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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.

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.

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

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)

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.

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.

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).

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.

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

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.

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.

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.

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.

Receptor diffusion plays an essential role in cellular signalling via the plasma membrane microenvironment and receptor interactions, but the regulation is not well understood. To aid in understanding of the key determinants of receptor diffusion and signalling, we developed agent-based models (ABMs) to explore the extent of dimerisation of the platelet- and megakaryocyte-specific receptor for collagen glycoprotein VI (GPVI). This approach assessed the importance of glycolipid enriched raft-like domains within the plasma membrane that lower receptor diffusivity. Our model simulations demonstrated that GPVI dimers preferentially concentrate in confined domains and, if diffusivity within domains is decreased relative to outside of domains, dimerisation rates are increased. While an increased amount of confined domains resulted in further dimerisation, merging of domains, which may occur upon membrane rearrangements, was without effect. Modelling of the proportion of the cell membrane which constitutes lipid rafts indicated that dimerisation levels could not be explained by these alone. Crowding of receptors by other membrane proteins was also an important determinant of GPVI dimerisation. Together, these results demonstrate the value of ABM approaches in exploring the interactions on a cell surface, guiding the experimentation for new therapeutic avenues.

Protein mutations, especially those which occur in the binding site, play an important role in inter-individual drug response and may alter binding affinity and thus impact the drug’s efficacy and side effects. Unfortunately, large-scale experimental screening of ligand-binding against protein variants is still time-consuming and expensive. Alternatively, in silico approaches can play a role in guiding those experiments. Methods ranging from computationally cheaper machine learning (ML) to the more expensive molecular dynamics have been applied to accurately predict the mutation effects. However, these effects have been mostly studied on limited and small datasets, while ideally a large dataset of binding affinity changes due to binding site mutations is needed. In this work, we used the PSnpBind database with six hundred thousand docking experiments to train a machine learning model predicting protein-ligand binding affinity for both wild-type proteins and their variants with a single-point mutation in the binding site. A numerical representation of the protein, binding site, mutation, and ligand information was encoded using 256 features, half of them were manually selected based on domain knowledge. A machine learning approach composed of two regression models is proposed, the first predicting wild-type protein-ligand binding affinity while the second predicting the mutated protein-ligand binding affinity. The best performing models reported an RMSE value within 0.5 $$-$$ - 0.6 kcal/mol-1 on an independent test set with an R2 value of 0.87 $$-$$ - 0.90. We report an improvement in the prediction performance compared to several reported models developed for protein-ligand binding affinity prediction. The obtained models can be used as a complementary method in early-stage drug discovery. They can be applied to rapidly obtain a better overview of the ligand binding affinity changes across protein variants carried by people in the population and narrow down the search space where more time-demanding methods can be used to identify potential leads that achieve a better affinity for all protein variants.

Emotions play a crucial role in human-human communications with complex socio-psychological nature. In order to enhance emotion communication in human-computer interaction, this paper studies emotion recognition from audio and visual signals in video clips, utilizing facial expressions and vocal utterances. Thereby, the study aims to exploit temporal information of audio-visual cues and detect their informative time segments. Attention mechanisms are used to exploit the importance of each modality over time. We propose a novel framework that consists of bi-modal time windows spanning short video clips labeled with discrete emotions. The framework employs two networks, with each one being dedicated to one modality. As input to a modality-specific network, we consider a time-dependent signal deriving from the embeddings of the video and audio modalities. We employ the encoder part of the Transformer on the visual embeddings and another one on the audio embeddings. The research in this paper introduces detailed studies and meta-analysis findings, linking the outputs of our proposition to research from psychology. Specifically, it presents a framework to understand underlying principles of emotion recognition as functions of three separate setups in terms of modalities: audio only, video only, and the fusion of audio and video. Experimental results on two datasets show that the proposed framework achieves improved accuracy in emotion recognition, compared to state-of-the-art techniques and baseline methods not using attention mechanisms. The proposed method improves the results over baseline methods by at least 5.4%. Our experiments show that attention mechanisms reduce the gap between the entropies of unimodal predictions, which increases the bimodal predictions’ certainty and, therefore, improves the bimodal recognition rates. Furthermore, evaluations with noisy data in different scenarios are presented during the training and testing processes to check the framework’s consistency and the attention mechanism’s behavior. The results demonstrate that attention mechanisms increase the framework’s robustness when exposed to similar conditions during the training and the testing phases. Finally, we present comprehensive evaluations of emotion recognition as a function of time. The study shows that the middle time segments of a video clip are essential in the case of using audio modality. However, in the case of video modality, the importance of time windows is distributed equally.

