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Archaeologists and computer scientists have both studied board games since the early days of their fields. Early archaeologists had an interest in identifying ways of playing the games of antiquity, and they applied diffusionist models fashionable at the time to trace the development of games from antiquity to the games played in nineteenth century Europe and North America. In time, a huge amount of data on ancient games was collected, and in the last thirty years archaeologists have studied games as they relate to social processes. In parallel to this, artificial intelligence (AI) research has utilized board games, primarily as testbeds for developing AI techniques, but also as an application domain. Archaeological and AI methods are combined in the Digital Ludeme Project, which documents the preserved knowledge of ancient games and uses computational techniques to evaluate research questions that can be addressed through AI playouts of proposed rulesets for games.

Reliable and accurate wind speed prediction has significant impact in many industrial sectors such as economic, business and management among others. This paper presents a new model for wind speed prediction based on Graph Attention Networks (GAT). In particular, the proposed model extends GAT architecture by equipping it with a learnable adjacency matrix as well as incorporating a new attention mechanism with the aim of obtaining attention scores per weather variable. The output of the GAT based model is combined with the LSTM layer in order to exploit both the spatial and temporal characteristics of the multivariate multidimensional historical weather data. Real weather data collected from several cities in Denmark and Netherlands are used to conduct the experiments and evaluate the performance of the proposed model. We show that in comparison to previous architectures used for wind speed prediction, the proposed model is able to better learn the complex input-output relationships of the weather data. Furthermore, thanks to the learned attention weights, the model provides an additional insights on the most important weather variables and cities for the studied prediction task., Comment: 8 pages, 5 figures

This paper demonstrates an unmanned aerial carrier as well as a new anchoring mechanism for connecting and transporting companion unmanned aerial vehicles (UAVs). Establishing this platform presents unique challenges including the requirements of precise localization of the platform, real-time environment mapping system, robust flight control approach, docking safety mechanism, and reliable anchor system for the companion UAV. To obtain the positioning information, a tightly-coupled visual-inertial optimization based odometry is implemented with a fisheye camera and an inertial measurement unit. A 3D map is updated in real-time using an Octomap framework. A nonlinear position model predictive controller cascaded with a DJI attitude controller is implemented for the flight control. Innovatively, we designed a lightweight anchoring mechanism for safe landing and reliable transportation of the companion UAV. Real-world experiments results suggest that the transportation system is a viab le approach to transport the companion UAV, and that the proposed anchoring mechanism allows for reliable operation.

Background The amount of available data, which can facilitate answering scientific research questions, is growing. However, the different formats of published data are expanding as well, creating a serious challenge when multiple datasets need to be integrated for answering a question. Results This paper presents a semi-automated framework that provides semantic enhancement of biomedical data, specifically gene datasets. The framework involved a concept recognition task using machine learning, in combination with the BioPortal annotator. Compared to using methods which require only the BioPortal annotator for semantic enhancement, the proposed framework achieves the highest results. Conclusions Using concept recognition combined with machine learning techniques and annotation with a biomedical ontology, the proposed framework can provide datasets to reach their full potential of providing meaningful information, which can answer scientific research questions.

HBr emissions from volcanoes lead rapidly to the formation of BrO within volcanic plumes and have an impact on tropospheric chemistry, at least at the local and regional scales. The motivation of this paper is to prepare a framework for further 3D modelling of volcanic halogen emissions in order to determine their fate within the volcanic plume and then in the atmosphere at the regional and global scales. The main aim is to evaluate the ability of the model to produce a realistic partitioning of bromine species within a grid box size typical of MOCAGE (Model Of atmospheric Chemistry At larGE scale) 3D (0.5∘ × 0.5∘). This work is based on a 1D single-column configuration of the global chemistry-transport model MOCAGE that has low enough computational cost to allow us to perform a large set of sensitivity simulations. This paper uses the emissions from the Mount Etna eruption on 10 May 2008. Several reactions are added to MOCAGE to represent the volcanic plume halogen chemistry. A simple plume parameterisation is also implemented and tested. The use of this parameterisation tends to only slightly limit the efficiency of BrO net production. Both simulations with and without the parameterisation give results for the partitioning of the bromine species, of ozone depletion and of the BrO/SO2 ratio that are consistent with previous studies. A series of test experiments were performed to evaluate the sensitivity of the results to the composition of the emissions (primary sulfate aerosols, Br radical and NO) and to the effective radius assumed for the volcanic sulfate aerosols. Simulations show that the plume chemistry is sensitive to all these parameters. We also find that the maximum altitude of the eruption changes the BrO production, which is linked to the vertical variability of the concentrations of oxidants in the background air. These sensitivity tests display changes in the bromine chemistry cycles that are generally at least as important as the plume parameterisation. Overall, the version of the MOCAGE chemistry developed for this study is suitable to produce the expected halogen chemistry in volcanic plumes during daytime and night-time.

