Miracle-dz/UniFAR-FTUs huggingface dataset:
A 3D Electromechanical Model of the Human Atria: A Realistic Framework for the Study of Atrial Fibrillation

Atrial fibrillation (AF) is the most common cardiac arrhythmia and it is mainly sustained by rapid ectopic activity, which affects the electromechanical dynamics of the cardiac tissue. The objective of the present work is to develop a highly detailed 3D electromechanical model of the human atria in order to assess how atrial fibrillation-induced electrical remodelling affects atrial mechanical contraction. The model incorporates regional tissue heterogeneities at the cell level by defining different electrical and tissular properties and different conduction areas. The preliminary studies here proposed suggest that the heterogeneity and the mechano-electric feedback is a relevant factor in the intracellular calcium handling and the contractility of the atria in arrhytmogenic dynamics.
MUSIC AND EMOTION: AN EMPIRICAL CRITIQUE OF A KEY ISSUE IN THE PHILOSOPHY OF MUSIC

The crux of much of the debate in the aesthetics of music has been the relationship of (instrumental) music and emotion, and specifically the issues of expression and induction of emotion by music. Kivy (e.g., 1989, 1990, 1999) has addressed these issues influentially, in part by means of the “emotivist” versus “cognitivist” dichotomy. A related topic has been the comparison of “musical emotions” to those that arise in the non-musical “real world. ” The aims of the paper are to show that some of the most important claims by the contemporary philosophers of music are empirically testable; to review some of the recent experimental work; and to demonstrate how such methodological and empirical advances can render certain aspects of the philosophical commentary obsolete. The paper concludes with the attempt to replace the concept of “musical emotions” by those of the measurable responses of “thrills/chills,” “being moved” and “aesthetic awe” ‐ all of these states being hierarchically related components in the Aesthetic Trinity theory (Konecni, 2004a, 2005a, 2007)
Structural Interpretation and Investigation of the Displacement Gradients of the Normal Fault System beneath the Horda Platform, the northern North Sea

The North Sea basin is one of the best-studied areas in the world with respect to the structural and sedimentary architecture of rift zones. The Base Cretaceous Unconformity, which defines a mappable horizon at the transition from synrift to postrift sequences associated with the JurassicCretaceous rift, is well known as a reference marker for both seismic and well log interpretations and covers most of the basin. This unconformity is interpreted at the locations of the Oygarden Fault Zone, the Troll Fault Block, the North Viking Graben, the Tampen Spur, the Snorre Fault Block, the Sogn Graben and the Horda Platform. The complexities of the unconformity have been established and vary with the structural and geographical position within the basin. However, as the Base Cretaceous Unconformity covers most of the northern North Sea, its structural time map, is used to derive the picture of post-structural framework of a rift basin and to locate essential structures in the deeper sections. Three main reflectors (Pre-Jurassic 1, Pre-Jurassic 2 and Top seismic basement) located beneath the Base Cretaceous Unconformity on the Horda Platform, and have been interpreted using 2D seismic reflection data. These three reflectors have been studied in order to investigate in detail the displacement gradients and possible linkage of the early fault system under the Horda Platform, and to evaluate their effect on the large-scale sediment architecture. A main reason to work on the structures under the Horda Platform is due to the fact that these structures are believed to have existed already in the early stages of the northern North Sea basin development. The extensional normal fault systems of both the Permo-Triassic and the Late Jurassic rifts are considered a key control on the geological structures and sedimentary architecture of the region as presently seen. The basin evolution related Permo-Triassic rifting is most pronounced on the eastern part of the Horda platform where its synrift geometry is obviously seen with the huge segment length and largest uplift explainable by a flexural stretching model. The rift axis is transferred to position at base of the Viking graben during the Late- Jurassic rifting with the smaller magnitude of extension than the Permo-Triassic as clearly seen by the less thickness of the synrift geometry. However, the structural evolution of normal faults and the basin architecture under the Horda Platform is particularly affected by the complex interaction of fault linkage, fault propagation, fault growth, and death of fault through times from the early stage to the final stage of the basin development. Apart from the effects of major tectonic controls, additionally, non-tectonic parameters, such as climate, sea or lake level changes, and differences in amount and type of sediment supply, should be taken into account to influence the stratigraphic and sedimentation patterns in the basin.
) Can an AI Analyze Arguments? Argument-Checking and the Challenges of Assessing the Quality of Online Information

Citation for published version (APA): Brave , R. , Russo, F. , Uzovic, O. , & Wagemans, J. (2023). Can an AI Analyze Arguments? Argument-Checking and the Challenges of Assessing the Quality of Online Information. In C. El Morr (Ed.), AI and Society: Tensions and Opportunities (pp. 267-281). (Chapman & Hall/CRC Artificial Intelligence and Robotics Series). CRC Press. https://doi.org/10.1201/9781003261247-20
Hispanic Spaces, Latino Places: Community and Cultural Diversity in Contemporary America

