nils-herrmann/clean_scirepeval huggingface dataset:
Universal audio synthesizer control with normalizing flows

The ubiquity of sound synthesizers has reshaped music production and even entirely defined new music genres. However, the increasing complexity and number of parameters in modern synthesizers make them harder to master. Hence, the development of methods allowing to easily create and explore with synthesizers is a crucial need. Here, we introduce a novel formulation of audio synthesizer control. We formalize it as finding an organized latent audio space that represents the capabilities of a synthesizer, while constructing an invertible mapping to the space of its parameters. By using this formulation, we show that we can address simultaneously automatic parameter inference, macro-control learning and audio-based preset exploration within a single model. To solve this new formulation, we rely on Variational Auto-Encoders (VAE) and Normalizing Flows (NF) to organize and map the respective auditory and parameter spaces. We introduce the disentangling flows, which allow to perform the invertible mapping between separate latent spaces, while steering the organization of some latent dimensions to match target variation factors by splitting the objective as partial density evaluation. We evaluate our proposal against a large set of baseline models and show its superiority in both parameter inference and audio reconstruction. We also show that the model disentangles the major factors of audio variations as latent dimensions, that can be directly used as macro-parameters. We also show that our model is able to learn semantic controls of a synthesizer by smoothly mapping to its parameters. Finally, we discuss the use of our model in creative applications and its real-time implementation in Ableton Live
Efficient Computation of Collisional ℓ-mixing Rate Coefficients in Astrophysical Plasmas

We present analytical expressions for direct evaluation of ℓ-mixing rate coefficients in proton-excited hydrogen atom collisions and describe a software package for efficient numerical evaluation of the collisional rate coefficients. Comparisons between rate coefficients calculated with various levels of approximation are discussed, highlighting their range of validity. These rate coefficients are benchmarked for radio recombination lines for hydrogen, evaluating the corresponding departure coefficients from local thermal equilibrium.
GASP – XX. From the loose spatially resolved to the tight global SFR–mass relation in local spiral galaxies

Exploiting the sample of 30 local star-forming, undisturbed late-type galaxies in different environments drawn from the GAs Stripping Phenomena in galaxies with MUSE (GASP), we investigate the spatially resolved star formation rate–mass ($\rm \Sigma _{SFR}$–$\rm \Sigma _\ast$) relation. Our analysis includes also the galaxy outskirts (up to >4 effective radii, re), a regime poorly explored by other Integral Field Spectrograph surveys. Our observational strategy allows us to detect H α out to more than 2.7re for 75 per cent of the sample. Considering all galaxies together, the correlation between the $\rm \Sigma _{SFR}$ and $\rm \Sigma _\ast$ is quite broad, with a scatter of 0.3 dex. It gets steeper and shifts to higher $\rm \Sigma _\ast$ values when external spaxels are excluded and moving from less to more massive galaxies. The broadness of the overall relation suggests galaxy-by-galaxy variations. Indeed, each object is characterized by a distinct $\rm \Sigma _{SFR}$ –$\rm \Sigma _\ast$ relation and in some cases the correlation is very loose. The scatter of the relation mainly arises from the existence of bright off-centre star-forming knots whose $\rm \Sigma _{SFR}$–$\rm \Sigma _\ast$ relation is systematically broader than that of the diffuse component. The $\rm \Sigma _{SFR}$–$\rm \Sigma _{tot \, gas}$ (total gas surface density) relation is as broad as the $\rm \Sigma _{SFR}$–$\rm \Sigma _\ast$ relation, indicating that the surface gas density is not a primary driver of the relation. Even though a large galaxy-by-galaxy variation exists, mean $\rm \Sigma _{SFR}$ and $\rm \Sigma _\ast$ values vary of at most 0.7 dex across galaxies. We investigate the relationship between the local and global SFR–M* relation, finding that the latter is driven by the existence of the size–mass relation.
Rapid early coeval star formation and assembly of the most-massive galaxies in the Universe

