nils-herrmann/clean_scirepeval huggingface dataset:
Fundamental Structures in Dynamic Communication Networks

In this paper I introduce a framework for modeling temporal communication networks and dynamical processes unfolding on such networks. The framework originates from the realization that there is a meaningful division of temporal communication networks into six dynamic classes, where the class of a network is determined by its generating process. In particular, each class is characterized by a fundamental structure: a temporal-topological network motif, which corresponds to the network representation of communication events in that class of network. These fundamental structures constrain network configurations: only certain configurations are possible within a dynamic class. In this way the framework presented here highlights strong constraints on network structures, which simplify analyses and shape network flows. Therefore the fundamental structures hold the potential to impact how we model temporal networks overall. I argue below that networks within the same class can be meaningfully compared, and modeled using similar techniques, but that integrating statistics across networks belonging to separate classes is not meaningful in general. This paper presents a framework for how to analyze networks in general, rather than a particular result of analyzing a particular dataset. I hope, however, that readers interested in modeling temporal networks will find the ideas and discussion useful in spite of the paper's more conceptual nature.
MusE GAs FLOw and Wind (MEGAFLOW) – III. Galactic wind properties using background quasars

We present results from our on-going MusE GAs FLOw and Wind (MEGAFLOW) survey, which consists of 22 quasar lines of sight, each observed with the integral field unit MUSE and the UVES spectrograph at the ESO Very Large Telescopes (VLT). The goals of this survey are to study the properties of the circumgalactic medium around z ∼ 1 star-forming galaxies. The absorption-line selected survey consists of 79 strong Mg ii absorbers (with rest-frame equivalent width ≳0.3 Å) and, currently, 86 associated galaxies within 100 projected kpc of the quasar with stellar masses (M⋆) from 109 to 1011 M⊙. We find that the cool halo gas traced by Mg ii is not isotropically distributed around these galaxies from the strong bi-modal distribution in the azimuthal angle of the apparent location of the quasar with respect to the galaxy major axis. This supports a scenario in which outflows are bi-conical in nature and co-exist with a co-planar gaseous structure extending at least up to 60–80 kpc. Assuming that absorbers near the minor axis probe outflows, the current MEGAFLOW sample allowed us to select 26 galaxy–quasar pairs suitable for studying winds. From this sample, using a simple geometrical model, we find that the outflow velocity only exceeds the escape velocity when M⋆ ≲ 4 × 109 M⊙, implying the cool material is likely to fall back except in the smallest haloes. Finally, we find that the mass loading factor η, the ratio between the ejected mass rate and the star formation rate, appears to be roughly constant with respect to the galaxy mass.
Incremental Class Discovery for Semantic Segmentation With RGBD Sensing

This work addresses the task of open world semantic segmentation using RGBD sensing to discover new semantic classes over time. Although there are many types of objects in the real-word, current semantic segmentation methods make a closed world assumption and are trained only to segment a limited number of object classes. Towards a more open world approach, we propose a novel method that incrementally learns new classes for image segmentation. The proposed system first segments each RGBD frame using both color and geometric information, and then aggregates that information to build a single segmented dense 3D map of the environment. The segmented 3D map representation is a key component of our approach as it is used to discover new object classes by identifying coherent regions in the 3D map that have no semantic label. The use of coherent region in the 3D map as a primitive element, rather than traditional elements such as surfels or voxels, also significantly reduces the computational complexity and memory use of our method. It thus leads to semi-real-time performance at 10.7 Hz when incrementally updating the dense 3D map at every frame. Through experiments on the NYUDv2 dataset, we demonstrate that the proposed method is able to correctly cluster objects of both known and unseen classes. We also show the quantitative comparison with the state-of-the-art supervised methods, the processing time of each step, and the influences of each component.
Structured Fusion Networks for Dialog

Neural dialog models have exhibited strong performance, however their end-to-end nature lacks a representation of the explicit structure of dialog. This results in a loss of generalizability, controllability and a data-hungry nature. Conversely, more traditional dialog systems do have strong models of explicit structure. This paper introduces several approaches for explicitly incorporating structure into neural models of dialog. Structured Fusion Networks first learn neural dialog modules corresponding to the structured components of traditional dialog systems and then incorporate these modules in a higher-level generative model. Structured Fusion Networks obtain strong results on the MultiWOZ dataset, both with and without reinforcement learning. Structured Fusion Networks are shown to have several valuable properties, including better domain generalizability, improved performance in reduced data scenarios and robustness to divergence during reinforcement learning.
Bernstein-Sato Functional Equations, V-Filtrations, and Multiplier Ideals of Direct Summands

