1,720,964 research outputs found
Jigsaw Puzzle Solving as a Consistent Labeling Problem
No full textThe thesis aims to develop a methodology for solving complex puzzle problems. In the first part of the research, we explore the idea of abstracting the jigsaw puzzle problem as a consistent labeling problem, a classical concept introduced in the 1980s by Hummel and Zucker for which a solid theory and powerful algorithms are available. A formal theory of consistency developed by Hummel and Zucker turned out to have intimate connections with non-cooperative game theory. The theory generalizes classical (boolean) constraint satisfaction problems to scenarios involving ``soft'' compatibility measures and probabilistic (as opposed to ``hard'') label assignments. The problem amounts to maximizing a well-known quadratic function over a probability space which we solve using relaxation labeling algorithms endowed with matrix balancing mechanisms to enforce one-to-one correspondence constraints. The second part addresses the problem of puzzles with eroded borders, a special challenging case of puzzle-solving. Solving puzzles with eroded borders is a common situation when dealing with the re-assembly of archaeological artifacts or ruined frescoes. In this particular condition, the puzzle's pieces do not align perfectly due to the erosion gaps; a direct matching of the patches is consequently unfeasible due to the lack of color and line continuations. To tackle this issue, we propose JiGAN, a GAN-based method for solving puzzles with ruined borders. The experiments on commonly used benchmark datasets demonstrate that our approach is able to address the problem of eroded borders and produce plausible reconstruction results
A Pareto-based multi-objective optimization algorithm to design energy-efficient shading devices
Full text linkIn this paper we address the problem of designing new energy-efficient static daylight devices that will surround the external windows of a residential building in Madrid. Shading devices can in fact largely influence solar gains in a building and improve thermal and lighting comforts by selectively intercepting the solar radiation and by reducing the undesirable glare. A proper shading device can therefore significantly increase the thermal performance of a building by reducing its energy demand in different climate conditions. In order to identify the set of optimal shading devices that allow a low energy consumption of the dwelling while maintaining high levels of thermal and lighting comfort for the inhabitants we derive a multi-objective optimization methodology based on Harmony Search and Pareto front approaches. The results show that the multi-objective approach here proposed is an effective procedure in designing energy efficient shading devices when a large set of conflicting objectives characterizes the performance of the proposed solutions
Solving Jigsaw Puzzles in the Wild: Human-Guided Reconstruction of Cultural Heritage Fragments
No full textReassembling real-world archaeological artifacts from fragments is challenging due to erosion, missing regions, irregular shapes, and large-scale ambiguity. Traditional jigsaw solvers, typically designed for clean, synthetic data, struggle especially with thousands of fragments, as in the RePAIR benchmark. We propose a human-in-the-loop (HIL) puzzle-solving framework tailored for real-world cultural heritage reconstruction. Our method combines an automatic relaxation-labeling solver with interactive human guidance, enabling users to iteratively lock verified placements, correct errors, and guide assembly toward semantic and geometric coherence. We introduce two complementary strategies, ie., Iterative Anchoring and Continuous Interactive Refinement, that support scalable reconstruction under varying ambiguity and size. Experiments on RePAIR groups show our hybrid approach significantly outperforms both fully automatic and manual methods in accuracy and efficiency, offering a practical solution for human-in-the-loop artifact reassembly
Model-based lead molecule design
No full text"Lead molecule" is a chemical compound deemed as a good candidate for drug discovery. Designing a lead molecule for optimization involves a complex phase in which researchers look for compounds that satisfy pharmaceutical properties and can then be investigated for drug development and clinical trials. Finding the optimal lead molecule is a hard problem that commonly requires searching in high dimensional and large experimental spaces. In this paper we propose to discover the optimal lead molecule by developing an evolutionary model-based approach where different classes of statistical models can achieve relevant information. The analysis is conducted comparing two different chemical representations of molecules: the amino-boronic acid representation and the chemical fragment representation. To deal with the high dimensionality of the fragment representation we adopt the Formal Concept Analysis and we then derive the evolutionary path on a reduced number of fragments. This approach has been tested on a particular data set of 2500 molecules and the achieved results show the very good performance of this strategy
Reducing dimensionality of molecular systems: a Bayesian non-parametric approach
No full textIn this paper we present a methodology that can be used to design experiments of complex systems characterized by a huge number of variables. The strategy combines the evolutionary principles with the information provided by statistical models tailored to the problem under consideration. Here, we are concerned with the process of design molecules, which is a quite challenging problem due to the presence of a high number of variables with a binary structure. Recent works on clustering of binary data and variable selection in the high-dimensional setting allow to develop an approach capable of recovering useful information derived from the incorporation of a grouping structure into the model
Nash Meets Wertheimer: Using Good Continuation in Jigsaw Puzzles
Full text linkJigsaw puzzle solving is a challenging task for computer
vision since it requires high-level spatial and semantic reasoning. To solve
the problem, existing approaches invariably use color and/or shape infor-
mation but in many real-world scenarios, such as in archaeological fresco
reconstruction, this kind of clues is often unreliable due to severe physical
and pictorial deterioration of the individual fragments. This makes state-
of-the-art approaches entirely unusable in practice. On the other hand, in
such cases, simple geometrical patterns such as lines or curves offer a pow-
erful yet unexplored clue. In an attempt to fill in this gap, in this paper
we introduce a new challenging version of the puzzle solving problem in
which one deliberately ignores conventional color and shape features and
relies solely on the presence of linear geometrical patterns. The recon-
struction process is then only driven by one of the most fundamental
principles of Gestalt perceptual organization, namely Wertheimer’s law
of good continuation. In order to tackle this problem, we formulate the
puzzle solving problem as the problem of finding a Nash equilibrium of
a (noncooperative) multiplayer game and use classical multi-population
replicator dynamics to solve it. The proposed approach is general and
allows us to deal with pieces of arbitrary shape, size and orientation. We
evaluate our approach on both synthetic and real-world data and com-
pare it with state-of-the-art algorithms. The results show the intrinsic
complexity of our purely line-based puzzle problem as well as the relative
effectiveness of our game-theoretic formulation
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Multi-objective Optimization in High-Dimensional Molecular Systems
No full textThe paper proposes a methodological approach to design complex experiments for multi-objective optimization. The strategy is based on evolutionary statistical inference to search for the optimal values in high-dimensional experimental spaces. We developed this approach to study a particular molecular system and discover the best molecules to be proposed as candidate drugs
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