Objectives: To investigate the association of polygenic risk score (PRS) and bladder cancer (BC) risk and whether this PRS can be offset by a healthy lifestyle. Methods: Individuals with BC (n = 563) and non-BC controls (n = 483 957) were identified in the UK Biobank, and adjusted Cox regression models were used. A PRS was constructed based on 34 genetic variants associated with BC development, while a healthy lifestyle score (HLS) was constructed based on three lifestyle factors (i.e., smoking, physical activity, and diet). Results: Overall, a negative interaction was observed between the PRS and the HLS (P = 0.02). A 7% higher and 28% lower BC risk per 1-standard deviation (SD) increment in PRS and HLS were observed, respectively. A simultaneous increment of 1 SD in both HLS and PRS was associated with a 6% lower BC risk. In addition, individuals with a high genetic risk and an unfavourable lifestyle showed an increased BC risk compared to individuals with low genetic risk and a favourable lifestyle (hazard ratio 1.55, 95% confidence interval 1.16–1.91; P for trend

High-level synthesis (HLS) enables automated conversion of high-level language algorithms into synthesizable register-transfer level code, allowing computation intensive algorithms to be accelerated on FPGAs. Most HLS tools have C++ as their input language, as it is widely known in both software and hardware industry. However, even though C++ receives a new standard every three years, the HLS tool vendors have mostly provided support and examples using C++98/03. Limiting to early C++ standards imposes a productivity penalty, since the newer standards provide both compilation time reductions and more concise, expressive, and maintainable way of writing code. In this study, we make the case for adopting modern C++ in HLS. We inspect the language features of C++11 and forward, and consider their benefits for HLS. We also test the present support for the modern language features with two state-of-the-art commercial HLS tools. Finally, we provide an extended example, demonstrating the increased clarity of code achieved using the newer standards. We note that the investigated HLS tools already have good support for modern C++ features, and urge their adoption to increase designer productivity. publishedVersion

Funding Information: This research was funded by Academy of Finland grants no. 314573 , 314602 , 332962 , and 335872 , and EU Horizon-2020 grant no. 957337 MARVEL. The authors would like to thank the APPLE consortium for the help in the project. Publisher Copyright: © 2023 The Author(s) | openaire: EC/H2020/957337/EU//MARVEL In order to study how early emotional experiences shape infant development, one approach is to analyze the emotional content of speech heard by infants, as captured by child-centered daylong recordings, and as analyzed by automatic speech emotion recognition (SER) systems. However, since large-scale daylong audio is initially unannotated and differs from typical speech corpora from controlled environments, there are no existing in-domain SER systems for the task. Based on existing literature, it is also unclear what is the best approach to deploy a SER system for a new domain. Consequently, in this study, we investigated alternative strategies for deploying a SER system for large-scale child-centered audio recordings from a neonatal hospital environment, comparing cross-corpus generalization, active learning (AL), and domain adaptation (DA) methods in the process. We first conducted simulations with existing emotion-labeled speech corpora to find the best strategy for SER system deployment. We then tested howthe findings generalize to our new initially unannotated dataset. As a result, we found that the studied AL method provided overall the most consistent results, being less dependent on the specifics of the training corpora or speech features compared to the alternative methods. However, in situations without the possibility to annotate data, unsupervised DA proved to be the best approach. We also observed that deployment of a SER system for real-world daylong child-centered audio recordings achieved a SER performance level comparable to those reported in literature, and that the amount of human effort required for the system deployment was overall relatively modest.

Projection-free optimization via different variants of the Frank-Wolfe (FW) method has become one of the cornerstones in large scale optimization for machine learning and computational statistics. Numerous applications within these fields involve the minimization of functions with self-concordance like properties. Such generalized self-concordant (GSC) functions do not necessarily feature a Lipschitz continuous gradient, nor are they strongly convex. Indeed, in a number of applications, e.g. inverse covariance estimation or distance-weighted discrimination problems in support vector machines, the loss is given by a GSC function having unbounded curvature, implying absence of theoretical guarantees for the existing FW methods. This paper closes this apparent gap in the literature by developing provably convergent FW algorithms with standard O(1/k) convergence rate guarantees. If the problem formulation allows the efficient construction of a local linear minimization oracle, we develop a FW method with linear convergence rate., This is an extended version of the conference paper arXiv:2002.04320

We present an $O(n^2)$-time algorithm to test whether an $n$-vertex directed partial $2$-tree is upward planar. This result improves upon the previously best known algorithm, which runs in $O(n^4)$ time., Appears in the Proceedings of the 30th International Symposium on Graph Drawing and Network Visualization (GD 2022)

Objective: Global (inter-patient) ECG classification for arrhythmia detection over Electrocardiogram (ECG) signal is a challenging task for both humans and machines. Automating this process with utmost accuracy is, therefore, highly desirable due to the advent of wearable ECG sensors. However, even with numerous deep learning approaches proposed recently, there is still a notable gap in the performance of global and patient-specific ECG classification performance. Methods: In this study, we propose a novel approach for inter-patient ECG classification using a compact 1D Self-ONN by exploiting morphological and timing information in heart cycles. We used 1D Self-ONN layers to automatically learn morphological representations from ECG data, enabling us to capture the shape of the ECG waveform around the R peaks. We further inject temporal features based on RR interval for timing characterization. The classification layers can thus benefit from both temporal and learned features for the final arrhythmia classification. Results: Using the MIT-BIH arrhythmia benchmark database, the proposed method achieves the highest classification performance ever achieved, i.e., 99.21% precision, 99.10% recall, and 99.15% F1-score for normal (N) segments; 82.19% precision, 82.50% recall, and 82.34% F1-score for the supra-ventricular ectopic beat (SVEBs); and finally, 94.41% precision, 96.10% recall, and 95.2% F1-score for the ventricular-ectopic beats (VEBs). Significance: As a pioneer application, the results show that compact and shallow 1D Self-ONNs with the feature injection can surpass all state-of-the-art deep models with a significant margin and with minimal computational complexity. Conclusion: This study has demonstrated that using a compact and superior network model, a global ECG classification can still be achieved with an elegant performance level even when no patient-specific information is used. publishedVersion

Abstract. Emotions are an important element of human interactions, including those in social media. Despite the prominence of text-based messages in online communication, little is known about how the emotional tone of the messages affects the emotions of the recipients. With three experiments, the current study investigated these effects in the context of online news discussions. Participants first read news discussion threads with a negative, neutral, and positive tone, and then rated their subjective emotional state in terms of valence and arousal. Results showed that negatively toned discussions induced more negative valence and higher arousal ratings than the other conditions. Positive discussions had an opposite effect. Emotionally toned online comments evidently affect the quality and the intensity of the readers’ subjective emotions. We discuss implications for future research and emotion mitigation strategies to improve online discussion quality.

