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Autism Spectrum Disorder (ASD) is a neurodivergent condition with a wide range of characteristics and support levels. Individuals with ASD can exhibit various combinations of traits such as difficulties in social interaction, communication, and language, alongside restricted interests and repetitive activities. Many adults with ASD live independently due to increased awareness and late diagnoses, which help them manage long-standing challenges. Predictability, clarity, and minimized sensory stimuli are crucial for the daily comfort of autistic individuals. In mobile applications, autistic users face significant cognitive overload compared to neurotypicals, resulting in higher effort and time to complete tasks. Urban mobility apps, essential for daily routines, often overlook the needs of autistic users, leading to cognitive overload issues. This study investigates the accessibility of urban mobility apps for autistic individuals using the Interfaces Accessibility Guide for Autism (GAIA). By evaluating various apps, we have identified a common gap regarding accessibility for people with Autism Spectrum Disorder (ASD). This limitation relates to the absence of a functionality that allows users on the autism spectrum to customize the characteristics of the textual and visual elements of the software, such as changing the text font, altering the font type, and adjusting text colors, as well as native audio guidance within the applications themselves. Currently, the only function in this context is for visually impaired people, which completely changes the user experience in terms of navigation., Comment: Author pre-print of article published in the XXIII Brazilian Symposium on Human Factors in Computing Systems (IHC'24)

We investigate the dispersion of solutes in active nematic fluids confined in narrow channels based on simulations of nematohydrodynamics. The study focuses on two pre-turbulent regimes: oscillatory flow, with net mass flux, and dancing flow, without net flux. Our findings reveal that the longitudinal dispersion of solutes, in both regimes, is determined by the second moments of the longitudinal and transverse components of the velocity field and proportional to the activity, suggesting a common mechanism for dispersion in these distinct flows. Finally, we found that the dispersion coefficient may increase up to one order of magnitude with respect to molecular diffusion in the dancing flow regime and that it is also enhanced in oscillatory flows, with potential for applications in natural biological environments and engineered devices.

Three-dimensional (3D) simulations of the structure of liquid crystal (LC) torons, topologically protected distortions of the LC director field, under material flows are rare but essential in microfluidic applications. Here, we show that torons adopt a steady-state configuration at low flow velocity before disintegrating at higher velocities, in line with experimental results. Furthermore, we show that under partial slip conditions at the boundaries, the flow induces a reversible elongation of the torons, also consistent with the experimental observations. These results are in contrast with previous simulation results for 2D skyrmions under similar flow conditions, highlighting the need for a 3D description of this LC soliton in relation to its coupling to the material flow. These findings pave the way for future studies of other topological solitons, like hopfions and heliknotons, in flowing soft matter systems.

In this work we consider some problems about a reflected graph map germ $f$ from $(\mathbb{C}^n,0)$ to $(\mathbb{C}^{n+1},0)$. A reflected graph map is a particular case of a reflection map, which is defined using an embedding of $\mathbb{C}^n$ in $\mathbb{C}^{p}$ and then applying the action of a reflection group $G$ on $\mathbb{C}^{p}$. In this work, we present a description of the presentation matrix of $f_*{\cal O}_n$ as an ${\cal O}_{n+1}$-module via $f$ in terms of the action of the associated reflection group $G$. We also give a description for a defining equation of the image of $f$ in terms of the action of $G$. Finally, we present an upper (and also a lower) bound for the multiplicity of the image of $f$ and some applications.

