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Play is a central aspect of childhood development, with games as a vital tool to promote it. However, neurodivergent children, especially those in neurodiverse environments, are underserved by HCI games research. Most existing work takes on a top-down approach, disregarding neurodivergent interest for the majority of the design process. Co-design is often proposed as a tool to create truly accessible and inclusive gaming experiences. Nevertheless, co-designing with neurodivergent children within neurodiverse groups brings about unique challenges, such as different communication styles, sensory needs and preferences. Building upon recommendations from prior work in neurodivergent, mixed-ability, and child-led co-design, we propose a concrete participatory game design kit for neurodiverse classrooms: PartiPlay. Moreover, we present preliminary findings from an in-the-wild experiment with the said kit, showcasing its ability to create an inclusive co-design process for neurodiverse groups of children. We aim to provide actionable steps for future participatory design research with neurodiverse children.

Synthetic data is gaining increasing relevance for training machine learning models. This is mainly motivated due to several factors such as the lack of real data and intra-class variability, time and errors produced in manual labeling, and in some cases privacy concerns, among others. This paper presents an overview of the 2nd edition of the Face Recognition Challenge in the Era of Synthetic Data (FRCSyn) organized at CVPR 2024. FRCSyn aims to investigate the use of synthetic data in face recognition to address current technological limitations, including data privacy concerns, demographic biases, generalization to novel scenarios, and performance constraints in challenging situations such as aging, pose variations, and occlusions. Unlike the 1st edition, in which synthetic data from DCFace and GANDiffFace methods was only allowed to train face recognition systems, in this 2nd edition we propose new sub-tasks that allow participants to explore novel face generative methods. The outcomes of the 2nd FRCSyn Challenge, along with the proposed experimental protocol and benchmarking contribute significantly to the application of synthetic data to face recognition., Comment: arXiv admin note: text overlap with arXiv:2311.10476

This paper proposes a semi-supervised methodology for training physics-informed machine learning methods. This includes self-training of physics-informed neural networks and physics-informed Gaussian processes in isolation, and the integration of the two via co-training. We demonstrate via extensive numerical experiments how these methods can ameliorate the issue of propagating information forward in time, which is a common failure mode of physics-informed machine learning.

This work proposes a policy learning algorithm for seeking generalised feedback Nash equilibria in $N_P$-players non-cooperative dynamic games. We consider linear-quadratic games with stochastic dynamics and design a best-response dynamics in which players update and communicate a parametrisation of their state-feedback policies. Our approach leverages the System Level Synthesis framework to formulate each player's update rule as the solution to a tractable robust optimisation problem. Under certain conditions, rates of convergence to a feedback Nash equilibrium can be established. The algorithm is showcased in exemplary problems ranging from the decentralised control of unstable systems to competition in oligopolistic markets., Comment: 18 pages, 8 figures; Minor revisions (including new example)

The flux of ultra-high energy cosmic rays reaching Earth above the ankle energy (5 EeV) can be described as a mixture of nuclei injected by extragalactic sources with very hard spectra and a low rigidity cutoff. Extragalactic magnetic fields existing between the Earth and the closest sources can affect the observed CR spectrum by reducing the flux of low-rigidity particles reaching Earth. We perform a combined fit of the spectrum and distributions of depth of shower maximum measured with the Pierre Auger Observatory including the effect of this magnetic horizon in the propagation of UHECRs in the intergalactic space. We find that, within a specific range of the various experimental and phenomenological systematics, the magnetic horizon effect can be relevant for turbulent magnetic field strengths in the local neighbourhood of order $B_{\rm rms}\simeq (50-100)\,{\rm nG}\,(20\rm{Mpc}/{d_{\rm s})( 100\,\rm{kpc}/L_{\rm coh}})^{1/2}$, with $d_{\rm s}$ the typical intersource separation and $L_{\rm coh}$ the magnetic field coherence length. When this is the case, the inferred slope of the source spectrum becomes softer and can be closer to the expectations of diffusive shock acceleration, i.e., $\propto E^{-2}$. An additional cosmic-ray population with higher source density and softer spectra, presumably also extragalactic and dominating the cosmic-ray flux at EeV energies, is also required to reproduce the overall spectrum and composition results for all energies down to 0.6~EeV., Comment: Matches published version

The conservation of tropical forests is a topic of significant social and ecological relevance due to their crucial role in the global ecosystem. Unfortunately, deforestation and degradation impact millions of hectares annually, necessitating government or private initiatives for effective forest monitoring. This study introduces a novel framework that employs the Univariate Marginal Distribution Algorithm (UMDA) to select spectral bands from Landsat-8 satellite, optimizing the representation of deforested areas. This selection guides a semantic segmentation architecture, DeepLabv3+, enhancing its performance. Experimental results revealed several band compositions that achieved superior balanced accuracy compared to commonly adopted combinations for deforestation detection, utilizing segment classification via a Support Vector Machine (SVM). Moreover, the optimal band compositions identified by the UMDA-based approach improved the performance of the DeepLabv3+ architecture, surpassing state-of-the-art approaches compared in this study. The observation that a few selected bands outperform the total contradicts the data-driven paradigm prevalent in the deep learning field. Therefore, this suggests an exception to the conventional wisdom that 'more is always better'., Comment: 9 pages, 4 figures, paper accepted for presentation at GECCO 2024

This paper focuses on additional inspections concerning the fermionic sector of the Standard Model Extension (SME). In this context, our main effort in this contribution is to investigate effects of Lorentz-symmetry violation (LSV) on the Klein Paradox, the Zitterbewegung and its phenomenology in connection to Condensed Matter Physics, Atomic Physics, and Astrophysics. Finally, we discuss a particular realization of LSV in the Dirac equation, considering an asymmetry between space and time due to a scale factor present in the linear momentum of the fermion, but which does not touch its time derivative. We go further and extend the implications of this asymmetry in the situation the scale factor becomes space-time dependent to compute its influence on the kinematics of the Compton effect with the extended dispersion relation for the fermion that scatters the photon., Comment: 15 pages, 2 figures

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