Miracle-dz/UniFAR-FTUs huggingface dataset:
Automated Simulations of Galaxy Morphology Evolution using Deep Learning and Particle Swarm Optimisation

The formation of Hoag-type galaxies with central spheroidal galaxies and outer stellar rings has yet to be understood in astronomy. We consider that these unique objects were formed from the past interaction between elliptical galaxies and gas-rich dwarf galaxies. We have modelled this potential formation process through simulation. These numerical simulations are a means of investigating this formation hypothesis, however the parameter space to be explored for these simulations is vast. Through the application of machine learning and computational science, we implement a new two-fold method to find the best model parameters for stellar rings in the simulations. First, test particle simulations are run to find a possible range of parameters for which stellar rings can be formed around elliptical galaxies (i.e. Hoag-type galaxies). A novel combination of particle swarm optimisation and Siamese neural networks has been implemented to perform the search over the parameter space and test the level of consistency between observations and simulations for numerous models. Upon the success of this initial step, we subsequently run full chemodynamical simulations for the derived range of model parameters in order to verify the output of the test particle simulations. We successfully find parameter sets at which stellar rings can be formed from the interaction between a gas-rich dwarf galaxy and a central elliptical galaxy. This is evidence that supports our hypothesis about the formation process of Hoag-type galaxies. In addition, this suggests that our new two-fold method has been successfully implemented in this problem search-space and can be investigated further in future applications. ~
Weyl Group Covers for Brieskorn’s Resolutions in All Characteristics and the Integral Cohomology of G/P

We unify results of Artin, Brieskorn, Slodowy and others by showing that, in all characteristics, the Artin component of the deformation space of a rational surface singularity has a ramified cover where simultaneous resolution exists and the Galois group of this cover is the Weyl group determined by the configuration of $(-2)$-curves in the minimal resolution. This verifies a conjecture made by Burns and Rapoport. We use an integral version of this to show that certain actions of Weyl groups on polynomial rings over the integers give rings of invariants that are also polynomial, and deduce that the integral cohomology rings of complete flag varieties $G/B$ of types $A,D$ or $E$ can be described as the corresponding rings of co-invariants.
The nature of the animacy organization in human ventral temporal cortex

The principles underlying the animacy organization of the ventral temporal cortex (VTC) remain hotly debated, with recent evidence pointing to an animacy continuum rather than a dichotomy. What drives this continuum? According to the visual categorization hypothesis, the continuum reflects the degree to which animals contain animal-diagnostic features. By contrast, the agency hypothesis posits that the continuum reflects the degree to which animals are perceived as (social) agents. Here, we tested both hypotheses with a stimulus set in which visual categorizability and agency were dissociated based on representations in convolutional neural networks and behavioral experiments. Using fMRI, we found that visual categorizability and agency explained independent components of the animacy continuum in VTC. Modeled together, they fully explained the animacy continuum. Finally, clusters explained by visual categorizability were localized posterior to clusters explained by agency. These results show that multiple organizing principles, including agency, underlie the animacy continuum in VTC.
Combining Distant and Direct Supervision for Neural Relation Extraction

In relation extraction with distant supervision, noisy labels make it difficult to train quality models. Previous neural models addressed this problem using an attention mechanism that attends to sentences that are likely to express the relations. We improve such models by combining the distant supervision data with an additional directly-supervised data, which we use as supervision for the attention weights. We find that joint training on both types of supervision leads to a better model because it improves the model’s ability to identify noisy sentences. In addition, we find that sigmoidal attention weights with max pooling achieves better performance over the commonly used weighted average attention in this setup. Our proposed method achieves a new state-of-the-art result on the widely used FB-NYT dataset.
How to Empower the Workforce - Analyzing Internal Crowd Work as a Neo-Socio-Technical System -

In this paper, we analyze internal crowd work as Neo-STS from an employee’s perspective. Based on qualitative interviews, we describe in our model how employees perceive empowerment through participation in internal crowd work. As our main contribution, we detail and extend existing research regarding internal crowd work, Neo-STS as well as empowerment by identifying structural antecedents that affect psychological empowerment of internal crowd workers.
Wild gardening as a sustainable intensification strategy in northwest Cambodian smallholder systems

