Miracle-dz/UniFAR-FTUs huggingface dataset:
The Role and Uses of Technologies in Mathematics Classrooms: Between Challenge and Modus Vivendi

Drawing support from theoretical frameworks, this article analyses the role and uses of technology in the teaching of mathematics, with specific reference to the complexity of integrating technology into teaching. Four aspects are discussed here: the changes that technology brings about in classroom mathematical activity, the difficulties students encounter in solving problems in a technology-rich environment, the evaluation of potential assignments and the creation of conditions appropriate for learning, and the difficulties faced by teachers who use technology in deciding when and how to intervene. This analysis is illustrated by examples taken primarily from dynamic geometry, but similar analyses could be conducted for computerized environments of other kinds. RésuméCet article porte sur le rôle et l’utilisation des technologies, plus spécifiquement sur la complexité de l’intégration des technologies en enseignement des mathématiques, en s’appuyant pour aborder cette question sur certains cadres théoriques. Quatre aspects y sont développés: les changements que provoquent ces technologies sur l’activité mathématique mobilisée à l’école, les difficultés que les élèves rencontrent dans la résolution de problèmes dans un tel environnement, l’analyse des tâches proposées et des conditions propices à un apprentissage, et la complexité de la gestion de l’intervention pour un enseignant qui intègre de telles technologies. Cette analyse est illustrée par des exemples provenant principalement d’un environnement de géométrie dynamique mais des analyses semblables peuvent être conduites dans d’autres types d’environnements informatiques.
Event Rate of Strongly Lensed Gravitational Waves of Stellar Binary Black Hole Mergers Produced by Dynamical Interactions

Gravitational waves emitted from stellar binary black hole (sBBH) mergers can be gravitationally lensed by intervening galaxies and detected by future ground-based detectors. A great amount of effort has been put into the estimation of the detection rate of lensed sBBH originating from the evolution of massive binary stars (EMBS channel). However, sBBHs produced by the dynamical interaction in dense clusters (dynamical channel) may also be dominant in our universe and their intrinsic distribution of physical properties can be significantly different from those produced by massive stars, especially mass and redshift distribution. In this paper, we investigate the event rate of lensed sBBHs produced via the dynamical channel by Monte Carlo simulations and the number is 16−12+4.7 yr−1 for the Einstein telescope and 24−17+6.8 yr−1 for Cosmic Explorer, of which the median is about ∼2 times the rate of sBBHs originating from the EMBS channel (calibrated by the local merger rate density estimated for the dynamical and the EMBS channel, i.e., ∼14−10+4.0 and 19−3.0+42Gpc−3yr−1 , respectively). Therefore, one may constrain the fraction of both the EMBS and dynamical channels through the comparison of the predicted and observed number of lensed sBBH events statistically.
Extending the Dixon and Coles model: an application to women's football data

The prevalent model by Dixon and Coles (1997) extends the double Poisson model where two independent Poisson distributions model the number of goals scored by each team by moving probabilities between the scores 0-0, 0-1, 1-0, and 1-1. We show that this is a special case of a multiplicative model known as the Sarmanov family. Based on this family, we create more suitable models by moving probabilities between scores and employing other discrete distributions. We apply the new models to women's football scores, which exhibit some characteristics different than that of men's football.
Femtoscopy of 
D
 mesons and light mesons upon unitarized effective field theories

