nils-herrmann/clean_scirepeval huggingface dataset:
CONSTRUCTING WADGE CLASSES

We show that, assuming the Axiom of Determinacy, every non-selfdual Wadge class can be constructed by starting with those of level 
$\omega _1$
 (that is, the ones that are closed under Borel preimages) and iteratively applying the operations of expansion and separated differences. The proof is essentially due to Louveau, and it yields at the same time a new proof of a theorem of Van Wesep (namely, that every non-selfdual Wadge class can be expressed as the result of a Hausdorff operation applied to the open sets). The exposition is self-contained, except for facts from classical descriptive set theory.
A Dark Hidden-Sector of Dirac fermions at the GeV scale

Our contribution sets out to investigate the GeV-scale phenomenology of a model based on an $SU_{L}(2) \times U_{Y}(1) \times U(1)_{X}$-gauge symmetry. The model accommodates, as a consequence of the symmetry-breaking pattern, a light gauge boson at the GeV-scale or below, allowing then to set up a new low energy physics. The fermion sector includes an exotic candidate to Dark Matter (DM) and the whole fermionic field content yields, as it is mandatory, the cancellation of the chiral anomaly. %In this strict sense, we refer to our proposal as a unified description : two different and exclusive symmetry-breaking patterns are treated with a single Lagrangian density and a common symmetry group. Furthermore, The light gauge boson and the exotic fermion are proposed as candidates to the so-called dark sector of the model that we try to describe in this contribution. The low-energy spectrum exhibits a hidden scalar field with mass fixed at the GeV level in the Higgs sector. We calculate the DM relic density associated with the dark fermion candidate by considering two annihilation processes in which the light gauge boson works as a portal for the DM detection. The observed relic density points to a mass of $3.4$ GeV for the DM fermion, while the light gauge boson mass is $8.0$ GeV. We also investigate the direct detection of DM in the low energy regime and obtain the region of parameters allowed by recent discussions on DM limits. Finally, the correction to the muon magnetic dipole moment is calculated considering the gauge boson mass at the GeV scale or below.
Fukaya–Seidel categories of Hilbert schemes and parabolic category $\mathcal{O}$

We realise Stroppel's extended arc algebra in the Fukaya-Seidel category of a natural Lefschetz fibration on the generic fiber of the adjoint quotient map on a type $A$ nilpotent slice with two Jordan blocks, and hence obtain a symplectic interpretation of certain parabolic two-block versions of Bernstein-Gelfan'd-Gelfan'd category $\mathcal{O}$. As an application, we give a new geometric construction of the spectral sequence from annular to ordinary Khovanov homology. The heart of the paper is the development of a cylindrical model to compute Fukaya categories of (affine open subsets of) Hilbert schemes of quasi-projective surfaces, which may be of independent interest.
Learning End-to-End Goal-Oriented Dialog with Maximal User Task Success and Minimal Human Agent Use

Neural end-to-end goal-oriented dialog systems showed promise to reduce the workload of human agents for customer service, as well as reduce wait time for users. However, their inability to handle new user behavior at deployment has limited their usage in real world. In this work, we propose an end-to-end trainable method for neural goal-oriented dialog systems that handles new user behaviors at deployment by transferring the dialog to a human agent intelligently. The proposed method has three goals: 1) maximize user’s task success by transferring to human agents, 2) minimize the load on the human agents by transferring to them only when it is essential, and 3) learn online from the human agent’s responses to reduce human agents’ load further. We evaluate our proposed method on a modified-bAbI dialog task, which simulates the scenario of new user behaviors occurring at test time. Experimental results show that our proposed method is effective in achieving the desired goals.
Gated Recurrent Neural Network Approach for Multilabel Emotion Detection in Microblogs

