Miracle-dz/UniFAR-FTUs huggingface dataset:
PLAYwithHEART: Study protocol to test the efficacy of a mindfulness, acceptance and compassion-based programme for adolescent athletes

Some studies have documented that athletes experience mental health challenges associated with the practice of sports. There is evidence of the benefits of mindfulness, acceptance, and compassion skills in athletes. The contribution of these processes has never been tested in an integrated and structured way. In this protocol, we aim to describe a controlled non-randomized trial to test a new integrative intervention based on contextual-behavioral therapies for adolescent athletes: the PLAYwithHEART programme. This trial is registered at ClinicalTrials.gov (Identifier: NCT04850872). The PLAYwithHEART programme will comprise eight weekly sessions (of about 45 minutes each) directed towards adolescent athletes. The PLAYwithHEART programme’s structure and contents are presented in this protocol. Athletes will be recruited through contact with sport clubs in Portugal. A total of 189 participants will be selected and assigned to one of two conditions: experimental group (who will participate in the programme) or control group (in the waitlist control condition). Outcome measure1 University of Coimbra, Center for Research in Neuropsychology and Cognitive Behavioural Intervention (CINEICC), Faculty of Psychology and Educational Sciences, Coimbra, Portugal. Email: sara.oliveira. uc@gmail.com. ORCID: https://orcid.org/0000-0003-2524-918X 2 University of Coimbra, Center for Research in Neuropsychology and Cognitive Behavioural Intervention (CINEICC), Faculty of Psychology and Educational Sciences, Coimbra, Portugal. Email: marina_cunha@ismt.pt. ORCID: https://orcid.org/0000-0002-5957-1903 3 University of Lisbon, FMH Faculty of Human Motricity, Lisbon, Portugal. Email: arosado@fmh.ulisboa. pt. ORCID: https://orcid.org/0000-0003-2336-0853 4 University of Coimbra, Center for Research in Neuropsychology and Cognitive Behavioural Intervention (CINEICC), Faculty of Psychology and Educational Sciences, Coimbra, Portugal. Email: claudiaferreira@fpce.uc. pt. ORCID: https://orcid.org/0000-0002-7020-9606 66 Sara Oliveira, Marina Cunha, António Rosado and Claudia Ferreira ment will be conducted at baseline, post-intervention, and at six-month follow-up. This programme aims to decrease the levels of anxiety in sport and increase the quality of life of athletes, by promoting skills such as mindfulness, acceptance, and self-compassion, and decreasing levels of shame and self-criticism, to deal with the challenges and demands of sport contexts. Results will contribute to reaching an optimized intervention for athletes.
E FFECTIVE TAX BURDEN ON INVESTMENT AT CORPORATE LEVEL IN M ACEDONIA

: This article presents the estimates of effective tax rates on investment at corporate level in Republic of Macedonia in the period from 2006 to 2012. In addition to accomplishing this research, 3 basic and most commonly applied indicators of the corporate income tax (CIT) burden will be used. They are the cost of capital, the effective marginal tax rate (EMTR) and the effective average tax rate (EATR), according to the Devereux-Griffith methodology. The results of the analysis will clearly show that the implemented domestic tax policy reform have transformed this country into one of the most, if not the most tax favorable country for investment in Europe.
Digital (Re)Evolution In An Italian Public Administration: Some Digital (Re)Evolution In An Italian Public Administration: Some Implications In Pandemic Era Implications In Pandemic Era

