The year 2022 is the 20th anniversary of Joseph Schafer and John Graham's paper titled "Missing data: Our view of the state of the art," currently the most highly cited paper in the history of Psychological Methods. Much has changed since 2002, as missing data methodologies have continually evolved and improved; the range of applications that are possible with modern missing data techniques has increased dramatically, and software options are light years ahead of where they were. This article provides an update on the state of the art that catalogs important innovations from the past two decades of missing data research. The paper addresses topics described in the original paper, including developments related to missing data theory, full information maximum likelihood, Bayesian estimation, multiple imputation, and models for missing not at random processes. The paper also describes newer factored regression specifications and missing data handling for multilevel models, both of which have been a focus of recent research. The paper concludes with a summary of the current software landscape and a discussion of several practical issues. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Since the mid-1980s it has been known that Byzantine Agreement can be solved with probability 1 asynchronously, even against an omniscient, computationally unbounded adversary that can adaptively corrupt up to f < n/3 parties. Moreover, the problem is insoluble with f ≥ n/3 corruptions. However, Bracha’s [ 13 ] 1984 protocol (see also Ben-Or [ 8 ]) achieved f < n/3 resilience at the cost of exponential expected latency 2 Θ ( n ) , a bound that has never been improved in this model with f = ⌊ (n-1)/3 ⌋ corruptions. In this article, we prove that Byzantine Agreement in the asynchronous, full information model can be solved with probability 1 against an adaptive adversary that can corrupt f < n/3 parties, while incurring only polynomial latency with high probability . Our protocol follows an earlier polynomial latency protocol of King and Saia [ 33 , 34 ], which had suboptimal resilience, namely f ≈ n /10 9 [ 33 , 34 ]. Resilience f = (n-1)/3 is uniquely difficult, as this is the point at which the influence of the Byzantine and honest players are of roughly equal strength. The core technical problem we solve is to design a collective coin-flipping protocol that eventually lets us flip a coin with an unambiguous outcome. In the beginning, the influence of the Byzantine players is too powerful to overcome, and they can essentially fix the coin’s behavior at will. We guarantee that after just a polynomial number of executions of the coin-flipping protocol, either (a) the Byzantine players fail to fix the behavior of the coin (thereby ending the game) or (b) we can “blacklist” players such that the blacklisting rate for Byzantine players is at least as large as the blacklisting rate for good players. The blacklisting criterion is based on a simple statistical test of fraud detection .

The application of Artificial Intelligence (AI) to screen drug molecules with potential therapeutic effects has revolutionized the drug discovery process, with significantly lower economic cost and time consumption than the traditional drug discovery pipeline. With the great power of AI, it is possible to rapidly search the vast chemical space for potential drug-target interactions (DTIs) between candidate drug molecules and disease protein targets. However, only a small proportion of molecules have labelled DTIs, consequently limiting the performance of AI-based drug screening. To solve this problem, a machine learning-based approach with great ability to generalize DTI prediction across molecules is desirable. Many existing machine learning approaches for DTI identification failed to exploit the full information with respect to the topological structures of candidate molecules. To develop a better approach for DTI prediction, we propose GraphormerDTI, which employs the powerful Graph Transformer neural network to model molecular structures. GraphormerDTI embeds molecular graphs into vector-format representations through iterative Transformer-based message passing, which encodes molecules' structural characteristics by node centrality encoding, node spatial encoding and edge encoding. With a strong structural inductive bias, the proposed GraphormerDTI approach can effectively infer informative representations for out-of-sample molecules and as such, it is capable of predicting DTIs across molecules with an exceptional performance. GraphormerDTI integrates the Graph Transformer neural network with a 1-dimensional Convolutional Neural Network (1D-CNN) to extract the drugs' and target proteins' representations and leverages an attention mechanism to model the interactions between them. To examine GraphormerDTI's performance for DTI prediction, we conduct experiments on three benchmark datasets, where GraphormerDTI achieves a superior performance than five state-of-the-art baselines for out-of-molecule DTI prediction, including GNN-CPI, GNN-PT, DeepEmbedding-DTI, MolTrans and HyperAttentionDTI, and is on a par with the best baseline for transductive DTI prediction. The source codes and datasets are publicly accessible at https://github.com/mengmeng34/GraphormerDTI.

