Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring language relations through syntactic distances and geographic proximity

Languages are grouped into families that share common linguistic traits. While this approach has been successful in understanding genetic relations between diverse languages, more analyses are needed to accurately quantify their relatedness, especially in less studied linguistic levels such as syntax. Here, we explore linguistic distances using series of parts of speech (POS) extracted from the Universal Dependencies dataset. Within an information-theoretic framework, we show that employing POS trigrams maximizes the possibility of capturing syntactic variations while being at the same time compatible with the amount of available data. Linguistic connections are then established by assessing pairwise distances based on the POS distributions. Intriguingly, our analysis reveals definite clusters that correspond to well known language families and groups, with exceptions explained by distinct morphological typologies. Furthermore, we obtain a significant correlation between language similarity and geographic distance, which underscores the influence of spatial proximity on language kinships.
Are the children and adolescents with congenital heart disease living in Southwestern Ontario really overweight and obese?

Abstract Objective To assess the prevalence of overweight and obesity in children with congenital heart disease and compare them with age-matched healthy children in Southwestern Ontario, Canada. Methods We compared the Center of Disease Control weight and body mass index z-scores of 1080 children, aged 2 to 18 years, who presented to our paediatric cardiology outpatient clinic from 2008 to 2010 for congenital heart disease with 1083 healthy controls. Results In all, 18.2% of the children with congenital heart disease and 20.8% of healthy children were identified to be either overweight or obese. Overall, the weight category distribution had been similar between the congenital heart disease and healthy control groups, as well as between the congenital heart disease subgroups. There was no difference in normal weight and overweight/obese categories between children with congenital heart disease and healthy children. The underweight category, however, showed a significantly higher prevalence in congenital heart disease compared with healthy children (6.8 and 4.5%, respectively, p = 0.03). Conclusion The prevalence of overweight/obesity did not differ in children with congenital heart disease compared with age-matched healthy children; however, it is still high (18.2%). Obesity may represent an additional risk factor for the long-term cardiovascular health of congenital heart disease patients aside from the underlying heart defect.
Modeling Sustainable City Trips: Integrating CO2 Emissions, Popularity, and Seasonality into Tourism Recommender Systems

In an era of information overload and complex decision-making processes, Recommender Systems (RS) have emerged as indispensable tools across diverse domains, particularly travel and tourism. These systems simplify trip planning by offering personalized recommendations that consider individual preferences and address broader challenges like seasonality, travel regulations, and capacity constraints. The intricacies of the tourism domain, characterized by multiple stakeholders, including consumers, item providers, platforms, and society, underscore the complexity of achieving balance among diverse interests. Although previous research has focused on fairness in Tourism Recommender Systems (TRS) from a multistakeholder perspective, limited work has focused on generating sustainable recommendations. Our paper introduces a novel approach for assigning a sustainability indicator (SF index) for city trips accessible from the users' starting point, integrating Co2e analysis, destination popularity, and seasonal demand. Our methodology involves comprehensive data gathering on transportation modes and emissions, complemented by analyses of destination popularity and seasonal demand. A user study validates our index, showcasing its practicality and efficacy in providing well-rounded and sustainable city trip recommendations. Our findings contribute significantly to the evolution of responsible tourism strategies, harmonizing the interests of tourists, local communities, and the environment while paving the way for future research in responsible and equitable tourism practices.
Adaptive Loss Weighting for Machine Learning Interatomic Potentials

Training machine learning interatomic potentials often requires optimizing a loss function composed of three variables: potential energies, forces, and stress. The contribution of each variable to the total loss is typically weighted using fixed coefficients. Identifying these coefficients usually relies on iterative or heuristic methods, which may yield sub-optimal results. To address this issue, we propose an adaptive loss weighting algorithm that automatically adjusts the loss weights of these variables during the training of potentials, dynamically adapting to the characteristics of the training dataset. The comparative analysis of models trained with fixed and adaptive loss weights demonstrates that the adaptive method not only achieves a more balanced predictions across the three variables but also improves overall prediction accuracy.
Sailing Through Point Clouds: Safe Navigation Using Point Cloud Based Control Barrier Functions

The capability to navigate safely in an unstructured environment is crucial when deploying robotic systems in real-world scenarios. Recently, control barrier function (CBF) based approaches have been highly effective in synthesizing safety-critical controllers. In this work, we propose a novel CBF-based local planner comprised of two components: Vessel and Mariner. The Vessel is a novel scaling factor based CBF formulation that synthesizes CBFs using only point cloud data. The Mariner is a CBF-based preview control framework that is used to mitigate getting stuck in spurious equilibria during navigation. To demonstrate the efficacy of our proposed approach, we first compare the proposed point cloud based CBF formulation with other point cloud based CBF formulations. Then, we demonstrate the performance of our proposed approach and its integration with global planners using experimental studies on the Unitree B1 and Unitree Go2 quadruped robots in various environments.
Test the Loop Quantum Gravity Theory via the Lensing Effect

