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When designing and evaluating an experiment or observational study, it is useful to have a realistic hypothesis regarding the average treatment effect. We present an approach to conceptualizing this average by first considering a distribution of effects. We demonstrate with examples in medicine, economics, and psychology.

This paper proposes a novel computationally efficient algorithm for optimal sizing of Battery Energy Storage Systems (BESS) considering renewable energy bidding strategies. Unlike existing two-stage methods, our algorithm enables the cooptimization of both by updating the BESS size during the training of the bidding policy, leveraging an extended reinforcement learning (RL) framework inspired by advancements in embodied cognition. By integrating the Deep Recurrent Q-Network (DRQN) with a distributed RL framework, the proposed algorithm effectively manages uncertainties in renewable generation and market prices while enabling parallel computation for efficiently handling long-term data.

This paper introduces the problem of selecting a satellite system architecture considering commercial on-orbit refueling (OOR). This problem answers two questions: "What design lifetime should the satellite have?" and "How much propellant should be carried at launch?" We formulate this as a mathematical optimization problem by adopting design lifetime and initial propellant mass as design variables and considering two objective functions to balance the returns and risks. To solve this problem, we develop a surrogate model-based framework grounded in a satellite lifecycle simulation. The framework captures various uncertainties and operational flexibility, and integrates a modified satellite sizing and cost model by adjusting traditional models with OOR. Based on the developed framework, we conduct a case study of GEO communication satellites to examine current target service performance and explore the potential of a new system architecture that diverges from traditional design trends.

This paper proposes topology optimization for considering shielding and penetrating features. Based on the fictitious physical model, which is a useful approach to control geometric features, the proposed method analyzes fictitious steady-state temperature fields and interprets target geometric features by examining the temperature change. First, the concept of topology optimization based on the level set method is introduced. Next, the basic idea of the fictitious physical model for considering geometric features is explained. Then, the differences between the shielding and penetrating features are clarified, and the fictitious physical model for evaluating these features is proposed. Furthermore, topology optimization for the minimum mean compliance problem with geometric conditions is formulated. Finally, 2D and 3D numerical examples are presented to validate the proposed method.

This paper presents an approach for the modelling of dependent random variables using generalised polynomial chaos. This allows to write chance-constrained optimization problems with respect to a joint distribution modelling dependencies between different stochastic inputs. Arbitrary dependencies are modelled by using Gaussian copulas to construct the joint distribution. The paper exploits the problem structure and develops suitable transformations to ensure tractability. The proposed method is applied to a probabilistic power reserve procurement problem. The effectiveness of the method to capture dependencies is shown by comparing the approach with a standard approach considering independent random variables.

Pavement deterioration modeling is important in providing information regarding the future state of the road network and in determining the needs of preventive maintenance or rehabilitation treatments. This research incorporated spatial dependence of road network into pavement deterioration modeling through a graph neural network (GNN). The key motivation of using a GNN for pavement performance modeling is the ability to easily and directly exploit the rich structural information in the network. This paper explored if considering spatial structure of the road network will improve the prediction performance of the deterioration models. The data used in this research comprises a large pavement condition data set with more than a half million observations taken from the Pavement Management Information System (PMIS) maintained by the Texas Department of Transportation. The promising comparison results indicates that pavement deterioration prediction models perform better when spatial relationship is considered.

Bacterial chemotaxis for E.coli is controlled by methylation of chemoreceptors, which in a biochemical pathway regulates the concentration of the CheY-P protein that finally controls the tumbling rate. As a consequence, the tumbling rate adjusts to changes in the concentration of relevant chemicals, to produce a biased random walk toward chemoattractants of against the repellers. Methylation is a slow process, implying that the internal concentration of CheY-P is not instantaneously adapted to the environment, and the tumbling rate presents memory. This implies that the Keller-Segel (KS) equations used to describe chemotaxis at the macroscopic scale, which assume a local relation between the bacterial flux and the chemical gradient, are not fully valid as memory and the associated nonlocal response are not considered. To derive the equations that replace the KS ones, we use a kinetic approach, in which a kinetic equation for the bacterial transport is written considering the dynamics of the protein concentration. When memory is large, the protein concentration field must be considered a relevant variable as the bacterial density. Working out the Chapman-Enskog (CE) method, the dyna

To address the problem of narrow recommendation ranges caused by an emphasis on prediction accuracy, serendipitous recommendations, which consider both usefulness and unexpectedness, have attracted attention. However, realizing serendipitous recommendations is challenging due to the varying proportions of usefulness and unexpectedness preferred by different users, which is influenced by their differing desires for knowledge. In this paper, we propose a method to estimate the proportion of usefulness and unexpectedness that each user desires based on their curiosity, and make recommendations that match this preference. The proposed method estimates a user's curiosity by considering both their long-term and short-term interests. Offline experiments were conducted using the MovieLens-1M dataset to evaluate the effectiveness of the proposed method. The experimental results demonstrate that our method achieves the same level of performance as state-of-the-art method while successfully providing serendipitous recommendations.

