nils-herrmann/clean_scirepeval huggingface dataset:
Exploiting a Stimuli Encoding Scheme of Spiking Neural Networks for Stream Learning

Stream data processing has gained progressive momentum with the arriving of new stream applications and big data scenarios. One of the most promising techniques in stream learning is the Spiking Neural Network, and some of them use an interesting population encoding scheme to transform the incoming stimuli into spikes. This study sheds lights on the key issue of this encoding scheme, the Gaussian receptive fields, and focuses on applying them as a pre-processing technique to any dataset in order to gain representativeness, and to boost the predictive performance of the stream learning methods. Experiments with synthetic and real data sets are presented, and lead to confirm that our approach can be applied successfully as a general pre-processing technique in many real cases.
WikiCREM: A Large Unsupervised Corpus for Coreference Resolution

Pronoun resolution is a major area of natural language understanding. However, large-scale training sets are still scarce, since manually labelling data is costly. In this work, we introduce WikiCREM (Wikipedia CoREferences Masked) a large-scale, yet accurate dataset of pronoun disambiguation instances. We use a language-model-based approach for pronoun resolution in combination with our WikiCREM dataset. We compare a series of models on a collection of diverse and challenging coreference resolution problems, where we match or outperform previous state-of-the-art approaches on 6 out of 7 datasets, such as GAP, DPR, WNLI, PDP, WinoBias, and WinoGender. We release our model to be used off-the-shelf for solving pronoun disambiguation.
Hebbian Graph Embeddings

Representation learning has recently been successfully used to create vector representations of entities in language learning, recommender systems and in similarity learning. Graph embeddings exploit the locality structure of a graph and generate embeddings for nodes which could be words in a language, products of a retail website; and the nodes are connected based on a context window. In this paper, we consider graph embeddings with an error-free associative learning update rule, which models the embedding vector of node as a non-convex Gaussian mixture of the embeddings of the nodes in its immediate vicinity with some constant variance that is reduced as iterations progress. It is very easy to parallelize our algorithm without any form of shared memory, which makes it possible to use it on very large graphs with a much higher dimensionality of the embeddings. We study the efficacy of proposed method on several benchmark data sets and favorably compare with state of the art methods. Further, proposed method is applied to generate relevant recommendations for a large retailer.
Quantum Algorithms for Portfolio Optimization

We develop the first quantum algorithm for the constrained portfolio optimization problem. The algorithm has running time Õ (n√r ζk/δ2 log (1/ϵ)), where r is the number of positivity and budget constraints, n is the number of assets in the portfolio, ϵ the desired precision, and δ, κ, ζ are problem-dependent parameters related to the well-conditioning of the intermediate solutions. If only a moderately accurate solution is required, our quantum algorithm can achieve a polynomial speedup over the best classical algorithms with complexity Õ (√rnω log(1/ϵ)), where ω is the matrix multiplication exponent that has a theoretical value of around 2.373, but is closer to 3 in practice. We also provide some experiments to bound the problem-dependent factors arising in the running time of the quantum algorithm, and these experiments suggest that for most instances the quantum algorithm can potentially achieve an O(n) speedup over its classical counterpart.
Spitzer Catalog of Herschel-selected Ultrared Dusty Star-forming Galaxies

