Miracle-dz/UniFAR-FTUs huggingface dataset:
Interactive Elaborative Storytelling: Engaging Children as Storytellers to Foster Vocabulary

Positive effects of shared reading for children’s language development are boosted by including instruction of word meanings and by increasing interactivity. The effects of engaging children as storytellers on vocabulary development have been less well studied. We developed an approach termed Interactive Elaborative Storytelling (IES), which employs both word-learning techniques and children’s storytelling in a shared-reading setting. To systematically investigate potential benefits of children as storytellers, we contrasted this approach to two experimental groups, an Elaborative Storytelling group employing word-learning techniques but no storytelling by children and a Read-Aloud group, excluding any additional techniques. The study was a 3 × 2 pre-posttest randomized design with 126 preschoolers spanning 1 week. Measured outcomes were receptive and expressive target vocabulary, story memory, and children’s behavior during story sessions. All three experimental groups made comparable gains on target words from pre- to posttest and there was no difference between groups in story memory. However, in the Elaborative Storytelling group, children were the least restless. Findings are discussed in terms of their contribution to optimizing shared reading as a method of fostering language.
Surrogate-assisted multiobjective optimization based on decomposition: a comprehensive comparative analysis

A number of surrogate-assisted evolutionary algorithms are being developed for tackling expensive multiobjective optimization problems. On the one hand, a relatively broad range of techniques from both machine learning and multiobjective optimization can be combined for this purpose. Different taxonomies exist in order to better delimit the design choices, advantages and drawbacks of existing approaches. On the other hand, assessing the relative performance of a given approach is a difficult task, since it depends on the characteristics of the problem at hand. In this paper, we focus on surrogate-assisted approaches using objective space decomposition as a core component. We propose a refined and fine-grained classification, ranging from EGO-like approaches to filtering or pre-screening. More importantly, we provide a comprehensive comparative study of a representative selection of state-of-the-art methods, together with simple baseline algorithms. We rely on selected benchmark functions taken from the bbob-biobj benchmarking test suite, that provides a variable range of objective function difficulties. Our empirical analysis highlights the effect of the available budget on the relative performance of each approach, and the impact of the training set and of the machine learning model construction on both solution quality and runtime efficiency.
Initializing the EM Algorithm for Data Clustering and Sub-population Detection

In this thesis our research on two loosely related parts are presented: initialization for the EM algorithm for model-based data clustering, and sub-population detection. Clustering is the task of putting objects into groups in such a way that the observations in the same group are more “similar” to each other than those in other groups. Clustering is a useful tool for summarizing the data, and hence could greatly reduce the complexity of a data set. Moreover, data could be compressed using clustering so that it take less space to store. There is no universally accepted definition of cluster, so there exist many different clustering algorithms. Among these algorithms, model-based clustering using the Gaussian mixture model is built on sound mathematical foundation, and is widely applied in many areas. However, the prevailing method for finding the maximum likelihood estimator (MLE), i.e., the expectation maximization (EM) algorithm, is very sensitive to initialization. Hence, the EM algorithm is very likely to stuck in a local maximum of the likelihood function, especially when the number of clusters in the data is large, but the problem of initializing the EM algorithm is not very well studied.
Procedural Tectonic Planets

We present a procedural method for authoring synthetic tectonic planets. Instead of relying on computationally demanding physically‐based simulations, we capture the fundamental phenomena into a procedural method that faithfully reproduces large‐scale planetary features generated by the movement and collision of the tectonic plates. We approximate complex phenomena such as plate subduction or collisions to deform the lithosphere, including the continental and oceanic crusts. The user can control the movement of the plates, which dynamically evolve and generate a variety of landforms such as continents, oceanic ridges, large scale mountain ranges or island arcs. Finally, we amplify the large‐scale planet model with either procedurally‐defined or real‐world elevation data to synthesize coherent detailed reliefs. Our method allows the user to control the evolution of an entire planet interactively, and to trigger specific events such as catastrophic plate rifting.
A Subspace Method for Fast Locally Injective Harmonic Mapping

We present a fast algorithm for low‐distortion locally injective harmonic mappings of genus 0 triangle meshes with and without cone singularities. The algorithm consists of two portions, a linear subspace analysis and construction, and a nonlinear non‐convex optimization for determination of a mapping within the reduced subspace. The subspace is the space of solutions to the Harmonic Global Parametrization (HGP) linear system [BCW17], and only vertex positions near cones are utilized, decoupling the variable count from the mesh density. A key insight shows how to construct the linear subspace at a cost comparable to that of a linear solve, extracting a very small set of elements from the inverse of the matrix without explicitly calculating it. With a variable count on the order of the number of cones, a tangential alternating projection method [HCW17] and a subsequent Newton optimization [CW17] are used to quickly find a low‐distortion locally injective mapping. This mapping determination is typically much faster than the subspace construction. Experiments demonstrating its speed and efficacy are shown, and we find it to be an order of magnitude faster than HGP and other alternatives.
An illumination algorithm approach to solving the micro-depot routing problem