Hadron femtoscopy has turned into a powerful tool for accessing space-time information of heavy-ion collisions as well as for studying final-state interactions of hadrons. Recently, heavy-flavor femtoscopy has become feasible using the ALICE detector at the LHC. We compute the correlation function of $D$ mesons and light mesons using an off-shell $T$-matrix approach to access the two-meson wave function, and predict the correlation functions involving charged $D^+, D^{*+},D_s^+$ and $D_s^{*+}$ with $\pi^\pm$ and $K^\pm$. From the obtained results -- all of them accessible in $p+p$ collision experiments -- we point up the case of $D^+ \pi^-$, which is sensitive to the lower state of the two-pole $D_0^* (2300)$ system. The presence of such poles imprints a depletion on the correlation function, which could potentially be detected in experiments. While preliminary ALICE data do not show evidence of this effect, we suggest to look into the $D_s^+ K^-$ system to explore the higher pole of the $D_0^* (2300)$, as the depletion in the correlation function is more pronounced. Using heavy-quark spin symmetry we also propose exploring the effect of the two poles of the $D_1(2430)$ and predict similar structures in the correlation functions of the $D^{*+} \pi^-$ and $D_s^{*+} K^-$ pairs.
AxonCallosumEM Dataset: Axon Semantic Segmentation of Whole Corpus Callosum cross section from EM Images

The electron microscope (EM) remains the predominant technique for elucidating intricate details of the animal nervous system at the nanometer scale. However, accurately reconstructing the complex morphology of axons and myelin sheaths poses a significant challenge. Furthermore, the absence of publicly available, large-scale EM datasets encompassing complete cross sections of the corpus callosum, with dense ground truth segmentation for axons and myelin sheaths, hinders the advancement and evaluation of holistic corpus callosum reconstructions. To surmount these obstacles, we introduce the AxonCallosumEM dataset, comprising a 1.83 times 5.76mm EM image captured from the corpus callosum of the Rett Syndrome (RTT) mouse model, which entail extensive axon bundles. We meticulously proofread over 600,000 patches at a resolution of 1024 times 1024, thus providing a comprehensive ground truth for myelinated axons and myelin sheaths. Additionally, we extensively annotated three distinct regions within the dataset for the purposes of training, testing, and validation. Utilizing this dataset, we develop a fine-tuning methodology that adapts Segment Anything Model (SAM) to EM images segmentation tasks, called EM-SAM, enabling outperforms other state-of-the-art methods. Furthermore, we present the evaluation results of EM-SAM as a baseline.
A catalogue of radio supernova remnants and candidate supernova remnants in the EMU/POSSUM Galactic pilot field

We use data from the pilot observations of the EMU/POSSUM surveys to study the ‘missing supernova remnant (SNR) problem’, the discrepancy between the number of Galactic SNRs that have been observed, and the number that are estimated to exist. The Evolutionary Map of the Universe (EMU) and the Polarization Sky Survey of the Universe’s Magnetism (POSSUM) are radio sky surveys that are conducted using the Australian Square Kilometre Array Pathfinder (ASKAP). We report on the properties of seven known SNRs in the joint Galactic pilot field, with an approximate longitude and latitude of 323° ≤ l ≤ 330° and −4° ≤ b ≤ 2°, respectively, and identify 21 SNR candidates. Of these, four have been previously identified as SNR candidates, three were previously listed as a single SNR, 13 have not been previously studied, and one has been studied in the infrared. These are the first discoveries of Galactic SNR candidates with EMU/POSSUM and, if confirmed, they will increase the SNR density in this field by a factor of 4. By comparing our SNR candidates to the known Galactic SNR population, we demonstrate that many of these sources were likely missed in previous surveys due to their small angular size and/or low surface brightness. We suspect that there are SNRs in this field that remain undetected due to limitations set by the local background and confusion with other radio sources. The results of this paper demonstrate the potential of the full EMU/POSSUM surveys to uncover more of the missing Galactic SNR population.
FOCUS: Object-Centric World Models for Robotics Manipulation

Understanding the world in terms of objects and the possible interplays with them is an important cognition ability, especially in robotics manipulation, where many tasks require robot-object interactions. However, learning such a structured world model, which specifically captures entities and relationships, remains a challenging and underexplored problem. To address this, we propose FOCUS, a model-based agent that learns an object-centric world model. Thanks to a novel exploration bonus that stems from the object-centric representation, FOCUS can be deployed on robotics manipulation tasks to explore object interactions more easily. Evaluating our approach on manipulation tasks across different settings, we show that object-centric world models allow the agent to solve tasks more efficiently and enable consistent exploration of robot-object interactions. Using a Franka Emika robot arm, we also showcase how FOCUS could be adopted in real-world settings.
Nonparametric Classification on Low Dimensional Manifolds using Overparameterized Convolutional Residual Networks