Within the last decade, Sustainable Intensification (SI) has emerged as a strategy to respond to future food security challenges. It incorporates increased food production without the cultivation of more land while incurring no net environmental cost. Frameworks attempting to measure SI often focus heavily on production indicators while overlooking other important indicators, such as impacts on economic, social, or human conditions. In this study, we evaluate the purposeful assemblage and management of neglected and underutilized species (NUS) in fringe areas around rural homesteads as a potential SI strategy. We use a recent SI assessment framework developed by the Feed the Future Innovation Lab for Collaborative Research on Sustainable Intensification (SIIL) that incorporates five measurable domains (productivity, economic, environmental, human condition, and social). We present findings from a qualitative case study in northwest Cambodia to assess local conceptu­alization of wild gardening, current uses of NUS, perceived benefits and challenges to their use and management, and the potential of wild gardening as a SI strategy. The qualitative methods employed 65 key informant interviews and four focus group discussions with both men and women partici­pants. Our results indicate that wild gardening is an important component of rural livelihoods in northeast Cambodia. However, a general lack of knowledge of strategic benefits, such as nutrition poten­tial, inhibits its use for maximum benefits. Wild gardening addresses multiple SI domains simultaneously and demonstrates the potential to be a promising SI strategy for improving rural livelihoods in Cambodia.
On the conformal method for the Einstein constraint equations

In this work, we use the global analysis and degree-theoretic methods introduced by Smale to study the existence and multiplicity of solutions of the vacuum Einstein constraint equations given by the conformal method of Lichnerowicz-Choquet-Bruhat-York. In particular this approach gives a simplified proof of the existence result of Holst-Nagy-Tsogtgerel-Maxwell, including a restricted uniqueness result. We also relate the method to the limit equation of Dahl-Gicquaud-Humbert and the non-existence result of Nguyen.
Amazon’s MTurk: A Currently Underutilised Resource for Survey Researchers?

A recent innovation in empirical academic research is the use of online labour markets as a source of data. One such market, Amazon’s Mechanical Turk (“MTurk”) has been used by studies published in high-quality accounting journals to source participants. Given the traction of this data source in high-calibre publications, it is timely to assess its current impact and future potential for accounting research. This paper examines the extent of adoption of MTurk as a data collection tool in leading accounting journals and specifically considers its adoption and suitability for survey research. Findings reveal that the use of MTurk in high-quality accounting publications is gathering momentum, with approximately the same number of articles published/accepted in 2019 as the total number of articles published in the preceding seven years. However, it is also found that nearly all the journal articles reviewed adopted MTurk for experimental research with only a limited presence in survey research. The study contributes to the literature by providing a comprehensive review of the adoption of MTurk in high-quality accounting journals by frequency, research method, and research participant type. Further, it analyses the unique methodological concerns that MTurk poses for survey-based accounting research, thereby providing researchers with guidance on its potential future usefulness and pitfalls to avoid. The paper concludes that difficulties in the availability of, and screening for, specific groups of participants may limit its potential for survey research until online labour market platforms are developed further.
Factors Affecting Freshman Engineering Students’ Attitudes Toward Mathematics

Students’ attitudes toward mathematics play a key role in their academic achievements. Still, concerns regarding the low grades in mathematics tests of engineering students have raised questions about the reasons for their poor performance. Prior research has mentioned the impact of engineering students’ attitudes on their grades. Few studies have attempted to explore the factors that influence and form engineering students’ attitudes toward mathematics. The current study aims to investigate the factors that affect freshman engineering students’ attitudes toward mathematics using a qualitative research design. Interviews with 26 participants enrolled at Imam Abdulrahman Bin Faisal University (IAU) in Saudi Arabia provided insight into the students’ experiences through survey questions. Thematic content analysis was used in this study to analyze the interviews. The results of the interview indicate that the freshman engineering students’ attitudes are most affected by their fathers and their teachers. The causes that form students’ attitudes toward mathematics can be divided into two parts: internal and external. The internal causes result from the students themselves, which include practice and preparation, assessments and grades, effect of English proficiency, and time management. The external causes include teachers’ characteristics and parental support.
Core Cosmology Library: Precision Cosmological Predictions for LSST

The Core Cosmology Library (CCL) provides routines to compute basic cosmological observables to a high degree of accuracy, which have been verified with an extensive suite of validation tests. Predictions are provided for many cosmological quantities, including distances, angular power spectra, correlation functions, halo bias, and the halo mass function through state-of-the-art modeling prescriptions available in the literature. Fiducial specifications for the expected galaxy distributions for the Large Synoptic Survey Telescope (LSST) are also included, together with the capability of computing redshift distributions for a user-defined photometric redshift model. A rigorous validation procedure, based on comparisons between CCL and independent software packages, allows us to establish a well-defined numerical accuracy for each predicted quantity. As a result, predictions for correlation functions of galaxy clustering, galaxy–galaxy lensing, and cosmic shear are demonstrated to be within a fraction of the expected statistical uncertainty of the observables for the models and in the range of scales of interest to LSST. CCL is an open source software package written in C, with a Python interface and publicly available at https://github.com/LSSTDESC/CCL.
Coordination in Distributed, Self-managing Work Teams: The Roles of Initiated and Received Task Interdependence