An increasing emphasis on reducing pollution and congestion in city centres combined with an increase in online shopping is changing the ways in which logistics companies address vehicle routing problems (VRP). We introduce the micro-depot-VRP, in which a single supply vehicle is used to supply a set of micro-depots distributed across a city; deliveries are then made from the micro-depot by couriers using electric vehicles, bicycles and on foot. We present a formal definition of the problem, and propose a representation that can be used with an optimisation algorithm to minimise the total cost associated with delivering packages. Using five instances created from real-data obtained from delivery companies operating within the City of Frankfurt, we apply an illumination algorithm in order to obtain a set of results that minimise costs but have differing characteristics in terms of emissions, distance travelled and number of couriers used. Results show that solutions can be obtained that have equivalent costs to the baseline standard VRP solution, but considerably improve on this in terms of minimising the secondary criteria relating to emissions, couriers and distance.
Generating Stochastic Wall Patterns On‐the‐fly with Wang Tiles

The game and movie industries always face the challenge of reproducing materials. This problem is tackled by combining illumination models and various textures (painted or procedural patterns). Generating stochastic wall patterns is crucial in the creation of a wide range of backgrounds (castles, temples, ruins…). A specific Wang tile set was introduced previously to tackle this problem, in an iterative fashion. However, long lines may appear as visual artifacts. We use this tile set in a new on‐the‐fly procedure to generate stochastic wall patterns. For this purpose, we introduce specific hash functions implementing a constrained Wang tiling. This technique makes possible the generation of boundless textures while giving control over the maximum line length. The algorithm is simple and easy to implement, and the wall structure we get from the tiles allows to achieve visuals that reproduce all the small details of artist painted walls.
Evolvability ES: scalable and direct optimization of evolvability

Designing evolutionary algorithms capable of uncovering highly evolvable representations is an open challenge in evolutionary computation; such evolvability is important in practice, because it accelerates evolution and enables fast adaptation to changing circumstances. This paper introduces evolvability ES, an evolutionary algorithm designed to explicitly and efficiently optimize for evolvability, i.e. the ability to further adapt. The insight is that it is possible to derive a novel objective in the spirit of natural evolution strategies that maximizes the diversity of behaviors exhibited when an individual is subject to random mutations, and that efficiently scales with computation. Experiments in 2-D and 3-D locomotion tasks highlight the potential of evolvability ES to generate solutions with tens of thousands of parameters that can quickly be adapted to solve different tasks and that can productively seed further evolution. We further highlight a connection between evolvability in EC and a recent and popular gradient-based meta-learning algorithm called MAML; results show that evolvability ES can perform competitively with MAML and that it discovers solutions with distinct properties. The conclusion is that evolvability ES opens up novel research directions for studying and exploiting the potential of evolvable representations for deep neural networks.
Fitness comparison by statistical testing in construction of SAT-based guess-and-determine cryptographic attacks

Algebraic cryptanalysis studies breaking ciphers by solving algebraic equations. Some of the promising approaches use SAT solvers for this purpose. Although the corresponding satisfiability problems are hard, their difficulty can often be lowered by choosing a set of variables to brute force over, and by solving each of the corresponding reduced problems using a SAT solver, which is called the guess-and-determine attack. In many successful cipher breaking attempts this set was chosen analytically, however, the nature of the problem makes evolutionary computation a good choice. We investigate one particular method for constructing guess-and-determine attacks based on evolutionary algorithms. This method estimates the fitness of a particular guessed bit set by Monte-Carlo simulations. We show that using statistical tests within the comparator of fitness values, which can be used to reduce the necessary number of samples, together with a dynamic strategy for the upper limit on the number of samples, speeds up the attack by a factor of 1.5 to 4.3 even on a distributed cluster.
EMOCS: evolutionary multi-objective optimisation for clinical scorecard generation

