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We present the results from an exploratory study that aimed to measure teachers' specialized knowledge in early mathematics during a pilot of an educational intervention using the Foundational Mathematical Knowledge for Teaching (FMKT) survey. The survey was administered to 323 teachers in the Kyrgyz Republic in 2021. We delve into survey results at two timepoints (pre- and post-intervention) to showcase the areas in which the intervention was successful and identify ongoing challenges in teacher knowledge. We found that the FMKT provided detailed, specific information on teacher learning and is an example of one way to center teacher knowledge in an instructional intervention.

Graph clustering plays a crucial role in graph representation learning but often faces challenges in achieving feature-space diversity. While Deep Modularity Networks (DMoN) leverage modularity maximization and collapse regularization to ensure structural separation, they do not explicitly encourage diversity in the feature space among clusters. We address this limitation by proposing Deep Modularity Networks with Diversity-Preserving Regularization (DMoN-DPR), which introduces three novel regularization terms: distance-based for inter-cluster separation, variance-based for intra-cluster diversity, and entropy-based for balanced assignments. Our method enhances clustering performance on benchmark datasets, namely Cora, CiteSeer, PubMed, Coauthor CS, and Coauthor Physics, achieving significant improvements in Normalized Mutual Information (NMI), and F1 scores. These results demonstrate the effectiveness of incorporating diversity-preserving regularizations in creating meaningful and interpretable clusters, especially in feature-rich datasets., Comment: Preprint

The commercial value of economically significant fruits, including date palm fruit (dates), is influenced by various factors, such as biochemical composition and morphological features like size, shape, and visual appearance, which are key determinants of their quality and market value. Dates are typically consumed at the dry stage (Tamar), during which they exhibit a wide range of physical characteristics, such as color, length, weight, and skin appearance. Understanding the genetic basis of these traits is crucial for improving crop quality and breeding new cultivars. In this study, we integrated a genome dataset from highly diverse date cultivars with phenotypes of dry fruit such as length, width, area, and weight, identifying multiple significant genetic loci (SNPs) associated with these traits. We also identified candidate genes located near the associated SNPs that are involved in biological processes such as cell differentiation, proliferation, growth, and the regulation of signalling pathways for growth regulators like auxin and abscisic acid, as observed in other plants. Gene expression analysis reveals that many of these genes are highly expressed in the early stage of fruit development when the fruit attains its maximum size and weight. These findings will enhance our understanding of genetic determinants of fruit size particularly at the commercially important Tamar stage.

The rapid expansion of urban areas challenges biodiversity conservation, requiring innovative ecosystem management. This study explores the role of Artificial Intelligence (AI) in urban biodiversity conservation, its applications, and a framework for implementation. Key findings show that: (a) AI enhances species detection and monitoring, achieving over 90% accuracy in urban wildlife tracking and invasive species management; (b) integrating data from remote sensing, acoustic monitoring, and citizen science enables large-scale ecosystem analysis; and (c) AI decision tools improve conservation planning and resource allocation, increasing prediction accuracy by up to 18.5% compared to traditional methods. The research presents an AI-Driven Framework for Urban Biodiversity Management, highlighting AI's impact on monitoring, conservation strategies, and ecological outcomes. Implementation strategies include: (a) standardizing data collection and model validation, (b) ensuring equitable AI access across urban contexts, and (c) developing ethical guidelines for biodiversity monitoring. The study concludes that integrating AI in urban biodiversity conservation requires balancing innovation with ecological wisdom and addressing data quality, socioeconomic disparities, and ethical concerns.

The prominence of artificial intelligence and machine learning in everyday life has led to efforts to foster AI literacy for all K-12 students. In this paper, we review how Hour of Code activities engage with the five big ideas of AI, in particular with machine learning and societal impact. We found that a large majority of activities focus on perception and machine learning, with little attention paid to representation and other topics. A surprising finding was the increased attention paid to critical aspects of computing. However, we also observed a limited engagement with hands-on activities. In the discussion, we address how future introductory activities could be designed to offer a broader array of topics, including the development of tools to introduce novices to artificial intelligence and machine learning and the design of more unplugged and collaborative activities.