Long-tailed data is a special type of multi-class imbalanced data with a very large amount of minority/tail classes that have a very significant combined influence. Long-tailed learning aims to build high-performance models on datasets with long-tailed distributions, which can identify all the classes with high accuracy, in particular the minority/tail classes. It is a cutting-edge research direction that has attracted a remarkable amount of research effort in the past few years. In this paper, we present a comprehensive survey of latest advances in long-tailed visual learning. We first propose a new taxonomy for long-tailed learning, which consists of eight different dimensions, including data balancing, neural architecture, feature enrichment, logits adjustment, loss function, bells and whistles, network optimization, and post hoc processing techniques. Based on our proposed taxonomy, we present a systematic review of long-tailed learning methods, discussing their commonalities and alignable differences. We also analyze the differences between imbalance learning and long-tailed learning approaches. Finally, we discuss prospects and future directions in this field., Comment: Current Under Revision at IEEE TNNLS. [This is the long/Full-length version of our Long-Tailed Learning Survey paper]

We present a calculation of the vector-isovector timelike form-factors of the pion and the kaon using lattice quantum chromodynamics. We calculate two-point correlation functions with $m_\pi \sim 280$ MeV, extracting both the finite-volume spectrum and matrix elements for these states created from the vacuum by a vector current. After determining the coupled-channel $\pi\pi, K\overline{K}$ scattering amplitudes, we perform the necessary correction for the significant finite-volume effects present in the current matrix elements, leading to the timelike form-factors. We find these to be dominated by the presence of the $\rho$ resonance, and we extract its decay constant by an analytic continuation of the amplitudes to the resonance pole. In addition, the spacelike pion form-factor is determined on the same lattice configurations, and a dispersive parameterization is used to simultaneously describe the spacelike and elastic timelike regions.

To advance our understanding of the magnetohydrodynamic (MHD) processes in liquid metals, in this paper we propose an approach combining the classical methods in the dynamo theory based on numerical simulations of the partial differential equations governing the evolution of the magnetic field with the statistical methods. In this study, we intend to answer the following ``optimization'' question: Can we find a statistical explanation what makes a flow to favour magnetic field generation in the linear regime (i.e. the kinematic dynamo is considered), where the Lorenz force is neglected? The flow is assumed to be steady and incompressible, and the magnetic field generation is governed by the magnetic induction equation. The behaviour of its solution is determined by the dominant (i.e. with the largest real part) eigenvalue of the magnetic induction operator. Considering an ensemble of 2193 randomly generated flows, we solved the kinematic dynamo problem and performed an attempt to find a correlation between the dominant eigenvalue and the standard quantities used in hydrodynamics -- vorticity and kinetic helicity. We have found that there is no visible relation between the property of the flow to be a kinematic dynamo and these quantities. This enables us to conclude that the problem requires a more elaborated approach to ``recognize'' if the flow is a dynamo or not; we plan to solve it using contemporary data-driven approach based on deep neural networks.

Microfluidics involves the manipulation of flows at the microscale, typically requiring external power sources to generate pressure gradients. Alternatively, harnessing flows from active fluids, which are usually chaotic, has been proposed as a paradigm for the development of micro-machines. Here, by combining experimental realizations and simulations, we demonstrate that the addition of triangular-shaped inclusions into an active nematic gel can locally break the fore-aft symmetry of active turbulence and stabilize flow fields with self-pumping capabilities. The proposed strategy has enabled us to generate wall-free and self-powered microfluidic systems capable of both cargo transport and mixing along with the downstream flow. We analyze the performance of these active pumps, both isolated and within cooperative ensembles in terms of their output velocity and hydrostatic pressure buildup. Finally, we demonstrate strategies to incorporate them into specifically designed microfluidic platforms to advantageously tailor the geometry of active flows. Our results reveal new possibilities for leveraging the self-organized mechanodynamics of active fluids., Comment: Supplementary figures and videos at DOI 10.17605/OSF.IO/N8G4C

Peer assessment has been widely studied as a replacement for traditional evaluation, not only by reducing the professors' workload but mainly by benefiting students' engagement and learning. Although several works successfully validate its accuracy and fairness, more research must be done on how students' pre-existing social relationships affect the grades they give their peers in an e-learning course. We developed a Moodle plugin to provide the platform with peer assessment capabilities in forums and used it on an MSc course. The plugin curated the reviewer set for a post based on the author's relationships and included rubrics to counter the possible interpersonal effects of peer assessment. Results confirm that peer assessment is reliable and accurate for works with at least three peer assessments, although students' grades are slightly higher. The impact of social relationships is noticeable when students who do not like another peer grade their work consistently lower than students who have a positive connection. However, this has little influence on the final aggregate peer grade. Our findings show that peer assessment can replace traditional evaluation in an e-learning environment where students are familiar with each other., Comment: 24 pages, 5 figures, 4 tables. Learning Environ Res (2024)