Detailed 3D quantification of tree structure plays a crucial role in understanding tree- and plot-level biophysical processes. Light detection and ranging (LiDAR) has led to a revolution in tree structural measurements and its 3D data are increasingly becoming publicly available. Yet, calculating structural metrics from LiDAR data can often be complex and time-consuming and potentially requires expert knowledge. We present the R package Individual Tree Structural Metrics (ITSMe), a toolbox that works with LiDAR tree point clouds and quantitative structure models (QSMs) derived from LiDAR point clouds to obtain individual tree structural metrics. It serves as a robust synthesis framework for researchers who want to readily obtain structural information from 3D data of individual trees. The package includes functions to determine basic structural metrics (tree height, diameter at breast height, diameter above buttresses, projected crown area, 3D alpha crown volume) from individual tree point clouds, as well as more complex structural metrics (individual tree component volumes, branch angle-, radius- and length-related metrics) from QSMs. The ITSMe package is an open-source package hosted on GitHub that will make the use of 3D data more straightforward and transparent for a range of end-users interested in exploiting tree structure information. publishedVersion

Individuals can hold contrasting views about distinct times: for example, dread over tomorrow’s appointment and excitement about next summer’s vacation. Yet, psychological measures of optimism often assess only one time point or ask participants to generalize about their future. Here, we address these limitations by developing the optimism curve, a measure of societal optimism that compares positivity toward different future times that was inspired by the Treasury bond yield curve. By performing sentiment analysis on over 3.5 million tweets that reference 23 future time points (2 days to 30 years), we measured how positivity differs across short-, medium-, and longer-term future references. We found a consistent negative association between positivity and the distance into the future referenced: From August 2017 to February 2020, the long-term future was discussed less positively than the short-term future. During the COVID-19 pandemic, this relationship inverted, indicating declining near-future- but stable distant-future-optimism. Our results demonstrate that individuals hold differentiated attitudes toward the near and distant future that shift in aggregate over time in response to external events. The optimism curve uniquely captures these shifting attitudes and may serve as a useful tool that can expand existing psychometric measures of optimism.

This paper deals with the challenging problem of simultaneously integrating interpretablility and reliability into prediction models in machine learning. It proposes to combine the interpretable models of decision rules with the reliable models based on conformal prediction. The result is a new technique of conformal decision rules. Given a test instance, the technique is capable of providing a point prediction, an explanation, and a confidence value for that prediction plus a prediction set. The experiments show when and how conformal decision rules can be used for interpretable and reliable machine learning.

Versatile Video Coding (VVC/H.266) is the latest video coding standard designed for a broad range of next-generation media applications. This paper explores the design space of practical VVC encoding by profiling the Fraunhofer Versatile Video Encoder (VVenC). All experiments were conducted over five 2160p video sequences and their downsampled versions under the random access (RA) condition. The exploration was performed by analyzing the rate-distortion-complexity (RDC) of the VVC block structure and coding tools. First, VVenC was profiled to provide a breakdown of coding block distribution and coding tool utilization in it. Then, the usefulness of each VVC coding tool was analyzed for its individual impact on overall RDC performance. Finally, our findings were elevated to practical implementation guidelines: the highest coding gains come with the multi type tree (MTT) structure, adaptive loop filter (ALF), cross component linear model (CCLM), and bi-directional optical flow (BDOF) coding tools, whereas multi transform selection (MTS) and affine motion estimation are the primary candidates for complexity reduction. To the best of our knowledge, this is the first work to provide a comprehensive RDC analysis for practical VVC encoding. It can serve as a basis for practical VVC encoder implementation or optimization on various computing platforms. publishedVersion

Existing hedging strategies are typically based on specific financial models: either the strategies are directly based on a given option pricing model or stock price and volatility models are used indirectly by generating synthetic data on which an agent is trained with reinforcement learning. In this paper, we train an agent in a pure data-driven manner. Particularly, we do not need any specifications on volatility or jump dynamics but use large empirical intra-day data from actual stock and option markets. The agent is trained for the hedging of derivative securities using deep reinforcement learning (DRL) with continuous actions. The training data consists of intra-day option price observations on S&P500 index over 6 years, and top of that, we use other data periods for validation and testing. We have two important empirical results. First, a DRL agent trained using synthetic data generated from a calibrated stochastic volatility model outperforms the classic Black–Scholes delta hedging strategy. Second, and more importantly, we find that a DRL agent, which is empirically trained directly using actual intra-day stock and option prices without the prior specification of the underlying volatility or jump processes, has superior performance compared with the use of synthetic data. This implies that DRL can capture the dynamics of S&P500 from the actual intra-day data and to self-learn how to hedge actual options efficiently. publishedVersion

Humans can make a plan by refining abstract actions. Dynamic logic, which enables reasoning about the dynamics of actions, does not support this form of planning. This paper investigates the extension of dynamic logic with a refinement relation that specifies how an abstract action can be refined into a more specific (composite) action. The paper investigates the properties of the refinement relation, the derivation of the refinement relation and a proof system based on a prefixed-tableau.