While there is widespread interest in supporting young people to critically evaluate machine learning-powered systems, there is little research on how we can support them in inquiring about how these systems work and what their limitations and implications may be. Outside of K-12 education, an effective strategy in evaluating black-boxed systems is algorithm auditing-a method for understanding algorithmic systems' opaque inner workings and external impacts from the outside in. In this paper, we review how expert researchers conduct algorithm audits and how end users engage in auditing practices to propose five steps that, when incorporated into learning activities, can support young people in auditing algorithms. We present a case study of a team of teenagers engaging with each step during an out-of-school workshop in which they audited peer-designed generative AI TikTok filters. We discuss the kind of scaffolds we provided to support youth in algorithm auditing and directions and challenges for integrating algorithm auditing into classroom activities. This paper contributes: (a) a conceptualization of five steps to scaffold algorithm auditing learning activities, and (b) examples of how youth engaged with each step during our pilot study.

In this work, we compare the domain-specific translation performance of open-source autoregressive decoder-only large language models (LLMs) with task-oriented machine translation (MT) models. Our experiments focus on the medical domain and cover four language pairs with varied resource availability: English-to-French, English-to-Portuguese, English-to-Swahili, and Swahili-to-English. Despite recent advancements, LLMs exhibit a clear gap in specialized translation quality compared to multilingual encoder-decoder MT models such as NLLB-200. In three out of four language directions in our study, NLLB-200 3.3B outperforms all LLMs in the size range of 8B parameters in medical translation. While fine-tuning LLMs such as Mistral and Llama improves their performance at medical translation, these models still fall short compared to fine-tuned NLLB-200 3.3B models. Our findings highlight the ongoing need for specialized MT models to achieve higher-quality domain-specific translation, especially in medium-resource and low-resource settings. As larger LLMs outperform their 8B variants, this also encourages pre-training domain-specific medium-sized LMs to improve quality and efficiency in specialized translation tasks.

Background: This study examines cardiometabolic (CM) risk factors in an urban South Asian population, integrating medical and Anthropological perspectives to explore the effects of socio-economic, lifestyle, gender-specific factors, and cultural norms on health outcomes. Results: Analysis indicates a high prevalence of MetS and Pre-MetS, particularly among females, with significant predictors including BMI, triglycerides, total cholesterol, and waist circumference, alongside socio-genetic and lifestyle factors. Employing Elastic Net logistic regression, the researcher rigorously validated models to evaluate their predictive performance while also describing the associations and prevalence of known risk factors. The use of this method underlines the importance of combining traditional risk factors with socio-genetic, biological, economic and lifestyle variables, while Anthropological insights reveal the impact of urbanization and socio-cultural norms on health behaviors. Conclusion: The study advocates for a multidisciplinary approach in public health strategies, emphasizing the complex interplay between genetic, environmental, biological and socio-cultural influences on cardiometabolic health. This dual approach aligns with descriptive and predictive model goals. The future research should further integrate biomedical sciences with socio-cultural studies to develop culturally sensitive interventions, aiming to address the growing challenge of CM diseases in urban South Asian contexts., Comment: 39 pages, 4 figures, 9 tables

Purpose: The purpose of this paper is to examine how a clinical interview protocol with failure artifact scenarios can capture changes in high school students' explanations of troubleshooting processes in physical computing activities. We focus on physical computing since finding and fixing hardware and software bugs is a highly contextual practice that involves multiple interconnected domains and skills. Approach: We developed and piloted a "failure artifact scenarios" clinical interview protocol. Youth were presented with buggy physical computing projects over video calls and asked for suggestions on how to fix them without having access to the actual project or its code. We applied this clinical interview protocol before and after an eight-week-long physical computing (more specifically, electronic textiles) unit. We analyzed matching pre- and post-interviews from 18 students at four different schools. Findings: Our findings demonstrate how the protocol can capture change in students' thinking about troubleshooting by eliciting students' explanations of specificity of domain knowledge of problems, multimodality of physical computing, iterative testing of failure artifact scenarios, and concreteness of troubleshooting and problem solving processes. Originality: Beyond tests and surveys used to assess debugging, which traditionally focus on correctness or student beliefs, our "failure artifact scenarios" clinical interview protocol reveals student troubleshooting-related thinking processes when encountering buggy projects. As an assessment tool, it may be useful to evaluate the change and development of students' abilities over time., Comment: arXiv admin note: text overlap with arXiv:2311.17212