People express their opinions and emotions freely in social media posts and online reviews that contain valuable feedback for multiple stakeholders such as businesses and political campaigns. Manually extracting opinions and emotions from large volumes of such posts is an impossible task. Therefore, automated processing of these posts to extract opinions and emotions is an important research problem. However, human emotion detection is a challenging task due to the complexity and nuanced nature. To overcome these barriers, researchers have extensively used techniques such as deep learning, distant supervision, and transfer learning. In this paper, we propose a novel Pyramid Attention Network (PAN) based model for emotion detection in microblogs. The main advantage of our approach is that PAN has the capability to evaluate sentences in different perspectives to capture multiple emotions existing in a single text. The proposed model was evaluated on a recently released dataset and the results achieved the state-of-the-art accuracy of 58.9%.
Sequential Pattern Mining of Longitudinal Adverse Events After Left Ventricular Assist Device Implant

Left ventricular assist devices (LVADs) are an increasingly common therapy for patients with advanced heart failure. However, implantation of the LVAD increases the risk of stroke, infection, bleeding, and other serious adverse events (AEs). Most post-LVAD AEs studies have focused on individual AEs in isolation, neglecting the possible interrelation, or causality between AEs. This study is the first to conduct an exploratory analysis to discover common sequential chains of AEs following LVAD implantation that are correlated with important clinical outcomes. This analysis was derived from 58,575 recorded AEs for 13,192 patients in International Registry for Mechanical Circulatory Support (INTERMACS) who received a continuous-flow LVAD between 2006 and 2015. The pattern mining procedure involved three main steps: (1) creating a bank of AE sequences by converting the AEs for each patient into a single, chronologically sequenced record, (2) grouping patients with similar AE sequences using hierarchical clustering, and (3) extracting temporal chains of AEs for each group of patients using Markov modeling. The mined results indicate the existence of seven groups of sequential chains of AEs, characterized by common types of AEs that occurred in a unique order. The groups were identified as: GRP1: Recurrent bleeding, GRP2: Trajectory of device malfunction & explant, GRP3: Infection, GRP4: Trajectories to transplant, GRP5: Cardiac arrhythmia, GRP6: Trajectory of neurological dysfunction & death, and GRP7: Trajectory of respiratory failure, renal dysfunction & death. These patterns of sequential post-LVAD AEs disclose potential interdependence between AEs and may aid prediction, and prevention, of subsequent AEs in future studies.
Electroencephalography based Classification of Long-term Stress using Psychological Labeling

Stress research is a rapidly emerging area in thefield of electroencephalography (EEG) based signal processing.The use of EEG as an objective measure for cost effective andpersonalized stress management becomes important in particularsituations such as the non-availability of mental health this http URL this study, long-term stress is classified using baseline EEGsignal recordings. The labelling for the stress and control groupsis performed using two methods (i) the perceived stress scalescore and (ii) expert evaluation. The frequency domain featuresare extracted from five-channel EEG recordings in addition tothe frontal and temporal alpha and beta asymmetries. The alphaasymmetry is computed from four channels and used as a feature.Feature selection is also performed using a t-test to identifystatistically significant features for both stress and control groups.We found that support vector machine is best suited to classifylong-term human stress when used with alpha asymmetry asa feature. It is observed that expert evaluation based labellingmethod has improved the classification accuracy up to 85.20%.Based on these results, it is concluded that alpha asymmetry maybe used as a potential bio-marker for stress classification, when labels are assigned using expert evaluation.
Network Based Pricing for 3D Printing Services in Two-Sided Manufacturing-as-a-Service Marketplace

This paper presents approaches to determine a network-based pricing for 3D printing services in the context of a two-sided manufacturing-as-a-service marketplace. The purpose of this study is to provide cost analytics to enable service bureaus to better compete in the market by moving away from setting ad hoc and subjective prices.



A data mining approach with machine learning methods is used to estimate a price range based on the profile characteristics of 3D printing service suppliers. The model considers factors such as supplier experience, supplier capabilities, customer reviews and ratings from past orders and scale of operations, among others, to estimate a price range for suppliers’ services. Data were gathered from existing marketplace websites, which were then used to train and test the model.