Recently, scholars such as Lewandowski, Ma, and Yang have successfully derived a quantum-corrected black hole model in loop quantum gravity (Phys.Rev.Lett.130.101501 (2023)), which is a modification of the Schwarzschild black hole. In this paper, we calculate the strong/weak gravitational lensing effects under the quantum-corrected black hole model, taking the supermassive black holes M87* and SgrA* as the subjects of study to explore the impact of the quantum correction parameter $\alpha$ on the positions of images and the Einstein ring. Our calculations show that in the case of strong gravitational lensing, the lensing coefficient $\bar{a}$ increases with an increase in the quantum correction parameter, while the deflection angle and the lensing coefficient $\bar{b}$ decrease with an increase in the quantum correction parameter. The quantum correction parameter $\alpha$ has a significant impact in the context of supermassive black holes. In weak gravitational lensing, the quantum correction parameter $\alpha$ plays a suppressive role. Our theoretical analysis suggests that with advancements in future astrophysical observation techniques, it might be possible to test loop quantum gravity theory using the gravitational lensing effect.
Collaborative Active Learning in Conditional Trust Environment

In this paper, we investigate collaborative active learning, a paradigm in which multiple collaborators explore a new domain by leveraging their combined machine learning capabilities without disclosing their existing data and models. Instead, the collaborators share prediction results from the new domain and newly acquired labels. This collaboration offers several advantages: (a) it addresses privacy and security concerns by eliminating the need for direct model and data disclosure; (b) it enables the use of different data sources and insights without direct data exchange; and (c) it promotes cost-effectiveness and resource efficiency through shared labeling costs. To realize these benefits, we introduce a collaborative active learning framework designed to fulfill the aforementioned objectives. We validate the effectiveness of the proposed framework through simulations. The results demonstrate that collaboration leads to higher AUC scores compared to independent efforts, highlighting the framework's ability to overcome the limitations of individual models. These findings support the use of collaborative approaches in active learning, emphasizing their potential to enhance outcomes through collective expertise and shared resources. Our work provides a foundation for further research on collaborative active learning and its practical applications in various domains where data privacy, cost efficiency, and model performance are critical considerations.
Safe and Robust Reinforcement-Learning: Principles and Practice

Reinforcement Learning (RL) has shown remarkable success in solving relatively complex tasks, yet the deployment of RL systems in real-world scenarios poses significant challenges related to safety and robustness. This paper aims to identify and further understand those challenges thorough the exploration of the main dimensions of the safe and robust RL landscape, encompassing algorithmic, ethical, and practical considerations. We conduct a comprehensive review of methodologies and open problems that summarizes the efforts in recent years to address the inherent risks associated with RL applications. After discussing and proposing definitions for both safe and robust RL, the paper categorizes existing research works into different algorithmic approaches that enhance the safety and robustness of RL agents. We examine techniques such as uncertainty estimation, optimisation methodologies, exploration-exploitation trade-offs, and adversarial training. Environmental factors, including sim-to-real transfer and domain adaptation, are also scrutinized to understand how RL systems can adapt to diverse and dynamic surroundings. Moreover, human involvement is an integral ingredient of the analysis, acknowledging the broad set of roles that humans can take in this context. Importantly, to aid practitioners in navigating the complexities of safe and robust RL implementation, this paper introduces a practical checklist derived from the synthesized literature. The checklist encompasses critical aspects of algorithm design, training environment considerations, and ethical guidelines. It will serve as a resource for developers and policymakers alike to ensure the responsible deployment of RL systems in many application domains.
LayoutFlow: Flow Matching for Layout Generation

Finding a suitable layout represents a crucial task for diverse applications in graphic design. Motivated by simpler and smoother sampling trajectories, we explore the use of Flow Matching as an alternative to current diffusion-based layout generation models. Specifically, we propose LayoutFlow, an efficient flow-based model capable of generating high-quality layouts. Instead of progressively denoising the elements of a noisy layout, our method learns to gradually move, or flow, the elements of an initial sample until it reaches its final prediction. In addition, we employ a conditioning scheme that allows us to handle various generation tasks with varying degrees of conditioning with a single model. Empirically, LayoutFlow performs on par with state-of-the-art models while being significantly faster.
HandBooster: Boosting 3D Hand-Mesh Reconstruction by Conditional Synthesis and Sampling of Hand-Object Interactions