Convolutional residual neural networks (ConvResNets), though overparameterized, can achieve remarkable prediction performance in practice, which cannot be well explained by conventional wisdom. To bridge this gap, we study the performance of ConvResNeXts, which cover ConvResNets as a special case, trained with weight decay from the perspective of nonparametric classification. Our analysis allows for infinitely many building blocks in ConvResNeXts, and shows that weight decay implicitly enforces sparsity on these blocks. Specifically, we consider a smooth target function supported on a low-dimensional manifold, then prove that ConvResNeXts can adapt to the function smoothness and low-dimensional structures and efficiently learn the function without suffering from the curse of dimensionality. Our findings partially justify the advantage of overparameterized ConvResNeXts over conventional machine learning models.
Edge-aware Multi-task Network for Integrating Quantification Segmentation and Uncertainty Prediction of Liver Tumor on Multi-modality Non-contrast MRI

Simultaneous multi-index quantification, segmentation, and uncertainty estimation of liver tumors on multi-modality non-contrast magnetic resonance imaging (NCMRI) are crucial for accurate diagnosis. However, existing methods lack an effective mechanism for multi-modality NCMRI fusion and accurate boundary information capture, making these tasks challenging. To address these issues, this paper proposes a unified framework, namely edge-aware multi-task network (EaMtNet), to associate multi-index quantification, segmentation, and uncertainty of liver tumors on the multi-modality NCMRI. The EaMtNet employs two parallel CNN encoders and the Sobel filters to extract local features and edge maps, respectively. The newly designed edge-aware feature aggregation module (EaFA) is used for feature fusion and selection, making the network edge-aware by capturing long-range dependency between feature and edge maps. Multi-tasking leverages prediction discrepancy to estimate uncertainty and improve segmentation and quantification performance. Extensive experiments are performed on multi-modality NCMRI with 250 clinical subjects. The proposed model outperforms the state-of-the-art by a large margin, achieving a dice similarity coefficient of 90.01$\pm$1.23 and a mean absolute error of 2.72$\pm$0.58 mm for MD. The results demonstrate the potential of EaMtNet as a reliable clinical-aided tool for medical image analysis.
Learning Discrete Weights and Activations Using the Local Reparameterization Trick

In computer vision and machine learning, a crucial challenge is to lower the computation and memory demands for neural network inference. A commonplace solution to address this challenge is through the use of binarization. By binarizing the network weights and activations, one can significantly reduce computational complexity by substituting the computationally expensive floating operations with faster bitwise operations. This leads to a more efficient neural network inference that can be deployed on low-resource devices. In this work, we extend previous approaches that trained networks with discrete weights using the local reparameterization trick to also allow for discrete activations. The original approach optimized a distribution over the discrete weights and uses the central limit theorem to approximate the pre-activation with a continuous Gaussian distribution. Here we show that the probabilistic modeling can also allow effective training of networks with discrete activation as well. This further reduces runtime and memory footprint at inference time with state-of-the-art results for networks with binary activations.
What Matters in Training a GPT4-Style Language Model with Multimodal Inputs?

Recent advancements in Large Language Models (LLMs) such as GPT4 have displayed exceptional multi-modal capabilities in following open-ended instructions given images. However, the performance of these models heavily relies on design choices such as network structures, training data, and training strategies, and these choices have not been extensively discussed in the literature, making it difficult to quantify progress in this field. To address this issue, this paper presents a systematic and comprehensive study, quantitatively and qualitatively, on training such models. We implement over 20 variants with controlled settings. Concretely, for network structures, we compare different LLM backbones and model designs. For training data, we investigate the impact of data and sampling strategies. For instructions, we explore the influence of diversified prompts on the instruction-following ability of the trained models. For benchmarks, we contribute the first, to our best knowledge, comprehensive evaluation set including both image and video tasks through crowd-sourcing. Based on our findings, we present Lynx, which performs the most accurate multi-modal understanding while keeping the best multi-modal generation ability compared to existing open-sourced GPT4-style models.
Implementing an intrinsically integrated game on Newtonian mechanics in the classroom: outcomes in terms of conceptual understanding and transfer

