Miracle-dz/UniFAR-FTUs huggingface dataset:
On the Empirical Substantiation of the Definition of “Deliberate Practice” (Ericsson et al., 1993) and “Deliberate Play” (Côté et al., 2007) in Youth Athletes

The concepts of deliberate practice (Ericsson, Krampe, & Tesch-Römer, 1993) and deliberate play (i.e., informal youth-led sport play with peers, but without a coach, such as backyard soccer, basketball in the driveway; Côté, Baker, & Abernethy, 2007) have been influential on research into the acquisition of athletic expertise. Deliberate practice was defined to be undertaken for the extrinsic motive to achieve an improved performance, with the further criteria that it be physically and mentally highly effortful and not inherently enjoyable. Deliberate play was defined to be highly enjoyable and to be undertaken for the intrinsic motive to maximize enjoyment. However, the authors ascribed these attributes a priori to athletes’ involvement in the activities but did not empirically measure the ascribed attributes. In this study, we examined whether these a priori pre-supposed attributes are underpinned by empirical correspondence in youth athletes. A total of 208 athletes (13.6 years; SD = 2.6) from several sports reported their motives and inherent enjoyment of coach-led practice and youth-led sport play in a questionnaire. The empirical findings did not provide support for some of the premises of the definitions of deliberate practice and deliberate play. Inherent enjoyment was high in both activities and did not differ. Youth athletes’ motives to engage in both sport activities comprised a broad multi-dimensional spectrum including health, general fitness, aesthetic experiences, social interaction, and recreation from daily hassle, not just performance or inherent enjoyment. Within the performance motive for coach-led practice, the intrinsic component was very pronounced while the extrinsic component was not. Furthermore, more successful youth athletes showed less empirical correspondence with premises of the deliberate practice concept than less successful athletes. The findings suggest that the definitions of deliberate practice and deliberate play should be empirically substantiated, rather than defining them by untested a priori premises .
Eﬀicient Interpolation of Rough Line Drawings

In traditional 2D animation, sketches drawn at distant keyframes are used to design motion, yet it would be far too labor-intensive to draw all the inbetween frames to fully visualize that motion. We propose a novel efficient interpolation algorithm that generates these intermediate frames in the artist’s drawing style. Starting from a set of registered rough vector drawings, we first generate a large number of candidate strokes during a pre-process, and then, at each intermediate frame, we select the subset of those that appropriately conveys the underlying interpolated motion, interpolates the stroke distributions of the key drawings, and introduces a minimum amount of temporal artifacts. In addition, we propose quantitative error metrics to objectively evaluate different stroke selection strategies. We demonstrate the potential of our method on various animations and drawing styles, and show its superiority over competing raster-and vector-based methods.
Currency Exchange Portfolio Risk Estimation Using Copula-Based Value at Risk and Conditional Value at Risk

—The recent currency ﬂuctuations, which have created uncertainty in currency markets about the movement of exchange rates, have spurred renewed interest in portfolio dependence modelling and risk modelling. Currency exchange portfolio risks can be measured using Value at Risk (VaR) and Conditional Value at Risk (CVaR) based on Monte Carlo simulation. However, this method of risk estimation involves considerable challenges owing to the complexity of modelling the joint multivariate distribution of the assets in the portfolio. Therefore, copula functions, such as the t -Student and Clay-ton copulas, have been proposed to measure the dependence structure of the return of a currency exchange portfolio. This study proposes the use of the copula-VaR and copula-CVaR approaches to make strategic choices in currency management when evaluating the risks of equal-and mixed-weighted port-folios of the returns of investment in ﬁve foreign currencies in Malaysia: the United States dollar, United Kingdom pound sterling, European Union euro, Japanese yen, and Singapore dollar. The generalised autoregressive conditional heteroscedas-ticity (GARCH)-copula models are also evaluated. We ﬁnd that the marginal distribution of the returns series of the currency exchange rates can be modelled using the Glosten– Jagannathan–Runkle (GJR)-GARCH model with the t -Student distribution, and the dependence structure of the currency exchange portfolio can be depicted by the t -Student copula. The best investment performance tends to the Singapore dollar.
Breeding patterns of the Great Tit (Parus major) in a pine plantation and a holm oak forest in a Mediterranean region (southern Spain)