Automatic markerless estimation of infant posture and motion from ordinary videos carries great potential for movement studies "in the wild", facilitating understanding of motor development and massively increasing the chances of early diagnosis of disorders. There is rapid development of human pose estimation methods in computer vision thanks to advances in deep learning and machine learning. However, these methods are trained on datasets featuring adults in different contexts. This work tests and compares seven popular methods (AlphaPose, DeepLabCut/DeeperCut, Detectron2, HRNet, MediaPipe/BlazePose, OpenPose, and ViTPose) on videos of infants in supine position. Surprisingly, all methods except DeepLabCut and MediaPipe have competitive performance without additional finetuning, with ViTPose performing best. Next to standard performance metrics (object keypoint similarity, average precision and recall), we introduce errors expressed in the neck-mid-hip ratio and additionally study missed and redundant detections and the reliability of the internal confidence ratings of the different methods, which are relevant for downstream tasks. Among the networks with competitive performance, only AlphaPose could run close to real time (27 fps) on our machine. We provide documented Docker containers or instructions for all the methods we used, our analysis scripts, and processed data at https://hub.docker.com/u/humanoidsctu and https://osf.io/x465b/., Comment: 21 pages, 3 figures, 14 tables

Predictive Maintenance (PdM) emerged as one of the pillars of Industry 4.0, and became crucial for enhancing operational efficiency, allowing to minimize downtime, extend lifespan of equipment, and prevent failures. A wide range of PdM tasks can be performed using Artificial Intelligence (AI) methods, which often use data generated from industrial sensors. The steel industry, which is an important branch of the global economy, is one of the potential beneficiaries of this trend, given its large environmental footprint, the globalized nature of the market, and the demanding working conditions. This survey synthesizes the current state of knowledge in the field of AI-based PdM within the steel industry and is addressed to researchers and practitioners. We identified 219 articles related to this topic and formulated five research questions, allowing us to gain a global perspective on current trends and the main research gaps. We examined equipment and facilities subjected to PdM, determined common PdM approaches, and identified trends in the AI methods used to develop these solutions. We explored the characteristics of the data used in the surveyed articles and assessed the practical implications of the research presented there. Most of the research focuses on the blast furnace or hot rolling, using data from industrial sensors. Current trends show increasing interest in the domain, especially in the use of deep learning. The main challenges include implementing the proposed methods in a production environment, incorporating them into maintenance plans, and enhancing the accessibility and reproducibility of the research., Comment: Preprint submitted to Engineering Applications of Artificial Intelligence

Topology establishes a unifying framework for a diverse range of scientific areas including particle physics, cosmology, and condensed matter physics. One of the most fascinating manifestations of topology in the context of condensed matter is the topological Hall effect, and its relative: the Skyrmion Hall effect. Skyrmions are stable vortex-like spin configurations in certain chiral magnets, and when subject to external electric currents can drift in the transverse direction to the current. These quasi-particles are characterised by a conserved topological charge which in the Skyrmion Hall effect plays the role of electric charges in the ordinary Hall effect. Recently, it has been shown that liquid crystals endowed with chiral properties serve as an ideal testbed for the fundamental investigation of topological solitons, including their two- and three-dimensional realisations. Here, we show experimentally and numerically that three-dimensional solitons aka "torons" exhibit a Hall-like effect when driven by shear flows: the torons are deflected in the direction perpendicular to the shear plane. The experimental results are rationalised in terms of the dynamic Ericksen-Leslie equations, which predict the emergence of the transverse component of the net mass flow, the magnitude of which scales as the 3rd power of the shear rate. The perturbation analysis highlights an interplay of the viscous and chiral elastic torques as the mechanism for the emergence of net transverse currents. Numerical simulations demonstrate, however, that torons are not merely dragged by the flow but move with their own transverse speed, much larger than the average flow velocity in the transverse direction. Our findings may enable responsive microfluidic applications relying on soft topological solitons., Comment: 10 pages, 9 figures, supplementary material and 3 supplementary videos