The "2-variable general-$\lambda$-matrix polynomials (2VG$\lambda$MP)" is a new family of matrix polynomials, introduced and studied in this article. These matrix polynomials are constructed using umbral and symbolic methods. We delve into the generating function, explicit series representation, differential equation, quasi-monomiality, summation formula, determinant representation, and various identities satisfied by these polynomials. Furthermore, a thorough investigation is conducted to ascertain the outcomes for the members of the general-$\lambda$-matrix family. Additionally, 3D graphs and figures showing the zeros distribution, real zeros, and stacks of zeros for a few members of the family of these polynomials are also presented in the article., Comment: 21 pages, 16 figures, 4 tables

Measuring fluctuations in matter's low energy excitations is the key to unveil the nature of the nonequilibrium response of materials. A promising outlook in this respect is offered by spectroscopic methods that address matter fluctuations by exploiting the statistical nature of light-matter interactions with weak few-photon probes. Here we report the first implementation of ultrafast phase randomized tomography, combining pump-probe experiments with quantum optical state tomography, to measure the ultrafast non-equilibrium dynamics in complex materials. Our approach utilizes a time-resolved multimode heterodyne detection scheme with phase-randomized coherent ultrashort laser pulses, overcoming the limitations of phase-stable configurations and enabling a robust reconstruction of the statistical distribution of phase-averaged optical observables. This methodology is validated by measuring the coherent phonon response in $\alpha$-quartz. By tracking the dynamics of the shot-noise limited photon number distribution of few-photon probes with ultrafast resolution, our results set an upper limit to the non-classical features of phononic state in $\alpha$-quartz and provide a pathway to access nonequilibrium quantum fluctuations in more complex quantum materials., Comment: 21 pages, 10 figures (including Supplementary Information)

Android apps rely on application programming interfaces (APIs) to access various functionalities of Android devices. These APIs however are regularly updated to incorporate new features while the old APIs get deprecated. Even though the importance of updating deprecated API usages with the recommended replacement APIs has been widely recognized, it is non-trivial to update the deprecated API usages. Therefore, the usages of deprecated APIs linger in Android apps and cause compatibility issues in practice. This paper introduces GUPPY, an automated approach that utilizes large language models (LLMs) to update Android deprecated API usages. By employing carefully crafted prompts, GUPPY leverages GPT-4, one of the most powerful LLMs, to update deprecated-API usages, ensuring compatibility in both the old and new API levels. Additionally, GUPPY uses GPT-4 to generate tests, identify incorrect updates, and refine the API usage through an iterative process until the tests pass or a specified limit is reached. Our evaluation, conducted on 360 benchmark API usages from 20 deprecated APIs and an additional 156 deprecated API usages from the latest API levels 33 and 34, demonstrates GUPPY's advantages over the state-of-the-art techniques.

Human perception is inherently multimodal. We integrate, for instance, visual, proprioceptive and tactile information into one experience. Hence, multimodal learning is of importance for building robotic systems that aim at robustly interacting with the real world. One potential model that has been proposed for multimodal integration is the multimodal variational autoencoder. A variational autoencoder (VAE) consists of two networks, an encoder that maps the data to a stochastic latent space and a decoder that reconstruct this data from an element of this latent space. The multimodal VAE integrates inputs from different modalities at two points in time in the latent space and can thereby be used as a controller for a robotic agent. Here we use this architecture and introduce information-theoretic measures in order to analyze how important the integration of the different modalities are for the reconstruction of the input data. Therefore we calculate two different types of measures, the first type is called single modality error and assesses how important the information from a single modality is for the reconstruction of this modality or all modalities. Secondly, the measures named loss of precision calculate the impact that missing information from only one modality has on the reconstruction of this modality or the whole vector. The VAE is trained via the evidence lower bound, which can be written as a sum of two different terms, namely the reconstruction and the latent loss. The impact of the latent loss can be weighted via an additional variable, which has been introduced to combat posterior collapse. Here we train networks with four different weighting schedules and analyze them with respect to their capabilities for multimodal integration.

Large language models (LLMs) have achieved a degree of success in generating coherent and contextually relevant text, yet they remain prone to a significant challenge known as hallucination: producing information that is not substantiated by the input or external knowledge. Previous efforts to mitigate hallucinations have focused on techniques such as fine-tuning models on high-quality datasets, incorporating fact-checking mechanisms, and developing adversarial training methods. While these approaches have shown some promise, they often address the issue at the level of individual model outputs, leaving unexplored the effects of inter-model interactions on hallucination. This study investigates the phenomenon of hallucination in LLMs through a novel experimental framework where multiple instances of GPT-4o-Mini models engage in a debate-like interaction prompted with questions from the TruthfulQA dataset. One model is deliberately instructed to generate plausible but false answers while the other models are asked to respond truthfully. The experiment is designed to assess whether the introduction of misinformation by one model can challenge the truthful majority to better justify their reasoning, improving performance on the TruthfulQA benchmark. The findings suggest that inter-model interactions can offer valuable insights into improving the accuracy and robustness of LLM outputs, complementing existing mitigation strategies.