This paper presents a novel approach aimed at bolstering network resilience against traffic accidents, particularly when users possess complete information about accident locations. Two new measures are introduced to evaluate the performance of the network and the importance of a link within the network. In addition, an objective function is designed to quantify optimal trip scheduling following an accident that guides investment decisions in network infrastructure. Additionally, we propose a model for addressing the Accident Improvement Problem and put forth a heuristic algorithm to solve this model. To illustrate the feasibility and effectiveness of the proposed methodologies, we present and analyze two illustrative examples, one at a small scale and the other at a medium scale. The findings underscore how the occurrence of accidents can markedly alter the importance of a link within the network during time. Unlike the prevailing trend in existing studies, which often overlook the repercussions of accidents on traffic flow along other links, our research highlights the importance of considering the impact of newcomers on the routes of existing travelers within the network. These findings demonstrate that such considerations can significantly influence the overall performance of the network in the event of an accident.

In passive imaging, one attempts to reconstruct some coefficients in a wave equation from correlations of observed randomly excited solutions to this wave equation. Many methods proposed for this class of inverse problem so far are only qualitative, e.g. trying to identify the support of a perturbation. Major challenges are the increase in dimensionality when computing correlations from primary data in a preprocessing step, and often very poor pointwise signal-to-noise ratios. In this paper, we propose an approach that addresses both of these challenges: it works only on the primary data while implicitly using the full information contained in the correlation data, and it provides quantitative estimates and convergence by iteration. Our work is motivated by helioseismic holography, a well-established imaging method to map heterogenities and flows in the solar interior. We show that the back-propagation used in classical helioseismic holography can be interpreted as the adjoint of the Fréchet derivative of the operator which maps the properties of the solar interior to the correlation data on the solar surface. The theoretical and numerical framework for passive imaging problems developed in this paper extends helioseismic holography to nonlinear problems and allows for quantitative reconstructions. We present a proof of concept in uniform media.

Viscoelasticity is mapped by dispersion in shearwave elastography. Incomplete spectral information of shearwaves is therefore used to estimate mechanical stiffness. We propose capturing the "full-waveform-information" of the shear wave spectra to better resolve complex shear modulus μ* (ω). Approach is validated on phantom models, animal tissues, and feasibility demonstrated on human post-delivery placenta. We captured robust estimates of μ* in ex-vivo livers subjected to water bath ablation, glutaraldehyde exposure and in the placenta. Complex modulus at 200 Hz is more reflective of tissue stiffness than cross-correlation estimate. Bias increased in phantoms with higher gelatin (G) (0.65: 6% G) and oil (O) (0.58: 6% G and 40% O) concentration, compared to elastic phantoms with low stiffness (0.33: 3% G). Actual tissues also reported higher bias in cross-correlation estimate (rabbit liver: 0.61, porcine liver: 2.20, and human placenta: 0.63). Stiffness is sensitive to ablation temperature, where the overall modulus changed from 3.02 KPa at 16 °C to 2.75 KPa at 56 °C in water bath. With exposure to Glutaraldehyde, the overall modulus increased from 2.37 to 9.03 KPa. Reconstruction errors in the loss modulus decreased by 68% with the power law compared to a Maxwell model in porcine livers with Cole-Cole inverse fitting. Omitting Shear wave attenuation leads to bias. Reconstruction of rheological response with a model is sensitive to its architecture and also the framework. We use "full spectral information" in ultrasound shear wave elastography to better map μ*(ω) changes in viscoelastic tissues.

This article is concerned with cluster consensus control of multiagent systems (MASs) with the fixed/switching topology under a dynamic event-trigger (DET) mechanism. A refined sampled-data-based DET scheme is proposed by introducing two dynamically adjusting threshold parameters to distinguish the different transmission requirements for neighboring agents intra and outer cluster. Faced with the difficulties of acquiring full state information among spatially distributed agents, output feedback is employed to construct cooperative control protocols. Both fixed and switching topologies are considered to execute the designed DET-based cooperative cluster consensus control protocols. By constructing appropriate Lyapunov-Krasovskii functionals (LKFs), some sufficient criteria in terms of matrix inequalities for the cluster consensus of MASs are derived, which can ensure that the error system with the proposed DET-based control strategy is asymptotically stable. Facing the nonconvex issue induced by output feedback, a particle swarm optimization (PSO)-based control design algorithm is novelly developed to calculate the control gains and event-triggering parameters jointly based on the derived stability criteria. The elements of the matrix variables are valued stochastically in certain ranges and the fitness function is designed as the accumulation of the weighting value of each matrix inequality. Finally, an application of multiple satellites formation flying is applied to numerically illustrate the effectiveness of the cluster consensus control strategy with the designed DET mechanism.

