ankles. External rotation using the Nault talar dome method and fibular length using the coronal method of Prior et al. The average, difference, and ratio (injured side/healthy side) and interobserver variability were calculated. There were 83 patients included (166 ankles, mean age 47 years, 77.1% male). A random set of 66 ankles (33 CT scans) were used to measure interobserver variability. The mean degrees of external rotation ranged from 6.6 to 7.7, mean difference ranged from 1.4 to 3.4 degrees, mean ratio ranged from 1.1 to 1.5, and interobserver variability ranged from 0.27 to 0.65. For fibular length, the mean ranged from 24.6 to 25.8 mm, mean difference in fibular length ranged from 0.5 to 2.1 mm, mean ratio ranged from 1.0 to 1.1 mm, and interobserver variability ranged from 0.45 to 0.73. Using bilateral ankle CT scans, mean differences in fibular rotation using the Nault talar dome method were 1.4-3.4 degrees. The distal fibular length had a mean difference between both sides of 0.5-2.1 mm. Although the intraclass correlation's were low, the interleg differences between patients were small, making them useful for clinical practice. Diagnostic Level III. See Instructions for Authors for a complete description of levels of evidence.

A mixed uncertain structural reliability analysis method considering random and convex set variables with correlation

AbstractIn this paper, a mixed model reliability analysis method is put forward for the problem of assessing the reliability of complex engineering structures containing both random and convex aggregate variables. By integrating the ellipsoidal model with correlation and the interval model, the uncertainty region characterized by the ellipsoidal model with correlation is optimized with full consideration of the limited amount of engineering structure sample data, and the risk region represented by the reliability model is redefined, a new mixed reliability assessment criterion is established, and the minimum safe nonprobability reliability index of the structure is built. A key reference is offered by the safety limit diagram of constant reliability indexes set for the first time for the optimal design of engineering structure reliability. The proposed mixed reliability model is compared and analyzed with three classical models. The sensitivity of nonprobability reliability indexes, influenced by random variable parameters and correlation coefficients, is analyzed. This verification confirms that the new reliability model not only provides an accurate assessment of engineering structures' reliability but also lowers the computational demands of engineering design. In this paper, aero‐engine blades and wind turbine blades are taken as examples to expound the validity of the reliability model built using this method and its importance to the structural safety analysis of actual engineering.

A Random Consideration Set Model for Demand Estimation, Assortment Optimization, and Pricing

Random Consideration Set Model We operationalize a microfounded consumer choice model—the random consideration set (RCS) choice model of Manzini and Mariotti [Manzini P, Mariotti M (2014) Stochastic choice and consideration sets. Econometrica 82(3):1153–1176]—that captures the limited attention of consumers, assuming that purchases are based on fixed preference orderings with consideration sets formed from independent attentions. We provide a condition for uniquely identifying model parameters and design an efficient algorithm for model parameters estimation. We offer a greedy-like algorithm for assortment optimization, adaptable for optimal assortment subject to cardinality constraint or discovering efficient sets. We extend the model to consider random product preferences, with a 1/2 performance-guaranteed approximation algorithm. Using data from a major U.S. airline, we find that the RCS model outperforms the mixed multinomial logit model in approximately half of the markets, particularly with smaller, less varied data sets.

Abstract 1169: Increased immunotherapy sensitivity associated with damaging IFN-gamma pathway mutations in NSCLC

Abstract Introduction: In vivo CRISPR screens across multiple murine models of malignancy revealed that loss of interferon gamma (IFN-γ)-mediated sensing predisposes to immune elimination. We assessed the association between damaging mutations in the IFN-γ pathway and clinical outcomes after immune checkpoint inhibition in the Stand Up to Cancer-Mark Foundation non-small cell lung cancer (NSCLC) cohort. Methods: The sensitizing IFN-γ pathway genes were defined as IFNGR1, IFNGR2, JAK1, JAK2, STAT1, IRF1 & IRF9. Damaging mutations (including missense mutations) in any of these genes was considered as loss of pathway activity. We used the best observed response (measured by RECIST v1.1 criteria across time points) to define outcome. Complete and partial response were categorized as responses (R), while stable and progressive disease were categorized as non-responses (NR). The percentage change in target lesions (CTL) was also assessed. Response data was analyzed using additive logistic (LogR) and linear regression (LinR). Overall survival (OS) and progression-free survival (PFS) were analyzed using Cox proportional-hazards models. Three predictor variable groups were established; the final models comprised IFN-γ pathway loss, tumor mutational burden (TMB), programmed death-ligand 1 (PD-L1) tumor proportion score (TPS), prior platinum chemotherapy, age, sex, histological subtype, initial tumor stage, and smoking pack years. Results: 309 patients had evaluable whole-exome sequencing data, treatment with PD-(L)1 inhibitor regimens, and matched radiological responses (121 R [39%], 188 NR [61%]); 306 had matched OS data; 297 had matched PFS data; and 227 had matched CTL data. 27 (7%) patients had damaging mutations in the IFN-γ pathway [IFNGR1 (3); IFNGR2 (1); JAK1 (6); JAK2 (8); STAT1 (3); IRF1 (1); IRF9 (3); and JAK2 & IRF9 (1)]. Significant relationships were observed in univariable models between IFN-γ pathway loss and both response (LogR: OR 0.17, p=2 × 10−4*; LinR: OR 0.70, p=4.8 × 10−4*) and survival (PFS: HR 0.45, p=0.0067*; OS: HR 0.51, p=0.037*). IFN-γ pathway loss is significantly associated with TMB (p=9.7 × 10−8*, Wilcoxon signed rank test). Multivariable models incorporating TMB and PD-L1 TPS confirmed the significance of IFN-γ pathway loss in both response (LogR: OR 0.29, p=0.050*; LinR: OR 0.71, p=0.0052*) and survival (PFS: HR 0.37, p=0.036*; OS: HR 0.27, p=0.030*). These relationships persisted in the final multivariable models (LogR: OR 0.32, p=0.11; LinR: OR 0.69, p=0.012*; PFS: HR 0.39, p=0.068; OS: HR 0.15, p=0.0038*). Random gene sets, of similar total protein coding sequence length, significantly associated with TMB but not with multivariable models of response or survival. Conclusion: In this NSCLC cohort, damaging IFN-γ pathway mutations associate with increased immunotherapy sensitivity, independently of TMB and other co-variates, as evidenced by multiple clinical outcomes. Citation Format: Alexander Azizi, Arvind Ravi, Natalie Vokes, Stephen-John Sammut, Justin Gainor, Gad Getz. Increased immunotherapy sensitivity associated with damaging IFN-gamma pathway mutations in NSCLC [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1169.