The current consensus on the formation and evolution of the brightest cluster galaxies is that their stellar mass forms early ($z$ ≳ 4) in separate galaxies that then eventually assemble the main structure at late times ($z$ ≲ 1). However, advances in observational techniques have led to the discovery of protoclusters out to $z$ ∼ 7. If these protoclusters assemble rapidly in the early Universe, they should form the brightest cluster galaxies much earlier than suspected by the late-assembly picture. Using a combination of observationally constrained hydrodynamical and dark-matter-only simulations, we show that the stellar assembly time of a sub-set of brightest cluster galaxies occurs at high redshifts ( $z$ > 3) rather than at low redshifts ($z$ < 1), as is commonly thought. We find, using isolated non-cosmological hydrodynamical simulations, that highly overdense protoclusters assemble their stellar mass into brightest cluster galaxies within ∼1 Gyr of evolution – producing massive blue elliptical galaxies at high redshifts ($z$ ≳ 1.5). We argue that there is a downsizing effect on the cluster scale wherein some of the brightest cluster galaxies in the cores of the most-massive clusters assemble earlier than those in lower mass clusters. In those clusters with $z$ = 0 virial mass ≥ 5 × 1014 M⊙, we find that $9.8{{\ \rm per\ cent}}$ have their cores assembly early, and a higher fraction of $16.4{{\ \rm per\ cent}}$ in those clusters above 1015 M⊙. The James Webb Space Telescope will be able to detect and confirm our prediction in the near future, and we discuss the implications to constraining the value of σ8.
Galactic cirri in deep optical imaging

The ubiquitous presence of Galactic cirri in deep optical images represents a major obstacle to study the low surface brightness features of extragalactic sources. To address this issue, we have explored the optical properties of cirri using g, r, i, and z bands in the Sloan Digital Sky Survey (SDSS) Stripe82 region. Using state-of-the-art, custom made, image processing techniques, including the modeling and removal of the instrumental scattered light produced by the stars, we managed to isolate the optical diffuse emission by the cirri, allowing their photometric characterization. We find that their optical colors are driven by the dust column density: The cirri become redder as their 100 μm emission increases. Remarkably, the optical colors of the Galactic cirri differ significantly from those of extragalactic sources, with a characteristic bluer r-i color for a given g-r, allowing one to detect these by using a simple color relation. Our results show the high potential of deep multi-band optical photometry, on its own, identifying the presence of cirri at a higher spatial resolution than those provided by far-infrared observations. The combination of very deep data and multi-band photometry (as the one produced by LSST and Euclid) would make it possible to build dust maps of unprecedented quality.
Quasi-Dirac neutrino oscillations at DUNE and JUNO

Quasi-Dirac neutrinos are obtained when the Lagrangian density of a neutrino mass model contains both Dirac and Majorana mass terms, and the Majorana terms are sufficiently small. This type of neutrinos introduces new mixing angles and mass splittings into the Hamiltonian, which will modify the standard neutrino oscillation probabilities. In this paper, we focus on the case where the new mass splittings are too small to be measured, but new angles and phases are present. We perform a sensitivity study for this scenario for the upcoming experiments DUNE and JUNO, finding that they will improve current bounds on the relevant parameters. Finally, we also explore the discovery potential of both experiments, assuming that neutrinos are indeed quasi-Dirac particles.
Evolution of Black Hole and Galaxy Growth in a Semi-numerical Galaxy Formation Model

We present a simple semi-numerical model designed to explore black hole growth and galaxy evolution. This method builds on a previous model for black hole accretion that uses a semi-numerical galaxy formation model and universal Eddington ratio distribution to describe the full active galactic nucleus (AGN) population by independently connecting galaxy and AGN growth to the evolution of the host dark matter halos. We fit observed X-ray luminosity functions up to a redshift of z ∼ 4, as well as investigate the evolution of the Eddington ratio distributions. We find that the Eddington ratio distribution evolves with redshift such that the slope of the low-Eddington accretion rate distribution increases with cosmic time, consistent with the behavior predicted in hydrodynamical simulations for galaxies with different gas fractions. We also find that the evolution of our average Eddington ratio is correlated with observed star formation histories, supporting a picture in which black holes and galaxies evolve together in a global sense. We further confirm the impact of luminosity limits on observed galaxy and halo properties by applying selection criteria to our fiducial model and comparing to surveys across a wide range of redshifts.
Applying Liouville's Theorem to Gaia Data