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Linear discriminant analysis (LDA) is a classical statistical machine-learning method, which aims to find a linear data transformation increasing class discrimination in an optimal discriminant subspace. Traditional LDA sets assumptions related to the Gaussian class distributions and single-label data annotations. In this article, we propose a new variant of LDA to be used in multilabel classification tasks for dimensionality reduction on original data to enhance the subsequent performance of any multilabel classifier. A probabilistic class saliency estimation approach is introduced for computing saliency-based weights for all instances. We use the weights to redefine the between-class and within-class scatter matrices needed for calculating the projection matrix. We formulate six different variants of the proposed saliency-based multilabel LDA (SMLDA) based on different prior information on the importance of each instance for their class(es) extracted from labels and features. Our experiments show that the proposed SMLDA leads to performance improvements in various multilabel classification problems compared to several competing dimensionality reduction methods. publishedVersion

The field of artificial intelligence based image enhancement has been rapidly evolving over the last few years and is able to produce impressive results on non-astronomical images. In this work, we present the first application of Machine Learning based super-resolution (SR) and de-noising (DN) to enhance X-ray images from the European Space Agency’s XMM-Newton telescope. Using XMM-Newton images in band [0.5, 2] keV from the European Photon Imaging Camera pn detector (EPIC-pn), we develop XMM-SuperRes and XMM-DeNoise – deep learning-based models that can generate enhanced SR and DN images from real observations. The models are trained on realistic XMM-Newton simulations such that XMM-SuperRes will output images with two times smaller point-spread function and with improved noise characteristics. The XMM-DeNoise model is trained to produce images with 2.5× the input exposure time from 20 to 50 ks. When tested on real images, DN improves the image quality by 8.2 per cent, as quantified by the global peak-signal-to-noise ratio. These enhanced images allow identification of features that are otherwise hard or impossible to perceive in the original or in filtered/smoothed images with traditional methods. We demonstrate the feasibility of using our deep learning models to enhance XMM-Newton X-ray images to increase their scientific value in a way that could benefit the legacy of the XMM-Newton archive.

The complexity of computing the flip distance between two triangulations of a simple convex polygon is unknown. Here we approach the problem from a parameterized complexity perspective and improve upon the 2k kernel of Lucas [12]. Specifically, we describe a kernel of size 4k3 and then show how it can be improved to (1 + c)k for every constant c > 0. By ensuring that the kernel consists of a single instance our result yields a kernel of the same magnitude (up to additive terms) for the almost equivalent rotation distance problem on rooted, ordered binary trees. The earlier work of Lucas left the kernel as a disjoint set of instances, potentially allowing very minor differences in the definition of the size of instances to accumulate, causing a constant-factor distortion in the kernel size when switching between flip distance and rotation distance formulations. Our approach avoids this sensitivity. We have also undertaken experiments to understand how much reduction is achieved by our kernel in practice.(c) 2023 The Author(s).

Publisher Copyright: © 2023 The Author(s) Context: Software startups are important drivers of economy on a global scale, and have become associated with innovation and high growth. However, the overwhelming majority of startups ends in failure. Many of these startup failures ultimately stem from software engineering issues, and requirements engineering (RE) ones in particular. Despite the emphasis placed on the importance of RE activities in the startup context, many startups continue to develop software without a clear market or customer, having never had meaningful contact with their would-be customer. Objective: We develop a method aimed at early-stage startups that is intended to help startups through the initial stages of the startup process: StartCards. The method emphasizes the importance of idea and product validation activities in particular in order to tackle anti-patterns related to (a lack of) RE in startups. This method is based on existing literature, both grey and academic literature. Method: StartCards was developed using the Canonical Action Research (CAR) approach, over the course of 4 AR cycles. During the AR process, the method was used by 44 student startup teams in a practical course setting. Data from the use of the method was collected through self-reporting in the form of modified learning diaries, mentoring meetings with the startup teams, and a qualitative survey. Results: We consider the current version of StartCards useful for early-stage startups based on the data we have collected. The method can also be used as a pedagogical tool in startup education. Conclusions: The paper presents the first published version of the method. While work on the method continues, the method is deemed ready for use.

The three-dimensional information in CT scans reveals notorious findings in the medical context, also for detecting symptoms of COVID-19 in chest CT scans. However, due to the lack of availability of large-scale datasets in 3D, the use of attention-based models in this field is proven to be difficult. With transfer learning, this work tackles this problem, investigating the performance of a pre-trained TimeSformer model, which was originally developed for video classification, on COVID-19 classification of three-dimensional chest CT scans. The attention-based model outperforms a DenseNet baseline. Furthermore, we propose three new attention schemes for TimeSformer improving the accuracy of the model by 1.5% and reducing runtime by almost 25% compared to the original attention scheme.