Problem definition: We study a robust screening problem where a seller attempts to sell a product to a buyer knowing only the moment and support information of the buyer’s valuation distribution. The objective is to maximize the competitive ratio relative to an optimal hindsight policy equipped with full valuation information. Methodology/results: We formulate the robust screening problem as a linear programming problem, which can be solved efficiently if the support of the buyer’s valuation is finite. When the support of the buyer’s valuation is continuous and the seller knows the mean and the upper and lower bounds of the support for the buyer’s valuation, we show that the optimal payment is a piecewise polynomial function of the valuation with a degree of at most two. Moreover, we derive the closed-form competitive ratio corresponding to the optimal mechanism. The optimal mechanism can be implemented by a randomized pricing mechanism whose price density function is a piecewise inverse function adjusted by a constant. When the mean and variance are known to the seller, we propose a feasible piecewise polynomial approximation of the optimal payment function with a degree of at most three. We also demonstrate that the optimal competitive ratio exhibits a logarithmic decay with respect to the coefficient of variation of the buyer’s valuation distribution. Managerial implications: Our general framework provides an approach to investigating the value of moment information in the robust screening problem. We establish that even a loose upper bound of support or a large variance can guarantee a good competitive ratio. Funding: The research of S. Wang is partially supported by the National Natural Science Foundation of China [Grant 72394395]. The research of S. Liu is partly supported by the National Natural Science Foundation of China [Grant NSFC-72072117]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.0072 .

When determining the optimal deductible level for an insurance policy, a policyholder faces two limitations. First, uncertainty arises from the randomness of future losses. Second, the opacity of the functional forms of the policyholder's loss distribution and utility function contributes to additional limitations. While the academic literature focuses on the former, we additionally include limited information on these functional forms in our model setting to reflect real-world decision-making. That is, we draw on an expected utility framework and analyze the relationship between optimal deductible levels under limited and full information. We also derive several decision rules under limited information in order to approximate the optimal deductible level under full information. To support real-world decision-making, these rules could be easily implemented in an online decision aid offered by an insurance broker, a comparison portal for insurance contracts or a consumer protection agency.

Rapid screening of copper-based bimetallic catalysts via automatic electrocatalysis platform: Electrocatalytic reduction of CO2 to C2+ products on europium-modified copper

Problem definition: We study supply chain contract design under uncertainty. In this problem, the retailer has full information about the demand distribution, whereas the supplier only has partial information drawn from historical demand realizations and contract terms. The supplier wants to optimize the contract terms, but she only has limited data on the true demand distribution. Methodology/results: We show that the classical approach for contract design is fragile in the small data regime by identifying cases where it incurs a large loss. We then show how to combine the historical demand and retailer data to improve the supplier’s contract design. On top of this, we propose a robust contract design model where the uncertainty set requires little prior knowledge from the supplier. We show how to optimize the supplier’s worst-case profit based on this uncertainty set. In the single-product case, the worst-case profit can be found with bisection search. In the multiproduct case, the worst-case profit can be found with a cutting plane algorithm. Managerial implications: Our framework demonstrates the importance of combining the demand and retailer information into the supplier’s contract design problem. We also demonstrate the advantage of our robust model by comparing it against classical data-driven approaches. This comparison sheds light on the value of information from interactions between agents in a game-theoretic setting and suggests that such information should be utilized in data-driven decision making. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0325 .

