Miracle-dz/UniFAR-FTUs huggingface dataset:
Marketing Decision Making In The Conditions Of Information Uncertainty And Business Risks On The Basis Of The Stratification Metamodeling Toolkit

The problematic subject area studied in this article is the sphere of marketing activity and the quality management system of industrial enterprises on the example of the enterprises of the electrical engineering industry. The article addresses the following problems of marketing management of enterprises producing electrotechnical products, such as reduction of financial demand from the side of potential consumers and unplanned negative changes in products market prices. It is substantiated that marketing decisions taken by the management of the enterprise concerning the pricing of production of own manufacturing are burdened with significant business risk and are accepted in the absence of reliable operational information about the current level of indirect production and nonproductive costs in the conditions of dynamic destabilizing influences from the outside of the enterprise. It is proposed to solve outlined problems using the instrumentation of a new object-oriented approach to the synthesis of complex models of the activities of industrial enterprises stratification metamodeling. The article describes a fragment of a stratification metamodel, which is a reference for the enterprises of the electrical engineering industry. The construction of this fragment of the stratification metamodel was carried out in three analytical contexts: conceptual modeling of marketing decision making in the conditions of information uncertainty, model decomposition of the economic problem into the multilayered hierarchy of the complex of problems and their mathematical models, information management of model knowledge and generating on this basis an effective marketing solution due to the synchronization of inputs and outputs of models of up-to-date management tasks that have created a coherent system. The system of interconnected economic and mathematical models of price, cost and profit management developed in this article is a scientific basis for the synthesis of artificial intelligence enhancement systems when adopting effective marketing decisions for the preventive elimination of the threat of price destabilization of the enterprise portfolio. Keywords—decision making; cost management; price conflict; a set of models; stratification metamodeling
Empowering the Italo-Australian community through news translation. A case study on Il Globo community newspaper

Within social relations in globalized and multilingual spaces affected by massive movements of people (e.g. Australia), globalisation encourages specificity at the micro-community level. “Ethnicity” here represents “a great source of comfort” (Spinner, 1994: 59) and community newspapers are indeed expressions of ethnicity, embodying an ongoing struggle between the multiple forces of globalisation represented by host and heritage cultures and languages, as well as providing an independent and meaningful voice for the community. This case study on the Italo-Australian community newspaper “Il Globo” explores translational aspects in the articles treating national issues in Australia (“Stati & Territori” section). Thanks to a senior journalist at “Il Globo” (IGJ henceforth), I retrieved English source texts (ST) and published Italian target texts (TT), affording a privileged look into news translation practices and outcomes. A corpus of 12 published TTs was analysed against the STs that informed and guided IGJ’s writing, paying special attention to how the journalist quoted her sources and dealt with cultural-specific items (CSIs, House, 2006). Finally, I discuss how this case study’s results can contribute to and offer a different perspective on the complex issues that link globalisation, language, and power within migrants’ communities and the role of ethnic media in today’s highly globalised multilingual and liquid (western) societies. 1. Il Globo in Melbourne: a safe place for the Italian community Il Globo, like any community newspaper conceived by migrants, was founded in 1959 with the aim of filling “the void of the lack of information about the homeland” (Mascitelli, 2009: 21). It consequently became the journalistic output of the growing Italian community settling in Melbourne during the post-war 1I will address the journalist using the feminine as a randomly chosen gender and I will not disclose her name for privacy reasons.
What motivates CAC 40 companies to disclose information on climate-related financial risks?

In this article, we examine the determinants of CAC 40 companies' compliance with the recommendations of the FSB Task Force on Climate Financial Reporting (TCFD). We use in this analysis a new index measuring the level of compliance of firms with TCFD's recommendations, the Comprehensive Compliance Index (CCI). Using a panel Tobit model, we find that environmentally friendly companies are more compliant with TCFD's recommendations, especially when operating in an industry with high environmental stakes. In addition, media environmental exposure increases the level of compliance of firms with TCFD's recommendations. We also find a strong influence of ownership structure on the CCI as a high shareholder concentration and a high proportion of foreign shareholders decrease the level of compliance with TCFD's recommendations. Overall, our results shed new light on the company's voluntary environmental reporting for regulators seeking to improve financial transparency in relation with climate risks. JEL Classification : G38; Q51; M41; Q56
Confounding by ill-health in the observed association between BMI and mortality: evidence from the HUNT Study using offspring as an