Reconstructing 3D hand mesh robustly from a single image is very challenging, due to the lack of diversity in existing real-world datasets. While data synthesis helps relieve the issue, the syn-to-real gap still hinders its usage. In this work, we present HandBooster, a new approach to uplift the data diversity and boost the 3D hand-mesh reconstruction performance by training a conditional generative space on hand-object interactions and purposely sampling the space to synthesize effective data samples. First, we construct versatile content-aware conditions to guide a diffusion model to produce realistic images with diverse hand appearances, poses, views, and backgrounds; favorably, accurate 3D annotations are obtained for free. Then, we design a novel condition creator based on our similarity-aware distribution sampling strategies to deliberately find novel and realistic interaction poses that are distinctive from the training set. Equipped with our method, several baselines can be significantly improved beyond the SOTA on the HO3D and DexYCB benchmarks. Our code will be released on https://github.com/hxwork/HandBooster_Pytorch.
Monitoring Biological and Chemical Trends in Temperate Still Waters Using Citizen Science

The involvement of volunteers in the monitoring of the environment holds great potential to gather information on a wider temporal and spatial scale than is currently possible. However, the mass involvement of citizens in monitoring freshwater health is a relatively new field and subject to uncertainty. Here, we examine 1192 samples collected across 46 temperate ponds ( 2 ha) by 120 volunteers trained through the FreshWater Watch citizen science programme to consider if the approach is able to (a) identify well established patterns in water quality and biological indicators (i.e., fish), and (b) provide a potentially useful basis for the identification of pollution sources in urban or peri-urban landscapes. Seasonal patterns observed agreed well with established principles of nutrient dynamics, algal bloom seasonality, and broad biological trends between ponds and lakes. Further, observational data collected by the volunteers suggested plausible links between the presence of residential discharge and water level fluctuation and significant increases in algal bloom observations between peri-urban and urban sites. We suggest that citizen science can have a role to play in complementing regulatory monitoring efforts and that local citizens should be empowered to become stewards of their local freshwater resources.
SingularTrajectory: Universal Trajectory Predictor Using Diffusion Model

There are five types of trajectory prediction tasks: deterministic, stochastic, domain adaptation, momentary observation, and few-shot. These associated tasks are defined by various factors, such as the length of input paths, data split and pre-processing methods. Interestingly, even though they commonly take sequential coordinates of observations as input and infer future paths in the same coordinates as output, designing specialized architectures for each task is still necessary. For the other task, generality issues can lead to sub-optimal performances. In this paper, we propose SingularTrajectory, a diffusion-based universal trajectory prediction framework to reduce the performance gap across the five tasks. The core of SingularTrajectory is to unify a variety of human dynamics representations on the associated tasks. To do this, we first build a Singular space to project all types of motion patterns from each task into one embedding space. We next propose an adaptive anchor working in the Singular space. Unlike traditional fixed anchor methods that sometimes yield unacceptable paths, our adaptive anchor enables correct anchors, which are put into a wrong location, based on a traversability map. Finally, we adopt a diffusion-based predictor to further enhance the prototype paths using a cascaded denoising process. Our unified framework ensures the generality across various benchmark settings such as input modality, and trajectory lengths. Extensive experiments on five public benchmarks demonstrate that SingularTrajectory substantially outperforms existing models, highlighting its effectiveness in estimating general dynamics of human movements. Code is publicly available at https://github.com/inhwanbae/SingularTrajectory .
Local (coarse) correlated equilibria in non-concave games

We investigate local notions of correlated equilibria, distributions of actions for smooth games such that players do not incur any regret against modifications of their strategies along a set of continuous vector fields. Our analysis shows that such equilibria are intrinsically linked to the projected gradient dynamics of the game. We identify the equivalent of coarse equilibria in this setting when no regret is incurred against any gradient field of a differentiable function. As a result, such equilibria are approximable when all players employ online (projected) gradient ascent with equal step-sizes as learning algorithms, and when their compact and convex action sets either (1) possess a smooth boundary, or (2) are polyhedra over which linear optimisation is ``trivial''. As a consequence, primal-dual proofs of performance guarantees for local coarse equilibria take the form of a generalised Lyapunov function for the gradient dynamics of the game. Adapting the regret matching framework to our setting, we also show that general local correlated equilibria are approximable when the set of vector fields is finite, given access to a fixed-point oracle for linear or conical combinations. For the class of affine-linear vector fields, which subsumes correlated equilibria of normal form games as a special case, such a fixed-point turns out to be the solution of a convex quadratic minimisation problem. Our results are independent of concavity assumptions on players' utilities.
Reasoning with spatial plans on the semantic web