Integrating topology optimization and additive manufacturing (AM) technology can facilitate innovative product development. However, laser powder bed fusion, which is the predominant method in metal AM, can lead to issues such as residual stress and deformation. Recently, topology optimization methods considering these stresses and deformations have been proposed; however, they suffer from challenges caused by an increased computational cost. In this study, we propose a method for reducing computational cost in topology optimization considering the deformation in AM. An inherent strain method-based analytical model is presented for simulating the residual stress and deformation in the AM process. Subsequently, a constraint condition to suppress the deformation is formulated, and a method to reduce the computational cost of the adjoint analysis in deriving sensitivity is proposed. The minimum mean compliance problem considering AM deformation and self-support constraints can then be incorporated into the level set-based topology optimization framework. Finally, numerical examples are presented for validating the effectiveness of the proposed topology optimization method.

This study investigates retrieval-augmented summarization by specifically examining the impact of exemplar summary lengths under length constraints, not covered by previous work. We propose a Diverse Length-aware Maximal Marginal Relevance (DL-MMR) algorithm to better control summary lengths. This algorithm combines the query relevance with diverse target lengths in retrieval-augmented summarization. Unlike previous methods that necessitate exhaustive exemplar exemplar relevance comparisons using MMR, DL-MMR considers the exemplar target length as well and avoids comparing exemplars to each other, thereby reducing computational cost and conserving memory during the construction of an exemplar pool. Experimental results showed the effectiveness of DL-MMR, which considers length diversity, compared to the original MMR algorithm. DL-MMR additionally showed the effectiveness in memory saving of 781,513 times and computational cost reduction of 500,092 times, while maintaining the same level of informativeness.

How to accurately quantify the uncertainty of stochastic dynamical responses affected by uncertain loads and structural parameters is an important issue in structural safety and reliability analysis. In this paper, the conditional uncertainty quantification analysis for the dynamical response of stochastic structures considering the measurement data with random error is studied in depth. A method for extracting the key measurement condition, which holds the most reference value for the uncertainty quantification of response, from the measurement data is proposed. Considering the key measurement condition and employing the principle of probability conservation and conditional probability theory, the quotient-form expressions for the conditional mean, conditional variance, and conditional probability density function of the stochastic structural dynamical response are derived and are referred to as the key conditional quotients (KCQ). A numerical method combining the non-equal weighted generalized Monte Carlo method, Dirac function smoothing technique, and online-offline coupled computational strategy is developed for calculating KCQs. Three linear/nonlinear stochastic dynamical exam

In recent years various results about locally symmetric manifolds were proven using probabilistic approaches. One of the approaches is to consider random manifolds by associating a probability measure to the space of discrete subgroups of the isometry Lie group. The main goals are to prove results about deterministic groups and manifolds by considering appropriate measures. In this overview paper we describe several such results, observing the evolution process of the measures involved. Starting with a result whose proof considered finitely supported measures (more precisely, measures supported on finitely many conjugacy classes) and proceeding with results which were outcome of the successful and popular theory of IRS (invariant random subgroups). In the last couple of years the theory has expanded to SRS (stationary random subgroups) allowing to deal with a lot more problems and establish stronger results. In the last section we shall review a very recent (yet unpublished) result whose proof make use of random subgroups which are not even stationary.

In functionally graded materials (FGMs) fabricated using directed energy deposition (DED) additive manufacturing (AM), cracks may form due to interdendritic stress during solidification, the formation of intermetallics, or the buildup of residual stresses. This study builds on our previously proposed concept of three-alloy FGM system feasibility diagrams for the identification of gradient pathways that avoid deleterious phases in FGMs by also considering solidification cracking. Here, five solidification cracking criteria were integrated into the feasibility diagrams, and equilibrium simulations were carried out based on Scheil results (termed Scheil-equilibrium simulation) to predict phase formation below the solidus temperature considering solidification microsegregation. The new feasibility diagrams were applied to four previously studied FGM systems, and the newly proposed approach predicted high crack susceptibility, detrimental phase formation, or interdendritic BCC phase formation in the experimentally observed cracking region. This demonstrates the utility of the proposed framework for crack prediction in the design of future FGMs gradient pathways.