Background: The observational association between mortality and body mass index (BMI) is U-shaped, leading to highly publicized suggestions that moderate overweight is beneﬁcial to health. However, it is unclear whether elevated mortality is caused by low BMI or if the association is confounded, for example by concurrent ill health. Methods: Using HUNT, a Norwegian prospective study, 32 452 mother-offspring and 27 747 father-offspring pairs were followed up to 2009. Conventional hazard ratios for parental mortality per standard deviation of BMI were estimated using Cox regression adjusted for behavioural and socioeconomic factors. To estimate hazard ratios with reduced susceptibility to confounding, particularly from concurrent ill health, the BMI of parents’ offspring was used as an instrumental variable for parents’ own BMI. The shape of mortality-BMI associations was assessed using cubic splines. Results: There were 18 365 parental deaths during follow-up. Conventional associations of mortality from all-causes, cardiovascular disease and cancer with parents’ own BMI were substantially nonlinear, with elevated mortality at both extremes and minima at 21–25 kg m (cid:2) 2 . Equivalent associations with offspring BMI were positive and there was no evidence of elevated parental mortality at low offspring BMI. The linear instrumental variable hazard ratio for all-cause mortality per standard deviation increase in BMI was 1.18 (95% conﬁdence interval: 1.10, 1.26), compared with
Faint LAEs near z > 4.7 C iv absorbers revealed by MUSE

We present the results from the search for Lyman Alpha emitters (LAEs) in the proximity of 11 CIV absorption systems at $z > 4.7$ in the spectrum of the QSO J1030+0524, using data from MUSE. We have found multiple LAE candidates close to four CIV systems at $z_{CIV} = 4.94$-5.74 with log$_{10}$(N CIV[cm$^{-2}$])$ > 13.5. $ At z = 5-6, CIV systems with W0(CIV) $> 0.2$ A seem more likely to have galaxies with Ly${\alpha}$ emission within $\rho 0.5 $ A is in the range 11 $ 4.9$ CIV systems suggest that the absorbing material is rather young, likely ejected in the recent past of the galaxies at these redshifts. The connection between faint LAEs and high-ionization absorption systems reported in this work, is potentially a consequence of the role of low mass galaxies in the early evolution of the circum-galactic and intergalactic media.
Symmetric Skip Connection Wasserstein GAN for High-Resolution Facial Image Inpainting

The state-of-the-art facial image inpainting methods achieved promising results but face realism preservation remains a challenge. This is due to limitations such as; failures in preserving edges and blurry artefacts. To overcome these limitations, we propose a Symmetric Skip Connection Wasserstein Generative Adversarial Network (S-WGAN) for high-resolution facial image inpainting. The architecture is an encoder-decoder with convolutional blocks, linked by skip connections. The encoder is a feature extractor that captures data abstractions of an input image to learn an end-to-end mapping from an input (binary masked image) to the ground-truth. The decoder uses learned abstractions to reconstruct the image. With skip connections, S-WGAN transfers image details to the decoder. Additionally, we propose a Wasserstein-Perceptual loss function to preserve colour and maintain realism on a reconstructed image. We evaluate our method and the state-of-the-art methods on CelebA-HQ dataset. Our results show S-WGAN produces sharper and more realistic images when visually compared with other methods. The quantitative measures show our proposed S-WGAN achieves the best Structure Similarity Index Measure (SSIM) of 0.94.
Photometric characterization of multiple populations in star clusters: the impact of the first dredge-up

The existence of star-to-star light-element abundance variations (multiple populations, MPs) in massive Galactic and extragalactic star clusters older than about 2 Gyr is by now well established. Photometry of red giant branch (RGB) stars has been and still is instrumental in enabling the detection and characterization of cluster MPs, through the appropriate choices of filters, colours and colour combinations, that are mainly sensitive to N and --to a lesser degree-- C stellar surface abundances. An important issue not yet properly addressed is that the translation of the observed widths of the cluster RGBs to abundance spreads must account for the effect of the first dredge-up on the surface chemical patterns, hence on the spectral energy distributions of stars belonging to the various MPs. We have filled this gap by studying theoretically the impact of the dredge-up on the predicted widths of RGBs in clusters hosting MPs. We find that for a given initial range of N abundances, the first dredge up reduces the predicted RGB widths in N-sensitive filters compared to the case when its effect on the stellar spectral energy distributions is not accounted for. This reduction is a strong function of age and has also a dependence on metallicity. The net effect is an underestimate of the initial N-abundance ranges from RGB photometry if the first dredge-up is not accounted for in the modelling, and also the potential determination of spurious trends of N-abundance spreads with age.
Unsupervised Enhancement of Real-World Depth Images Using Tri-Cycle GAN