There are several reasons why citizens, businesses and civil servants need access to regulations. Unfortunately, traditional approaches that aim to provide this access fall short, especially in the area of spatial planning. Fairly straight-forward questions such as "where will I be able to perform this kind of activity" or "is this activity allowed here" are not answered automatically by current systems. There are many attempts to create one-stop-shop front-ends to eGovernment, but these are seldom built from the perspective of the user. This paper describes our work on what we call a 'Legal Atlas'. Using various Semantic Web technologies we combine distributed geospatial data, textual data and controlled vocabularies in order to support users in answering questions such as those mentioned above.
Common Sense Enhanced Knowledge-based Recommendation with Large Language Model

Knowledge-based recommendation models effectively alleviate the data sparsity issue leveraging the side information in the knowledge graph, and have achieved considerable performance. Nevertheless, the knowledge graphs used in previous work, namely metadata-based knowledge graphs, are usually constructed based on the attributes of items and co-occurring relations (e.g., also buy), in which the former provides limited information and the latter relies on sufficient interaction data and still suffers from cold start issue. Common sense, as a form of knowledge with generality and universality, can be used as a supplement to the metadata-based knowledge graph and provides a new perspective for modeling users' preferences. Recently, benefiting from the emergent world knowledge of the large language model, efficient acquisition of common sense has become possible. In this paper, we propose a novel knowledge-based recommendation framework incorporating common sense, CSRec, which can be flexibly coupled to existing knowledge-based methods. Considering the challenge of the knowledge gap between the common sense-based knowledge graph and metadata-based knowledge graph, we propose a knowledge fusion approach based on mutual information maximization theory. Experimental results on public datasets demonstrate that our approach significantly improves the performance of existing knowledge-based recommendation models.
Scaling Laws For Dense Retrieval

Scaling up neural models has yielded significant advancements in a wide array of tasks, particularly in language generation. Previous studies have found that the performance of neural models frequently adheres to predictable scaling laws, correlated with factors such as training set size and model size. This insight is invaluable, especially as large-scale experiments grow increasingly resource-intensive. Yet, such scaling law has not been fully explored in dense retrieval due to the discrete nature of retrieval metrics and complex relationships between training data and model sizes in retrieval tasks. In this study, we investigate whether the performance of dense retrieval models follows the scaling law as other neural models. We propose to use contrastive log-likelihood as the evaluation metric and conduct extensive experiments with dense retrieval models implemented with different numbers of parameters and trained with different amounts of annotated data. Results indicate that, under our settings, the performance of dense retrieval models follows a precise power-law scaling related to the model size and the number of annotations. Additionally, we examine scaling with prevalent data augmentation methods to assess the impact of annotation quality, and apply the scaling law to find the best resource allocation strategy under a budget constraint. We believe that these insights will significantly contribute to understanding the scaling effect of dense retrieval models and offer meaningful guidance for future research endeavors.
Coherent Collections of Rules Describing Exceptional Materials Identified with a Multi-Objective Optimization of Subgroups

Using a modest amount of data from a large population, subgroup discovery (SGD) identifies outstanding subsets of data with respect to a certain property of interest of that population. The SGs are described by"rules". These are constraints on key descriptive parameters that characterize the material or the environment. These parameters and constraints are obtained by maximizing a quality function that establishes a tradeoff between SG size and utility, i.e., between generality and exceptionality. The utility function measures how outstanding a SG is. However, this approach does not give a unique solution, but typically many SGs have similar quality-function values. Here, we identify coherent collections of SGs of a"Pareto region"presenting various size-utility tradeoffs and define a SG similarity measure based on the Jaccard index, which allows us to hierarchically cluster these optimal SGs. These concepts are demonstrated by learning rules that describe perovskites with high bulk modulus. We show that SGs focusing on exceptional materials exhibit a high quality-function value but do not necessarily maximize it. We compare the mean shift with the cumulative Jensen-Shannon divergence ($D_{sJS}$) as utility functions and show that the SG rules obtained with $D_{cJS}$ are more focused than those obtained with the mean shift.
Connections between metric differentiability and rectifiability

We combine Kirchheim's metric differentials with Cheeger charts in order to establish a non-embeddability principle for any collection $\mathcal C$ of Banach (or metric) spaces: if a metric measure space $X$ bi-Lipschitz embeds in some element in $\mathcal C$, and if every Lipschitz map $X\to Y\in \mathcal C$ is differentiable, then $X$ is rectifiable. This gives a simple proof of the rectifiability of Lipschitz differentiability spaces that are bi-Lipschitz embeddable in Euclidean space, due to Kell--Mondino. Our principle also implies a converse to Kirchheim's theorem: if all Lipschitz maps from a domain space to arbitrary targets are metrically differentiable, the domain is rectifiable. We moreover establish the compatibility of metric and w$^*$-differentials of maps from metric spaces in the spirit of Ambrosio--Kirchheim.