Inspired by recent terrorist attacks on cereal production fields in Iraq, we introduce and study two types of Stackelberg games. In these games, the leader wants to maximize its production (e.g., cereal), while the follower tries to destroy this production as much as possible. In the first model, the leader can protect its production by spreading his production resources over multiple regions. In the second model, the leader can also decide to allocate some extra protection resources to the regions. For both games, we are interested in a follower's and leader's optimal strategy. We characterise optimal strategies for the follower and present two linear time algorithms (one for each game) that find an optimal strategy for the leader. (c) 2021 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )

Phylogenetic trees are leaf-labelled trees used to model the evolution of species. Here we explore the practical impact of kernelization (i.e. data reduction) on the NP-hard problem of computing the TBR distance between two unrooted binary phylogenetic trees. This problem is better-known in the literature as the maximum agreement forest problem, where the goal is to partition the two trees into a minimum number of common, non-overlapping subtrees. We have implemented two well-known reduction rules, the subtree and chain reduction, and five more recent, theoretically stronger reduction rules, and compare the reduction achieved with and without the stronger rules. We find that the new rules yield smaller reduced instances and thus have clear practical added value. In many cases they also cause the TBR distance to decrease in a controlled fashion. Next, we compare the achieved reduction to the known worst-case theoretical bounds of 15k-9 and 11k-9 respectively, on the number of leaves of the two reduced trees, where k is the TBR distance, observing in both cases a far larger reduction in practice. As a by-product of our experimental framework we obtain a number of new insights into the actual computation of TBR distance. We find, for example, that very strong lower bounds on TBR distance can be obtained efficiently by randomly sampling certain carefully constructed partitions of the leaf labels, and identify instances which seem particularly challenging to solve exactly. The reduction rules have been implemented within our new solver Tubro which combines kernelization with an Integer Linear Programming (ILP) approach. Tubro also incorporates a number of additional features, such as a cluster reduction and a practical upper-bounding heuristic, and it can leverage combinatorial insights emerging from the proofs of correctness of the reduction rules to simplify the ILP., Comment: Updated version. New figures and analysis. Accepted for journal publication

Analyzing the relationships among various drugs is an essential issue in the field of computational biology. Different kinds of informative knowledge, such as drug repurposing, can be extracted from drug-drug relationships. Scientific literature represents a rich source for the retrieval of knowledge about the relationships between biological concepts, mainly drug-drug, disease-disease, and drug-disease relationships. In this paper, we propose DDREL as a general-purpose method that applies deep learning on scientific literature to automatically extract the graph of syntactic and semantic relationships among drugs. DDREL remarkably outperforms the existing human drug network method and a random network respected to average similarities of drugs’ anatomical therapeutic chemical (ATC) codes. DDREL is able to shed light on the existing deficiency of the ATC codes in various drug groups. From the DDREL graph, the history of drug discovery became visible. In addition, drugs that had repurposing score 1 (diflunisal, pargyline, fenofibrate, guanfacine, chlorzoxazone, doxazosin, oxymetholone, azathioprine, drotaverine, demecarium, omifensine, yohimbine) were already used in additional indication. The proposed DDREL method justifies the predictive power of textual data in PubMed abstracts. DDREL shows that such data can be used to 1- Predict repurposing drugs with high accuracy, and 2- Reveal existing deficiencies of the ATC codes in various drug groups.

In this paper we develop the use of monads, a concept popularised in the functional language Haskell, to provide a general framework for studying control systems with uncertainty. We first develop the theory of monads, notably dependent products and conditioning. We then provide three main applications, namely computing the input-output behaviour, designing observers, and studying compositionality for uncertain systems. Copyright (C) 2022 The Authors.

The human brain effortlessly solves the complex computational task of sound localization using a mixture of spatial cues. How the brain performs this task in naturalistic listening environments (e.g. with reverberation) is not well understood. In the present paper, we build on the success of deep neural net-works at solving complex and high-dimensional problems [1] to develop goal-driven, neurobiological-inspired convolutional neural network (CNN) models of human spatial hearing. After training, we visual-ize and quantify feature representations in intermediate layers to gain insights into the representational mechanisms underlying sound location encoding in CNNs. Our results show that neurobiological-inspired CNN models trained on real-life sounds spatialized with human binaural hearing characteristics can accu-rately predict sound location in the horizontal plane. CNN localization acuity across the azimuth resem-bles human sound localization acuity, but CNN models outperform human sound localization in the back. Training models with different objective functions -that is, minimizing either Euclidean or angular dis-tance -modulates localization acuity in particular ways. Moreover, different implementations of binaural integration result in unique patterns of localization errors that resemble behavioral observations in humans. Finally, feature representations reveal a gradient of spatial selectivity across network layers, starting with broad spatial representations in early layers and progressing to sparse, highly selective spa-tial representations in deeper layers. In sum, our results show that neurobiological-inspired CNNs are a valid approach to modeling human spatial hearing. This work paves the way for future studies combining neural network models with empirical measurements of neural activity to unravel the complex compu-tational mechanisms underlying neural sound location encoding in the human auditory pathway. (c) 2021 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