This study investigates the influence of human-machine interface (HMI) design on passenger trust in autonomous electric vertical take-off and landing (eVTOL) vehicles. An immersive simulator-based experiment was conducted with 34 participants, exposing them to four HMI conditions: baseline, movement, hazard detection, and full information condition. As related measures of passenger trust, we collected self-reported measures including trust, perceived safety, perceived reliability, and intention to use. In addition, physiological measures including gaze behavior, electrodermal activity, and heart rate were collected. The results indicated that movement and hazard detection information improved passenger trust, suggesting that HMI design could play a crucial role in enhancing the acceptance of autonomous eVTOLs. In addition, gaze behavior showed a stronger relationship with self-reported trust than other physiological measures. The findings underscore the importance of HMI design in fostering passenger trust, which is critical to the success of urban air mobility.

Facemasks have been employed to mitigate the spread of SARS-CoV-2. The community effect of providing cloth facemasks on COVID-19 morbidity and mortality is unknown. In a cluster randomised trial in urban Bissau, Guinea-Bissau, clusters (geographical areas with an average of 19 houses), were randomised to an intervention or control arm using computer-generated random numbers. Between 20 July 2020 and 22 January 2021, trial participants (aged 10+ years) living in intervention clusters (n = 90) received two 2-layer cloth facemasks, while facemasks were only distributed later in control clusters (n = 91). All participants received information on COVID-19 prevention. Trial participants were followed through a telephone interview for COVID-19-like illness (3+ symptoms), care seeking, and mortality for 4 months. End-of-study home visits ensured full mortality information and distribution of facemasks to the control group. Individual level information on outcomes by trial arm was compared in logistic regression models with generalised estimating equation-based correction for cluster. Facemasks use was mandated. Facemask use in public areas was assessed by direct observation. We enrolled 39,574 trial participants among whom 95% reported exposure to groups of >20 persons and 99% reported facemasks use, with no difference between trial arms. Observed use was substantially lower (~40%) with a 3%, 95%CI: 0-6% absolute difference between control and intervention clusters. Half of those wearing a facemask wore it correctly. Few participants (532, 1.6%) reported COVID-19-like illness; proportions did not differ by trial arm: Odds Ratio (OR) = 0.81, 95%CI: 0.57-1.15. 177 (0.6%) participants reported consultations and COVID-19-like illness (OR = 0.83, 95%CI: 0.56-1.24); 89 participants (0.2%) died (OR = 1.34, 95%CI: 0.89-2.02). Hence, though trial participants were exposed to many people, facemasks were mostly not worn or not worn correctly. Providing facemasks and messages about correct use did not substantially increase their use and had limited impact on morbidity and mortality. Trial registration: clinicaltrials.gov: NCT04471766.

Seismic waves can shake mountainous landscapes, triggering thousands of landslides. Regional-scale landslide models primarily rely on shaking intensity parameters obtained by simplifying ground motion time-series into peak scalar values. Such an approach neglects the contribution of ground motion phase and amplitude and their variations over space and time. Here, we address this problem by developing an explainable deep-learning model able to treat the entire wavefield and benchmark it against a model equipped with scalar intensity parameters. The experiments run on the area affected by the 2015 Mw7.8 Gorkha, Nepal earthquake reveal a 16% improvement in predictive capacity when incorporating full waveforms. This improvement is achieved mainly on gentle (~25°) hillslopes exposed to low ground shaking (~0.2 m/s). Moreover, we can largely attribute this improvement to the ground motion before and much after the peak velocity arrival. This underscores the limits of single-intensity measures and the untapped potential of full waveform information.