Digital educational games have demonstrated large variations in learning outcomes and transfer. Furthermore, educational games are usually embedded in a larger educational setting. This case study evaluates in detail a lesson around an educational game designed to foster transfer. The game, Newtons Race, is an intrinsically integrated game on Newtonian mechanics. The learning goal of the lesson is understanding and applying the relationship between forces and motion. Outside of the game, lesson activities include a debriefing session, a generalisation assignment, and an assignment on transfer situations. This lesson was evaluated using a mixed-methods approach. A pre-post test design (N=27) demonstrated a large significant learning effect (p = .002, d = .908). Transfer, as measured within the posttest, was also fostered significantly. In the qualitative part of the study, students written statements on the worksheets and students utterances during the discussion were analyzed using open coding. 79 percent Of all quotes were coded as scientifically correct.
Static Collocated Piecewise Fuzzy Control Design of Quasi-Linear Parabolic PDE Systems Subject to Periodic Boundary Conditions

This paper presents a Lyapunov and partial differential equation (PDE)-based methodology to solve static collocated piecewise fuzzy control design of quasi-linear parabolic PDE systems subject to periodic boundary conditions. Two types of piecewise control, i.e., globally piecewise control and locally piecewise control are considered, respectively. A Takagi–Sugeno (T–S) fuzzy PDE model that is constructed via local sector nonlinearity method is first employed to accurately describe spatiotemporal dynamics of quasi-linear PDEs. Based on the T–S fuzzy PDE model, a static collocated piecewise fuzzy feedback controller is constructed to guarantee the locally exponential stability of the resulting closed-loop system. Sufficient conditions for the existence of such fuzzy controller are developed by applying vector-valued Poincaré–Wirtinger inequality and its variants and a linear matrix inequality (LMI) relaxation technique. These sufficient conditions are presented in terms of standard LMIs. Finally, the performance of the suggested fuzzy controller is illustrated by numerical simulation results of a nonlinear PDE system described by quasi-linear FitzHugh–Nagumo equation with periodic boundary conditions.
ChildPlay: A New Benchmark for Understanding Children’s Gaze Behaviour

Gaze behaviors such as eye-contact or shared attention are important markers for diagnosing developmental disorders in children. While previous studies have looked at some of these elements, the analysis is usually performed on private datasets and is restricted to lab settings. Furthermore, all publicly available gaze target prediction benchmarks mostly contain instances of adults, which makes models trained on them less applicable to scenarios with young children. In this paper, we propose the first study for predicting the gaze target of children and interacting adults. To this end, we introduce the ChildPlay dataset: a curated collection of short video clips featuring children playing and interacting with adults in uncontrolled environments (e.g. kindergarten, therapy centers, preschools etc.), which we annotate with rich gaze information. We further propose a new model for gaze target prediction that is geometrically grounded by explicitly identifying the scene parts in the 3D field of view (3DFoV) of the person, leveraging recent geometry preserving depth inference methods. Our model achieves state of the art results on benchmark datasets and ChildPlay. Furthermore, results show that looking at faces prediction performance on children is much worse than on adults, and can be significantly improved by fine-tuning models using child gaze annotations. Our dataset is available at https://www.idiap. ch/en/dataset/childplay-gaze. Code will be made available soon.
Astronomical Camera Based on a CCD261-84 Detector with Increased Sensitivity in the Near-Infrared

