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Several decades of research in the field of machine learning have resulted in a multitude of different algorithms for solving a broad range of problems. To tackle a new application, a researcher typically tries to map their problem onto one of these existing methods, often influenced by their familiarity with specific algorithms and by the availability of corresponding software implementations. In this study, we describe an alternative methodology for applying machine learning, in which a bespoke solution is formulated for each new application. The solution is expressed through a compact modelling language, and the corresponding custom machine learning code is then generated automatically. This model-based approach offers several major advantages, including the opportunity to create highly tailored models for specific scenarios, as well as rapid prototyping and comparison of a range of alternative models. Furthermore, newcomers to the field of machine learning do not have to learn about the huge range of traditional methods, but instead can focus their attention on understanding a single modelling environment. In this study, we show how probabilistic graphical models, coupled with efficient inference algorithms, provide a very flexible foundation for model-based machine learning, and we outline a large-scale commercial application of this framework involving tens of millions of users. We also describe the concept of probabilistic programming as a powerful software environment for model-based machine learning, and we discuss a specific probabilistic programming language called Infer.NET, which has been widely used in practical applications.

Lifted graphical models provide a language for expressing dependencies between different types of entities, their attributes, and their diverse relations, as well as techniques for probabilistic reasoning in such multi-relational domains. In this survey, we review a general form for a lifted graphical model, a par-factor graph, and show how a number of existing statistical relational representations map to this formalism. We discuss inference algorithms, including lifted inference algorithms, that efficiently compute the answers to probabilistic queries over such models. We also review work in learning lifted graphical models from data. There is a growing need for statistical relational models (whether they go by that name or another), as we are inundated with data which is a mix of structured and unstructured, with entities and relations extracted in a noisy manner from text, and with the need to reason effectively with this data. We hope that this synthesis of ideas from many different research groups will provide an accessible starting point for new researchers in this expanding field.

Phylogenetic models have become increasingly complex, and phylogenetic data sets have expanded in both size and richness. However, current inference tools lack a model specification language that can concisely describe a complete phylogenetic analysis while remaining independent of implementation details. We introduce a new lightweight and concise model specification language, ‘LPhy’, which is designed to be both human and machine-readable. A graphical user interface accompanies ‘LPhy’, allowing users to build models, simulate data, and create natural language narratives describing the models. These narratives can serve as the foundation for manuscript method sections. Additionally, we present a command-line interface for converting LPhy-specified models into analysis specification files (in XML format) compatible with the BEAST2 software platform. Collectively, these tools aim to enhance the clarity of descriptions and reporting of probabilistic models in phylogenetic studies, ultimately promoting reproducibility of results.

The rapid expansion of offshore wind energy necessitates robust and cost-effective electrical collector system (ECS) designs that prioritize lifetime operational reliability. Traditional optimization approaches often simplify reliability considerations or fail to holistically integrate them with economic and technical constraints. This paper introduces a novel, two-stage optimization framework for offshore wind farm (OWF) ECS planning that systematically incorporates reliability. The first stage employs Mixed-Integer Linear Programming (MILP) to determine an optimal radial network topology, considering linearized reliability approximations and geographical constraints. The second stage enhances this design by strategically placing tie-lines using a Mixed-Integer Quadratically Constrained Program (MIQCP). This stage leverages a dynamic-aware adaptation of Multi-Source Multi-Terminal Network Reliability (MSMT-NR) assessment, with its inherent nonlinear equations successfully transformed into a solvable MIQCP form for loopy networks. A benchmark case study demonstrates the framework’s efficacy, illustrating how increasing the emphasis on reliability leads to more distributed and interconnected network topologies, effectively balancing investment costs against enhanced system resilience.

Score-based algorithms that learn Bayesian Network (BN) structures provide solutions ranging from different levels of approximate learning to exact learning. Approximate solutions exist because exact learning is generally not applicable to networks of moderate or higher complexity. In general, approximate solutions tend to sacrifice accuracy for speed, where the aim is to minimise the loss in accuracy and maximise the gain in speed. While some approximate algorithms are optimised to handle thousands of variables, these algorithms may still be unable to learn such high dimensional structures. Some of the most efficient score-based algorithms cast the structure learning problem as a combinatorial optimisation of candidate parent sets. This paper explores a strategy towards pruning the size of candidate parent sets, and which could form part of existing score-based algorithms as an additional pruning phase aimed at high dimensionality problems. The results illustrate how different levels of pruning affect the learning speed relative to the loss in accuracy in terms of model fitting, and show that aggressive pruning may be required to produce approximate solutions for high complexity problems.