Low quality depth poses a considerable challenge to computer vision algorithms. In this work we aim to enhance highly degraded, real-world depth images acquired by a low-cost sensor, for which an analytical noise model is unavailable. In the absence of clean ground-truth, we approach the task as an unsupervised domain-translation between the low-quality sensor domain and a high-quality sensor domain, represented using two unpaired training sets. We employ the highly-successful Cycle-GAN to this task, but find it to perform poorly in this case. Identifying the sources of the failure, we introduce several modifications to the framework, including a larger generator architecture, depth-specific losses that take into account missing pixels, and a novel Tri-Cycle loss which promotes information-preservation while addressing the asymmetry between the domains. We show that the resulting framework dramatically improves over the original Cycle-GAN both visually and quantitatively, extending its applicability to more challenging and asymmetric translation tasks.
Button Simulation and Design via FDVV Models

Designing a push-button with desired sensation and performance is challenging because the mechanical construction must have the right response characteristics. Physical simulation of a button's force-displacement (FD) response has been studied to facilitate prototyping; however, the simulations' scope and realism have been limited. In this paper, we extend FD modeling to include vibration (V) and velocity-dependence characteristics (V). The resulting FDVV models better capture tactility characteristics of buttons, including snap. They increase the range of simulated buttons and the perceived realism relative to FD models. The paper also demonstrates methods for obtaining these models, editing them, and simulating accordingly. This end-to-end approach enables the analysis, prototyping, and optimization of buttons, and supports exploring designs that would be hard to implement mechanically.
Collisional ferrohydrodynamics of magnetic fluid droplets on superhydrophobic surfaces

The study reports the aspects of postimpact hydrodynamics of ferrofluid droplets on superhydrophobic SH surfaces in the presence of a horizontal magnetic field. A wide gamut of dynamics was observed by varying the impact Weber number We, the Hartmann number Ha and the magnetic field strength manifested through the magnetic Bond number Bom. For a fixed We 60, we observed that at moderately low Bom 300, droplet rebound off the SH surface is suppressed. The noted We is chosen to observe various impact outcomes and to reveal the consequent ferrohydrodynamic mechanisms. We also show that ferrohydrodynamic interactions leads to asymmetric spreading, and the droplet spreads preferentially in a direction orthogonal to the magnetic field lines. We show analytically that during the retraction regime, the kinetic energy of the droplet is distributed unequally in the transverse and longitudinal directions due to the Lorentz force. This ultimately leads to suppression of droplet rebound. We study the role of Bom at fixed We 60, and observed that the liquid lamella becomes unstable at the onset of retraction phase, through nucleation of holes, their proliferation and rupture after reaching a critical thickness only on SH surfaces, but is absent on hydrophilic surfaces. We propose an analytical model to predict the onset of instability at a critical Bom. The analytical model shows that the critical Bom is a function of the impact We, and the critical Bom decreases with increasing We. We illustrate a phase map encompassing all the post impact ferrohydrodynamic phenomena on SH surfaces for a wide range of We and Bom.
Visually Guided Self Supervised Learning of Speech Representations

Self supervised representation learning has recently attracted a lot of research interest for both the audio and visual modalities. However, most works typically focus on a particular modality or feature alone and there has been very limited work that studies the interaction between the two modalities for learning self supervised representations. We propose a framework for learning audio representations guided by the visual modality in the context of audiovisual speech. We employ a generative audio-to-video training scheme in which we animate a still image corresponding to a given audio clip and optimize the generated video to be as close as possible to the real video of the speech segment. Through this process, the audio encoder network learns useful speech representations that we evaluate on emotion recognition and speech recognition. We achieve state of the art results for emotion recognition and competitive results for speech recognition. This demonstrates the potential of visual supervision for learning audio representations as a novel way for self-supervised learning which has not been explored in the past. The proposed unsupervised audio features can leverage a virtually unlimited amount of training data of unlabelled audiovisual speech and have a large number of potentially promising applications.
Deep-Learning-Based Pedestrian Inertial Navigation: Methods, Data Set, and On-Device Inference

Modern inertial measurements units (IMUs) are small, cheap, energy efficient, and widely employed in smart devices and mobile robots. Exploiting inertial data for accurate and reliable pedestrian navigation supports is a key component for emerging Internet of Things applications and services. Recently, there has been a growing interest in applying deep neural networks (DNNs) to motion sensing and location estimation. However, the lack of sufficient labelled data for training and evaluating architecture benchmarks has limited the adoption of DNNs in IMU-based tasks. In this article, we present and release the Oxford Inertial Odometry Data Set (OxIOD), a first-of-its-kind public data set for deep-learning-based inertial navigation research with fine-grained ground truth on all sequences. Furthermore, to enable more efficient inference at the edge, we propose a novel lightweight framework to learn and reconstruct pedestrian trajectories from raw IMU data. Extensive experiments show the effectiveness of our data set and methods in achieving accurate data-driven pedestrian inertial navigation on resource-constrained devices.
Understanding and Mitigating the Security Risks of Content Inclusion in Web Browsers