Cette etude compare les patrons de reproduction en nichoir de Mesanges charbonnieres (Parus major) entre deux habitats forestiers adjacents: une plantation de pins et une foret mediterraneenne a Quercus rotundifolia. La densite des reproducteurs s'est accrue dans les deux forets quand la densite des nichoirs a ete augmentee. La saison de reproduction a commence plus tot dans la pinede, mais tendit a se terminer en meme temps dans les deux habitats. L'effort reproducteur fut plus important dans la plantation mais en terme de volume d'oeuf plutot que de taille de ponte. La periode dans la saison a eu un effet sur la taille de la ponte mais pas sur celle des oeufs, laquelle est restee constante tout au long de la saison de reproduction. Le succes reproducteur fut associe a l'habitat, avec des effectifs et des poids de jeunes a l'envol plus eleves dans la pinede. Ces parametres ont montre une tendance saisonniere dans la pinede mais pas dans la chenaie. Nos resultats montrent que la Mesange charbonniere affiche une meilleure performance reproductrice dans la pinede bien que son installation n'y soit que recente.
Dissipation induced extended-localized transition

Mobility edge (ME), representing the critical energy that distinguishes between extended and localized states, is a key concept in understanding the transition between extended (metallic) and localized (insulating) states in disordered and quasiperiodic systems. Here we explore the impact of dissipation on a quasiperiodic system featuring MEs by calculating steady-state density matrix and analyzing quench dynamics with sudden introduction of dissipation, and demonstrate that dissipation can lead the system into specific states predominantly characterized by either extended or localized states, irrespective of the initial state. Our results establish the use of dissipation as a new avenue for inducing transitions between extended and localized states, and for manipulating dynamic behaviors of particles.
Key Frame Mechanism for Efficient Conformer Based End-to-End Speech Recognition

Recently, Conformer as a backbone network for end-to-end automatic speech recognition achieved state-of-the-art performance. The Conformer block leverages a self-attention mechanism to capture global information, along with a convolutional neural network to capture local information, resulting in improved performance. However, the Conformer-based model encounters an issue with the self-attention mechanism, as computational complexity grows quadratically with the length of the input sequence. Inspired by previous Connectionist Temporal Classification (CTC) guided blank skipping during decoding, we introduce intermediate CTC outputs as guidance into the downsampling procedure of the Conformer encoder. We define the frame with non-blank output as key frame. Specifically, we introduce the key frame-based self-attention (KFSA) mechanism, a novel method to reduce the computation of the self-attention mechanism using key frames. The structure of our proposed approach comprises two encoders. Following the initial encoder, we introduce an intermediate CTC loss function to compute the label frame, enabling us to extract the key frames and blank frames for KFSA. Furthermore, we introduce the key frame-based downsampling (KFDS) mechanism to operate on high-dimensional acoustic features directly and drop the frames corresponding to blank labels, which results in new acoustic feature sequences as input to the second encoder. By using the proposed method, which achieves comparable or higher performance than vanilla Conformer and other similar work such as Efficient Conformer. Meantime, our proposed method can discard more than 60% useless frames during model training and inference, which will accelerate the inference speed significantly.
Native spaces and generalization of Wu functions

We prove that the native space of a Wu function is a dense subspace of a Sobolev space. An explicit characterization of the native spaces of Wu functions is given. Three definitions of Wu functions are introduced and proven to be equivalent. Based on these new equivalent definitions and the so called $f$-form tricks, we can generalize the Wu functions into the even-dimensional spaces $\R^{2k}$, while the original Wu functions are only defined in the odd-dimensional spaces $\R^{2k+1}$. Such functions in even-dimensional spaces are referred to as the `missing Wu functions'. Furthermore we can generalize the Wu functions into `fractional'-dimensional spaces. We call all these Wu functions the generalized Wu functions. The closed form of the generalized Wu functions are given in terms of hypergeometric functions. Finally we prove that the Wu functions and the missing Wu functions can be written as linear combinations of the generalized Wendland functions.
DYNAMITE: Dynamic Interplay of Mini-Batch Size and Aggregation Frequency for Federated Learning with Static and Streaming Dataset

Federated Learning (FL) is a distributed learning paradigm that can coordinate heterogeneous edge devices to perform model training without sharing private data. While prior works have focused on analyzing FL convergence with respect to hyperparameters like batch size and aggregation frequency, the joint effects of adjusting these parameters on model performance, training time, and resource consumption have been overlooked, especially when facing dynamic data streams and network characteristics. This paper introduces novel analytical models and optimization algorithms that leverage the interplay between batch size and aggregation frequency to navigate the trade-offs among convergence, cost, and completion time for dynamic FL training. We establish a new convergence bound for training error considering heterogeneous datasets across devices and derive closed-form solutions for co-optimized batch size and aggregation frequency that are consistent across all devices. Additionally, we design an efficient algorithm for assigning different batch configurations across devices, improving model accuracy and addressing the heterogeneity of both data and system characteristics. Further, we propose an adaptive control algorithm that dynamically estimates network states, efficiently samples appropriate data batches, and effectively adjusts batch sizes and aggregation frequency on the fly. Extensive experiments demonstrate the superiority of our offline optimal solutions and online adaptive algorithm.
A Study on Knowledge Graph Embeddings and Graph Neural Networks for Web Of Things