Object tracking is one of the fundamental processes for many high level applications in the field of machine vision. Many challenges in this field remain unsolved despite the development of several tracking perspectives in recent years. A tracking system can be defined in four steps including object detection, appearance modeling, data association, and trajectory estimation. The idea of combining these processes is attractive, but it raises new challenges. The focus of this paper is on the integration of these processes by relying on the use of local structural information. Quantitative and qualitative comparison of the results of the experiments obtained from the proposed method with some works done shows the improvement of the proposed method.

Measurement of well-being has been a highly debated topic since the end of the last century. While some specific aspects are still open issues, a multidimensional approach as well as the construction of shared and well-rooted systems of indicators are now accepted as the main route to measure this complex phenomenon. A meaningful effort, in this direction, is that of the Italian “Equitable and Sustainable Well-being” (BES) system of indicators, developed by the Italian National Institute of Statistics (ISTAT) and the National Council for Economics and Labour (CNEL). The BES framework comprises a number of atomic indicators measured yearly at regional level and reflecting the different domains of well-being (e.g. Health, Education, Work & Life Balance, Environment,...). In this work we aim at dealing with the multidimensionality of the BES system of indicators and try to answer three main research questions: (I) What is the structure of the relationships among the BES atomic indicators; (II) What is the structure of the relationships among the BES domains; (III) To what extent the structure of the relationships reflects the current BES theoretical framework. We address these questions by implementing Bayesian Networks (BNs), a widely accepted class of multivariate statistical models, particularly suitable for handling reasoning with uncertainty. Implementation of a BN results in a set of nodes and a set of conditional independence statements that provide an effective tool to explore associations in a system of variables. In this work, we also suggest two strategies for encoding prior knowledge in the BN estimating algorithm so that the BES theoretical framework can be represented into the network.

Cost-effectiveness analysis (CEA) is used increasingly in medicine to determine whether the health benefit of an intervention is worth the economic cost. Discrete event simulation (DES) is playing an increasing role in CEA thanks to several advantages, such as the possibility of modeling time and heterogeneous populations. It is usually implemented with general-purpose programming languages or commercial software packages. To our knowledge, no artificial intelligence technique has been applied to DES for CEA. Our objective is to develop a graphical representation, an algorithm, and a software tool that allows non-programmers to easily build models and perform CEA. We present DESnets (discrete event simulation networks) as a new type of probabilistic graphical model inspired by probabilistic influence diagrams, an algorithm for evaluating and an implementation as an OpenMarkov plug-in. DESnets are compared qualitatively and empirically with six alternative tools using as a running example a model about osteoporosis by the British National Institute for Health and Care Excellence (NICE). In our experiments, the implementation of DESnets allowed the building of a typical DES model declaratively. Its evaluation process ranked among the most efficient. DESnets compare favorably with alternative tools in terms of ease of use, expressive power, transparency, and computational efficiency.

Bayesian Networks structure learning (BNSL) is a troublesome problem that aims to search for an optimal structure. An exact search tends to sacrifice a significant amount of time and memory to promote accuracy, while the local search can tackle complex networks with thousands of variables but commonly gets stuck in a local optimum. In this paper, two novel and practical operators and a derived operator are proposed to perturb structures and maintain the acyclicity. Then, we design a framework, incorporating an influential perturbation factor integrated by three proposed operators, to escape current local optimal and improve the dilemma that outcomes trap in local optimal. The experimental results illustrate that our algorithm can output competitive results compared with the state-of-the-art constraint-based method in most cases. Meanwhile, our algorithm reaches an equivalent or better solution found by the state-of-the-art exact search and hybrid methods.

In order for robots to effectively understand natural language commands, they must be able to acquire meaning representations that can be mapped to perceptual features in the external world. Previous approaches to learning these grounded meaning representations require detailed annotations at training time. In this paper, we present an approach to grounded language acquisition which is capable of jointly learning a policy for following natural language commands such as “Pick up the tire pallet,” as well as a mapping between specific phrases in the language and aspects of the external world; for example the mapping between the words “the tire pallet” and a specific object in the environment. Our approach assumes a parametric form for the policy that the robot uses to choose actions in response to a natural language command that factors based on the structure of the language. We use a gradient method to optimize model parameters. Our evaluation demonstrates the effectiveness of the model on a corpus of commands given to a robotic forklift by untrained users.