Aequitas Flow is an open-source framework and toolkit for end-to-end Fair Machine Learning (ML) experimentation, and benchmarking in Python. This package fills integration gaps that exist in other fair ML packages. In addition to the existing audit capabilities in Aequitas, the Aequitas Flow module provides a pipeline for fairness-aware model training, hyperparameter optimization, and evaluation, enabling easy-to-use and rapid experiments and analysis of results. Aimed at ML practitioners and researchers, the framework offers implementations of methods, datasets, metrics, and standard interfaces for these components to improve extensibility. By facilitating the development of fair ML practices, Aequitas Flow hopes to enhance the incorporation of fairness concepts in AI systems making AI systems more robust and fair.

Sand is a highly dissipative system, where the local spatial arrangements and densities depend strongly on the applied forces, resulting in fluid-like or solid-like behaviour. This makes sand swimming challenging and intriguing, raising questions about the nature of the motion and how to optimize the design of artificial swimmers able to swim in sand. Recent experiments suggest that lateral undulatory motion enables efficient locomotion, with a non-monotonic dependence of the swimming speed on the undulatory frequency and the height of the sediment bed. Here, we propose a quasi-2D granular model, where the effect of the bed height is modeled by a coarse-grained frictional force with the substrate. We show that the optimal frequency coincides with the second vibrational mode of the swimmer and explain the underlying mechanism through a characterization of the rheology of the medium. Potential implications in the design of artificial swimmers are discussed.

Predictive Maintenance applications are increasingly complex, with interactions between many components. Black box models are popular approaches based on deep learning techniques due to their predictive accuracy. This paper proposes a neural-symbolic architecture that uses an online rule-learning algorithm to explain when the black box model predicts failures. The proposed system solves two problems in parallel: anomaly detection and explanation of the anomaly. For the first problem, we use an unsupervised state of the art autoencoder. For the second problem, we train a rule learning system that learns a mapping from the input features to the autoencoder reconstruction error. Both systems run online and in parallel. The autoencoder signals an alarm for the examples with a reconstruction error that exceeds a threshold. The causes of the signal alarm are hard for humans to understand because they result from a non linear combination of sensor data. The rule that triggers that example describes the relationship between the input features and the autoencoder reconstruction error. The rule explains the failure signal by indicating which sensors contribute to the alarm and allowing the identification of the component involved in the failure. The system can present global explanations for the black box model and local explanations for why the black box model predicts a failure. We evaluate the proposed system in a real-world case study of Metro do Porto and provide explanations that illustrate its benefits., Comment: 26 pages

Illegal landfills are a critical issue due to their environmental, economic, and public health impacts. This study leverages aerial imagery for environmental crime monitoring. While advances in artificial intelligence and computer vision hold promise, the challenge lies in training models with high-resolution literature datasets and adapting them to open-access low-resolution images. Considering the substantial quality differences and limited annotation, this research explores the adaptability of models across these domains. Motivated by the necessity for a comprehensive evaluation of waste detection algorithms, it advocates cross-domain classification and super-resolution enhancement to analyze the impact of different image resolutions on waste classification as an evaluation to combat the proliferation of illegal landfills. We observed performance improvements by enhancing image quality but noted an influence on model sensitivity, necessitating careful threshold fine-tuning., Comment: This article has been accepted by the Symposium on Intelligent Data Analysis (IDA 2024)