The model demonstrates an accuracy of 65 per cent for US-based suppliers and 59 per cent for Europe-based suppliers to classify a supplier’s 3D printer listing in one of the seven price categories. The improvement over baseline accuracy of 25 per cent demonstrates that machine learning-based methods are promising for network-based pricing in manufacturing marketplaces



Conventional methodologies for pricing services through activity-based costing are inefficient in strategically priced 3-D printing service offering in a connected marketplace. As opposed to arbitrarily determining prices, this work proposes an approach to determine prices through data mining methods to estimate competitive prices. Such tools can be built into online marketplaces to help independent service bureaus to determine service price rates.
Lung Nodules Detection and Segmentation Using 3D Mask-RCNN

Accurate assessment of Lung nodules is a time consuming and error prone ingredient of the radiologist interpretation work. Automating 3D volume detection and segmentation can improve workflow as well as patient care. Previous works have focused either on detecting lung nodules from a full CT scan or on segmenting them from a small ROI. We adapt the state of the art architecture for 2D object detection and segmentation, MaskRCNN, to handle 3D images and employ it to detect and segment lung nodules from CT scans. We report on competitive results for the lung nodule detection on LUNA16 data set. The added value of our method is that in addition to lung nodule detection, our framework produces 3D segmentations of the detected nodules.
Direct detection of nuclear scattering of sub-Gev dark matter using molecular excitations

We propose a novel direct detection concept to search for dark matter with 100~keV to 100~MeV masses. Such dark matter can scatter off molecules in a gas and transfer an $\mathcal{O}(1)$ fraction of its kinetic energy to excite a vibrational and rotational state. The excited ro-vibrational mode relaxes rapidly and produces a spectacular multi-infrared-photon signal, which can be observed with ultrasensitive photodetectors. We discuss in detail a gas target consisting of carbon monoxide molecules, which enable efficient photon emission even at a relatively low temperature and high vapor pressure. The emitted photons have an energy in the range 180~meV to 265~meV. By mixing together carbon monoxide molecules of different isotopes, including those with an odd number of neutrons, we obtain sensitivity to both spin-independent interactions and spin-dependent interactions with the neutron. We also consider hydrogen fluoride, hydrogen bromide, and scandium hydride molecules, which each provide sensitivity to spin-dependent interactions with the proton. The proposed detection concept can be realized with near-term technology and allows for the exploration of orders of magnitude of new dark matter parameter space.
Mapping the Stellar Halo with the H3 Spectroscopic Survey

Modern theories of galaxy formation predict that the Galactic stellar halo was hierarchically assembled from the accretion and disruption of smaller systems. This hierarchical assembly is expected to produce a high degree of structure in the combined phase and chemistry space; this structure should provide a relatively direct probe of the accretion history of our Galaxy. Revealing this structure requires precise 3D positions (including distances), 3D velocities, and chemistry for large samples of stars. The Gaia satellite is delivering proper motions and parallaxes for >1 billion stars to G ≈ 20. However, radial velocities and metallicities will only be available to G ≈ 15, which is insufficient to probe the outer stellar halo (≳10 kpc). Moreover, parallaxes will not be precise enough to deliver high-quality distances for stars beyond ∼10 kpc. Identifying accreted systems throughout the stellar halo therefore requires a large ground-based spectroscopic survey to complement Gaia. Here we provide an overview of the H3 Stellar Spectroscopic Survey, which will deliver precise stellar parameters and spectrophotometric distances for ≈200,000 stars to r = 18. Spectra are obtained with the Hectochelle instrument at the MMT, which is configured for the H3 Survey to deliver resolution R ≈ 23,000 spectra covering the wavelength range 5150–5300 Å. The survey is optimized for stellar halo science and therefore focuses on high Galactic latitude fields ( ), sparsely sampling 15,000 sq. degrees. Targets are selected on the basis of Gaia parallaxes, enabling very efficient selection of bona fide halo stars. The survey began in the fall of 2017 and has collected 88,000 spectra to-date. All of the data, including the derived stellar parameters, will eventually be made publicly available via the survey website: h3survey.rc.fas.harvard.edu.
Cosmic perturbations, baryon asymmetry, and dark matter from the minimal supersymmetric standard model