While coordination is assumed to contribute to distributed self-managing work team performance, our knowledge about the factors influencing coordination in such team settings is limited. In the present study, we investigate the moderating roles of initiated and received task interdependence on the relationship between self-management and coordination perceptions in distributed teams that rely on electronic communication tools to interact. A field survey study of 110 employees in 40 distributed teams demonstrated that when there are high levels of initiated task interdependence and low levels of received task interdependence, team self-management is associated with stronger perceived coordination in distributed teams. Based on these results, we discuss theoretical and practical implications for distributed self-managing teams.
Binary Matrix Completion With Nonconvex Regularizers

Many practical problems involve the recovery of a binary matrix from partial information, so the binary matrix completion (BMC) technique has increasingly been of interest in machine learning. In particular, we consider a special case of the BMC problems, in which only a subset of positive elements can be observed. In recent years, convex regularization-based methods are the mainstream approaches for this task. However, applications of nonconvex surrogates in standard matrix completion have demonstrated better empirical performance. Accordingly, we propose a novel BMC model with nonconvex regularizers and provide the recovery guarantee for the model. Furthermore, to solve the resultant nonconvex optimization problem, we improve the popular proximal algorithm with acceleration strategies. It can be guaranteed that the convergence rate of the algorithm is on the order of <inline-formula> <tex-math notation="LaTeX">${1/T}$ </tex-math></inline-formula>, where <inline-formula> <tex-math notation="LaTeX">$T$ </tex-math></inline-formula> is the number of iterations. The extensive experiments conducted on both synthetic and real-world data sets demonstrate the superiority of the proposed approach over other competing methods.
Dense 3D Face Decoding Over 2500FPS: Joint Texture & Shape Convolutional Mesh Decoders

3D Morphable Models (3DMMs) are statistical models that represent facial texture and shape variations using a set of linear bases and more particular Principal Component Analysis (PCA). 3DMMs were used as statistical priors for reconstructing 3D faces from images by solving non-linear least square optimization problems. Recently, 3DMMs were used as generative models for training non-linear mappings (\ie, regressors) from image to the parameters of the models via Deep Convolutional Neural Networks (DCNNs). Nevertheless, all of the above methods use either fully connected layers or 2D convolutions on parametric unwrapped UV spaces leading to large networks with many parameters. In this paper, we present the first, to the best of our knowledge, non-linear 3DMMs by learning joint texture and shape auto-encoders using direct mesh convolutions. We demonstrate how these auto-encoders can be used to train very light-weight models that perform Coloured Mesh Decoding (CMD) in-the-wild at a speed of over 2500 FPS.
On Making a Difference: Towards an Integrative Framework for Green IT and Green IS Adoption

Organizations and society nowadays face significant challenges. Organizations are required to fundamentally digital transform by assimilating Information Technology (IT) and Information System (IS) assets. Society faces an increasingly severe global climate disruption and needs to become more environmentally friendly. Green IT (GIT) and Green IS (GIS), as technologies and initiatives that seek to reduce the negative impacts of IT/IS on the environment, are a response to this. They can help organizations to gain a competitive advantage while also addressing broadscale environmental issues. We undertake a literature review to frame the general GIT/GIS adoption process. We provide an overarching understanding by modeling a sequence of five cognitive adoption phases (outset, pre-adoption, adoption, post-adoption, and outcome) on four levels (environmental, societal, organizational, and individual). By recognizing that GIT/GIS adoption has multiple drivers and outcomes, we provide an extensive perspective on GIT/GIS adoption.
Branched Multi-Task Networks: Deciding what layers to share

In the context of multi-task learning, neural networks with branched architectures have often been employed to jointly tackle the tasks at hand. Such ramified networks typically start with a number of shared layers, after which different tasks branch out into their own sequence of layers. Understandably, as the number of possible network configurations is combinatorially large, deciding what layers to share and where to branch out becomes cumbersome. Prior works have either relied on ad hoc methods to determine the level of layer sharing, which is suboptimal, or utilized neural architecture search techniques to establish the network design, which is considerably expensive. In this paper, we go beyond these limitations and propose an approach to automatically construct branched multi-task networks, by leveraging the employed tasks' affinities. Given a specific budget, i.e. number of learnable parameters, the proposed approach generates architectures, in which shallow layers are task-agnostic, whereas deeper ones gradually grow more task-specific. Extensive experimental analysis across numerous, diverse multi-tasking datasets shows that, for a given budget, our method consistently yields networks with the highest performance, while for a certain performance threshold it requires the least amount of learnable parameters.
$k$-means clustering of extremes