Thanks to the wide range of features offered by web browsers, modern websites include various types of content such as JavaScript and CSS in order to create interactive user interfaces. Browser vendors also provided extensions to enhance web browsers with additional useful capabilities that are not necessarily maintained or supported by default. However, included content can introduce security risks to users of these websites, unbeknownst to both website operators and users. In addition, the browser's interpretation of the resource URLs may be very different from how the web server resolves the URL to determine which resource should be returned to the browser. The URL may not correspond to an actual server-side file system structure at all, or the web server may internally rewrite parts of the URL. This semantic disconnect between web browsers and web servers in interpreting relative paths (path confusion) could be exploited by Relative Path Overwrite (RPO). On the other hand, even tough extensions provide useful additional functionality for web browsers, they are also an increasingly popular vector for attacks. Due to the high degree of privilege extensions can hold, extensions have been abused to inject advertisements into web pages that divert revenue from content publishers and potentially expose users to malware. In this thesis, I propose novel research into understanding and mitigating the security risks of content inclusion in web browsers to protect website publishers as well as their users.
Quantum embeddings for machine learning

Quantum classifiers are trainable quantum circuits used as machine learning models. The first part of the circuit implements a quantum feature map that encodes classical inputs into quantum states, embedding the data in a high-dimensional Hilbert space; the second part of the circuit executes a quantum measurement interpreted as the output of the model. Usually, the measurement is trained to distinguish quantum-embedded data. We propose to instead train the first part of the circuit---the embedding---with the objective of maximally separating data classes in Hilbert space, a strategy we call quantum metric learning. As a result, the measurement minimizing a linear classification loss is already known and depends on the metric used: for embeddings separating data using the l1 or trace distance, this is the Helstrom measurement, while for the l2 or Hilbert-Schmidt distance, it is a simple overlap measurement. This approach provides a powerful analytic framework for quantum machine learning and eliminates a major component in current models, freeing up more precious resources to best leverage the capabilities of near-term quantum information processors.
Almost everywhere convergence of prolate spheroidal series

In this paper, we show that the expansions of functions from $L^p$-Paley-Wiener type spaces in terms of the prolate spheroidal wave functions converge almost everywhere for $1<p<\infty$, even in the cases when they might not converge in $L^p$-norm. We thereby consider the classical Paley-Wiener spaces $PW_c^p\subset L^p(\mathcal{R})$ of functions whose Fourier transform is supported in $[-c,c]$ and Paley-Wiener like spaces $B_{\alpha,c}^p\subset L^p(0,\infty)$ of functions whose Hankel transform $\mathcal{H}^\alpha$ is supported in $[0,c]$. As a side product, we show the continuity of the projection operator $P_c^\alpha f:=\mathcal{H}^\alpha(\chi_{[0,c]}\cdot \mathcal{H}^\alpha f)$ from $L^p(0,\infty)$ to $L^q(0,\infty)$, $1<p\leq q<\infty$.
Dominance Move calculation using a MIP approach for comparison of multi and many-objective optimization solution sets

Dominance move (DoM) is a binary quality indicator that can be used in multiobjective optimization. It can compare solution sets while representing some important features such as convergence, spread, uniformity, and cardinality. DoM has an intuitive concept and considers the minimum move of one set needed to weakly Pareto dominate the other set. Despite the aforementioned properties, DoM is hard to calculate. The original formulation presents an efficient and exact method to calculate it in a biobjective case only. This work presents a new approach to calculate and extend DoM to deal with three or more objectives. The idea is to use a mixed integer programming (MIP) approach to calculate DoM. Some initial experiments, in the biobjective space, were done to verify the model correctness. Furthermore, other experiments, using three, five, and ten objective functions were done to show how the model behaves in higher dimensional cases. Algorithms such as IBEA, MOEAD, NSGAIII, NSGAII, and SPEA2 were used to generate the solution sets, however any other algorithms could be used with DoM indicator. The results have confirmed the effectiveness of the MIP DoM in problems with more than three objective functions. Final notes, considerations, and future research are discussed to exploit some solution sets particularities and improve the model and its use for other situations.
Zooming In on Equity Factor Crowding

Crowding is most likely an important factor in the deterioration of strategy performance, the increase of trading costs and the development of systemic risk. We study the imprints of \emph{crowding} on both anonymous market data and a large database of metaorders from institutional investors in the U.S. equity market. We propose direct metrics of crowding that capture the presence of investors contemporaneously trading the same stock in the same direction by looking at fluctuations of the imbalances of trades executed on the market. We identify significant signs of crowding in well known equity signals, such as Fama-French factors and especially Momentum. We show that the rebalancing of a Momentum portfolio can explain between 1-2\% of order flow, and that this percentage has been significantly increasing in recent years.