In a planar L-drawing of a directed graph (digraph) each edge e is represented as a polyline composed of a vertical segment starting at the tail of e and a horizontal segment ending at the head of e. Distinct edges may overlap, but not cross. Our main focus is on bimodal graphs, i.e., digraphs admitting a planar embedding in which the incoming and outgoing edges around each vertex are contiguous. We show that every plane bimodal graph without 2-cycles admits a planar L-drawing. This includes the class of upward-plane graphs. Finally, outerplanar digraphs admit a planar L-drawing - although they do not always have a bimodal embedding - but not necessarily with an outerplanar embedding., Appears in the Proceedings of the 28th International Symposium on Graph Drawing and Network Visualization (GD 2020)

As a sequel to an investigation of the standard (partisan) versions of Col and Snort on rectangular boards, we defined impartial versions of both games (dubbed iCol and iSnort). These have the same coloring conditions as their partisan versions, but either player is allowed to use at any move a black or a white stone. For these two games similar strategies show that with both dimensions odd the first player can win, otherwise it is a second-player win. For both Linear versions, analyses using Combinatorial Game Theory show that the even-length chains have value 0, the odd-length chains value *.

In healthcare settings, compliance with clinical protocols and medical guidelines is important to ensure high-quality, safe and effective treatment of patients. How to measure compliance and how to represent compliance information in an interpretable and actionable way is still an open challenge. In this paper, we propose new metrics for compliance assessments. For this purpose, we use two fuzzy aggregation techniques, namely the OWA operator and the Sugeno integral. The proposed measures take into consideration three factors: (i) the degree of compliance with a single activity, (ii) the degree of compliance of a patient, and (iii) the importance of the activities. The proposed measures are applied to two clinical protocols used in practice. We demonstrate that the proposed measures for compliance can further aid clinicians in assessing the aspect of protocol compliance when evaluating the effectiveness of implemented clinical protocols.Keywords: clinical protocols; protocol compliance; protocol adherence; protocol conformance; aggregation; OWA operator; Sugeno integral

The rooted subtree prune and regraft (rSPR) distance between two rooted binary phylogenetic trees is a well-studied measure of topological dissimilarity that is NP-hard to compute. Here we describe an improved linear kernel for the problem. In particular, we show that if the classical subtree and chain reduction rules are augmented with a modified type of chain reduction rule, the resulting trees have at most 9k−3 leaves, where k is the rSPR distance; and that this bound is tight. In comparison, the previous best-known linear kernel is of size at most 28k. To achieve this improvement we introduce cyclic generators, which can be viewed as cyclic analogues of the generators used in the phylogenetic networks literature. As a corollary to our main result we also give an improved weighted linear kernel for the minimum hybridization problem on two rooted binary phylogenetic trees.

The electrocardiogram (ECG) is the standard method in clinical practice to non-invasively analyze the electrical activity of the heart, from electrodes placed on the body’s surface. The ECG can provide a cardiologist with relevant information to assess the condition of the heart and the possible presence of cardiac pathology. Nonetheless, the global view of the heart’s electrical activity given by the ECG cannot provide fully detailed and localized information about abnormal electrical propagation patterns and corresponding substrates on the surface of the heart. Electrocardiographic imaging, also known as the inverse problem in electrocardiography, tries to overcome these limitations by non-invasively reconstructing the heart surface potentials, starting from the corresponding body surface potentials, and the geometry of the torso and the heart. This problem is ill-posed, and regularization techniques are needed to achieve a stable and accurate solution. The standard approach is to use zero-order Tikhonov regularization and the L-curve approach to choose the optimal value for the regularization parameter. However, different methods have been proposed for computing the optimal value of the regularization parameter. Moreover, regardless of the estimation method used, this may still lead to over-regularization or under-regularization. In order to gain a better understanding of the effects of the choice of regularization parameter value, in this study, we first focused on the regularization parameter itself, and investigated its influence on the accuracy of the reconstruction of heart surface potentials, by assessing the reconstruction accuracy with high-precision simultaneous heart and torso recordings from four dogs. For this, we analyzed a sufficiently large range of parameter values. Secondly, we evaluated the performance of five different methods for the estimation of the regularization parameter, also in view of the results of the first analysis. Thirdly, we investigated the effect of using a fixed value of the regularization parameter across all reconstructed beats. Accuracy was measured in terms of the quality of reconstruction of the heart surface potentials and estimation of the activation and recovery times, when compared with ground truth recordings from the experimental dog data. Results show that values of the regularization parameter in the range (0.01–0.03) provide the best accuracy, and that the three best-performing estimation methods (L-Curve, Zero-Crossing, and CRESO) give values in this range. Moreover, a fixed value of the regularization parameter could achieve very similar performance to the beat-specific parameter values calculated by the different estimation methods. These findings are relevant as they suggest that regularization parameter estimation methods may provide the accurate reconstruction of heart surface potentials only for specific ranges of regularization parameter values, and that using a fixed value of the regularization parameter may represent a valid alternative, especially when computational efficiency or consistency across time is required.