Nowadays, the COVID-19 pandemic condition has generated an acceleration of change in public administration. The challenges around the COVID‐19 pandemic have further spurred public organizations into action by increasing their awareness of the need to accelerate digital transformation. The digital revolution in public administration is defined as "e-government". The context of public administration appears to be increasingly complex and requires skills able to better manage the digital transformation under-way. The role of the Human Resources Manager, in this particular historical phase, in which the work is digitalized, is transformed. The figure of the Human Resources Manager (HR Manager) overlaps with that of the Chief Digital Officier (CDO). In recent years, the role of the Chief Digital Officer has emerged and attracted scholars’ attention. However, the role of the CDO is still in its nascent and not well-defined stages, the role means different things for different organizations. On this basis, the main objective of the research is understanding if the HR manager is also a CDO in this historical phase in public organizations; trying to understand if the CDO is going to play the role of change agent in these organizations. This study is based on qualitative analysis.
BETWEEN ACTUAL AND VIRTUAL REALITY. TECHNOLOGIES AND FUNCTIONALITIES OF DEVICES IN THE BUYING PROCESS FROM THE PERSPECTIVE OF (POLISH) CONSUMERS

In modern times, the buying process involves utilisation of technologies from the virtual world as well as appropriate devices and their functionalities. Strategies of this type are also appreciated by young Polish people for whom balancing between actual and virtual reality is becoming an intrinsic element of consumer awareness. Based on selected papers and research reports, including their own and ones not yet published, the authors of the paper present various ways of moving between two realities throughout the whole buying process, from the stage of creating needs to completion of the purchase. In this respect, they draw attention to such issues as the kind of devices and their functionalities used or preferred in this process, the differentiation of actions in the individual buying categories, the involvement of third parties in the buying process, responses to messages, and willingness to disclose personal information. In addition to the empirical character (presentation of the relevant data obtained during the research) of the study, the authors consider the extent of autonomy and rationality of consumer decisions. The paper presents empirical research as it was based on the authors’ consumer studies, the full descriptions of which, along with the conclusions, are included in two reports constituting elements of a project implemented by the Social Research Institute at the Institute of Philosophy and Sociology of the Pedagogical University of Krakow (IFiS UP) and the Interia Group, under the name ‘Studies on the construction of an innovative platform, compliant with the latest global trends and enabling creation of services in the “Shopping Assistant” model’, funded by the Regional Operating Programme for Mazowieckie Province for 2014–2020 (Priority axis 1: Knowledge Economy; Action 1.2 Research and innovation in enterprises, Sub‑measure 1.2.1 R&D projects for enterprises).
Distributionally Preference Robust Optimization in Multi-Attribute Decision Making

Utility preference robust optimization (PRO) has recently been proposed to deal with optimal decision making problems where the decision maker's (DM) preference over gains and losses is ambiguous. In this paper, we take a step further to investigate the case that the DM's preference is not only ambiguous but also potentially inconsistent or even displaying some kind of randomness. We propose a distributionally preference robust optimization (DPRO) approach where the DM's preference is represented by a random utility function and the ambiguity is described by a set of probability distributions of the random utility. An obvious advantage of the new DPRO model is that it no longer concerns the DM's preference inconsistency. In the case when the random utility functions are of piecewise linear structure, we propose two statistical methods for constructing the ambiguity set: an ellipsoidal method and a bootstrap method both of which are fundamentally based on the idea of confidence region with the sample mean of the random parameters, and demonstrate how the resulting DPRO can be solved by a cutting surface algorithm and an MISOCP respectively. We also show how the DPRO models with general random utility functions may be approximated by those with piecewise linear random utility functions. Finally, we apply the proposed DPRO model in car manufacturing and facility location planning and show how the random samples may be extracted by multinomial logit method and conjoint analysis/machine learning. The paper is the first attempt to use distributionally robust optimization methods for PRO.
Spencer's theorem in nearly input-sparsity time

A celebrated theorem of Spencer states that for every set system $S_1,\dots, S_m \subseteq [n]$, there is a coloring of the ground set with $\{\pm 1\}$ with discrepancy $O(\sqrt{n\log(m/n+2)})$. We provide an algorithm to find such a coloring in near input-sparsity time $\tilde{O}(n+\sum_{i=1}^{m}|S_i|)$. A key ingredient in our work, which may be of independent interest, is a novel width reduction technique for solving linear programs, not of covering/packing type, in near input-sparsity time using the multiplicative weights update method.
On the Generalization and Adaption Performance of Causal Models