The Milky Way is filled with the tidally-disrupted remnants of globular clusters and dwarf galaxies. Determining the properties of these objects -- in particular, initial masses and density profiles -- is relevant to both astronomy and dark matter physics. However, most direct measures of mass cannot be applied to tidal debris, as the systems of interest are no longer in equilibrium. Since phase-space density is conserved during adiabatic phase mixing, Liouville's theorem provides a connection between stellar kinematics as measured by observatories such as Gaia and the original mass of the disrupted system. Accurately recovering the phase-space density is complicated by uncertainties resulting from measurement errors and orbital integration, which both effectively inject entropy into the system, preferentially decreasing the measured density. In this paper, we demonstrate that these two issues can be overcome. First, we measure the phase-space density of the globular cluster M4 in Gaia data, and use Liouville's theorem to derive its mass. We then show that, for tidally disrupted systems, the orbital parameters and thus phase-space density can be inferred by minimizing the phase-space entropy of cold stellar streams. This work is therefore a proof of principle that true phase-space density can be measured and the original properties of the star cluster reconstructed in systems of astrophysical interest.
Neutrino non-standard interactions: A status report

This report summarizes the present status of neutrino non-standard interactions (NSI). After a brief overview, several aspects of NSIs are discussed, including connection to neutrino mass models, model-building and phenomenology of large NSI with both light and heavy mediators, NSI phenomenology in both short- and long-baseline neutrino oscillation experiments, neutrino cross-sections, complementarity of NSI with other low- and high-energy experiments, fits with neutrino oscillation and scattering data, DUNE sensitivity to NSI, effective field theory of NSI, as well as the relevance of NSI to dark matter and cosmology. 
We also discuss the open questions and interesting future directions that can be pursued by the community at large. 
This report is based on talks and discussions during the Neutrino Theory Network NSI workshop held at Washington University in St.~Louis from May 29-31, 2019
Enumeration of Preferred Extensions in Almost Oriented Digraphs

In this paper, we present enumeration algorithms to list all preferred extensions of an argumentation framework. This task is equivalent to enumerating all maximal semikernels of a directed graph. For directed graphs on $n$ vertices, all preferred extensions can be enumerated in $O^*(3^{n/3})$ time and there are directed graphs with $\Omega(3^{n/3})$ preferred extensions. We give faster enumeration algorithms for directed graphs with at most $0.8004\cdot n$ vertices occurring in $2$-cycles. In particular, for oriented graphs (digraphs with no 2-cycles) one of our algorithms runs in time $O(1.2321^n)$, and we show that there are oriented graphs with $\Omega(3^{n/6}) > \Omega(1.2009^n)$ preferred extensions. 
A combination of three algorithms leads to the fastest enumeration times for various proportions of the number of vertices in $2$-cycles. The most innovative one is a new 2-stage sampling algorithm, combined with a new parameterized enumeration algorithm, analyzed with a combination of the recent monotone local search technique (STOC 2016) and an extension thereof (ICALP 2017).
Constraining nuclear star cluster formation using MUSE-AO observations of the early-type galaxy FCC 47