Scalar fields in the minimal supersymmetric standard model may have large field values during inflation. Because of approximate global symmetry, it is plausible that the phase directions of them are nearly massless during inflation and obtain quantum fluctuations, which may be the origin of the cosmic perturbations. If perturbations are produced through Q-ball formation, baryon asymmetry and dark matter can be consistently generated. Significant baryon and dark matter isocurvature perturbations are produced, but they are predicted to nearly compensate each other. The lepton asymmetry is much larger than the baryon asymmetry. The scenario predicts local non-Gaussianity of $f_{\rm NL} = 5/3$. Implication to the mass spectrum of supersymmetric particles is discussed.
ZZ Instantons and the Non-Perturbative Dual of c = 1 String Theory

We study the effect of ZZ instantons in c = 1 string theory, and demonstrate that they give rise to non-perturbative corrections to scattering amplitudes that do not saturate unitarity within the closed string sector. Beyond the leading non-perturbative order, logarithmic divergences are canceled between worldsheet diagrams of different topologies, due to the Fischler-Susskind-Polchinski mechanism. We propose that the closed string vacuum in c = 1 string theory is non-perturbatively dual to a state of the matrix quantum mechanics in which all scattering states up to a given energy with no incoming flux from the "other side" of the potential are occupied by free fermions. Under such a proposal, we find detailed agreement of non-perturbative corrections to closed string amplitudes in the worldsheet description and in the dual matrix model.
Optical transient from an explosion close to the stellar surface

We study the hydrodynamic evolution of an explosion close to the stellar surface, and give predictions for the radiation from such an event. We show that such an event will give rise to a multiwavelength transient. We apply this model to describe a precursor burst to the peculiar supernova iPTF14hls, which occurred in 1954, 60 yr before the supernova. We propose that the new generation of optical surveys might detect similar transients, and that they can be used to identify supernova progenitors well before the explosion.
Large Molecular Gas Reservoirs in Star-forming Cluster Galaxies

We present CO (2−1) observations of 72 galaxies in the nearby, disturbed Antlia Cluster with the Atacama Pathfinder Experiment telescope. The galaxies in our sample are selected to span a wide range of stellar masses (108 M⊙ ≲ M⋆ ≲ 1010 M⊙) and star formation rates (0.0005 M⊙ yr−1 < SFR < 0.3 M⊙ yr−1). Reaching a depth of 23 mJy in 50 km s−1 channels, we report a total CO detection rate of 37.5% and a CO detection rate of 86% for sources within 1 dex of the main sequence. We compare our sample with a similar sample of galaxies in the field, finding that, for a fixed stellar mass and SFR, galaxies in the Antlia Cluster have comparable molecular gas reservoirs to field galaxies. We find that ∼41% (11/27) of our CO detections display non-Gaussian CO (2−1) emission-line profiles, and a number of these sources display evidence of quenching in their optical images. We also find that the majority of our sample lies either just below or far below the main sequence of field galaxies, further hinting at potential ongoing quenching. We conclude that the Antlia Cluster represents an intermediate environment between fields and dense clusters, where the gentler intracluster medium (ICM) allows the cluster members to retain their reservoirs of molecular gas, but in which the disturbed ICM is just beginning to influence the member galaxies, resulting in high SFRs and possible ongoing quenching.
Tension with the flat ΛCDM model from a high-redshift Hubble diagram of supernovae, quasars, and gamma-ray bursts