* Acknowledgments * Introduction (Daniel D. Arreola) *1. Hispanic American Legacy, Latino American Diaspora (Daniel D. Arreola) * Part I. Continuous Communities *2. The Plaza in Las Vegas, New Mexico: A Community Gathering Place (Jeffrey S. Smith) *3. Social Geography of Laredo, Texas, Neighborhoods: Distinctiveness and Diversity in a Majority-Hispanic Place (Michael S. Yoder and Renee LaPerriere de Gutierrez) * Part II. Discontinuous Communities *4. Barrio under Siege: Latino Sense of Place in San Francisco, California (Brian J. Godfrey) *5. Globalization of the Barrio: Transformation of the Latino Cultural Landscapes of San Diego, California (Lawrence A. Herzog) *6. Barrio Space and Place in Southeast Los Angeles, California (James R. Curtis) * Part III. New Communities *7. Changing Latinization of New York City (Ines M. Miyares) *8. Soccer and Latino Cultural Space: Metropolitan Washington Futbol Leagues (Marie Price and Courtney Whitworth) *9. The Cultural Landscape of a Puerto Rican Neighborhood in Cleveland, Ohio (Albert Benedict and Robert B. Kent) *10. Latinos in Polynucleated Kansas City (Steven L. Driever) *11. Latino Commerce in Northern Nevada (Kate A. Berry) *12. Se Venden Aqui: Latino Commercial Landscapes in Phoenix, Arizona (Alex Oberle) *13. Hispanics in the American South and the Transformation of the Poultry Industry (William Kandel and Emilio A. Parrado) *14. Hispanization of Hereford, Texas (Terrence W. Haverluk) * References * Contributors * Index
Interpolation for Robust Learning: Data Augmentation on Wasserstein Geodesics

We propose to study and promote the robustness of a model as per its performance through the interpolation of training data distributions. Specifically, (1) we augment the data by finding the worst-case Wasserstein barycenter on the geodesic connecting subpopulation distributions of different categories. (2) We regularize the model for smoother performance on the continuous geodesic path connecting subpopulation distributions. (3) Additionally, we provide a theoretical guarantee of robustness improvement and investigate how the geodesic location and the sample size contribute, respectively. Experimental validations of the proposed strategy on \textit{four} datasets, including CIFAR-100 and ImageNet, establish the efficacy of our method, e.g., our method improves the baselines' certifiable robustness on CIFAR10 up to $7.7\%$, with $16.8\%$ on empirical robustness on CIFAR-100. Our work provides a new perspective of model robustness through the lens of Wasserstein geodesic-based interpolation with a practical off-the-shelf strategy that can be combined with existing robust training methods.
Hierarchical Grammar-Induced Geometry for Data-Efficient Molecular Property Prediction

The prediction of molecular properties is a crucial task in the field of material and drug discovery. The potential benefits of using deep learning techniques are reflected in the wealth of recent literature. Still, these techniques are faced with a common challenge in practice: Labeled data are limited by the cost of manual extraction from literature and laborious experimentation. In this work, we propose a data-efficient property predictor by utilizing a learnable hierarchical molecular grammar that can generate molecules from grammar production rules. Such a grammar induces an explicit geometry of the space of molecular graphs, which provides an informative prior on molecular structural similarity. The property prediction is performed using graph neural diffusion over the grammar-induced geometry. On both small and large datasets, our evaluation shows that this approach outperforms a wide spectrum of baselines, including supervised and pre-trained graph neural networks. We include a detailed ablation study and further analysis of our solution, showing its effectiveness in cases with extremely limited data. Code is available at https://github.com/gmh14/Geo-DEG.
AN INTERACTIVE APPROACH TO IDENTIFY CRICKET SHOTS THROUGH DEEP LEARNING MECHANISM

Numerous sports have attracted a lot of interest and popularity recently. The absence of any sporting activities throughout the height of the current outbreak had caused a sizable number of people to crave to see some game being played. In India, cricket is undoubtedly the most well-liked sport, with millions of fans who passionately follow the matches. As a result of their intense interest in the game, fans analysis each player’s skills in great detail, especially their shot selection. The popularity of fantasy leagues and other services of a similar nature has increased interest in assessing players so that they might be chosen for their teams. The manual process for identifying better shots is one of the most time-consuming .
Supplementary Material to Neural Texture Synthesis with Guided Correspondence