Herein, we describe the design, implementation and operation principles of an astronomical camera system, based on a large-format CCD261-84 detector with an extremely thick 200 μm substrate. The DINACON-V controller was used with the CCD to achieve high performance and low noise. The CCD system photometric characteristics are presented. A spatial autocorrelation analysis of flat-field images was performed to reveal the dependence of substrate voltage on the lateral charge spreading. The investigation of the dispersion index for the optimal choice of exposure time is discussed. Studies of the patterns of fringes were carried out in comparison with previous detectors. The amplitude of fringes with CCD261-84 was significantly lower, compared to previous-generation detectors. The results of using a new camera for imaging and spectral observations at the Russian 6 m telescope with the SCORPIO-2 multimode focal reducer are considered. The developed CCD camera system makes it possible to significantly increase the sensitivity in the 800–1000 spectral range.
The logical relations of opposition and how to teach them

Based on the mental models theory, in this paper, I try to identify which of the relations of opposition in Aristotelian logic can be often difficult to understand for the students. In this way, assuming the distinction that the theory proposes between canonical and noncanonical models, I analyze all of the relations of opposition in order to detect which of them require to manipulate canonical models and, therefore, more cognitive effort. In addition, I comment on the kinds of sentences that should be used as examples to make the learning process of the problematic relations easier.
Fuzzing with Quantitative and Adaptive Hot-Bytes Identification

Fuzzing has emerged as a powerful technique for finding security bugs in complicated real-world applications. American fuzzy lop (AFL), a leading fuzzing tool, has demonstrated its powerful bug finding ability through a vast number of reported CVEs. However, its random mutation strategy is unable to generate test inputs that satisfy complicated branching conditions (e.g., magic-byte comparisons, checksum tests, and nested if-statements), which are commonly used in image decoders/encoders, XML parsers, and checksum tools. Existing approaches (such as Steelix and Neuzz) on addressing this problem assume unrealistic assumptions such as we can satisfy the branch condition byte-to-byte or we can identify and focus on the important bytes in the input (called hot-bytes) once and for all. In this work, we propose an approach called \tool~which is designed based on the following principles. First, there is a complicated relation between inputs and branching conditions and thus we need not only an expressive model to capture such relationship but also an informative measure so that we can learn such relationship effectively. Second, different branching conditions demand different hot-bytes and we must adjust our fuzzing strategy adaptively depending on which branches are the current bottleneck. We implement our approach as an open source project and compare its efficiency with other state-of-the-art fuzzers. Our evaluation results on 10 real-world programs and LAVA-M dataset show that \tool~achieves sustained increases in branch coverage and discovers more bugs than other fuzzers.
Expressiveness Results for an Inductive Logic of Separated Relations

In this paper we study a Separation Logic of Relations (SLR) and compare its expressiveness to (Monadic)Second Order Logic (M)SO. SLR is based on the well-known Symbolic Heap fragment of Separation Logic, whose formulae are composed of points-to assertions, inductively defined predicates, with the separating conjunction as the only logical connective. SLR generalizes the Symbolic Heap fragment by supporting general relational atoms, instead of only points-to assertions. In this paper, we restrict ourselves to finite relational structures, and hence only consider Weak (M)SO, where quantification ranges over finite sets. Our main results are that SLR and MSO are incomparable on structures of unbounded treewidth, while SLR can be embedded in SO in general. Furthermore, MSO becomes a strict subset of SLR, when the treewidth of the models is bounded by a parameter and all vertices attached to some hyperedge belong to the interpretation of a fixed unary relation symbol. We also discuss the problem of identifying a fragment of SLR that is equivalent to MSO over models of bounded treewidth.
Non-Concave Utility Maximization with Transaction Costs

This paper studies a finite-horizon portfolio selection problem with non-concave terminal utility and proportional transaction costs. The commonly used concavification principle for terminal value is no longer valid here, and we establish a proper theoretical characterization of this problem. We first give the asymptotic terminal behavior of the value function, which implies any transaction close to maturity only provides a marginal contribution to the utility. After that, the theoretical foundation is established in terms of a novel definition of the viscosity solution incorporating our asymptotic terminal condition. Via numerical analyses, we find that the introduction of transaction costs into non-concave utility maximization problems can prevent the portfolio from unbounded leverage and make a large short position in stock optimal despite a positive risk premium and symmetric transaction costs.