In platelets, elevated cytosolic Ca 2+ is a crucial second messenger, involved in most functional responses, including shape change, secretion, aggregation and procoagulant activity. The platelet Ca 2+ response consists of Ca 2+ mobilization from endoplasmic reticulum stores, complemented with store-operated or receptor-operated Ca 2+ entry pathways. Several channels can contribute to the Ca 2+ entry, but their relative contribution is unclear upon stimulation of ITAM-linked receptors such as glycoprotein VI (GPVI) and G-protein coupled receptors such as the protease-activated receptors (PAR) for thrombin. We employed a 96-well plate high-throughput assay with Fura-2-loaded human platelets to perform parallel [Ca 2+] i measurements in the presence of EGTA or CaCl 2. Per agonist condition, this resulted in sets of EGTA, CaCl 2 and Ca 2+ entry ratio curves, defined by six parameters, reflecting different Ca 2+ ion fluxes. We report that threshold stimulation of GPVI or PAR, with a variable contribution of secondary mediators, induces a maximal Ca 2+ entry ratio of 3-7. Strikingly, in combination with Ca 2+-ATPase inhibition by thapsigargin, the maximal Ca 2+ entry ratio increased to 400 (GPVI) or 40 (PAR), pointing to a strong receptor-dependent enhancement of store-operated Ca 2+ entry. By pharmacological blockage of specific Ca 2+ channels in platelets, we found that, regardless of GPVI or PAR stimulation, the Ca 2+ entry ratio was strongest affected by inhibition of ORAI1 (2-APB, Synta66) > Na +/Ca 2+ exchange (NCE) > P2× 1 (only initial). In contrast, inhibition of TRPC6, Piezo1/2 or STIM1 was without effect. Together, these data reveal ORAI1 and NCE as dominating Ca 2+ carriers regulating GPVI- and PAR-induced Ca 2+ entry in human platelets.

Dysphagia can stem from various etiologies and cause several serious complications. Instrumental evaluation methods for swallowing require special equipment not available everywhere. Thus, an instrumental means to evaluate swallowing that could be used outside a hospital setting would be critical. Dual-axis accelerometers have been utilized in earlier research to recognize swallowing movements. However, no textile-based approaches have been reported. In this study, we developed a textile-based prototype device for identifying swallowing movements. The device used accelerometers and gyroscopes, with eight sensors attached to the fabric. Two female participants were asked to perform two tasks while wearing the device around their neck: sitting still and taking 10 sips of water. The sensor attached to the middle of the thyroid notch level and the two sensors horizontally aligned to both sides of the hyoid bone level were the most accurate in recognizing swallowing movements. No sensor alone could recognize all swallows. However, all the swallows were identified using the combined data from the sensors. Thus, based on these preliminary results, it seems like a textile-based device using accelerometers and gyroscopes could identify swallowing movements. publishedVersion

We consider monotone inclusions defined on a Hilbert space where the operator is given by the sum of a maximal monotone operator $T$ and a single-valued monotone, Lipschitz continuous, and expectation-valued operator $V$. We draw motivation from the seminal work by Attouch and Cabot on relaxed inertial methods for monotone inclusions and present a stochastic extension of the relaxed inertial forward-backward-forward (RISFBF) method. Facilitated by an online variance reduction strategy via a mini-batch approach, we show that (RISFBF) produces a sequence that weakly converges to the solution set. Moreover, it is possible to estimate the rate at which the discrete velocity of the stochastic process vanishes. Under strong monotonicity, we demonstrate strong convergence, and give a detailed assessment of the iteration and oracle complexity of the scheme. When the mini-batch is raised at a geometric (polynomial) rate, the rate statement can be strengthened to a linear (suitable polynomial) rate while the oracle complexity of computing an $\epsilon$-solution improves to $O(1/\epsilon)$. Importantly, the latter claim allows for possibly biased oracles, a key theoretical advancement allowing for far broader applicability. By defining a restricted gap function based on the Fitzpatrick function, we prove that the expected gap of an averaged sequence diminishes at a sublinear rate of $O(1/k)$ while the oracle complexity of computing a suitably defined $\epsilon$-solution is $O(1/\epsilon^{1+a})$ where $a>1$. Numerical results on two-stage games and an overlapping group Lasso problem illustrate the advantages of our method compared to stochastic forward-backward-forward (SFBF) and SA schemes., Comment: Small clarifications in Section 4.2

The aim of this article is in developing unifying frequency domain theory for robust regulation of multi-input multi-output systems. The main theoretical results achieved are a new formulation of the internal model principle, solvability conditions for the robust regulation problem, and a parameterization of all robustly regulating controllers. The main results are formulated with minimal assumptions and without using coprime factorizations, thus guaranteeing their applicability with a very general class of systems. In addition to theoretical results, the design of robust controllers is addressed. The results are illustrated by two examples involving a delay and a heat equation. publishedVersion

Accompanied by the upsurging adoption of open source software (OSS) in a wide range of complex software-intensive systems, practitioners often raise multiple concerns about their maintainability and sustainability. A system that embeds unmaintained components is fragile towards various risks which could result in dire consequences. To address such concerns, this paper introduces the OSS Abandonment Risk Assessment (OSSARA) model, which evaluates the abandonment risk by synthesizing the prediction of potential abandonment of the embedded OSS components. publishedVersion

Gamification has been widely used to design better educational systems aiming to increase students’ concentration, motivation, engagement, flow experience, and others positive experiences. With advances in research on gamification in education, over the past few years, many studies have highlighted the need to tailor the gamification design properties to match individual students’ needs, characteristics and preferences. Thus, different studies have been conducted to personalize the gamification in education. However, the results are still contradictory and need to be better understood to advance this field. To provide a complete understanding of this research domain, we conducted a systematic literature review to summarize the results and discussions on studies that cover the field of tailored gamified education. Following a systematic process, we analysed 2108 studies and identified 19 studies to answer our research questions. The results indicate that most of the studies only consider students’ gamer types to tailor the systems, and most of the experiments do not provide sufficient statistical evidence, especially regarding learning performance using tailored gamified systems. Based on the results, we also provided an agenda with different challenges, opportunities, and research directions to improve the literature on tailored gamification in education. Our study contributes to the field of gamification design in education.