This paper investigates the existence and properties of a Bernstein-Sato functional equation in nonregular settings. In particular, we construct $D$-modules in which such formal equations can be studied. The existence of the Bernstein-Sato polynomial for a direct summand of a polynomial over a field is proved in this context. It is observed that this polynomial can have zero as a root, or even positive roots. Moreover, a theory of $V$-filtrations is introduced for nonregular rings, and the existence of these objects is established for what we call differentially extensible summands. This family of rings includes toric, determinantal, and other invariant rings. This new theory is applied to the study of multiplier ideals of singular varieties. Finally, we extend known relations among the objects of interest in the smooth case to the setting of singular direct summands of polynomial rings.
Hidden Markov Models derived from Behavior Trees

Behavior trees are rapidly attracting interest in robotics and human task-related motion tracking. However no algorithms currently exist to track or identify parameters of BTs under noisy observations. We report a new relationship between BTs, augmented with statistical information, and Hidden Markov Models. Exploiting this relationship will allow application of many algorithms for HMMs (and dynamic Bayesian networks) to data acquired from BT-based systems.
Happiness Entailment: Automating Suggestions for Well-Being

Understanding what makes people happy is a central topic in psychology. Prior work has mostly focused on developing self-reporting assessment tools for individuals and relies on experts to analyze the periodic reported assessments. One of the goals of the analysis is to understand what actions are necessary to encourage modifications in the behaviors of the individuals to improve their overall well-being. In this paper, we outline a complementary approach; on the assumption that the user journals her happy moments as short texts, a system can analyze these texts and propose sustainable suggestions for the user that may lead to an overall improvement in her well-being. We prototype one necessary component of such a system, the Happiness Entailment Recognition (HER)module, which takes as input a short text describing an event, a candidate suggestion, and outputs a determination about whether the suggestion is more likely to be good for this user based on the event described. This component is implemented as a neural network model with two encoders, one for the user input and one for the candidate actionable suggestion, with additional layers to capture psychologically significant features in the happy moment and suggestion. Our model achieves an AU-ROC of 0.831 and outperforms our baseline as well as the current state-of-the-art Textual Entailment model from AllenNLP by more than 48% of improvements, confirming the uniqueness and complexity of the HER task.
Non-linear obstructions for consistent new general relativity

We revisit the field content and consistency of the New General Relativity family of theories. These theories are constructed in a geometrical framework with a flat and metric-compatible connection, so the affine structure is entirely determined by the torsion. The action is given by a local and parity-preserving quadratic form of the torsion with three free parameters. It is well-known that a special choice of parameters gives an equivalent to General Relativity and that the spectrum of the general linear theory around Minkowski contains an additional 2-form field. It has been suggested that the viability of these theories at linear order requires the 2-form field to feature a gauge symmetry so that it describes a massless Kalb-Ramond field. In this work we revisit these previous results and compute the cubic interactions. We also obtain the decoupling limit of the theories and show that the required gauge symmetry for the 2-form at linear order cannot be extended to higher orders. This signals towards a pathological behaviour of these theories and singles out the equivalent of General Relativity as the only consistent New General Relativity theory with a stable Minkowski background that includes gravity.
Trading via image classification

The art of systematic financial trading evolved with an array of approaches, ranging from simple strategies to complex algorithms, all relying primarily on aspects of time-series analysis (e.g., Murphy, 1999; De Prado, 2018; Tsay, 2005). After visiting the trading floor of a leading financial institution, we noticed that traders always execute their trade orders while observing images of financial time-series on their screens. In this work, we build upon image recognition's success (e.g., Krizhevsky et al., 2012; Szegedy et al., 2015; Zeiler and Fergus, 2014; Wang et al., 2017; Koch et al., 2015; LeCun et al., 2015) and examine the value of transforming the traditional time-series analysis to that of image classification. We create a large sample of financial time-series images encoded as candlestick (Box and Whisker) charts and label the samples following three algebraically-defined binary trade strategies (Murphy, 1999). Using the images, we train over a dozen machine-learning classification models and find that the algorithms efficiently recover the complicated, multiscale label-generating rules when the data is visually represented. We suggest that the transformation of continuous numeric time-series classification problem to a vision problem is useful for recovering signals typical of technical analysis.
Sustainable Business Models: A Review

