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Instance Space Analysis is a methodology to evaluate algorithm performance across diverse problem fields. Through visualisation and exploratory data analysis techniques, Instance Space Analysis offers objective, data-driven insights into the diversity of test instances, algorithm behaviour, and algorithm strengths and weaknesses. As such, it supports automated algorithm selection and synthetic test instance generation, increasing testing reliability in optimisation, machine learning, and scheduling fields. This paper introduces instancespace, a Python package that implements an automated pipeline for Instance Space Analysis. This package supports research by streamlining the testing process, providing unbiased metrics, and facilitating more informed algorithmic design and deployment decisions, particularly for complex and safety-critical systems.

Experimental evaluation is crucial in AI research, especially for assessing algorithms across diverse tasks. Many studies often evaluate a limited set of algorithms, failing to fully understand their strengths and weaknesses within a comprehensive portfolio. This paper introduces an Item Response Theory (IRT) based analysis tool for algorithm portfolio evaluation called AIRT-Module. Traditionally used in educational psychometrics, IRT models test question difficulty and student ability using responses to test questions. Adapting IRT to algorithm evaluation, the AIRT-Module contains a Shiny web application and the R package airt. AIRT-Module uses algorithm performance measures to compute anomalousness, consistency, and difficulty limits for an algorithm and the difficulty of test instances. The strengths and weaknesses of algorithms are visualised using the difficulty spectrum of the test instances. AIRT-Module offers a detailed understanding of algorithm capabilities across varied test instances, thus enhancing comprehensive AI method assessment. It is available at https://sevvandi.shinyapps.io/AIRT/ ., Comment: 10 Pages, 6 Figures. Submitted to SoftwareX

Remote patient monitoring has emerged as a prominent non-invasive method, using digital technologies and computer vision (CV) to replace traditional invasive monitoring. While neonatal and pediatric departments embrace this approach, Pediatric Intensive Care Units (PICUs) face the challenge of occlusions hindering accurate image analysis and interpretation. \textit{Objective}: In this study, we propose a hybrid approach to effectively segment common occlusions encountered in remote monitoring applications within PICUs. Our approach centers on creating a deep-learning pipeline for limited training data scenarios. \textit{Methods}: First, a combination of the well-established Google DeepLabV3+ segmentation model with the transformer-based Segment Anything Model (SAM) is devised for occlusion segmentation mask proposal and refinement. We then train and validate this pipeline using a small dataset acquired from real-world PICU settings with a Microsoft Kinect camera, achieving an Intersection-over-Union (IoU) metric of 85\%. \textit{Results}: Both quantitative and qualitative analyses underscore the effectiveness of our proposed method. The proposed framework yields an overall classification performance with 92.5\% accuracy, 93.8\% recall, 90.3\% precision, and 92.0\% F1-score. Consequently, the proposed method consistently improves the predictions across all metrics, with an average of 2.75\% gain in performance compared to the baseline CNN-based framework. \textit{Conclusions}: Our proposed hybrid approach significantly enhances the segmentation of occlusions in remote patient monitoring within PICU settings. This advancement contributes to improving the quality of care for pediatric patients, addressing a critical need in clinical practice by ensuring more accurate and reliable remote monitoring., Comment: Under revision

A simplicial complex is said to be {\em critical} (or {\em forbidden}) for the 3-sphere $S^3$ if it cannot be embedded in $S^3$ but after removing any one point, it can be embedded. We show that if a multibranched surface cannot be embedded in $S^3$, it contains a critical complex which is a union of a multibranched surface and a (possibly empty) graph. We exhibit all critical complexes for $S^3$ which are contained in $K_5 \times S^1$ and $K_{3,3} \times S^1$ families. We also classify all critical complexes for $S^3$ which can be decomposed into $G\times S^1$ and $H$, where $G$ and $H$ are graphs. In spite of the above property, there exist complexes which cannot be embedded in $S^3$, but they do not contain any critical complexes. From the property of those examples, we define an equivalence relation on all simplicial complexes $\mathcal{C}$ and a partially ordered set of complexes $(\mathcal{C}/\mathord\sim; \subseteqq)$, and refine the definition of critical. According to the refined definition of critical, we show that if a complex $X$ cannot be embedded in $S^3$, then there exists $[X']\subseteqq [X]$ such that $[X']$ is critical for $[S^3]$., Comment: 17 pages, 14 figures

An integral framed, closed pure n-braid B' in the 3-sphere describes a positive Artin presentation, if the braid B can be put on a disk with holes such that each relation describes a positive path and these paths are disjoint. In the present paper we classify the closed, pure n-braids B' in the 3-sphere, such that B represents a positive Artin presentation. Also we prove that if such B describes a positive Artin presentation then B' is strongly invertible. Such positive Artin presentation give us the fundamental group of closed, connected and orientable 3-manifolds M, and in fact, by giving an example, we show that there exist 3-manifolds whose fundamental group does not admit a positive Artin presentation., Comment: 18 pages, 11 figures