Clinical scorecards of risk factors associated with disease severity or mortality outcome are used by clinicians to make treatment decisions and optimize resources. This study develops an automated tool or framework based on evolutionary algorithms for the derivation of scorecards from clinical data. The techniques employed are based on the NSGA-II Multi-objective Optimization Genetic Algorithm (GA) which optimizes the Pareto-front of two clinically-relevant scorecard objectives, size and accuracy. Three automated methods are presented which improve on previous manually derived scorecards. The first is a hybrid algorithm which uses the GA for feature selection and a decision tree for scorecard generation. In the second, the GA generates the full scorecard. The third is an extended full scoring system in which the GA also generates the scorecard scores. In this system combinations of features and thresholds for each scorecard point are selected by the algorithm and the evolutionary process is used to discover near-optimal Pareto-fronts of scorecards for exploration by expert decision makers. This is shown to produce scorecards that improve upon a human derived example for C.Difficile, an important infection found globally in communities and hospitals, although the methods described are applicable to any disease where the required data is available.
Generating Color Scribble Images using Multi‐layered Monochromatic Strokes Dithering

Color scribbling is a unique form of illustration where artists use compact, overlapping, and monochromatic scribbles at microscopic scale to create astonishing colorful images at macroscopic scale. The creation process is skill‐demanded and time‐consuming, which typically involves drawing monochromatic scribbles layer‐by‐layer to depict true‐color subjects using a limited color palette delicately. In this work, we present a novel computational framework for automatic generation of color scribble images from arbitrary raster images. The core contribution of our work lies in a novel color dithering model tailor‐made for synthesizing a smooth color appearance using multiple layers of overlapped monochromatic strokes. Specifically, our system reconstructs the appearance of the input image by (i) generating layers of monochromatic scribbles based on a limited color palette derived from input image, and (ii) optimizing the drawing sequence among layers to minimize the visual color dissimilarity between dithered image and original image as well as the color banding artifacts. We demonstrate the effectiveness and robustness of our algorithm with various convincing results synthesized from a variety of input images with different stroke patterns. The experimental study further shows that our approach faithfully captures the scribble style and the color presentation at respectively microscopic and macroscopic scales, which is otherwise difficult for state‐of‐the‐art methods.
Control parameter sensitivity analysis of the multi-guide particle swarm optimization algorithm

This paper conducts a sensitivity analysis of the recently proposed multi-objective optimizing algorithm, namely the multi-guide particle swarm optimization algorithm (MGPSO). The MGPSO uses subswarms to explore the search space, where each subswarm optimises one of the multiple objectives. A bounded archive is used to share previously found non-dominated solutions between subswarms. A third term, the archive guide, is added to the velocity update equation that represents a randomly selected solution from the archive. The influence of the archive guide on a particle is controlled by the archive balance coefficient and is proportional to the social guide. The original implementation of the MGPSO used static values randomly sampled from a uniform distribution in the range [0,1] for the archive balance coefficient. This paper investigates a number of approaches to dynamically adjust this control parameter. These approaches are evaluated on a variety of multi-objective optimization problems. It is shown that a linearly increasing strategy and stochastic strategies outperformed the standard approach to initializing the archive balance coefficient on two-objective and three objective optimization problems.
Learning a Generative Model for Multi‐Step Human‐Object Interactions from Videos

Creating dynamic virtual environments consisting of humans interacting with objects is a fundamental problem in computer graphics. While it is well‐accepted that agent interactions play an essential role in synthesizing such scenes, most extant techniques exclusively focus on static scenes, leaving the dynamic component out. In this paper, we present a generative model to synthesize plausible multi‐step dynamic human‐object interactions. Generating multi‐step interactions is challenging since the space of such interactions is exponential in the number of objects, activities, and time steps. We propose to handle this combinatorial complexity by learning a lower dimensional space of plausible human‐object interactions. We use action plots to represent interactions as a sequence of discrete actions along with the participating objects and their states. To build action plots, we present an automatic method that uses state‐of‐the‐art computer vision techniques on RGB videos in order to detect individual objects and their states, extract the involved hands, and recognize the actions performed. The action plots are built from observing videos of everyday activities and are used to train a generative model based on a Recurrent Neural Network (RNN). The network learns the causal dependencies and constraints between individual actions and can be used to generate novel and diverse multi‐step human‐object interactions. Our representation and generative model allows new capabilities in a variety of applications such as interaction prediction, animation synthesis, and motion planning for a real robotic agent.
An improved merge search algorithm for the constrained pit problem in open-pit mining