BackgroundAppetitive traits and parent feeding styles are associated with body mass index in children, yet their associations with child diet quality are unclear. ObjectiveThe objective was to examine relations of appetitive traits and parental feeding style with diet quality in 3.5-year-old children. DesignThe study was a secondary, cross-sectional analysis of data from Sprouts, a follow-up study of the Pregnancy Eating Attributes Study (PEAS). Birthing parents completed the Child Eating Behavior Questionnaire, Caregiver’s Feeding Styles Questionnaire, and proxy 24-hour dietary recalls for their children from February 2019 to December 2020. Participants/settingParticipants were 162 birthing parents (early pregnancy BMI ≥ 18.5 and absence of preexisting diabetes, any medical condition contraindicating study participation, self-reported eating disorder, or medications that could affect diet or weight) and their children living in North Carolina. Main outcome measuresHealthy Eating Index—2015 (HEI-2015) total scores were calculated. Statistical analyses performedPath modeling was conducted using PROC CALIS with full information maximum likelihood (FIML) to account for missing data (< 2% of all data in dataset). Associations of child appetitive traits and parental feeding style with child HEI-2015 scores, adjusting for exclusive breastfeeding duration and household income–poverty ratio, were examined. Tests of simple effects were conducted in subsamples split by parental feeding style. Hypotheses were formulated during data collection. ResultsA 1-standard deviation (SD) greater food fussiness was associated with a 2.4-point lower HEI-2015 total score (P = .02; 95% confidence interval [CI] [–4.32, –0.48]) in children. When parental feeding style was authoritarian, a 1-SD greater food responsiveness was associated with a 4.1-point higher HEI-2015 total score (P = .007; 95% CI [1.12, 7.01]) in children. When parental feeding style was authoritative, a 1-SD greater slowness in eating was associated with a 5.8-point lower HEI-2015 total score (P = .01; 95% CI [–10.26, –1.33]) in children. ConclusionsParental feeding style may modify the association of appetitive traits with diet quality in young children. Future research could determine whether matching parent feeding styles to child appetitive trait profiles improves child diet quality.

In this paper, we propose an effective compressive sensing algorithm based on Gaussian entropy for complex-data. Compared with the traditional mean squared error (MSE) method, we consider the full second order statistics information of Gaussian noise in the new algorithm, including relevant information and conjugate information, which makes the recovered signal closer to the original input signal. Simulation results of the synthesized 1D signal and 2D signal show that the proposed algorithm has better performance than the MSE method.

Heterogeneous information network (HIN) embedding aims to map heterogeneous nodes to the low-dimensional vector space. The existing embedding models cannot determine the optimal length of semantics automatically and reveal full semantic information adaptively for different heterogeneous networks. To address this challenge, an HIN embedding model with adaptive semantic mining is proposed. First, we project heterogeneous nodes into the same space and aggregate the features of target types in the first-degree range. Then, the semantics of different node types is combined through the attention mechanism, and latent meta-paths are mined using the attention coefficients. Finally, multiple feature aggregation layers are stacked with residual blocks. The residual weights control the proportion of semantics transferred between layers to aggregate more distal features selectively. In addition, we designed the Selected DropLink unit to remove links which transfer negative information, which can further improve the resistance of model to over-smoothing. Experiments show that our model can obtain more accurate embedding results and can automatically mine complex semantic connections between heterogeneous nodes without prior definition of meta-path and semantic depth.

Optimization-based state estimation is useful for handling of constrained linear or nonlinear dynamical systems. It has an ideal form, known as full information estimation (FIE) which uses all past measurements to perform state estimation, and also a practical counterpart, known as moving-horizon estimation (MHE) which uses most recent measurements of a limited length to perform the estimation. This work reveals a generic link from robust stability of FIE to that of MHE, showing that the former implies at least a weaker robust stability of MHE which implements a long enough horizon. The implication strengthens to strict robust stability of MHE if the corresponding FIE satisfies a mild Lipschitz continuity condition. The revealed implications are then applied to derive new sufficient conditions for robust stability of MHE, which further reveals an intrinsic relation between the existence of a robustly stable FIE/MHE and the system being incrementally input/output-to-state stable.

Protein annotation has long been a challenging task in computational biology. Gene Ontology (GO) has become one of the most popular frameworks to describe protein functions and their relationships. Prediction of a protein annotation with proper GO terms demands high-quality GO term representation learning, which aims to learn a low-dimensional dense vector representation with accompanying semantic meaning for each functional label, also known as embedding. However, existing GO term embedding methods, which mainly take into account ancestral co-occurrence information, have yet to capture the full topological information in the GO-directed acyclic graph (DAG). In this study, we propose a novel GO term representation learning method, PO2Vec, to utilize the partial order relationships to improve the GO term representations. Extensive evaluations show that PO2Vec achieves better outcomes than existing embedding methods in a variety of downstream biological tasks. Based on PO2Vec, we further developed a new protein function prediction method PO2GO, which demonstrates superior performance measured in multiple metrics and annotation specificity as well as few-shot prediction capability in the benchmarks. These results suggest that the high-quality representation of GO structure is critical for diverse biological tasks including computational protein annotation.

Are nocturnal awakenings at age 1 predictive of sleep duration and efficiency at age 6: Results from two birth cohorts