We aim to identify and characterize cores in the high-mass proto-cluster W49, determine their evolutionary stages and measure the associated lifetimes. We built a catalog of 129 cores extracted from an ALMA 1.3 mm continuum image at 0.26" (2900 au) angular resolution. The association between cores and Hyper/Ultra Compact HII (H/UC HII) regions was established from the analysis of VLA 3.3 cm continuum and H30$\alpha$ line observations. We also looked for emission of hot molecular cores (HMCs) using the methyl formate doublet at 218.29 GHz. We identified 40 cores associated with an H/UC HII region and 19 HMCs over the ALMA mosaic. The 52 cores with an H/UC HII region and/or a HMC are assumed to be high-mass protostellar cores, while the rest of the core population likely consists in prestellar cores and low-mass protostellar cores. We found a good agreement between the two tracers of ionized gas, with 23 common detections and only four cores detected at 3.3 cm and not in H30$\alpha$. The spectral indexes from 3.3 cm to 1.3 mm range from 1, for the youngest cores with partially optically thick free-free emission, to about -0.1, that is optically thin free-free emission obtained for cores likely more evolved. Using as a reference the H/UC HII regions, we found the statistical lifetimes of the HMC and massive protostellar phases in W49N to be about $6\times10^4$ yr and $1.4\times10^5$ yr, respectively. We also showed that HMC can co-exist with H/UC HII regions during a short fraction of the core lifetime, about $2\times10^4$ yr. This indicates a rapid dispersal of the inner molecule envelope once the HC HII is formed., Comment: 15 pages, 7 figures. Accepted for publication in A&A

The use of the open innovation paradigm has been, over the past years, getting special attention in the public sector. Motivated by an urban environment that is increasingly more complex and challenging, several government agencies have been allocating financial resources and efforts to promote open and participative government initiatives. As a way to try and understand this scenario, a systematic review of the literature was conducted, to provide a comprehensive analysis of the scientific papers that were published, seeking to capture, classify, evaluate and synthesize how the use of this paradigm has been put into practice in the public sector. In total, 4,741 preliminary studies were analyzed. From this number, only 37 articles were classified as potentially relevant and moved forward, going through the process of data extraction and analysis. From the data obtained, it was possible to verify that the use of this paradigm started to be reported with a higher frequency in the literature since 2013 and, among the main findings, we highlight the reports of experiences, approach propositions, of understanding how the phenomenon occurs and theoretical reflections. It was also possible to verify that the use of open innovation through social media was one of the pioneer techniques of engagement between the public sector and citizens. In conclusion, the reports confirm that the main challenges of this paradigm applied to the public sector are associated with their respective bureaucratic aspects, therefore lacking a bigger reflection on the procedures and methods to be used in the public sphere., Comment: 33 pages, 6 figures and 18 tables

We present S+t-SNE, an adaptation of the t-SNE algorithm designed to handle infinite data streams. The core idea behind S+t-SNE is to update the t-SNE embedding incrementally as new data arrives, ensuring scalability and adaptability to handle streaming scenarios. By selecting the most important points at each step, the algorithm ensures scalability while keeping informative visualisations. Employing a blind method for drift management adjusts the embedding space, facilitating continuous visualisation of evolving data dynamics. Our experimental evaluations demonstrate the effectiveness and efficiency of S+t-SNE. The results highlight its ability to capture patterns in a streaming scenario. We hope our approach offers researchers and practitioners a real-time tool for understanding and interpreting high-dimensional data., Comment: This preprint has not undergone peer review or any post-submission improvements or corrections. We will soon add a link to the final version of this contribution that underwent peer-review and post-acceptance improvements and was presented at IDA2024 (https://ida2024.org/)

A surface in contact with the isotropic phase of a passive liquid crystal can induce nematic order over distances that range from microscopic to macroscopic when the nematic-isotropic interface undergoes an orientational-wetting transition. If the nematic is active, what happens to the interface? Does it propagate and, if it does, is its structure different from the passive one? In this paper, we address these questions. We investigate how the active nematic-isotropic interface is affected by the anchoring strength of the surface, the bulk ordering field and the activity. We find that while passive interfaces are one-dimensional the active ones exhibit two dynamical regimes: a passive-like regime and a propagating regime where the interfaces propagate until the entire domain is active nematic. Active interfaces break the translational symmetry within the interfacial plane above a threshold activity, where the active nematic fluctuations, which are ultimately responsible for the emergence of an active turbulent nematic phase, drive non-steady dynamical interfacial regimes.