Nuclear star clusters (NSCs) are found in at least 70% of all galaxies, but their formation path is still unclear. In the most common scenarios, NSCs form in-situ from the galaxy’s central gas reservoir, through the merging of globular clusters (GCs), or through a combination of both. As the scenarios pose different expectations for angular momentum and stellar population properties of the NSC in comparison to the host galaxy and the GC system, it is necessary to characterise the stellar light, NSC, and GCs simultaneously. The large NSC (reff = 66 pc) and rich GC system of the early-type Fornax cluster galaxy FCC 47 (NGC 1336) render this galaxy an ideal laboratory to constrain NSC formation. Using Multi Unit Spectroscopic Explorer science verification data assisted by adaptive optics, we obtained maps for the stellar kinematics and stellar-population properties of FCC 47. We extracted the spectra of the central NSC and determined line-of-sight velocities of 24 GCs and metallicities of five. The galaxy shows the following kinematically decoupled components (KDCs): a disk and a NSC. Our orbit-based dynamical Schwarzschild model revealed that the NSC is a distinct kinematic feature and it constitutes the peak of metallicity and old ages in FCC 47. The main body consists of two counter-rotating populations and is dominated by a more metal-poor population. The GC system is bimodal with a dominant metal-poor population and the total GC system mass is ∼17% of the NSC mass (∼7 × 108 M⊙).Conclusions. The rotation, high metallicity, and high mass of the NSC cannot be explained by GC-inspiral alone. It most likely requires additional, quickly quenched, in-situ formation. The presence of two KDCs likely are evidence of a major merger that has significantly altered the structure of FCC 47, indicating the important role of galaxy mergers in forming the complex kinematics in the galaxy-NSC system.
Learning Representations from Imperfect Time Series Data via Tensor Rank Regularization

There has been an increased interest in multimodal language processing including multimodal dialog, question answering, sentiment analysis, and speech recognition. However, naturally occurring multimodal data is often imperfect as a result of imperfect modalities, missing entries or noise corruption. To address these concerns, we present a regularization method based on tensor rank minimization. Our method is based on the observation that high-dimensional multimodal time series data often exhibit correlations across time and modalities which leads to low-rank tensor representations. However, the presence of noise or incomplete values breaks these correlations and results in tensor representations of higher rank. We design a model to learn such tensor representations and effectively regularize their rank. Experiments on multimodal language data show that our model achieves good results across various levels of imperfection.
Rare Disease Detection by Sequence Modeling with Generative Adversarial Networks

Rare diseases affecting 350 million individuals are commonly associated with delay in diagnosis or misdiagnosis. To improve those patients' outcome, rare disease detection is an important task for identifying patients with rare conditions based on longitudinal medical claims. In this paper, we present a deep learning method for detecting patients with exocrine pancreatic insufficiency (EPI) (a rare disease). The contribution includes 1) a large longitudinal study using 7 years medical claims from 1.8 million patients including 29,149 EPI patients, 2) a new deep learning model using generative adversarial networks (GANs) to boost rare disease class, and also leveraging recurrent neural networks to model patient sequence data, 3) an accurate prediction with 0.56 PR-AUC which outperformed benchmark models in terms of precision and recall.
Diminishing the Effect of Adversarial Perturbations via Refining Feature Representation

Deep neural networks are highly vulnerable to adversarial examples, which imposes severe security issues for these state-of-the-art models. Many defense methods have been proposed to mitigate this problem. However, a lot of them depend on modification or additional training of the target model. In this work, we analytically investigate each layer's representation of non-perturbed and perturbed images and show the effect of perturbations on each of these representations. Accordingly, a method based on whitening coloring transform is proposed in order to diminish the misrepresentation of any desirable layer caused by adversaries. Our method can be applied to any layer of any arbitrary model without the need of any modification or additional training. Due to the fact that the full whitening of the layer's representation is not easily differentiable, our proposed method is superbly robust against white-box attacks. Furthermore, we demonstrate the strength of our method against some state-of-the-art black-box attacks.
Learning to aggregate feature representations