The largest Herschel extragalactic surveys, H-ATLAS and HerMES, have selected a sample of “ultrared” dusty star-forming galaxies (DSFGs) with rising SPIRE flux densities (S500 > S350 > S250; the so-called “500 μm risers”) as an efficient way for identifying DSFGs at higher redshift (z > 4). In this paper, we present a large Spitzer follow-up program of 300 Herschel ultrared DSFGs. We have obtained high-resolution Atacama Large Millimeter/submillimeter Array, Northern Extended Millimeter Array, and SMA data for 63 of them, which allow us to securely identify the Spitzer/IRAC counterparts and classify them as gravitationally lensed or unlensed. Within the 63 ultrared sources with high-resolution data, ∼65% appear to be unlensed and ∼27% are resolved into multiple components. We focus on analyzing the unlensed sample by directly performing multiwavelength spectral energy distribution modeling to derive their physical properties and compare with the more numerous z ∼ 2 DSFG population. The ultrared sample has a median redshift of 3.3, stellar mass of 3.7 × 1011 M⊙, star formation rate (SFR) of 730 M⊙ yr−1, total dust luminosity of 9.0 × 1012 L⊙, dust mass of 2.8 × 109 M⊙, and V-band extinction of 4.0, which are all higher than those of the ALESS DSFGs. Based on the space density, SFR density, and stellar mass density estimates, we conclude that our ultrared sample cannot account for the majority of the star-forming progenitors of the massive, quiescent galaxies found in infrared surveys. Our sample contains the rarer, intrinsically most dusty, luminous, and massive galaxies in the early universe that will help us understand the physical drivers of extreme star formation.
Coarse-to-fine Optimization for Speech Enhancement

In this paper, we propose the coarse-to-fine optimization for the task of speech enhancement. Cosine similarity loss  has proven to be an effective metric to measure similarity of speech signals. However, due to the large variance of the enhanced speech with even the same cosine similarity loss in high dimensional space, a deep neural network learnt with this loss might not be able to predict enhanced speech with good quality. Our coarse-to-fine strategy optimizes the cosine similarity loss for different granularities so that more constraints are added to the prediction from high dimension to relatively low dimension. In this way, the enhanced speech will better resemble the clean speech. Experimental results show the effectiveness of our proposed coarse-to-fine optimization in both discriminative models and generative models. Moreover, we apply the coarse-to-fine strategy to the adversarial loss in generative adversarial network (GAN) and propose dynamic perceptual loss, which dynamically computes the adversarial loss from coarse resolution to fine resolution. Dynamic perceptual loss further improves the accuracy and achieves state-of-the-art results compared with other generative models.
Modeling the Gaia Color-Magnitude Diagram with Bayesian Neural Flows to Constrain Distance Estimates

We demonstrate an algorithm for learning a flexible color-magnitude diagram from noisy parallax and photometry measurements using a normalizing flow, a deep neural network capable of learning an arbitrary multi-dimensional probability distribution. We present a catalog of 640M photometric distance posteriors to nearby stars derived from this data-driven model using Gaia DR2 photometry and parallaxes. Dust estimation and dereddening is done iteratively inside the model and without prior distance information, using the Bayestar map. The signal-to-noise (precision) of distance measurements improves on average by more than 48% over the raw Gaia data, and we also demonstrate how the accuracy of distances have improved over other models, especially in the noisy-parallax regime. Applications are discussed, including significantly improved Milky Way disk separation and substructure detection. We conclude with a discussion of future work, which exploits the normalizing flow architecture to allow us to exactly marginalize over missing photometry, enabling the inclusion of many surveys without losing coverage.
Tidal circularization of gaseous planets orbiting white dwarfs

A gas giant planet which survives the giant branch stages of evolution at a distance of many au and then is subsequently perturbed sufficiently close to a white dwarf will experience orbital shrinkage and circularization due to star–planet tides. The circularization time-scale, when combined with a known white dwarf cooling age, can place coupled constraints on the scattering epoch as well as the active tidal mechanisms. Here, we explore this coupling across the entire plausible parameter phase space by computing orbit shrinkage and potential self-disruption due to chaotic f-mode excitation and heating in planets on orbits with eccentricities near unity, followed by weakly dissipative equilibrium tides. We find that chaotic f-mode evolution activates only for orbital pericentres which are within twice the white dwarf Roche radius, and easily restructures or destroys ice giants but not gas giants. This type of internal thermal destruction provides an additional potential source of white dwarf metal pollution. Subsequent tidal evolution for the surviving planets is dominated by non-chaotic equilibrium and dynamical tides which may be well-constrained by observations of giant planets around white dwarfs at early cooling ages.
The case of NGC 6302: the impact of shocks in the derivation of nitrogen abundances