Learning models that offer robust out-of-distribution generalization and fast adaptation is a key challenge in modern machine learning. Modelling causal structure into neural networks holds the promise to accomplish robust zero and few-shot adaptation. Recent advances in differentiable causal discovery have proposed to factorize the data generating process into a set of modules, i.e. one module for the conditional distribution of every variable where only causal parents are used as predictors. Such a modular decomposition of knowledge enables adaptation to distributions shifts by only updating a subset of parameters. In this work, we systematically study the generalization and adaption performance of such modular neural causal models by comparing it to monolithic models and structured models where the set of predictors is not constrained to causal parents. Our analysis shows that the modular neural causal models outperform other models on both zero and few-shot adaptation in low data regimes and offer robust generalization. We also found that the effects are more significant for sparser graphs as compared to denser graphs.
Redundancy in Deep Linear Neural Networks

Conventional wisdom states that deep linear neural networks benefit from expressiveness and optimization advantages over a single linear layer. This paper suggests that, in practice, the training process of deep linear fully-connected networks using conventional optimizers is convex in the same manner as a single linear fully-connected layer. This paper aims to explain this claim and demonstrate it. Even though convolutional networks are not aligned with this description, this work aims to attain a new conceptual understanding of fully-connected linear networks that might shed light on the possible constraints of convolutional settings and non-linear architectures.
Learning in Distributed Contextual Linear Bandits Without Sharing the Context

Contextual linear bandits is a rich and theoretically important model that has many practical applications. Recently, this setup gained a lot of interest in applications over wireless where communication constraints can be a performance bottleneck, especially when the contexts come from a large $d$-dimensional space. In this paper, we consider a distributed memoryless contextual linear bandit learning problem, where the agents who observe the contexts and take actions are geographically separated from the learner who performs the learning while not seeing the contexts. We assume that contexts are generated from a distribution and propose a method that uses $\approx 5d$ bits per context for the case of unknown context distribution and $0$ bits per context if the context distribution is known, while achieving nearly the same regret bound as if the contexts were directly observable. The former bound improves upon existing bounds by a $\log(T)$ factor, where $T$ is the length of the horizon, while the latter achieves information theoretical tightness.
A Fourier representation of kernel Stein discrepancy with application to Goodness-of-Fit tests for measures on infinite dimensional Hilbert spaces

Kernel Stein discrepancy (KSD) is a widely used kernel-based measure of discrepancy between probability measures. It is often employed in the scenario where a user has a collection of samples from a candidate probability measure and wishes to compare them against a specified target probability measure. KSD has been employed in a range of settings including goodness-of-fit testing, parametric inference, MCMC output assessment and generative modelling. However, so far the method has been restricted to finite-dimensional data. We provide the first analysis of KSD in the generality of data lying in a separable Hilbert space, for example functional data. The main result is a novel Fourier representation of KSD obtained by combining the theory of measure equations with kernel methods. This allows us to prove that KSD can separate measures and thus is valid to use in practice. Additionally, our results improve the interpretability of KSD by decoupling the effect of the kernel and Stein operator. We demonstrate the efficacy of the proposed methodology by performing goodness-of-fit tests for various Gaussian and non-Gaussian functional models in a number of synthetic data experiments.
Analyzing Folktales of Different Regions Using Topic Modeling and Clustering

This paper employs two major natural language processing techniques, topic modeling and clustering, to find patterns in folktales and reveal cultural relationships between regions. In particular, we used Latent Dirichlet Allocation and BERTopic to extract the recurring elements as well as K-means clustering to group folktales. Our paper tries to answer the question what are the similarities and differences between folktales, and what do they say about culture. Here we show that the common trends between folktales are family, food, traditional gender roles, mythological figures, and animals. Also, folktales topics differ based on geographical location with folktales found in different regions having different animals and environment. We were not surprised to find that religious figures and animals are some of the common topics in all cultures. However, we were surprised that European and Asian folktales were often paired together. Our results demonstrate the prevalence of certain elements in cultures across the world. We anticipate our work to be a resource to future research of folktales and an example of using natural language processing to analyze documents in specific domains. Furthermore, since we only analyzed the documents based on their topics, more work could be done in analyzing the structure, sentiment, and the characters of these folktales.
Receding Horizon Inverse Reinforcement Learning