The Algonauts challenge requires to construct a multi-subject encoder of images to brain activity. Deep networks such as ResNet-50 and AlexNet trained for image classification are known to produce feature representations along their intermediate stages which closely mimic the visual hierarchy. However the challenges introduced in the Algonauts project, including combining data from multiple subjects, relying on very few similarity data points, solving for various ROIs, and multi-modality, require devising a flexible framework which can efficiently accommodate them. Here we build upon a recent state-of-the-art classification network (SE-ResNeXt-50) and construct an adaptive combination of its intermediate representations. While the pretrained network serves as a backbone of our model, we learn how to aggregate feature representations along five stages of the network. During learning, our method enables to modulate and screen outputs from each stage along the network as governed by the optimized objective. We applied our method to the Algonauts2019 fMRI and MEG challenges. Using the combined fMRI and MEG data, our approach was rated among the leading five for both challenges. Surprisingly we find that for both the lower and higher order areas (EVC and IT) the adaptive aggregation favors features stemming at later stages of the network.
Investigating The Piece-Wise Linearity And Benchmark Related To Koczy-Hirota Fuzzy Linear Interpolation

Fuzzy Rule Interpolation (FRI) reasoning methods have been introduced to address sparse fuzzy rule bases and reduce complexity. The first FRI method was the Koczy and Hirota (KH) proposed "Linear Interpolation". Besides, several conditions and criteria have been suggested for unifying the common requirements FRI methods have to satisfy. One of the most conditions is restricted the fuzzy set of the conclusion must preserve a Piece-Wise Linearity (PWL) if all antecedents and consequents of the fuzzy rules are preserving on PWL sets at {\alpha}-cut levels. The KH FRI is one of FRI methods which cannot satisfy this condition. Therefore, the goal of this paper is to investigate equations and notations related to PWL property, which is aimed to highlight the problematic properties of the KH FRI method to prove its efficiency with PWL condition. In addition, this paper is focusing on constructing benchmark examples to be a baseline for testing other FRI methods against situations that are not satisfied with the linearity condition for KH FRI.
ML-Based Fault Injection for Autonomous Vehicles: A Case for Bayesian Fault Injection

The safety and resilience of fully autonomous vehicles (AVs) are of significant concern, as exemplified by several headline-making accidents. While AV development today involves verification, validation, and testing, end-to-end assessment of AV systems under accidental faults in realistic driving scenarios has been largely unexplored. This paper presents DriveFI, a machine learning-based fault injection engine, which can mine situations and faults that maximally impact AV safety, as demonstrated on two industry-grade AV technology stacks (from NVIDIA and Baidu). For example, DriveFI found 561 safety-critical faults in less than 4 hours. In comparison, random injection experiments executed over several weeks could not find any safety-critical faults.
Is artificial data useful for biomedical Natural Language Processing algorithms?

A major obstacle to the development of Natural Language Processing (NLP) methods in the biomedical domain is data accessibility. This problem can be addressed by generating medical data artificially. Most previous studies have focused on the generation of short clinical text, and evaluation of the data utility has been limited. We propose a generic methodology to guide the generation of clinical text with key phrases. We use the artificial data as additional training data in two key biomedical NLP tasks: text classification and temporal relation extraction. We show that artificially generated training data used in conjunction with real training data can lead to performance boosts for data-greedy neural network algorithms. We also demonstrate the usefulness of the generated data for NLP setups where it fully replaces real training data.
Optimal Bookmaking

We introduce a general framework for continuous-time betting markets, in which a bookmaker can dynamically control the prices of bets on outcomes of random events. In turn, the prices set by the bookmaker affect the rate or intensity of bets placed by gamblers. The bookmaker seeks a price process that maximizes his expected (utility of) terminal wealth. We obtain explicit solutions or characterizations to the bookmaker's optimal bookmaking problem in various interesting models.
GPU-based Parallel Computation Support for Stan

This paper details an extensible OpenCL framework that allows Stan to utilize heterogeneous compute devices. It includes GPU-optimized routines for the Cholesky decomposition, its derivative, other matrix algebra primitives and some commonly used likelihoods, with more additions planned for the near future. Stan users can now benefit from speedups offered by GPUs with little effort and without changes to their existing Stan code. We demonstrate the practical utility of our work with two examples -- logistic regression and Gaussian process regression.