Graph data structures are widely used to store relational information between several entities. With data being generated worldwide on a large scale, we see a significant growth in the generation of knowledge graphs. Thing in the future is Orange's take on a knowledge graph in the domain of the Web Of Things (WoT), where the main objective of the platform is to provide a digital representation of the physical world and enable cross-domain applications to be built upon this massive and highly connected graph of things. In this context, as the knowledge graph grows in size, it is prone to have noisy and messy data. In this paper, we explore state-of-the-art knowledge graph embedding (KGE) methods to learn numerical representations of the graph entities and, subsequently, explore downstream tasks like link prediction, node classification, and triple classification. We also investigate Graph neural networks (GNN) alongside KGEs and compare their performance on the same downstream tasks. Our evaluation highlights the encouraging performance of both KGE and GNN-based methods on node classification, and the superiority of GNN approaches in the link prediction task. Overall, we show that state-of-the-art approaches are relevant in a WoT context, and this preliminary work provides insights to implement and evaluate them in this context.
Causal machine learning for single-cell genomics

Advances in single-cell omics allow for unprecedented insights into the transcription profiles of individual cells. When combined with large-scale perturbation screens, through which specific biological mechanisms can be targeted, these technologies allow for measuring the effect of targeted perturbations on the whole transcriptome. These advances provide an opportunity to better understand the causative role of genes in complex biological processes such as gene regulation, disease progression or cellular development. However, the high-dimensional nature of the data, coupled with the intricate complexity of biological systems renders this task nontrivial. Within the machine learning community, there has been a recent increase of interest in causality, with a focus on adapting established causal techniques and algorithms to handle high-dimensional data. In this perspective, we delineate the application of these methodologies within the realm of single-cell genomics and their challenges. We first present the model that underlies most of current causal approaches to single-cell biology and discuss and challenge the assumptions it entails from the biological point of view. We then identify open problems in the application of causal approaches to single-cell data: generalising to unseen environments, learning interpretable models, and learning causal models of dynamics. For each problem, we discuss how various research directions - including the development of computational approaches and the adaptation of experimental protocols - may offer ways forward, or on the contrary pose some difficulties. With the advent of single cell atlases and increasing perturbation data, we expect causal models to become a crucial tool for informed experimental design.
Understanding the Inner Workings of Language Models Through Representation Dissimilarity

As language models are applied to an increasing number of real-world applications, understanding their inner workings has become an important issue in model trust, interpretability, and transparency. In this work we show that representation dissimilarity measures, which are functions that measure the extent to which two model's internal representations differ, can be a valuable tool for gaining insight into the mechanics of language models. Among our insights are: (i) an apparent asymmetry in the internal representations of model using SoLU and GeLU activation functions, (ii) evidence that dissimilarity measures can identify and locate generalization properties of models that are invisible via in-distribution test set performance, and (iii) new evaluations of how language model features vary as width and depth are increased. Our results suggest that dissimilarity measures are a promising set of tools for shedding light on the inner workings of language models.
The representation of arctic soils in the land surface model: The importance of mosses

Mosses dominate the surface cover in high northern latitudes and have the potential to play a key role in modifying the thermal and hydrologic regime of Arctic soils. These modifications in turn feed back to influence surface energy exchanges and hence may affect regional climate. However, mosses are poorly represented in models of the land surface. In this study the NCAR Land Surface Model (LSM) was modified in two ways. First, additional soil texture types including mosses and lichens were added to more realistically represent northern soils. Second, the LSM was also modified so that a different soil texture type could be specified for each layer. Several experiments were performed using climate data from an Arctic tundra site in 1995. The model was run for a homogeneous loam soil column and then also for columns that included moss, lichen, peat, and sand. The addition of a surface layer of moss underlain by peat and loam had a substantial impact on modeled surface processes. First, moss acted as an insulative layer producing cooler summer temperatures (6.9 8C lower at 0.5 m) and warmer winter temperatures (2.38C higher at 0.5 m) when compared with a homogenous loam soil column. Second, a soil column with a moss surface had a greater surface infiltration, leading to greater storage of soil moisture in lower layers when compared with a homogeneous loam column. Last, moss modulated the surface energy exchanges by decreasing soil heat flux (57% in July) and increasing turbulent fluxes of heat (67% in July) and moisture (15% in July). Mosses were also more effective contributors to total latent heating than was a bare loam surface. These results suggest that the addition of moss and the ability to prescribe different soil textures for different soil layers result in a more plausible distribution of heat and water within the column and that these modifications should be incorporated into regional and global climate models.
Sampling Balanced Forests of Grids in Polynomial Time