Mechrez et al. [6] proposed the Contextual loss for image transformation with non-aligned data. Heitz et al. [5] proved it unsuitable for texture synthesis but didn’t analyze why. In this section, we will try to figure out the reason and make clear the difference between the Contextual loss and our Guided Correspondence loss (besides the difference in features involved, since we considers the matching diversity and additional guidance in the patch distance). The Contextual loss measures image similarity based on semantic features. The motivation behind its design is to search for correspondence pairs that are significant enough between all source and target samples. Briefly, it first considers the similarity of a target sample with respect to the context of the entire source image. Then all the target samples are involved as the context for each source sample, and the overall similarity is defined therefrom. To be more specific, as illustrated in Figure 1, the first step of Contextual loss is to find the nearest neighbor for each target/output sample ti. Then the similarity of ti to all source/reference samples in S is normalized by the similarity of ti to its nearest neighbor. After the normalization, the second step is to choose the correspondence with the maximum similarity among all target samples for each source sample sj . Finally, these similarities are summed up as the contextual similarity between the entire S and T . The so-designed Contextual loss may result in ill matches for some target samples: 1) There might be more than one match for some target samples, and 2) There might be no match found for some target samples. Figure 1 shows an illustration of this situation (see also Figure 3 in [6]). These configurations are considered reasonable for image
Parallel Online Clustering of Bandits via Hedonic Game

Contextual bandit algorithms appear in several applications, such as online advertisement and recommendation systems like personalized education or personalized medicine. Individually-tailored recommendations boost the performance of the underlying application; nevertheless, providing individual suggestions becomes costly and even implausible as the number of users grows. As such, to efficiently serve the demands of several users in modern applications, it is imperative to identify the underlying users’ clusters, i.e., the groups of users for which a single recommendation might be (near-)optimal. We propose CLUB-HG, a novel algorithm that integrates a game-theoretic approach into clustering inference. Our algorithm achieves Nash equilibrium at each inference step and discovers the underlying clusters. We also provide regret analysis within a standard linear stochastic noise setting. Finally, experiments on synthetic and real-world datasets show the superior performance of our proposed algo-rithm compared to the state-of-the-art algorithms.
Height and economic development in Spain, 1850-1958

This paper investigates the relationship between height and economic development in Spain in the modern period. The relation is investigated using recently constructed times series with recruitment data of conscripts. We observed changes in average height along the analyzed period. These variations could be explained by different indicators of economic development such as for consumption of hygiene products, for deflator of private consumption, income per capita, schooling rate, infant mortality and trade (grade of openness). We model human stature as a Vector Autoregressive Model (VAR) and we proceed to estimate a Vector Autoregressive Equilibrium Correction Model (VECqM) to quantify the height response to different changes in the different explanatory variables. The analysis shows that there is a long- run relationship between height, income, and other indicators of economic development in Spain as consumption of hygiene products, and openness.
Active Learning based Structural Inference

In this paper, we propose a novel framework Active Learning based Structural Inference (ALaSI), to infer the existence of directed connections from observed agents’ states over a time period in a dynamical system. With the help of deep active learning, ALaSI is competent in learning the representation of connections with a relatively small pool of prior knowledge. Moreover, based on information theory, the proposed inter-and out-of-scope message learning pipelines are remarkably beneficial to structural inference for large dynamical systems. We evaluate ALaSI on various large datasets including simulated systems and real-world networks, to demonstrate that ALaSI is able to outperform previous methods in precisely inferring the existence of connections in large systems under either supervised learning or unsupervised learning.
Revisiting Bayesian network learning with small vertex cover

The problem of structure learning in Bayesian networks asks for a directed acyclic graph (DAG) that maximizes a given scoring function. Since the problem is NP-hard, research effort has been put into discovering restricted classes of DAGs for which the search problem can be solved in polynomial time. Here, we initiate investigation of questions that have received less attention thus far: Are the known polynomial algorithms close to the best possible, or is there room for signiﬁ-cant improvements? If the interest is in Bayesian learning, that is, in sampling or weighted counting of DAGs, can we obtain similar complexity results? Focusing on DAGs with bounded vertex cover number—a class studied in Korhonen and Parviainen’s seminal work (NIPS 2015)—we answer the questions in the afﬁrmative. We also give, apparently the ﬁrst, proof that the counting problem is # P-hard in general. In addition, we show that under the vertex-cover constraint counting is # W[1]-hard.
Heuristics and biases

A heuristic is a word from the Greek meaning ‘to discover’. It is an approach to problem-solving that takes one’s personal experience into account. Heuristics provide strategies to scrutinize a limited number of signals and/or alternative choices in decision-making. Heuristics diminish the work of retrieving and storing information in memory and of streamlining the decision-making process by reducing the amount of integrated information necessary in making the choice or passing judgement. However, whilst heuristics can speed up our problem-solving and decision-making processes, they can introduce errors and bias judgements. This article looks at commonly used heuristics and their human psychology origins. Understanding how heuristics work can give us better insight into our personal biases and influences and (perhaps) lead to better problem-solving and decision-making.
Orthogonality-Enforced Latent Space in Autoencoders: An Approach to Learning Disentangled Representations