PurposeLocation-based games (LBGs) have afforded novel information technology (IT) developments in how people interact with the physical world. Namely, LBGs have spurred a wave of territoriality (i.e. controlling) and exploration (i.e. discovering) of augmented physical space that are driven by different social dynamics related to group formation, social connectivity and altruism. The aim of this study is to investigate this dynamic and how it is further related to the use intensity of location-based IT.Design/methodology/approachThis work presents a structural equation model that connects social dimensions of play to territorial control and exploration, and playing intensity. The model was tested with psychometric data gathered from a global sample of Pokémon GO players (N = 515).FindingsIn the tested sample, players' social self-efficacy and altruism were positively associated with team identification. Team identification, in turn, was positively associated with both territorial control and exploration tendency. Territorial control had a significant relationship with playing intensity; however, exploration tendency did not. This implies territorial control is the stronger predictor of playing intensity.Practical implicationsThe findings suggest that human primal urges to conquer and control geographical territory may surface in the digital reimagination of physical space. LBGs offer opportunities for making use of new forms of play (territorial control and exploration) in motivating locative behaviours.Originality/valueThis research quantifies the relationships between a social predisposition, team identification, territorial control, exploration tendency and playing intensity in the context of Pokémon GO. It contributes new knowledge to the understanding of territorial behaviour (control and exploration) in location-based IT.

A rectangular dual of a graph G is a contact representation of G by axis-aligned rectangles such that (i) no four rectangles share a point and (ii) the union of all rectangles is a rectangle. The partial representation extension problem for rectangular duals asks whether a given partial rectangular dual can be extended to a rectangular dual, that is, whether there exists a rectangular dual where some vertices are represented by prescribed rectangles. The simultaneous representation problem for rectangular duals asks whether two (or more) given graphs that share a subgraph admit rectangular duals that coincide on the shared subgraph. Combinatorially, a rectangular dual can be described by a regular edge labeling (REL), which determines the orientations of the rectangle contacts.We describe linear-time algorithms for the partial representation extension problem and the simultaneous representation problem for rectangular duals when each input graph is given together with a REL. Both algorithms are based on formulations as linear programs, yet they have geometric interpretations and can be seen as extensions of the classic algorithm by Kant and He that computes a rectangular dual for a given graph. (C) 2022 Elsevier B.V. All rights reserved.

Cartograms are popular for visualizing numerical data for administrative regions in thematic maps. When there are multiple data values per region (over time or from different datasets) shown as animated or juxtaposed cartograms, preserving the viewer's mental map in terms of stability between multiple cartograms is another important criterion alongside traditional cartogram criteria such as maintaining adjacencies. We present a method to compute stable stable Demers cartograms, where each region is shown as a square scaled proportionally to the given numerical data and similar data yield similar cartograms. We enforce orthogonal separation constraints using linear programming, and measure quality in terms of keeping adjacent regions close (cartogram quality) and using similar positions for a region between the different data values (stability). Our method guarantees the ability to connect most lost adjacencies with minimal-length planar orthogonal polylines. Experiments show that our method yields good quality and stability on multiple quality criteria. publishedVersion

Evolutionary game theory mathematically conceptualizes and analyzes biological interactions where one’s fitness not only depends on one’s own traits, but also on the traits of others. Typically, the individuals are not overtly rational and do not select, but rather inherit their traits. Cancer can be framed as such an evolutionary game, as it is composed of cells of heterogeneous types undergoing frequency-dependent selection. In this article, we first summarize existing works where evolutionary game theory has been employed in modeling cancer and improving its treatment. Some of these game-theoretic models suggest how one could anticipate and steer cancer’s eco-evolutionary dynamics into states more desirable for the patient via evolutionary therapies. Such therapies offer great promise for increasing patient survival and decreasing drug toxicity, as demonstrated by some recent studies and clinical trials. We discuss clinical relevance of the existing game-theoretic models of cancer and its treatment, and opportunities for future applications. Moreover, we discuss the developments in cancer biology that are needed to better utilize the full potential of game-theoretic models. Ultimately, we demonstrate that viewing tumors with evolutionary game theory has medically useful implications that can inform and create a lockstep between empirical findings and mathematical modeling. We suggest that cancer progression is an evolutionary competition between different cell types and therefore needs to be viewed as an evolutionary game.