We prove that a polynomial fraction of the set of $k$-component forests in the $m \times n$ grid graph have equal numbers of vertices in each component, for any constant $k$. This resolves a conjecture of Charikar, Liu, Liu, and Vuong, and establishes the first provably polynomial-time algorithm for (exactly or approximately) sampling balanced grid graph partitions according to the spanning tree distribution, which weights each $k$-partition according to the product, across its $k$ pieces, of the number of spanning trees of each piece. Our result follows from a careful analysis of the probability a uniformly random spanning tree of the grid can be cut into balanced pieces. Beyond grids, we show that for a broad family of lattice-like graphs, we achieve balance up to any multiplicative $(1 \pm \varepsilon)$ constant with constant probability, and up to an additive constant with polynomial probability. More generally, we show that, with constant probability, components derived from uniform spanning trees can approximate any given partition of a planar region specified by Jordan curves. These results imply polynomial time algorithms for sampling approximately balanced tree-weighted partitions for lattice-like graphs. Our results have applications to understanding political districtings, where there is an underlying graph of indivisible geographic units that must be partitioned into $k$ population-balanced connected subgraphs. In this setting, tree-weighted partitions have interesting geometric properties, and this has stimulated significant effort to develop methods to sample them.
Counting the Bugs in ChatGPT's Wugs: A Multilingual Investigation into the Morphological Capabilities of a Large Language Model

Large language models (LLMs) have recently reached an impressive level of linguistic capability, prompting comparisons with human language skills. However, there have been relatively few systematic inquiries into the linguistic capabilities of the latest generation of LLMs, and those studies that do exist (i) ignore the remarkable ability of humans to generalize, (ii) focus only on English, and (iii) investigate syntax or semantics and overlook other capabilities that lie at the heart of human language, like morphology. Here, we close these gaps by conducting the first rigorous analysis of the morphological capabilities of ChatGPT in four typologically varied languages (specifically, English, German, Tamil, and Turkish). We apply a version of Berko's (1958) wug test to ChatGPT, using novel, uncontaminated datasets for the four examined languages. We find that ChatGPT massively underperforms purpose-built systems, particularly in English. Overall, our results -- through the lens of morphology -- cast a new light on the linguistic capabilities of ChatGPT, suggesting that claims of human-like language skills are premature and misleading.
Invariance is Key to Generalization: Examining the Role of Representation in Sim-to-Real Transfer for Visual Navigation

The data-driven approach to robot control has been gathering pace rapidly, yet generalization to unseen task domains remains a critical challenge. We argue that the key to generalization is representations that are (i) rich enough to capture all task-relevant information and (ii) invariant to superfluous variability between the training and the test domains. We experimentally study such a representation -- containing both depth and semantic information -- for visual navigation and show that it enables a control policy trained entirely in simulated indoor scenes to generalize to diverse real-world environments, both indoors and outdoors. Further, we show that our representation reduces the A-distance between the training and test domains, improving the generalization error bound as a result. Our proposed approach is scalable: the learned policy improves continuously, as the foundation models that it exploits absorb more diverse data during pre-training.
Structure of the Earth from Glacio-Isostatic Rebound

The deglaciated regions of Fennoscandia and North America have, since the disappearance of the ice sheet, experienced broad, domal uplifts of the earth’s surface which are shown in the raised and tilted strandlines of the sea and lakes. Because they closely coincide in shape and position with the deglaciated area, these uplifts arc believed to be the result of the relaxation of the earth to the removal of the ice load. Their study will therefore given information on the dccp structure and theology of the earth. Apart from horizontal scale, in which they differ by a factor of about 2, the principal uplift of North America (the Laurentidc uplift) and the Fcnnoscandian uplift have many points of similarity. The central region of each is occupied by a shallow sea and surrounded by a Precambrian shield. In both regions the shield merges in one direction with a continental platform and in the other with heavily glacicrized mountains that border the adjacent ocean. Both have a broadly elliptical shape with an cllipticity of about 0.8, but thc major axis of the Laurcntidc uplift trends northwest, and that of the Fcnnoscandian uplift, northeast. To the north of each is a smaller region of rebound. In North America this is the Innuitian uplift (Blake 6, Walcott 65), centered over the Queen Elizabeth Islands and contiguous with the Laurcntide to the south; in Fennoscandia, the island of Spitzbergcn, once covered by ice, has bccn uplifted and the area of rebound may extend southward over the area of present day shallow seas (the northern part of the British Islands is also dcglaciatcd and has undergone glacio-isostatic rebound). The similarities may be understood as the result of the growth of ice caps in regions of similar geology, physiography, history, and latitude. Because of these geological similarities we can infer that beneath both regions earth structure and rhcoldgy are also closely similar. Comparativ~ studies of the glacio-isostatic rebound of North America and Fennoscandia have a special geophysical significance, therefore, as we can reasonably expect any differences in response to be directly attributable to differences in scale. Until recently the North American uplifts were inadequately known and the Fcnnoscandian data have been the main body of information in this field of study,