The use of optical cavities on resonance with material excitations allows controlling light-matter interaction in both the regimes of weak and strong coupling. We study here the multimode vibrational coupling of low energy phonons in the charge-density-wave material 1T-TaS$_{2}$ across its insulator-to-metal phase transition. For this purpose, we embed 1T-TaS$_{2}$ into THz Fabry-P\'erot cryogenic cavities tunable in frequency within the spectral range of the vibrational modes of the insulating phase and track the linear response of the coupled phonons across the insulator-to-metal transition. In the low temperature dielectric state, we reveal the signatures of a multimode vibrational strong collective coupling. The observed polariton modes inherit character from all the vibrational resonances as a consequence of the cavity-mediated hybridization. We reveal that the vibrational strong collective coupling is suppressed across the insulator-to-metal transition as a consequence of the phonon-screening induced by the free charges. Our findings emphasize how the response of cavity-coupled vibrations can be modified by the presence of free charges, uncovering a new direction toward the tuning of coherent light-matter interaction in cavity-confined correlated materials., Comment: 24 pages, 9 figures (including Supplementary Material)

The formation of large social groups having uniform opinions influenced by mass media is currently an important topic in the social sciences. In this work, we explore and extend an off-lattice, two-dimensional Potts model (Eur. Phys. J. B 87, 78 [2014]) that describes the formation and dynamics of opinions in social groups according to individual consequence and agreement between neighbors. This model was originally obtained by the application of the maximum entropy principle, a general method in statistical inference, and using the same methodology we have now included the influence of mass media as a constant external field. By means of microcanonical Monte Carlo Metropolis simulations on a setup with two regions with opposing external influences, we have shown the presence of metastable states associated to the formation of clusters aligned with the locally imposed opinion. Our results suggest that, for some values of the total energy of the system, only a single cluster with a uniform opinion survives, thus the presence of two large, opposing groups is not a thermodynamically stable configuration.

Walking as a form of active travel is essential in promoting sustainable transport. It is thus crucial to accurately predict pedestrian crossing intention and avoid collisions, especially with the advent of autonomous and advanced driver-assisted vehicles. Current research leverages computer vision and machine learning advances to predict near-misses; however, this often requires high computation power to yield reliable results. In contrast, this work proposes a low-complexity ensemble-learning approach that employs contextual data for predicting the pedestrian's intent for crossing. The pedestrian is first detected, and their image is then compressed using skeleton-ization, and contextual information is added into a stacked ensemble-learning approach. Our experiments on different datasets achieve similar pedestrian intent prediction performance as the state-of-the-art approaches with 99.7% reduction in computational complexity. Our source code and trained models will be released upon paper acceptance

We consider a sequence $\mathbf{T} = (\mathcal{T}_n : n \in \mathbb{N}^+)$ of trees $\mathcal{T}_n$ where, for some $\Delta \in \mathbb{N}^+$ every $\mathcal{T}_n$ has height at most $\Delta$ and as $n \to \infty$ the minimal number of children of a nonleaf tends to infinity. We can view every tree as a (first-order) $\tau$-structure where $\tau$ is a signature with one binary relation symbol. For a fixed (arbitrary) finite and relational signature $\sigma \supseteq \tau$ we consider the set $\mathbf{W}_n$ of expansions of $\mathcal{T}_n$ to $\sigma$ and a probability distribution $\mathbb{P}_n$ on $\mathbf{W}_n$ which is determined by a (parametrized/lifted) Probabilistic Graphical Model (PGM) $\mathbb{G}$ which can use the information given by $\mathcal{T}_n$. The kind of PGM that we consider uses formulas of a many-valued logic that we call $PLA^*$ with truth values in the unit interval $[0, 1]$. We also use $PLA^*$ to express queries, or events, on $\mathbf{W}_n$. With this setup we prove that, under some assumptions on $\mathbf{T}$, $\mathbb{G}$, and a (possibly quite complex) formula $\varphi(x_1, \ldots, x_k)$ of $PLA^*$, as $n \to \infty$, if $a_1, \ldots, a_k$ are vertices of the tree $\mathcal{T}_n$ then the value of $\varphi(a_1, \ldots, a_k)$ will, with high probability, be almost the same as the value of $\psi(a_1, \ldots, a_k)$, where $\psi(x_1, \ldots, x_k)$ is a ``simple'' formula the value of which can always be computed quickly (without reference to $n$), and $\psi$ itself can be found by using only the information that defines $\mathbf{T}$, $\mathbb{G}$ and $\varphi$. A corollary of this, subject to the same conditions, is a probabilistic convergence law for $PLA^*$-formulas., Comment: arXiv admin note: text overlap with arXiv:2401.04802