The emerging field of \emph{value awareness engineering} claims that software agents and systems should be value-aware, i.e. they must make decisions in accordance with human values. In this context, such agents must be capable of explicitly reasoning as to how far different courses of action are aligned with these values. For this purpose, values are often modelled as preferences over states or actions, which are then aggregated to determine the sequences of actions that are maximally aligned with a certain value. Recently, additional value admissibility constraints at this level have been considered as well. However, often relaxed versions of these constraints are needed, and this increases considerably the complexity of computing value-aligned policies. To obtain efficient algorithms that make value-aligned decisions considering admissibility relaxation, we propose the use of learning techniques, in particular, we have used constrained reinforcement learning algorithms. In this paper, we present two algorithms, $ε\text{-}ADQL$ for strategies based on local alignment and its extension $ε\text{-}CADQL$ for a sequence of decisions. We have validated their efficiency in a water dist

The Lottery Ticket Hypothesis (LTH) showed that by iteratively training a model, removing connections with the lowest global weight magnitude and rewinding the remaining connections, sparse networks can be extracted. This global comparison removes context information between connections within a layer. Here we study means for recovering some of this layer distributional context and generalise the LTH to consider weight importance values rather than global weight magnitudes. We find that given a repeatable training procedure, applying different importance metrics leads to distinct performant lottery tickets with little overlapping connections. This strongly suggests that lottery tickets are not unique

Robot developers develop various types of robots for satisfying users' various demands. Users' demands are related to their backgrounds and robots suitable for users may vary. If a certain developer would offer a robot that is different from the usual to a user, the robot-specific software has to be changed. On the other hand, robot-software developers would like to reuse their developed software as much as possible to reduce their efforts. We propose the system design considering hardware-level reusability. For this purpose, we begin with the learning-from-observation framework. This framework represents a target task in robot-agnostic representation, and thus the represented task description can be shared with various robots. When executing the task, it is necessary to convert the robot-agnostic description into commands of a target robot. To increase the reusability, first, we implement the skill library, robot motion primitives, only considering a robot hand and we regarded that a robot was just a carrier to move the hand on the target trajectory. The skill library is reusable if we would like to the same robot hand. Second, we employ the generic IK solver to quickly swap a rob

A ubiquitous feature of quantum mechanical theories is the existence of states of superposition. This is expected to be no different for a quantum gravity theory. Guided by this consideration and others we consider a framework in which classical reference frames may be in superposition relative to one another. Mirroring standard quantum mechanics we introduce a complex-valued wavefunctional, which takes as input the transformations between the coordinates, $Ψ[x(x')]$, with the interpretation that an interaction between the reference frames may select a particular transformation with probability distribution given by the Born rule - $P[x(x')] = \text{probability distribution functional} \equiv \vert Ψ[x(x')] \vert^2$. The cases of two and three reference frames in superposition are considered explicitly. It is shown that the set of transformations is closed. A rule for transforming wavefunctions from one system to another system in superposition is proposed and consistency with the Schrodinger equation is demonstrated.

Mathematical models of thrombosis are currently used to study clinical scenarios of pathological thrombus formation. Most of these models involve inherent uncertainties that must be assessed to increase the confidence in model predictions and identify avenues of improvement for both thrombosis modeling and anti-platelet therapies. In this work, an uncertainty quantification analysis of a multi-constituent thrombosis model is performed considering a common assay for platelet function (PFA-100). The analysis is performed using a polynomial chaos expansion as a parametric surrogate for the thrombosis model. The polynomial approximation is validated and used to perform a global sensitivity analysis via computation of Sobol' coefficients. Six out of fifteen parameters were found to be influential in the simulation variability considering only individual effects. Nonetheless, parameter interactions are highlighted when considering the total Sobol' indices. In addition to the sensitivity analysis, the surrogate model was used to compute the PFA-100 closure times of 300,000 virtual cases that align well with clinical data. The current methodology could be used including common anti-platele

This paper explores optimal service resource management strategy, a continuous challenge for health information service to enhance service performance, optimise service resource utilisation and deliver interactive health information service. An adaptive optimal service resource management strategy was developed considering a value co-creation model in health information service with a focus on collaborative and interactive with users. The deep reinforcement learning algorithm was embedded in the Internet of Things (IoT)-based health information service system (I-HISS) to allocate service resources by controlling service provision and service adaptation based on user engagement behaviour. The simulation experiments were conducted to evaluate the significance of the proposed algorithm under different user reactions to the health information service.