This paper introduces a very general discrete covering location model that accounts for uncertainty and time-dependent aspects. A MILP formulation is proposed for the problem. Afterwards, it is observed that most of the models existing in the literature related with covering location can be considered as particular cases of this formulation. In order to tackle large instances of this problem a Lagrangian relaxation based heuristic is developed. A computational study is addressed to check the potentials and limits of the formulation and some variants proposed for the problem, as well as to evaluate the heuristic. Finally, different measures to report the relevance of considering a multi-period stochastic setting are studied.

In the evolving field of machine learning, ensuring fairness has become a critical concern, prompting the development of algorithms designed to mitigate discriminatory outcomes in decision-making processes. However, achieving fairness in the presence of group-specific concept drift remains an unexplored frontier, and our research represents pioneering efforts in this regard. Group-specific concept drift refers to situations where one group experiences concept drift over time while another does not, leading to a decrease in fairness even if accuracy remains fairly stable. Within the framework of federated learning, where clients collaboratively train models, its distributed nature further amplifies these challenges since each client can experience group-specific concept drift independently while still sharing the same underlying concept, creating a complex and dynamic environment for maintaining fairness. One of the significant contributions of our research is the formalization and introduction of the problem of group-specific concept drift and its distributed counterpart, shedding light on its critical importance in the realm of fairness. In addition, leveraging insights from prior research, we adapt an existing distributed concept drift adaptation algorithm to tackle group-specific distributed concept drift which utilizes a multi-model approach, a local group-specific drift detection mechanism, and continuous clustering of models over time. The findings from our experiments highlight the importance of addressing group-specific concept drift and its distributed counterpart to advance fairness in machine learning.

In the realm of power systems, the increasing involvement of residential users in load forecasting applications has heightened concerns about data privacy. Specifically, the load data can inadvertently reveal the daily routines of residential users, thereby posing a risk to their property security. While federated learning (FL) has been employed to safeguard user privacy by enabling model training without the exchange of raw data, these FL models have shown vulnerabilities to emerging attack techniques, such as Deep Leakage from Gradients and poisoning attacks. To counteract these, we initially employ a Secure-Aggregation (SecAgg) algorithm that leverages multiparty computation cryptographic techniques to mitigate the risk of gradient leakage. However, the introduction of SecAgg necessitates the deployment of additional sub-center servers for executing the multiparty computation protocol, thereby escalating computational complexity and reducing system robustness, especially in scenarios where one or more sub-centers are unavailable. To address these challenges, we introduce a Markovian Switching-based distributed training framework, the convergence of which is substantiated through rigorous theoretical analysis. The Distributed Markovian Switching (DMS) topology shows strong robustness towards the poisoning attacks as well. Case studies employing real-world power system load data validate the efficacy of our proposed algorithm. It not only significantly minimizes communication complexity but also maintains accuracy levels comparable to traditional FL methods, thereby enhancing the scalability of our load forecasting algorithm.

Persistent motion of microswimmers near boundaries is known to result in surface accumulation. Recently it was shown experimentally that surface accumulation of microswimmers is impacted primarily by steric forces and short-ranged hydrodynamics. A way to control surface accumulation is by reducing the contact surface area between swimmers and surface by modifying its topography, typically through the application of microscale structure. In this work, we introduce a three-dimensional(3D) model of a microswimmer navigating a volume bounded by a top and bottom surface. We describe the swimmer-surface interaction with an effective short-ranged hydrodynamic alignment force, and study numerically the effect of surface textures, modelled by convex obstacles, on the surface accumulation of chiral and non-chiral microswimmers. We find that, depending on the angular velocity of the swimmer, and the alignment force, convex obstacles can either hinder or enhance surface accumulation. We discuss potential applications to sorting of microswimmers by their angular velocity.