Circumbinary planets - planets that orbit both stars in a binary system - offer the opportunity to study planet formation and orbital migration in a different environment compare to single stars. However, despite the fact that > 90% of binary systems in the solar neighbourhood are spectrally resolved double-lined binaries, there has been only one detection of a circumbinary planet orbitting a double-lined binary using the radial velocity method so far. Spectrally disentangling both components of a binary system is hard to do accurately. Weak spectral lines blend with one another in a time-varying way, and inaccuracy in spectral modelling can lead to an inaccurate estimation of the radial-velocity of each component. This inaccuracy adds scatter to the measurements that can hide the weak radial-velocity signature of circumbinary exoplanets. We have obtained new high signal-to-noise and high-resolution spectra with the SOPHIE spectrograph, mounted on the 193cm telescope at Observatoire de Haute-Provence (OHP) for six, bright, double-lined binaries for which a circumbinary exoplanet detection has been attempted in the past. To extract radial-velocities we use the DOLBY code, a recent method of spectral disentangling using Gaussian processes to model the time-varying components. We analyse the resulting radial-velocities with a diffusive nested sampler to seek planets, and compute sensitivity limits. We do not detect any new circumbinary planet. However, we show that the combination of new data, new radial-velocity extraction methods, and new statistical methods to determine a dataset's sensitivity to planets leads to an approximately one order of magnitude improvement compared to previous results. This improvement brings us into the range of known circumbinary exoplanets and paves the way for new campaigns of observations targeting double-lined binaries., Comment: 19 pages, 8 figures, 7 tables. Accepted for publication in MNRAS

Consider a collection of data generators which could represent, e.g., humans equipped with a smart-phone or wearables. We want to train a personalized (or tailored) model for each data generator even if they provide only small local datasets. The available local datasets might fail to provide sufficient statistical power to train high-dimensional models (such as deep neural networks) effectively. One possible solution is to identify similar data generators and pool their local datasets to obtain a sufficiently large training set. This paper proposes a novel method for sequentially identifying similar (or relevant) data generators. Our method is similar in spirit to active sampling methods but does not require exchange of raw data. Indeed, our method evaluates the relevance of a data generator by evaluating the effect of a gradient step using its local dataset. This evaluation can be performed in a privacy-friendly fashion without sharing raw data. We extend this method to non-parametric models by a suitable generalization of the gradient step to update a hypothesis using the local dataset provided by a data generator.

The rhythmic pumping motion of the heart stands as a cornerstone in life, as it circulates blood to the entire human body through a series of carefully timed contractions of the individual chambers. Changes in the size, shape and movement of the chambers can be important markers for cardiac disease and modeling this in relation to clinical demography or disease is therefore of interest. Existing methods for spatio-temporal modeling of the human heart require shape correspondence over time or suffer from large memory requirements, making it difficult to use for complex anatomies. We introduce a novel conditional generative model, where the shape and movement is modeled implicitly in the form of a spatio-temporal neural distance field and conditioned on clinical demography. The model is based on an auto-decoder architecture and aims to disentangle the individual variations from that related to the clinical demography. It is tested on the left atrium (including the left atrial appendage), where it outperforms current state-of-the-art methods for anatomical sequence completion and generates synthetic sequences that realistically mimics the shape and motion of the real left atrium. In practice, this means we can infer functional measurements from a static image, generate synthetic populations with specified demography or disease and investigate how non-imaging clinical data effect the shape and motion of cardiac anatomies., Comment: Accepted for MICCAI2024

This paper describes our system submission to the International Conference on Spoken Language Translation (IWSLT 2024) for Irish-to-English speech translation. We built end-to-end systems based on Whisper, and employed a number of data augmentation techniques, such as speech back-translation and noise augmentation. We investigate the effect of using synthetic audio data and discuss several methods for enriching signal diversity., Comment: IWSLT 2024