Noting the importance of factorizing (or disen-tangling) the latent space, we propose a novel, non-probabilistic disentangling framework for au-toencoders, based on the principles of symmetry transformations that are independent of one another. To the best of our knowledge, this is the first deterministic model that is aiming to achieve dis-entanglement based on autoencoders using only a reconstruction loss without pairs of images or labels, by explicitly introducing inductive biases into a model architecture through Euler encoding. The proposed model is then compared with a number of state-of-the-art models, relevant to dis-entanglement, including symmetry-based models and generative models. Our evaluation using six different disentanglement metrics, including the unsupervised disentanglement metric we propose here in this paper, shows that the proposed model can offer better disentanglement, especially when variances of the features are different, where other methods may struggle. We believe that this model opens several opportunities for linear disentangled representation learning based on deterministic au-toencoders.
Maximizing submodular functions under submodular constraints

We consider the problem of maximizing submodular functions under submodular constraints by formulating the problem in two ways: SCSK-C and D IFF -C. Given two submodular functions f and g where f is monotone, the objective of SCSK-C problem is to find a set S of size at most k that maximizes f ( S ) under the constraint that g ( S ) ≤ θ , for a given value of θ . The problem of D IFF -C focuses on finding a set S of size at most k such that h ( S ) = f ( S ) − g ( S ) is maximized. It is known that these problems are highly inapproximable and do not admit any constant factor multiplicative approximation algorithms unless NP is easy. Known approximation algorithms involve data-dependent approximation factors that are not efficiently computable. We initiate a study of the design of approximation algorithms where the approximation factors are efficiently computable. For the problem of SCSK-C, we prove that the greedy algorithm produces a solu-tion whose value is at least (1 − 1 /e ) f (OPT) − A , where A is the data-dependent additive error. For the D IFF -C problem, we design an algorithm that uses the SCSK-C greedy algorithm as a subrou-tine. This algorithm produces a solution whose value is at least (1 − 1 /e ) h (OPT) − B , where B is also a data-dependent additive error. A salient feature of our approach is that the additive error terms can be computed efficiently, thus enabling us to ascertain the quality of the solutions produced.
Contribution to the Routledge “Handbook on Inequalities and the Life Course” Optimising the use of measures of social stratification in research with intersectional and longitudinal analytical priorities

Many different approaches are available to measure the social stratification position of individuals. It is well known that different approaches can be associated with different theoretical and empirical properties. Nevertheless there is little consistent advice when confronting two important and interconnected considerations that affect many analyses of inequalities: how can we best exploit stratification measures when an intersectional and/or longitudinal understanding is prioritised? This paper will review the features of a number of important candidate measures of social stratification and discuss the challenges and opportunities for adapting conventional practices in ways that can take better account of intersectional and longitudinal analytical considerations
BEV-Guided Multi-Modality Fusion for Driving Perception

Integrating multiple sensors and addressing diverse tasks in an end-to-end algorithm are challenging yet critical topics for autonomous driving. To this end, we introduce BEVGuide, a novel Bird's Eye- View (BEV) representation learning framework, representing the first attempt to unify a wide range of sensors under direct BEV guidance in an end-to-end fashion. Our architecture accepts input from a diverse sensor pool, including but not limited to Camera, Lidar and Radar sensors, and extracts BEV feature embeddings using a versatile and general transformer backbone. We design a BEV-guided multi-sensor attention block to take queries from BEV embeddings and learn the BEV representation from sensor-specific features. BEVGuide is efficient due to its lightweight backbone design and highly flexible as it supports almost any input sensor configurations. Extensive experiments demonstrate that our framework achieves exceptional performance in BEV perception tasks with a diverse sensor set. Project page is at https://yunzeman.github. io/BEVGuide.
Incremental Pacing Induces Sustained Reentry in a Computational Model of Brugada Syndrome

Brugada Syndrome is a form of idiopathic ventricular fibrillation. To date, there is no definitive theory about how ventricular fibrillation is initiated, or a consensus on its substrate. In this work, we report a computational study aimed at determining the role of the pacing protocol on the induction of sustained arrhythmias in Brugada Syndrome. We developed a computational model of Brugada Syndrome, including both structural and electrophysiological abnormalities in a slab of transmurally heterogeneous cardiac tissue. Starting from the clinical observation that cardiac episodes are more frequent at rest, we studied the effect of incremental pacing (i.e., with increasing pacing cycle length) on the generation of reentrant activity. Our results suggest that incremental pacing can unmask the arrhythmogenic substrate of Brugada Syndrome.