Surrogate modelling techniques have seen growing attention in recent years when applied to both modelling and optimisation of industrial design problems. These techniques are highly relevant when assessing the performance of a particular design carries a high cost, as the overall cost can be mitigated via the construction of a model to be queried in lieu of the available high-cost source. The construction of these models can sometimes employ other sources of information which are both cheaper and less accurate. The existence of these sources however poses the question of which sources should be used when constructing a model. Recent studies have attempted to characterise harmful data sources to guide practitioners in choosing when to ignore a certain source. These studies have done so in a synthetic setting, characterising sources using a large amount of data that is not available in practice. Some of these studies have also been shown to potentially suffer from bias in the benchmarks used in the analysis. In this study, we present a characterisation of harmful low-fidelity sources using only the limited data available to train a surrogate model. We employ recently developed benchmark filtering techniques to conduct a bias-free assessment, providing objectively varied benchmark suites of different sizes for future research. Analysing one of these benchmark suites with the technique known as Instance Space Analysis, we provide an intuitive visualisation of when a low-fidelity source should be used and use this analysis to provide guidelines that can be used in an applied industrial setting.

Search-based software testing (SBST) is now a mature area, with numerous techniques developed to tackle the challenging task of software testing. SBST techniques have shown promising results and have been successfully applied in the industry to automatically generate test cases for large and complex software systems. Their effectiveness, however, is problem-dependent. In this paper, we revisit the problem of objective performance evaluation of SBST techniques considering recent methodological advances -- in the form of Instance Space Analysis (ISA) -- enabling the strengths and weaknesses of SBST techniques to be visualized and assessed across the broadest possible space of problem instances (software classes) from common benchmark datasets. We identify features of SBST problems that explain why a particular instance is hard for an SBST technique, reveal areas of hard and easy problems in the instance space of existing benchmark datasets, and identify the strengths and weaknesses of state-of-the-art SBST techniques. In addition, we examine the diversity and quality of common benchmark datasets used in experimental evaluations.

We show an infinite family of hyperbolic knots that have an exceptional surgery producing a graph manifold containing five disjoint, and non parallel incompressible tori., Comment: 9 pages, 7 figures

The transient number of a knot K, denoted tr(K), is the minimal number of simple arcs that have to be attached to K, in order that K can be homotoped to a trivial knot in a regular neighborhood of the union of K and the arcs. We give a lower bound for tr(K) in terms of the rank of the first homology group of the double branched cover of K. In particular, if t(K)=1, then the first homology group of the double branched cover of K is cyclic. Using this, we can calculate the transient number of many knots in the tables and show that there are knots with arbitrarily large transient number., Comment: 21 pages, 3 figures

In black-box optimization, it is essential to understand why an algorithm instance works on a set of problem instances while failing on others and provide explanations of its behavior. We propose a methodology for formulating an algorithm instance footprint that consists of a set of problem instances that are easy to be solved and a set of problem instances that are difficult to be solved, for an algorithm instance. This behavior of the algorithm instance is further linked to the landscape properties of the problem instances to provide explanations of which properties make some problem instances easy or challenging. The proposed methodology uses meta-representations that embed the landscape properties of the problem instances and the performance of the algorithm into the same vector space. These meta-representations are obtained by training a supervised machine learning regression model for algorithm performance prediction and applying model explainability techniques to assess the importance of the landscape features to the performance predictions. Next, deterministic clustering of the meta-representations demonstrates that using them captures algorithm performance across the space and detects regions of poor and good algorithm performance, together with an explanation of which landscape properties are leading to it., Comment: To appear at GECCO 2023

The barren plateau phenomenon is one of the main obstacles to implementing variational quantum algorithms in the current generation of quantum processors. Here, we introduce a method capable of avoiding the barren plateau phenomenon in the variational determination of the geometric measure of entanglement for a large number of qubits. The method is based on measuring compatible two-qubit local functions whose optimization allows for achieving a well-suited initial condition, from which a global function can be further optimized without encountering a barren plateau. We analytically demonstrate that the local functions can be efficiently estimated and optimized. Numerical simulations up to 18-qubit GHZ and W states demonstrate that the method converges to the exact value. In particular, the method allows for escaping from barren plateaus induced by hardware noise or global functions defined on high-dimensional systems. Numerical simulations with noise are in agreement with experiments carried out on IBM's quantum processors for 7 qubits.