Problem definition: Streaming platforms such as Spotify are popular media services where content creators may offer their content. Because these platforms operate in a highly competitive market, content creators may leave the platform and join elsewhere. This paper studies conditions under which content creators have no incentives to leave the platform and thus stability can be preserved. Methodology/results: We introduce a stylized model for streaming platform situations and associate these situations with a cooperative game. We focus on the (non)emptiness of the core to analyze the stability of the streaming platforms. It turns out that both stable and unstable streaming platforms exist. We show that for streaming platforms operating in a market where users have completely opposite streaming behavior, stability cannot always be preserved. However, in markets where users are more similar in their streaming behavior, stability can be preserved. We further analyze the stability of streaming platforms by means of numerical experiments. Our results indicate that stability of streaming platforms generally is a delicate matter. Managerial implications: Streaming platforms are more likely to be stable in markets where users are similar in their streaming behavior. To avoid that content creators leave the platform, it is therefore recommended to focus on particular market segments where these similarities occur.

A rectangular dual of a plane graph G is a contact representations of G by interior-disjoint axis-aligned rectangles such that (i) no four rectangles share a point and (ii) the union of all rectangles is a rectangle. A rectangular dual gives rise to a regular edge labeling (REL), which captures the orientations of the rectangle contacts.We study the problem of morphing between two rectangular duals of the same plane graph. If we require that, at any time throughout the morph, there is a rectangular dual, then a morph exists only if the two rectangular duals realize the same REL. Therefore, we allow intermediate contact representations of non-rectangular polygons of constant complexity. Given an n-vertex plane graph, we show how to compute in O(n(3)) time a piecewise linear morph that consists of O(n(2)) linear morphing steps.

Uncertainty is unavoidable in modeling dynamical systems and it may be represented mathematically by differential inclusions. In the past, we proposed an algorithm to compute validated solutions of differential inclusions; here we provide several theo-retical improvements to the algorithm, including its extension to piecewise constant and sinusoidal approximations of uncertain inputs, updates on the affine approximation bounds and a generalized formula for the analytical error. The approach proposed is able to achieve higher order convergence with respect to the current state-of-the-art. We implemented the methodology in Ariadne, a library for the verification of continuous and hybrid systems. For evaluation purposes, we introduce ten systems from the literature, with varying degrees of nonlinearity, number of variables and uncertain inputs. The results are hereby compared with two state-of-the-art approaches to time-varying uncertainties in nonlinear systems.(c) 2022 Published by Elsevier Ltd.

One way to help users navigate debated topics online is to apply stance detection in web search. Automatically identifying whether search results are against, neutral, or in favor could facilitate diversification efforts and support interventions that aim to mitigate cognitive biases. To be truly useful in this context, however, stance detection models not only need to make accurate (cross-topic) predictions but also be sufficiently explainable to users when applied to search results - an issue that is currently unclear. This paper presents a study into the feasibility of using current stance detection approaches to assist users in their web search on debated topics. We train and evaluate 10 stance detection models using a stance-annotated data set of 1204 search results. In a preregistered user study (N = 291), we then investigate the quality of stance detection explanations created using different explainability methods and explanation visualization techniques. The models we implement predict stances of search results across topics with satisfying quality (i.e., similar to the state-of-the-art for other data types). However, our results reveal stark differences in explanation quality (i.e., as measured by users' ability to simulate model predictions and their attitudes towards the explanations) between different models and explainability methods. A qualitative analysis of textual user feedback further reveals potential application areas, user concerns, and improvement suggestions for such explanations. Our findings have important implications for the development of user-centered solutions surrounding web search on debated topics.