As the amount and complexity of software for automating heavy-duty mobile machinery is increasing, software engineering in this domain is becoming more important. To characterize the industry's current state of software engineering and its issues to guide future research, we performed an empirical exploratory study. We interviewed 16 software engineering professionals from 13 different companies conducting business in heavy-duty mobile machines and their automation. The interviews were analyzed qualitatively, and quantification of the analysis results is presented. We first create an overview of software engineering in the heavy-duty mobile machinery industry. We then identify problem areas affecting software development and discuss some of the possible solutions found in literature. Our findings indicate that the major problem areas faced in the industry that require more research are its digital transformation, autonomous machine functional safety, low availability of workforce for developing software for robotic mobile machines and the lack of established software standards. publishedVersion

Local, spatial state-action features can be used to effectively train linear policies from self-play in a wide variety of board games. Such policies can play games directly, or be used to bias tree search agents. However, the resulting feature sets can be large, with a significant amount of overlap and redundancies between features. This is a problem for two reasons. Firstly, large feature sets can be computationally expensive, which reduces the playing strength of agents based on them. Secondly, redundancies and correlations between fea -tures impair the ability for humans to analyse, interpret, or understand tactics learned by the policies. We look towards decision trees for their ability to perform feature selection, and serve as interpretable models. Previous work on distilling policies into decision trees uses states as inputs, and distributions over the complete action space as outputs. In con -trast, we propose and evaluate a variety of decision tree types, which take state-action pairs as inputs, and provide various different types of outputs on a per-action basis. An empirical evaluation over 43 different board games is presented, and two of those games are used as case studies where we attempt to interpret the discovered features.(c) 2022 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Background: The optimal way to determine repolarization time (RT) from the intracardiac unipolar electrogram (UEG) has been a topic of debate for decades. RT is typically determined by either the Wyatt method or the "alternative method," which both consider UEG T-wave slope, but differently.Objective: To determine the optimal method to measure RT on the UEG.Methods: Seven pig hearts surrounded by an epicardial sock with 100 electrodes were Langendorff-perfused with selective cannulation of the left anterior descending (LAD) coronary artery and submersed in a torso-shaped tank containing 256 electrodes on the torso surface. Repolarization was prolonged in the non-LAD-regions by infusing dofetilide and shortened in the LAD-region using pinacidil. RT was determined by the Wyatt (t(Wyatt)) and alternative (t(Alt)) methods, in both invasive (recorded with epicardial electrodes) and in non-invasive UEGs (reconstructed with electrocardiographic imaging). t(Wyatt) and t(Alt) were compared to local effective refractory period (ERP).Results: With contact mapping, mean absolute error (MAE) of t(Wyatt) and t(Alt) vs. ERP were 21 ms and 71 ms, respectively. Positive T-waves typically had an earlier ERP than negative T-waves, in line with theory. t(Wyatt) -but not t(Alt)-shortened by local infusion of pinacidil. Similar results were found for the non-invasive UEGs (MAE of t(Wyatt) and t(Alt) vs. ERP were 30 ms and 92 ms, respectively).Conclusion: The Wyatt method is the most accurate to determine RT from (non) invasive UEGs, based on novel and historical analyses. Using it to determine RT could unify and facilitate repolarization assessment and amplify its role in cardiac electrophysiology. The authors would like to thank Ruben Coronel, MD, PhD, Amsterdam UMC and IHU Liryc, Bordeaux, Bastiaan J. Boukens, PhD, CARIM, Maastricht University and Amsterdam UMC, and André G. Kléber, MD, PhD, BIDMC, Harvard Medical School, Boston, for their useful discussions on T-wave genesis and RT determination in the UEG, and Roel L.H.M.G. Spätjens, BSc, Maastricht University Medical Center+, for his suggestions on the figures. Moreover, the authors wish to thank Michele Orini, PhD, UC London and Barts Heart Centre, London, for providing data from a previous study (Orini et al., 2019) for additional analyses

Expressing identity socially involves a balance between conformity and innovation. One can adopt existing labels to express belonging to a certain community or introduce new labels to express an individual sense of identity. In such a process of co-creation, the existing identity labels of a community shape one’s sense of identity, while individual expression changes that of a community. Social media has introduced new opportunities to study the expression of collective identity. Here we study the group behavior of individuals defining their identities with hashtag self-labels in their Twitter profiles from mid-2017 through 2019. These timelines of personal self-labeling show behavior incorporating innovation, conservation, and social conformity when defining self. We show that the collective co-labeling of popular concepts in the context of identity, such as #resist and #maga, follow the dynamics of a modified Yule–Simon model balancing novelty and conformity. The dynamics of identity expression resemble the collective tagging processes of folksonomies, indicating a similarity between the collective tagging of external objects and the collective labeling of ourselves. Our work underpins a better understanding of how online environments mediate the evolution of collective identity which plays an increasingly important role in the establishment of community values and identity politics.

BACKGROUND AND AIMS: This study aims to identify sex-specific transcriptional differences and signaling pathways in circulating monocytes contributing to cardiovascular disease.METHODS AND RESULTS: We generated sex-biased gene expression signatures by comparing male versus female monocytes of coronary artery disease (CAD) patients (n = 450) from the Center for Translational Molecular Medicine-Circulating Cells Cohort. Gene set enrichment analysis demonstrated that monocytes from female CAD patients carry stronger chemotaxis and migratory signature than those from males. We then inferred cytokine signaling activities based on CytoSig database of 51 cytokine and growth factor regulation profiles. Monocytes from females feature a higher activation level of EGF, IFN1, VEGF, GM-CSF, and CD40L pathways, whereas IL-4, INS, and HMGB1 signaling was seen to be more activated in males. These sex differences were not observed in healthy subjects, as shown for an independent monocyte cohort of healthy subjects (GSE56034, n = 485). More pronounced GM-CSF signaling in monocytes of female CAD patients was confirmed by the significant enrichment of GM-CSF-activated monocyte signature in females. As we show these effects were not due to increased plasma levels of the corresponding ligands, sex-intrinsic differences in monocyte signaling regulation are suggested. Consistently, regulatory network analysis revealed jun-B as a shared transcription factor activated in all female-specific pathways except IFN1 but suppressed in male-activated IL-4.CONCLUSIONS: We observed overt CAD-specific sex differences in monocyte transcriptional profiles and cytokine- or growth factor-induced responses, which provide insights into underlying mechanisms of sex differences in CVD.