Multi-objective optimization problems with constraints (CMOPs) are generally considered more challenging than those without constraints. This in part can be attributed to the creation of infeasible regions generated by the constraint functions, and/or the interaction between constraints and objectives. In this paper, we explore the relationship between constrained multi-objective evolutionary algorithms (CMOEAs) performance and CMOP instances characteristics using Instance Space Analysis (ISA). To do this, we extend recent work focused on the use of Landscape Analysis features to characterise CMOP. Specifically, we scrutinise the multi-objective landscape and introduce new features to describe the multi-objective-violation landscape, formed by the interaction between constraint violation and multi-objective fitness. Detailed evaluation of problem-algorithm footprints spanning six CMOP benchmark suites and fifteen CMOEAs, illustrates that ISA can effectively capture the strength and weakness of the CMOEAs. We conclude that two key characteristics, the isolation of non-dominate set and the correlation between constraints and objectives evolvability, have the greatest impact on algorithm performance. However, the current benchmarks problems do not provide enough diversity to fully reveal the efficacy of CMOEAs evaluated.

In the last few years, electric utility companies have increasingly invested into transactive energy systems. This trend was primarily caused by the integration of distributed energy resources (DERs) and internet-of-things (IoT) devices into their existing distribution networks. Influenced by the general interest in blockchain technologies, many industry specialists are considering new, more efficient peer-to-peer market structures for DERs. Since blockchain-based energy exchanges can automate transactions between their members and provide increased levels of security thanks to smart contracts, these new initiatives may eventually revolutionize how customers interact with utility companies. In this paper, we explore the trade-off between cost and traceability in the form of on-chain and off-chain solutions. We also propose ZipZap, a first step towards a blockchain-based local smart grid system. ZipZap is an ERC-1155 compliant solution with four different prototypes: Heavyweight, Featherweight, Lightweight and Weightless. The first three prototypes were developed in Solidity and deployed using Ethereum. Heavyweight is fully on-chain, whereas Featherweight and Lightweight showcase various levels of hybridization. Weightless, in turn, was deployed using Quorum, a gas-free alternative to Ethereum. Our evaluation uses realistic parameters and measures the impact of different types of metadata storage scopes, with some Ethereum prototypes showcasing gas cost reductions of more than 97% in comparison to our fully on-chain baseline.

The relationship of network structure and dynamics is one of most extensively investigated problems in the theory of complex systems of the last years. Understanding this relationship is of relevance to a range of disciplines -- from Neuroscience to Geomorphology. A major strategy of investigating this relationship is the quantitative comparison of a representation of network architecture (structural connectivity) with a (network) representation of the dynamics (functional connectivity). Analysing such SC/FC relationships has over the past years contributed substantially to our understanding of the functional role of network properties, such as modularity, hierarchical organization, hubs and cycles. Here, we show that one can distinguish two classes of functional connectivity -- one based on simultaneous activity (co-activity) of nodes the other based on sequential activity of nodes. We delineate these two classes in different categories of dynamical processes -- excitations, regular and chaotic oscillators -- and provide examples for SC/FC correlations of both classes in each of these models. We expand the theoretical view of the SC/FC relationships, with conceptual instances of the SC and the two classes of FC for various application scenarios in Geomorphology, Freshwater Ecology, Systems Biology, Neuroscience and Social-Ecological Systems. Seeing the organization of a dynamical processes in a network either as governed by co-activity or by sequential activity allows us to bring some order in the myriad of observations relating structure and function of complex networks.

An almost-fibered knot is a knot whose complement possesses a circular thin position in which there is one and only one weakly incompressible Seifert surface and one incompressible Seifert surface. Infinite examples of almost-fibered knots are known. In this article, we show the existence of infinitely many hyperbolic genus one knots that are not almost-fibered., Comment: arXiv admin note: text overlap with arXiv:1812.06930 by other authors

We show an infinite family of satellite knots that can be unknotted by a single band move, but such that there is no band unknotting the knots which is disjoint from the satellite torus., Comment: 10 pages, 5 figures

In this paper, we give a lower bound for the maximum and minimum genus of a multibranched surface by the first Betti number and the minimum and maximum genus of the boundary of the neighborhood of it, respectively. As its application, we show that the maximum and minimum genus of $G\times S^1$ is equal to twice of the maximum and minimum genus of $G$ for a graph $G$, respectively. This provides an interplay between graph theory and 3-manifold theory., Comment: 9 pages, 2 figures

The genus of satellite tunnel number one knots and torti-rational knots is computed using the tools introduced by Floyd and Hatcher. An implementation of an algorithm is given to compute genus and slopes of minimal genus Seifert surfaces for such knots.

In this paper, we characterize non-hyperbolic 3-component links in the 3-sphere whose exteriors contain essential 3-punctured spheres with non-integral boundary slopes. We also show the existence of embeddings of some multibranched surfaces in the 3-sphere which satisfy some homological conditions to be embedded in the 3-sphere., Comment: 11 pages, 7 figures