In the current framework, the standard parametrization of our Universe is the so-called Lambda cold dark matter (ΛCDM) model. Recently, a ∼4σ tension with the ΛCDM model was shown to exist via a model-independent parametrization of a Hubble diagram of type Ia supernovae (SNe Ia) from the JLA survey and quasars. Model-independent approaches and independent samples over a wide redshift range are key to testing this tension and any possible systematic errors. Here we present an analysis of a combined Hubble diagram of SNe Ia, quasars, and gamma-ray bursts (GRBs) to check the agreement of the quasar and GRB cosmological parameters at high redshifts (z >  2) and to test the concordance flat ΛCDM model with improved statistical accuracy. We build a Hubble diagram with SNe Ia, quasars, and GRBs, where quasars are standardised through the observed non-linear relation between their ultraviolet and X-ray emission and GRBs through the correlation between the spectral peak energy and the isotropic-equivalent radiated energy (the so-called Amati relation). We fit the data with cosmographic models consisting of a fourth-order logarithmic polynomial and a fifth-order linear polynomial, and compare the results with the expectations from a flat ΛCDM model. We confirm the tension between the best-fit cosmographic parameters and the ΛCDM model at ∼4σ with SNe Ia and quasars, at ∼2σ with SNe Ia and GRBs, and at > 4σ with the whole SNe Ia+quasars+GRB data set. The completely independent high-redshift Hubble diagrams of quasars and GRBs are fully consistent with each other, strongly suggesting that the deviation from the standard model is not due to unknown systematic effects but to new physics.
Sub-damped Lyman α systems in the XQ-100 survey – I. Identification and contribution to the cosmological H i budget

Sub-damped Lyman α systems (subDLAs; H i column densities of 19.0 ≤ logN(H i) < 20.3) are rarely included in the cosmic H i census performed at redshifts zabs ≳ 1.5, yet are expected to contribute significantly to the overall H i mass budget of the Universe. In this paper, we present a blindly selected sample of 155 subDLAs found along 100 quasar sightlines (with a redshift path-length ΔX = 475) in the XQ-100 legacy survey to investigate the contribution of subDLAs to the H i mass density of the Universe. The impact of X-Shooter’s spectral resolution on Ly α absorber identification is evaluated, and found to be sufficient for reliably finding absorbers down to a column density of logN(H i) ≥ 18.9. We compared the implications of searching for subDLAs solely using H i absorption versus the use of metal lines to confirm the identification, and found that metal-selection techniques would have missed 75 subDLAs. Using a bootstrap Monte Carlo simulation, we computed the column density distribution function (f(N, X)) and the cosmological H i mass density ($\Omega _{\rm H\,{\small I}}$) of subDLAs and compared with our previous work based on the XQ-100 damped Lyman α systems. We do not find any significant redshift evolution in f(N, X) or $\Omega _{\rm H\,{\small I}}$ for subDLAs. However, subDLAs contribute 10–20 per cent of the total $\Omega _{\rm H\,{\small I}}$ measured at redshifts 2 < z < 5, and thus have a small but significant contribution to the H i budget of the Universe.
Untangling the Galaxy. I. Local Structure and Star Formation History of the Milky Way

Gaia DR2 provides unprecedented precision in measurements of the distance and kinematics of stars in the solar neighborhood. Through applying unsupervised machine learning on DR2's 5D data set (3D position + 2D velocity), we identify a number of clusters, associations, and comoving groups within 1 kpc and (many of which have not been previously known). We estimate their ages with the precision of ∼0.15 dex. Many of these groups appear to be filamentary or string-like, oriented in parallel to the Galactic plane, and some span hundreds of parsec in length. Most of these string lack a central cluster, indicating that their filamentary structure is primordial, rather than the result of tidal stripping or dynamical processing. The youngest strings (<100 Myr) are orthogonal to the Local Arm. The older ones appear to be remnants of several other arm-like structures that cannot be presently traced by dust and gas. The velocity dispersion measured from the ensemble of groups and strings increase with age, suggesting a timescale for dynamical heating of ∼300 Myr. This timescale is also consistent with the age at which the population of strings begins to decline, while the population in more compact groups continues to increase, suggesting that dynamical processes are disrupting the weakly bound string populations, leaving only individual clusters to be identified at the oldest ages. These data shed a new light on the local galactic structure and a large-scale cloud collapse.