During the past two decades of e-commerce growth, the concept of a business model has become increasingly popular. More recently, the research on this realm has grown rapidly, with diverse research activity covering a wide range of application areas. Considering the sustainable development goals, the innovative business models have brought a competitive advantage to improve the sustainability performance of organizations. The concept of the sustainable business model describes the rationale of how an organization creates, delivers, and captures value, in economic, social, cultural, or other contexts, in a sustainable way. The process of sustainable business model construction forms an innovative part of a business strategy. Different industries and businesses have utilized sustainable business models’ concept to satisfy their economic, environmental, and social goals simultaneously. However, the success, popularity, and progress of sustainable business models in different application domains are not clear. To explore this issue, this research provides a comprehensive review of sustainable business models literature in various application areas. Notable sustainable business models are identified and further classified in fourteen unique categories, and in every category, the progress -either failure or success- has been reviewed, and the research gaps are discussed. Taxonomy of the applications includes innovation, management and marketing, entrepreneurship, energy, fashion, healthcare, agri-food, supply chain management, circular economy, developing countries, engineering, construction and real estate, mobility and transportation, and hospitality. The key contribution of this study is that it provides an insight into the state of the art of sustainable business models in various application areas and future research directions. This paper concludes that popularity and the success rate of sustainable business models in all application domains have been increased along with the increasing use of advanced technologies.
Testing gravity with gravitational waves from binary black hole mergers: Contributions from amplitude corrections

The detection of gravitational waves has offered us the opportunity to explore the dynamical and strong-field regime of gravity. Because matched filtering is more sensitive to variations in the gravitational waveform phase than the amplitude, many tests of gravity with gravitational waves have been carried out using only the former. Such studies cannot probe the non-Einsteinian effects that may enter only in the amplitude. Besides, if not accommodated in the waveform template, a non-Einsteinian effect in the amplitude may induce systematic errors on other parameters such as the luminosity distance. In this paper, we derive constraints on a few modified theories of gravity (Einstein-dilaton-Gauss-Bonnet gravity, scalar-tensor theories, and varying-$G$ theories), incorporating both phase and amplitude corrections. We follow the model-independent approach of the parametrized post-Einsteinian formalism. We perform Fisher analyses with Monte-Carlo simulations using the LIGO/Virgo posterior samples. We find that the contributions from amplitude corrections can be comparable to the ones from the phase corrections in case of massive binaries like GW150914. Also, constraints derived by incorporating both phase and amplitude corrections differ from the ones with phase corrections only by 4% at most, which supports many of the previous studies that only considered corrections in the phase. We further derive reliable constraints on the time-evolution of a scalar field in a scalar-tensor theory for the first time with gravitational waves.
VIS3COS: II. Nature and nurture in galaxy structure and morphology

We study the impact of local density and stellar mass on the structure and morphology of approximately 500 quiescent and star-forming galaxies from the VIMOS Spectroscopic Survey of a Superstructure in COSMOS ((VISCOS)-C-3). We perform bulge-to-disc decomposition of the surface brightness profiles and find similar to 41 +/- 3% of >10(10) M-circle dot galaxies to be best fitted with two components. We complement our analysis with non-parametric morphological measurements and qualitative visual classifications. We find that both galaxy structure and morphology depend on stellar mass and environment for our sample as a whole. We only find an impact of the environment on galaxy size for galaxies more massive than 10(11 )M(circle dot). We find higher Sersic indices (n) and bulge-to-total ratios (B/T) in high-density regions when compared to low-density counterparts at similar stellar masses. We also find that galaxies with higher stellar mass have steeper light profiles (high n, B/T) compared to galaxies with lower stellar mass. Using visual classifications, we find a morphology-density relation at z similar to 0.84 for galaxies more massive than 10(10) M-circle dot, with elliptical galaxies being dominant at high-density regions and disc galaxies more common in low-density regions. However, when splitting the sample into colour-colour-selected star-forming and quiescent sub-populations, there are no statistically significant differences between low- and high-density regions. We find that quiescent galaxies are smaller, have higher Sersic indices (for single profiles, around n similar to 4), and higher bulgeto-total light ratios (for decomposed profiles, around B/T similar to 0.5) when compared to star-forming counterparts (n similar to 1 and B/T similar to 0.3, for single and double profiles, respectively). We confirm these trends with non-parametric quantities, finding quiescent galaxies to be smoother (lower asymmetry, lower M-20) and to have most of their light over smaller areas (higher concentration and Gini coefficient) than star-forming galaxies. Overall, we find a stronger dependence of structure and morphology on stellar mass than on local density and these relations are strongly correlated with the quenching fraction. The change in average structure or morphology corresponds to a change in the relative fractions of blue disc-like galaxies and red elliptical galaxies with stellar mass and environment. We hypothesise that the processes responsible for the quenching of star formation must also affect the galaxy morphology on similar timescales.
IC 4665 DANCe