The $k$-means clustering algorithm and its variant, the spherical $k$-means clustering, are among the most important and popular methods in unsupervised learning and pattern detection. In this paper, we explore how the spherical $k$-means algorithm can be applied in the analysis of only the extremal observations from a data set. By making use of multivariate extreme value analysis we show how it can be adopted to find "prototypes" of extremal dependence and we derive a consistency result for our suggested estimator. In the special case of max-linear models we show furthermore that our procedure provides an alternative way of statistical inference for this class of models. Finally, we provide data examples which show that our method is able to find relevant patterns in extremal observations and allows us to classify extremal events.
Quality of Life in Children Diagnosed With Non-classic Congenital Adrenal Hyperplasia

Background Non-classical congenital adrenal hyperplasia (NC-CAH) is a chronic disease characterised by excessive androgen production that may negatively affect the quality of life (QoL) of affected patients. Pediatric Quality of Life Inventory 4.0 (PedsQL™) is a validated tool to assess health-related QoL (HRQoL). Methods A cross-sectional study including 19 patients with NC-CAH was carried out in the pediatric endocrinology department. NC-CAH patients who agreed to participate were included. Anthropometric data was collected. PedsQL™ was applied to the patients and their parents. Patients were divided into four groups according to age: 2-4, 5-7, 8-12, and 13-18 years old. The control group consisted of healthy individuals from the instrument's validation studies for the Portuguese population and the standard control population used in the PedsQL™ validation study. Results The only difference found concerns the parents' score results for children aged 8-12, which showed physical health and emotional dimension scores significantly higher (86.16±9.86 vs.68.90±23.02 p=0.004, 69.17±14.14 vs. 65.82±19.24 p=0.004), while psychosocial health's score and total scale score were significantly lower than the control group (59.99±9.90 vs. 69.34±14.07 p=0.047, 73.11±4.65 vs.78.86±16.61 p=0.017). Conclusion HRQoL scores are not negatively affected by NC-CAH in most group ages, with the exception of the parents' reports on HRQoL for children aged 8-12. Further studies with a greater number of patients are needed to determine the impact of this chronic disease on the HRQoL of children.
Proper motions of the satellites of M31

We predict the range of proper motions of 19 satellite galaxies of M31 that would rotationally stabilize the M31 plane of satellites consisting of 15–20 members as identified by Ibata et al. Our prediction is based purely on the current positions and line-of-sight velocities of these satellites and the assumption that the plane is not a transient feature. These predictions are therefore independent of the current debate about the formation history of this plane. We further comment on the feasibility of measuring these proper motions with future observations by the THEIA satellite mission as well as the currently planned observations by HST and JWST.
Retaining the learning professional: A survival study on workplace learning in professional service firms

Funding information Maastricht University Abstract Lowering professional turnover is of paramount importance for professional service firms, as with each professional, crucial proprietary knowledge leaves the firm. Based on the need to retain this crucial knowledge in the firm, this study explores whether factors that drive learning at work also mitigate professionals' turnover behavior. Building on insights from both workplace learning and turnover research, this study follows 96 professional auditors across a period of 5 years to determine how drivers for workplace learning at the organizational, the social interaction, and the individual level relate to turnover behavior. Through survival analysis, we find that those auditors who experience a supportive learning climate at the organizational level were less likely to leave their firm and profession, while those who score high on individual-level reflection were more likely to leave their firm and profession. We also found that professionals scoring high on reflection leave more quickly when they perceive to work in an unsupportive learning climate— when they experience low synergy across workplace learning levels. Especially observable behaviors, such as providing DOI: 10.1002/hrdq.21436
Modeling Expert Judgments of Insider Threat Using Ontology Structure: Effects of Individual Indicator Threat Value and Class Membership

We describe research on a comprehensive ontology of sociotechnical and organizational factors for insider threat (SOFIT) and results of an expert knowledge elicitation study. The study examined how alternative insider threat assessment models may reflect associations among constructs beyond the relationships defined in the hierarchical class structure. Results clearly indicate that individual indicators contribute differentially to expert judgments of insider threat risk. Further, models based on ontology class structure more accurately predict expert judgments. There is some (although weak) empirical evidence that other associations among constructs— such as the roles that indicators play in an insider threat exploit—may also contribute to expert judgments of insider threat risk. These findings contribute to ongoing research aimed at development of more effective insider threat decision support tools.