Research exploring CycleGAN-based synthetic image generation has recently accelerated in the medical community due to its ability to leverage unpaired images effectively. However, a commonly established drawback of the CycleGAN, the introduction of artifacts in generated images, makes it unreliable for medical imaging use cases. In an attempt to address this, we explore the effect of structure losses on the CycleGAN and propose a generalized frequency-based loss that aims at preserving the content in the frequency domain. We apply this loss to the use-case of cone-beam computed tomography (CBCT) translation to computed tomography (CT)-like quality. Synthetic CT (sCT) images generated from our methods are compared against baseline CycleGAN along with other existing structure losses proposed in the literature. Our methods (MAE: 85.5, MSE: 20433, NMSE: 0.026, PSNR: 30.02, SSIM: 0.935) quantitatively and qualitatively improve over the baseline CycleGAN (MAE: 88.8, MSE: 24244, NMSE: 0.03, PSNR: 29.37, SSIM: 0.935) across all investigated metrics and are more robust than existing methods. Furthermore, no observable artifacts or loss in image quality were observed. Finally, we demonstrated that sCTs generated using our methods have superior performance compared to the original CBCT images on selected downstream tasks.

Although most existing recommender systems support single users, there are many scenarios where these systems target the needs of groups. Traits such as group mood, emotional contagion, and interpersonal relationships are often ill-defined characteristics, tend to mutate over time, and are usually missing from the systems’ modeling, even though they play an indispensable part in group modeling. Furthermore, producing timely and accurate recommendations for groups that are explainable, fair, and privacy-protecting is a notoriously tricky challenge since group members may have divergent views and needs. The second GMAP workshop aims at bringing together a community of researchers focused on group modeling, adaptation, and personalization. The objective is to explore the challenges and opportunities of developing effective methods and tools to support group decision-making. The workshop, we brought together researchers from several disciplines, including Psychology, Computer Science, and Organizational Behavior, to discuss their latest research and ideas on this topic. It also provided opportunities for participants to share their research and experiences and to collaborate and network with other researchers in this field. The long-term goal is to foster a vibrant and inclusive community of researchers committed to advancing our understanding of group modeling, adaptation, and personalization by bringing together experts from different disciplines and perspectives. Throughout this workshop, we aim to identify critical challenges and opportunities in this area and develop a shared research agenda to guide future work.

Adverse phenomena such as the search engine manipulation effect (SEME), where web search users change their attitude on a topic following whatever most highly-ranked search results promote, represent crucial challenges for research and industry. However, the current lack of automatic methods to comprehensively measure or increase viewpoint diversity in search results complicates the understanding and mitigation of such effects. This paper proposes a viewpoint bias metric that evaluates the divergence from a pre-defined scenario of ideal viewpoint diversity considering two essential viewpoint dimensions (i.e., stance and logic of evaluation). In a case study, we apply this metric to actual search results and find considerable viewpoint bias in search results across queries, topics, and search engines that could lead to adverse effects such as SEME. We subsequently demonstrate that viewpoint diversity in search results can be dramatically increased using existing diversification algorithms. The methods proposed in this paper can assist researchers and practitioners in evaluating and improving viewpoint diversity in search results.

Advanced science 10(5), 2203053 (2023). doi:10.1002/advs.202203053, Published by Wiley-VCH, Weinheim

Highly complex and irregular atrial activation patterns during atrial fibrillation (AF) can occasionally be interrupted by repetitive atrial activation patterns (RAAPs). These patterns are thought to be generated by mechanisms that initiate or maintain AF episodes are therefore, might be more diverse in patients with more complex forms of AF We quantified RAAP diversity by the half decay time of the ratio of the unprecedented RAAPs to the total num{###} ber of RAAPs in a goat model with different durations of sustained AF[3 weeks (3wkAF, n=8) and 22 weeks (22wkAF, n=8)]. 32 recordings from left and right atria (LA/RA) of each goat were analyzed. 24 out of 32 curves could be modeled as exponential decay functions with adjusted R-squared > 0.75 while others presented more irregular decaying patterns (3wkAF LA:2 RA:3,22wkAF LA: 1 RA:2). Half decay rates were significantly shorter in LAs of 3wkAF goats (δ3wkAF=23.67s vs. δ22wkAF=32.86s,p < 0.05, Mann-Whitney U-test). There was no significant difference in RA.