The study of star formation is extremely challenging, due to the lack of complete and clean samples of young nearby clusters and star-forming regions. The recent Gaia DR2 catalogue complemented with the deep ground-based COSMIC DANCe catalogue offers a new database of unprecedented accuracy to revisit the membership of clusters and star-forming regions. The 30 Myr open cluster IC 4665 is one of the few well-known clusters of this age and it is an excellent target where evolutionary models can be tested and planetary formation studied.
Aims. We provide a comprehensive membership analysis of IC 4665 and study the following properties: empirical isochrones, distance, magnitude distribution, present-day system mass function, and spatial distribution.
Methods. We used the Gaia DR2 catalogue together with the DANCe catalogue to look for members via a probabilistic model of the distribution of the observable quantities in both the cluster and background populations.
Results. We obtained a final list of 819 candidate members that cover a 12.4 magnitude range (7 <  J <  19.4). We find that 50% are new candidates, and we estimate a conservative contamination rate of 20%. This unique sample of members allows us to obtain a present-day system mass function in the range of 0.02–6 M⊙, which reveals a number of details not seen in previous studies. In addition, we find that a spherically symmetric spatial distribution is favoured by our final list of members for this young open cluster.
Conclusions. Our membership analysis represents a significant increase in the quantity and quality (low contamination) with respect to previous studies. It offers an excellent opportunity to revisit other fundamental parameters such as age.
Cosmic Expansion History from Line-Intensity Mapping.

Line-intensity mapping (LIM) of emission from star-forming galaxies can be used to measure the baryon acoustic oscillation (BAO) scale as far back as the epoch of reionization. This provides a standard cosmic ruler to constrain the expansion rate of the Universe at redshifts which cannot be directly probed otherwise. In light of growing tension between measurements of the current expansion rate using the local distance ladder and those inferred from the cosmic microwave background, extending the constraints on the expansion history to bridge between the late and early Universe is of paramount importance. Using a newly derived methodology to robustly extract cosmological information from LIM, which minimizes the inherent degeneracy with unknown astrophysics, we show that present and future experiments can gradually improve the measurement precision of the expansion rate history, ultimately reaching percent-level constraints on the BAO scale. Specifically, we provide detailed forecasts for the SPHEREx satellite, which will target the Hα and Lyman-α lines, for a near-future stage-2 experiment targeting CII, and for the ground-based COMAP instrument-as well as a future stage-3 experiment-that will target the CO rotational lines. Besides weighing in on the so-called Hubble tension, reliable LIM cosmic rulers can enable wide-ranging tests of dark matter, dark energy, and modified gravity.
User’s guide to extracting cosmological information from line-intensity maps

Line-intensity mapping (LIM) provides a promising way to probe cosmology, reionization and galaxy evolution. However, its sensitivity to cosmology and astrophysics at the same time is also a nuisance. Here we develop a comprehensive framework for modelling the LIM power spectrum, which includes redshift space distortions and the Alcock-Paczynski effect. We then identify and isolate degeneracies with astrophysics so that they can be marginalized over. We study the gains of using the multipole expansion of the anisotropic power spectrum, providing an accurate analytic expression for their covariance, and find a 10%-60% increase in the precision of the baryon acoustic oscillation scale measurements when including the hexadecapole in the analysis. We discuss different observational strategies when targeting other cosmological parameters, such as the sum of neutrino masses or primordial non-Gaussianity, finding that fewer and wider bins are typically more optimal. Overall, our formalism facilitates an optimal extraction of cosmological constraints robust to astrophysics.
Spherical collapse in coupled quintessence with a 
ΛCDM
 background

In this work we study the growth of cold dark matter density perturbations in the nonlinear regime on a conformally coupled quintessence model in which the background is designed to mimic a $\Lambda$CDM cosmology. The spherical collapse of overdense regions is analyzed. We highlight the role of the coupling on the overall dynamics, trace the evolution of the density contrast throughout the cosmic history and compute perturbative parameters such as the critical density contrast. We find that the coupling has the influence of delaying the collapse due to the slower growth of matter perturbations. We follow to compute the cluster number counts using the Press-Schechter and Sheth-Tormen mass functions. In both cases, the transfer of energy between the dark energy field and dark matter suppresses the number of objects at low redshifts and enhances the number at high redshifts. Finally, we compute the expected cluster number counts for the future eROSITA mission and the current South Pole Telescope survey.
GraphXNET-Chest X-Ray Classification Under Extreme Minimal Supervision

The task of classifying X-ray data is a problem of both theoretical and clinical interest. Whilst supervised deep learning methods rely upon huge amounts of labelled data, the critical problem of achieving a good classification accuracy when an extremely small amount of labelled data is available has yet to be tackled. In this work, we introduce a novel semi-supervised framework for X-ray classification which is based on a graph-based optimisation model. To the best of our knowledge, this is the first method that exploits graph-based semi-supervised learning for X-ray data classification. Furthermore, we introduce a new multi-class classification functional with carefully selected class priors which allows for a smooth solution that strengthens the synergy between the limited number of labels and the huge amount of unlabelled data. We demonstrate, through a set of numerical and visual experiments, that our method produces highly competitive results on the ChestX-ray14 data set whilst drastically reducing the need for annotated data.