For propositional beliefs, there are well-established connections between belief revision, defeasible conditionals, and nonmonotonic inference. In argumentative contexts, such connections have not yet been investigated. On the one hand, the exact relationship between formal argumentation and nonmonotonic inference relations is a research topic that keeps on eluding researchers despite recently intensified efforts, whereas argumentative revision has been studied in numerous works during recent years. In this paper, we show that relationships between belief revision, defeasible conditionals, and nonmonotonic inference similar to those in propositional logic hold in argumentative contexts as well. We first define revision operators for abstract dialectical frameworks, and use such revision operators to define dynamic conditionals by means of the Ramsey test. We show that such conditionals can be equivalently defined using a total preorder over three-valued interpretations, and study the inferential behaviour of the resulting conditional inference relations.

Software vulnerabilities play a major role, as there are multiple risks associated, including loss and manipulation of private data. The software engineering research community has been contributing to the body of knowledge by proposing several empirical studies on vulnerabilities and automated techniques to detect and remove them from source code. The reliability and generalizability of the findings heavily depend on the quality of the information mineable from publicly available datasets of vulnerabilities as well as on the availability and suitability of those databases. In this paper, we seek to understand the anatomy of the currently available vulnerability databases through a systematic mapping study where we analyze (1) what are the popular vulnerability databases adopted; (2) what are the goals for adoption; (3) what are the other sources of information adopted; (4) what are the methods and techniques; (5) which tools are proposed. An improved understanding of these aspects might not only allow researchers to take informed decisions on the databases to consider when doing research but also practitioners to establish reliable sources of information to inform their security policies and standards. publishedVersion

Participation and social inclusion influence individuals’ health and well-being. These factors can be easily disturbed, especially for those with disabilities. Designers and engineers have tried harnessing technology to assist people via producing prototypes of assistive devices, such as smart clothing and furniture. This study approaches that user surface and inspects the user’s needs for participation through clothing and furniture. We thus arranged two similar workshops with student participants (n = 37) from four different educational units, creating 10 innovative concepts to support participation and social inclusion. All aimed to support participation via improved self-regulation, increased safety, or environmental control. Most of the concepts were connectible to another device, such as a mobile phone. All devices were made adjustable to meet personal preferences. This study aligns with previous ones by concluding that assistive technology should be unobtrusive, give timely responses, and interact with other devices. These initial concepts are ready to be turned into tangible prototypes.Article highlights Participation and social inclusion have remarkable meaning for an individual’s well-being and health. Commonly, assistive technology aims to solve challenges in daily living by promoting health and well-being. For this reason, we arranged two similar co-creation workshops and asked the participants to innovate smart clothing and furniture concepts that will promote greater participation and more social inclusion. This study also identified users’ needs, such as increased safety and independence, supported communication, self-regulation and awareness, and an effective learning tool. The majority of the concepts were designed to be adjustable to meet personal preferences, let individuals interact with other devices (such as a mobile phone), and give timely responses.

A morph is a continuous transformation between two representations of a graph. We consider the problem of morphing between contact representations of a plane graph. In an $${\mathcal {F}}$$ F -contact representation of a plane graph G, vertices are realized by internally disjoint elements from a family $${\mathcal {F}}$$ F of connected geometric objects. Two such elements touch if and only if their corresponding vertices are adjacent. These touchings also induce the same embedding as in G. In a morph between two $${\mathcal {F}}$$ F -contact representations we insist that at each time step (continuously throughout the morph) we have an $${\mathcal {F}}$$ F -contact representation. We focus on the case when $$\mathcal {F}$$ F is the family of triangles in $$\mathbb {R}^2$$ R 2 that are the lower-right half of axis-parallel rectangles. Such RT-representations exist for every plane graph and right triangles are one of the simplest families of shapes supporting this property. Moreover, they naturally correspond to 3-orientations. Thus, they provide a natural case to study regarding morphs of contact representations of plane graphs. We characterize the pairs of RT-representations admitting a morph between each other via the respective 3-orientations. Our characterization leads to a polynomial-time algorithm to decide whether there is a morph between two RT-representations of an n-vertex plane triangulation, and, if so, computes a morph with $${\mathcal {O}}(n^2)$$ O ( n 2 ) steps. Each of these steps is a linear morph moving the endpoints of each triangle at constant speed along straight-line trajectories. Our characterization also implies that for 4-connected plane triangulations there is a morph between every pair of RT-representations where the “top-most” triangle in both representations corresponds to the same vertex.

Social robots are becoming important agents in several sectors of people's lives. They can act in diferent contexts, e.g., public spaces, schools, and homes. Operating, programming and interacting with these robots will be an essential skill in the future. We present a qualitative and explorative study on how family members collaboratively learn (co-learn) about social robots at their homes. Our one-month in the wild study took place at homes of eight families (N=32) in Finland. We defned a novel model for co-learning about and with a social robot at home, Robocamp. In Robocamp, Alpha Mini robot was introduced and left within the families, who were then provided with weekly robotic challenges to be conducted with the robot. The research data was collected by semi-structured interviews and online diaries. This study provides novel insights about family-based co-learning with social robots in the home context. It also ofers recommendations for implementing family-based co-learning with social robots at homes. publishedVersion