Inverse reinforcement learning (IRL) seeks to infer a cost function that explains the underlying goals and preferences of expert demonstrations. This paper presents receding horizon inverse reinforcement learning (RHIRL), a new IRL algorithm for high-dimensional, noisy, continuous systems with black-box dynamic models. RHIRL addresses two key challenges of IRL: scalability and robustness. To handle high-dimensional continuous systems, RHIRL matches the induced optimal trajectories with expert demonstrations locally in a receding horizon manner and 'stitches' together the local solutions to learn the cost; it thereby avoids the 'curse of dimensionality'. This contrasts sharply with earlier algorithms that match with expert demonstrations globally over the entire high-dimensional state space. To be robust against imperfect expert demonstrations and control noise, RHIRL learns a state-dependent cost function 'disentangled' from system dynamics under mild conditions. Experiments on benchmark tasks show that RHIRL outperforms several leading IRL algorithms in most instances. We also prove that the cumulative error of RHIRL grows linearly with the task duration.
Discovery of the most luminous quasar of the last 9 Gyr

Abstract We report the discovery of a bright ( $g = 14.5$ mag (AB), $K = 11.9$ mag (Vega)) quasar at redshift $z=0.83$ — the optically brightest (unbeamed) quasar at $z>0.4$ . SMSS J114447.77-430859.3, at a Galactic latitude of $b=+18.1^{\circ}$ , was identified by its optical colours from the SkyMapper Southern Survey (SMSS) during a search for symbiotic binary stars. Optical and near-infrared spectroscopy reveals broad Mg ii, H $\unicode{x03B2}$ , H $\unicode{x03B1}$ , and Pa $\unicode{x03B2}$ emission lines, from which we measure a black hole mass of $\log_{10}\! (M_{\mathrm{BH}}/\mathrm{M}_{\odot}) = 9.4 \pm 0.5$ . With its high luminosity, $L_{\mathrm{bol}} = (4.7\pm1.0)\times10^{47}\,\mathrm{erg\,s}^{-1}$ or $M_{i}(z=2) = -29.74$ mag (AB), we estimate an Eddington ratio of $\approx1.4$ . As the most luminous quasar known over the last ${\sim}$ 9 Gyr of cosmic history, having a luminosity $8\times$ greater than 3C 273, the source offers a range of potential follow-up opportunities.
Weak Cosmic Censorship Conjecture of Hairy Black Holes in Einstein Gravity

Apart from the Kerr-Newman black holes, the hairy black holes in general relativity have been widely investigated in the gravity and cosmology. In this paper, we extend the Sorce-Wald method to prove the weak cosmic censorship conjecture (WCCC) of the static and spherically symmetric hairy black holes in the Einstein gravity without using the explicit expressions of the metric and lagrangian for matter fields. We examine the WCCC in the collision process without requiring the spherically symmetry of the perturbation matter fields. After assuming the stability condition of spacetime and applying the Gaussian null coordinates into the variational identities, we derive the first two order perturbation inequalities which reflects the null energy condition of the matter fields. As a result, we find that nearly extremal static hairy black hole cannot be destroyed in the above perturbation process under the second-order approximation. Our result implies that the valid of the WCCC is universal for the hairy black hole in the Einstein gravity as long as the matter fields satisfy the null energy condition under the perturbation level.
A simple data-driven method to optimise the penalty strengths of penalised models and its application to non-parametric smoothing