Conventional mixed-integer programming (MIP) solvers can struggle with many large-scale combinatorial problems, as they contain too many variables and constraints. Meta-heuristics can be applied to reduce the size of these problems by removing or aggregating variables or constraints. Merge search algorithms achieve this by generating populations of solutions, either by heuristic construction [4], or by finding neighbours to an initial solution [12]. This paper presents a merge search algorithm that improves the population generation heuristic in [12] and utilises a variable grouping heuristic that exploits the common information across a population to aggregate groups of variables in order to create a reduced subproblem. The algorithm is tested on some well known benchmarks for a complex problem called the constrained pit (CPIT) problem and it is compared to results produced by a merge search algorithm previously used on the same problem and the results published on the minelib [9] website.
Novelty search: a theoretical perspective

Novelty Search is an exploration algorithm driven by the novelty of a behavior. The same individual evaluated at different generations has different fitness values. The corresponding fitness landscape is thus constantly changing and if, at the scale of a single generation, the metaphor of a fitness landscape with peaks and valleys still holds, this is not the case anymore at the scale of the whole evolutionary process. How does this kind of algorithms behave? Is it possible to define a model that would help understand how it works? This understanding is critical to analyse existing Novelty Search variants and design new and potentially more efficient ones. We assert that Novelty Search asymptotically behaves like a uniform random search process in the behavior space. This is an interesting feature, as it is not possible to directly sample in this space: the algorithm has a direct access to the genotype space only, whose relationship to the behavior space is complex. We describe the model and check its consistency on a classical Novelty Search experiment. We also show that it sheds a new light on results of the literature and suggests future research work.
Elastic Correspondence between Triangle Meshes

We propose a novel approach for shape matching between triangular meshes that, in contrast to existing methods, can match crease features. Our approach is based on a hybrid optimization scheme, that solves simultaneously for an elastic deformation of the source and its projection on the target. The elastic energy we minimize is invariant to rigid body motions, and its non‐linear membrane energy component favors locally injective maps. Symmetrizing this model enables feature aligned correspondences even for non‐isometric meshes. We demonstrate the advantage of our approach over state of the art methods on isometric and non‐isometric datasets, where we improve the geodesic distance from the ground truth, the conformal and area distortions, and the mismatch of the mean curvature functions. Finally, we show that our computed maps are applicable for surface interpolation, consistent cross‐field computation, and consistent quadrangular remeshing of a set of shapes.
Novel cortex lytic enzymes in Bacillus megaterium QM B1551 spores.

Present models for spore germination in Bacillus species include a requirement for either the SleB or CwlJ cortex lytic enzymes to efficiently depolymerise the spore cortex. Previous work has demonstrated that B. megaterium spores may differ to other species in this regard, since sleB cwlJ null mutant spores complemented with the gene in trans for the non-peptidoglycan lysin YpeB can efficiently degrade the cortex. Here we identify two novel cortex lytic enzymes, encoded at the BMQ_2391 and BMQ_3234 loci, that are essential for cortex hydrolysis in the absence of SleB and CwlJ. Ellipsoid localisation microscopy places the BMQ_3234 protein within the inner-spore coat, a region of the spore that is populated by other cortex lytic enzymes. The findings reinforce the idea that there is a degree of variation in mechanisms of cortex hydrolysis across the Bacillales, raising potential implications for environmental decontamination strategies based upon targeted inactivation of components of the spore germination apparatus.
Electrical impedance tomography identifies a distinct change in regional phase angle delay pattern in ventilation filling prior to a spontaneous pneumothorax.

Pneumothoraces are common in preterm infants and are a major cause of morbidity. Early detection and treatment of pneumothoraces is vital to minimize further respiratory compromise. Electrical Impedance Tomography (EIT) has been suggested as a method of rapidly detecting pneumothoraces at the bedside. Our objective was to define the EIT-derived regional phase-angle differences in filling characteristics before and during spontaneous pneumothoraces in preterm lambs. 124 - 127 d gestation preterm lambs were ventilated on high frequency oscillatory ventilation for 120 min. EIT data and cardiorespiratory parameters were monitored continuously and recorded for 3 min every 15 min. Six animals spontaneously developed a pneumothorax within a gravity non-dependent quadrant of the lung and were included for this analysis. Changes in End Expiratory Lung Impedance (EELI), ventilation and phase angle delay were calculated in the four lung quadrants at the onset of the pneumothorax and 15 and 30 min prior. At the onset of the pneumothorax all animals showed a clear increase in EELI in the affected lung quadrant. 15 and 30 min before the pneumothorax there was a significant phase angle delay between the non-dependent and dependent lung. At one min prior to pneumothorax this phase angle delay was isolated just to the affected quadrant (non-dependent). These findings are the first description of the events within the lung at initiation of a pneumothorax, demonstrating distinct predictive changes in air filling characteristics prior to the pneumothorax occurring. This suggests EIT may be able to accurately identify the onset of a pneumothorax.