Recursive filtering for two-dimensional systems with amplify-and-forward relays: Handling degraded measurements and dynamic biases

This paper is concerned with the recursive filtering issue for a type of two-dimensional shift-varying systems communicated via imperfect networks. The communication network undergoes degraded measurements, stochastic biases, and channel noises, all reflecting real-world constraints. The degraded measurements are governed by three random variable sets with known statistical details, while the stochastic biases follow specific dynamic equations. An amplify-and-forward (AF) relay is implemented to bolster the reliability of transmission from the sensor to the distant filter. The main goal of this study is to formulate an AF relay-based recursive filtering algorithm that can predict system states with the desired performance specification. By employing the inductive technique and meticulous stochastic analysis, an upper bound for the filtering error variance is initially set based on the solutions to recursive difference equations, following which the filter gain is intricately designed to minimize this upper bound at each point in time. Furthermore, a theoretical analysis is conducted on the effect of degradation coefficients on the filtering performance. Finally, the effectiveness of the proposed recursive filtering approach is demonstrated using a numerical example.

The Reliability Inference for Multicomponent Stress–Strength Model under the Burr X Distribution

The reliability of the multicomponent stress–strength system was investigated under the two-parameter Burr X distribution model. Based on the structure of the system, the type II censored sample of strength and random sample of stress were obtained for the study. The maximum likelihood estimators were established by utilizing the type II censored Burr X distributed strength and complete random stress data sets collected from the multicomponent system. Two related approximate confidence intervals were achieved by utilizing the delta method under the asymptotic normal distribution theory and parametric bootstrap procedure. Meanwhile, point and confidence interval estimators based on alternative generalized pivotal quantities were derived. Furthermore, a likelihood ratio test to infer the equality of both scalar parameters is provided. Finally, a practical example is provided for illustration.

Forward Selection of Relevant Factors by Means of MDR-EFE Method

The suboptimal procedure under consideration, based on the MDR-EFE algorithm, provides sequential selection of relevant (in a sense) factors affecting the studied, in general, non-binary random response. The model is not assumed linear, the joint distribution of the factors vector and response is unknown. A set of relevant factors has specified cardinality. It is proved that under certain conditions the mentioned forward selection procedure gives a random set of factors that asymptotically (with probability tending to one as the number of observations grows to infinity) coincides with the “oracle” one. The latter means that the random set, obtained with this algorithm, approximates the features collection that would be identified, if the joint distribution of the features vector and response were known. For this purpose the statistical estimators of the prediction error functional of the studied response are proposed. They involve a new version of regularization. This permits to guarantee not only the central limit theorem for normalized estimators, but also to find the convergence rate of their first two moments to the corresponding moments of the limiting Gaussian variable.

Random genetic drift sets an upper limit on mRNA splicing accuracy in metazoans

Most eukaryotic genes undergo alternative splicing (AS), but the overall functional significance of this process remains a controversial issue. It has been noticed that the complexity of organisms (assayed by the number of distinct cell types) correlates positively with their genome-wide AS rate. This has been interpreted as evidence that AS plays an important role in adaptive evolution by increasing the functional repertoires of genomes. However, this observation also fits with a totally opposite interpretation: given that ‘complex’ organisms tend to have small effective population sizes (Ne), they are expected to be more affected by genetic drift, and hence more prone to accumulate deleterious mutations that decrease splicing accuracy. Thus, according to this ‘drift barrier’ theory, the elevated AS rate in complex organisms might simply result from a higher splicing error rate. To test this hypothesis, we analyzed 3496 transcriptome sequencing samples to quantify AS in 53 metazoan species spanning a wide range of Ne values. Our results show a negative correlation between Ne proxies and the genome-wide AS rates among species, consistent with the drift barrier hypothesis. This pattern is dominated by low abundance isoforms, which represent the vast majority of the splice variant repertoire. We show that these low abundance isoforms are depleted in functional AS events, and most likely correspond to errors. Conversely, the AS rate of abundant isoforms, which are relatively enriched in functional AS events, tends to be lower in more complex species. All these observations are consistent with the hypothesis that variation in AS rates across metazoans reflects the limits set by drift on the capacity of selection to prevent gene expression errors.