Information of interest can often only be extracted from data by model fitting. When the functional form of such a model can not be deduced from first principles, one has to make a choice between different possible models. A common approach in such cases is to minimise the information loss in the model by trying to reduce the number of fit variables (or the model flexibility, respectively) as much as possible while still yielding an acceptable fit to the data. Model selection via the Akaike Information Criterion (AIC) provides such an implementation of Occam's razor. We argue that the same principles can be applied to optimise the penalty-strength of a penalised maximum-likelihood model. However, while in typical applications AIC is used to choose from a finite, discrete set of maximum-likelihood models the penalty optimisation requires to select out of a continuum of candidate models and these models violate the maximum-likelihood condition. We derive a generalised information criterion AICp that encompasses this case. It naturally involves the concept of effective free parameters which is very flexible and can be applied to any model, be it linear or non-linear, parametric or non-parametric, and with or without constraint equations on the parameters. We show that the generalised AICp allows an optimisation of any penalty-strength without the need of separate Monte-Carlo simulations. As an example application, we discuss the optimisation of the smoothing in non-parametric models which has many applications in astrophysics, like in dynamical modeling, spectral fitting or gravitational lensing.
DeepVerge: Classification of Roadside Verge Biodiversity and Conservation Potential

Open space grassland is being increasingly farmed or built upon, leading to a ramping up of conservation efforts targeting roadside verges. Approximately half of all UK grassland species can be found along the country's 500,000 km of roads, with some 91 species either threatened or near threatened. Careful management of these"wildlife corridors"is therefore essential to preventing species extinction and maintaining biodiversity in grassland habitats. Wildlife trusts have often enlisted the support of volunteers to survey roadside verges and identify new"Local Wildlife Sites"as areas of high conservation potential. Using volunteer survey data from 3,900 km of roadside verges alongside publicly available street-view imagery, we present DeepVerge; a deep learning-based method that can automatically survey sections of roadside verges by detecting the presence of positive indicator species. Using images and ground truth survey data from the rural county of Lincolnshire, DeepVerge achieved a mean accuracy of 88%. Such a method may be used by local authorities to identify new local wildlife sites, and aid management and environmental planning in line with legal and government policy obligations, saving thousands of hours of manual labour.
What-is and How-to for Fairness in Machine Learning: A Survey, Reflection, and Perspective

We review and reflect on fairness notions proposed in machine learning literature and make an attempt to draw connections to arguments in moral and political philosophy, especially theories of justice. We survey dynamic fairness inquiries and further consider the long-term impact induced by current prediction and decision. We present a flowchart that encompasses implicit assumptions and expected outcomes of different fairness inquiries on the data-generating process, the predicted outcome, and the induced impact, respectively. We demonstrate the importance of matching the mission (what kind of fairness to enforce) and the means (which appropriate fairness spectrum to analyze) to fulfill the intended purpose.
Individually Fair Learning with One-Sided Feedback

We consider an online learning problem with one-sided feedback, in which the learner is able to observe the true label only for positively predicted instances. On each round, $k$ instances arrive and receive classification outcomes according to a randomized policy deployed by the learner, whose goal is to maximize accuracy while deploying individually fair policies. We first extend the framework of Bechavod et al. (2020), which relies on the existence of a human fairness auditor for detecting fairness violations, to instead incorporate feedback from dynamically-selected panels of multiple, possibly inconsistent, auditors. We then construct an efficient reduction from our problem of online learning with one-sided feedback and a panel reporting fairness violations to the contextual combinatorial semi-bandit problem (Cesa-Bianchi&Lugosi, 2009, Gy\"{o}rgy et al., 2007). Finally, we show how to leverage the guarantees of two algorithms in the contextual combinatorial semi-bandit setting: Exp2 (Bubeck et al., 2012) and the oracle-efficient Context-Semi-Bandit-FTPL (Syrgkanis et al., 2016), to provide multi-criteria no regret guarantees simultaneously for accuracy and fairness. Our results eliminate two potential sources of bias from prior work: the"hidden outcomes"that are not available to an algorithm operating in the full information setting, and human biases that might be present in any single human auditor, but can be mitigated by selecting a well chosen panel.