Southeast Asia (SEA) is a region rich in linguistic diversity and cultural variety, with over 1,300 indigenous languages and a population of 671 million people. However, prevailing AI models suffer from a significant lack of representation of texts, images, and audio datasets from SEA, compromising the quality of AI models for SEA languages. Evaluating models for SEA languages is challenging due to the scarcity of high-quality datasets, compounded by the dominance of English training data, raising concerns about potential cultural misrepresentation. To address these challenges, we introduce SEACrowd, a collaborative initiative that consolidates a comprehensive resource hub that fills the resource gap by providing standardized corpora in nearly 1,000 SEA languages across three modalities. Through our SEACrowd benchmarks, we assess the quality of AI models on 36 indigenous languages across 13 tasks, offering valuable insights into the current AI landscape in SEA. Furthermore, we propose strategies to facilitate greater AI advancements, maximizing potential utility and resource equity for the future of AI in SEA., Comment: https://seacrowd.github.io/ Accepted in EMNLP 2024

Precisely stabilizing laser frequency is crucial for advancing laser technology and unlocking the full potential of various quantum technologies. Here, we propose a compact device for stabilizing frequency of a semiconductor laser through mode coupling effects, which provides enhanced sensitivity. Our proposed architecture features a main ridge waveguide with a Bragg grating, flanked by two curved ridge waveguides. This configuration exhibits an optical phenomenon characterized by a transmission crossing at the wavelength of the Bragg grating. Using particle swarm optimization strategy and employing efficient figures of merit, we achieve a high transmission crossing. The observed asymmetric transmission crossing not only holds the promise for an efficient and compact on-chip laser frequency stabilizer, but also fosters the development of novel sensing platforms with heightened sensitivity.

The pursuit of understanding fundamental particle interactions has reached unparalleled precision levels. Particle physics detectors play a crucial role in generating low-level object signatures that encode collision physics. However, simulating these particle collisions is a demanding task in terms of memory and computation which will be exasperated with larger data volumes, more complex detectors, and a higher pileup environment in the High-Luminosity LHC. The introduction of "Fast Simulation" has been pivotal in overcoming computational bottlenecks. The use of deep-generative models has sparked a surge of interest in surrogate modeling for detector simulations, generating particle showers that closely resemble the observed data. Nonetheless, there is a pressing need for a comprehensive evaluation of their performance using a standardized set of metrics. In this study, we conducted a rigorous evaluation of three generative models using standard datasets and a diverse set of metrics derived from physics, computer vision, and statistics. Furthermore, we explored the impact of using full versus mixed precision modes during inference. Our evaluation revealed that the CaloDiffusion and CaloScore generative models demonstrate the most accurate simulation of particle showers, yet there remains substantial room for improvement. Our findings identified areas where the evaluated models fell short in accurately replicating Geant4 data.

In this conceptual paper, we review existing literature on artificial intelligence/machine learning (AI/ML) education to identify three approaches to how learning and teaching ML could be conceptualized. One of them, a data-driven approach, emphasizes providing young people with opportunities to create data sets, train, and test models. A second approach, learning algorithm-driven, prioritizes learning about how the learning algorithms or engines behind how ML models work. In addition, we identify efforts within a third approach that integrates the previous two. In our review, we focus on how the approaches: (1) glassbox and blackbox different aspects of ML, (2) build on learner interests and provide opportunities for designing applications, (3) integrate ethics and justice. In the discussion, we address the challenges and opportunities of current approaches and suggest future directions for the design of learning activities.

Located at the bottom of the main sequence, ultracool dwarf stars are widespread in the solar neighbourhood. Nevertheless, their extremely low luminosity has left their planetary population largely unexplored, and only one of them, TRAPPIST-1, has so far been found to host a transiting planetary system. In this context, we present the SPECULOOS project's detection of an Earth-sized planet in a 17 h orbit around an ultracool dwarf of M6.5 spectral type located 16.8 pc away. The planet's high irradiation (16 times that of Earth) combined with the infrared luminosity and Jupiter-like size of its host star make it one of the most promising rocky exoplanet targets for detailed emission spectroscopy characterization with JWST. Indeed, our sensitivity study shows that just ten secondary eclipse observations with the Mid-InfraRed Instrument/Low-Resolution Spectrometer on board JWST should provide strong constraints on its atmospheric composition and/or surface mineralogy.