High nitrogen abundance is characteristic of Type I planetary nebulae, as well as their highly filamentary structure. In the present work, we test the hypothesis of shocks as a relevant excitation mechanism for a Type I nebula, NGC 6302, using recently released diagnostic diagrams to distinguish shocks from photoexcitation. The construction of the diagrams depends on emission-line ratios and kinematical information. NGC 6302 shows the relevance of shocks in peripheral regions and their importance to the whole nebula. Using shocks, we question the usual assumption of ionization correction factor calculation, justifying a warning regarding broadly used abundance derivation methods. From kinematical analysis, we derive a new distance for NGC 6302 of $805\pm 143\,$ pc.
Automated quantum programming via reinforcement learning for combinatorial optimization

We develop a general method for incentive-based programming of hybrid quantum-classical computing systems using reinforcement learning, and apply this to solve combinatorial optimization problems on both simulated and real gate-based quantum computers. Relative to a set of randomly generated problem instances, agents trained through reinforcement learning techniques are capable of producing short quantum programs which generate high quality solutions on both types of quantum resources. We observe generalization to problems outside of the training set, as well as generalization from the simulated quantum resource to the physical quantum resource.
Structural and stellar-population properties versus bulge types in Sloan Digital Sky Survey central galaxies

This paper studies pseudo-bulges (P-bulges) and classical bulges (C-bulges) in Sloan Digital Sky Survey (SDSS) central galaxies using the new bulge indicator ΔΣ1, which measures relative central stellar-mass surface density within 1 kpc. We compare ΔΣ1 to the established bulge-type indicator Δ〈μe〉 from Gadotti (2009) and show that classifying by ΔΣ1 agrees well with Δ〈μe〉. ΔΣ1 requires no bulge–disc decomposition and can be measured on SDSS images out to z = 0.07. Bulge types using it are mapped on to 20 different structural and stellar-population properties for 12 000 SDSS central galaxies with masses 10.0 < log M*/M⊙ < 10.4. New trends emerge from this large sample. Structural parameters show fairly linear log–log relations versus ΔΣ1 and Δ〈μe〉 with only moderate scatter, while stellar-population parameters show a highly non-linear ‘elbow’ in which specific star formation rate remains roughly flat with increasing central density and then falls rapidly at the elbow, where galaxies begin to quench. P-bulges occupy the low-density end of the horizontal arm of the elbow and are universally star forming, while C-bulges occupy the elbow and the vertical branch and exhibit a wide range of star formation rates at a fixed density. The non-linear relation between central density and star formation rate has been seen before, but this mapping on to bulge class is new. The wide range of star formation rates in C-bulges helps to explain why bulge classifications using different parameters have sometimes disagreed in the past. The elbow-shaped relation between density and stellar indices suggests that central structure and stellar populations evolve at different rates as galaxies begin to quench.
The Progenitors of Calcium-strong Transients

A new class of faint, spectroscopically peculiar transients has emerged in the last decade. We term these events “calcium-strong transients” (CaSTs) because of their atypically high calcium-to-oxygen nebular line ratios. Previous studies have struggled to deduce the identity of their progenitors, due to a combination of their extremely extended radial distributions with respect to their host galaxies and their relatively high rate of occurrence. In this work, we find that the CaST radial distribution is consistent with the radial distribution of two populations of stars: old (ages >5 Gyr), low-metallicity (Z/Z⊙ < 0.3) stars, and globular clusters. While no obvious progenitor scenario arises from considering old, metal-poor stars, the alternative production site of globular clusters leads us to narrow down the list of possible candidates to three binary scenarios: mergers of helium and oxygen/neon white dwarfs; tidal disruptions of helium white dwarfs by neutron stars; and stable accretion from low-mass helium-burning stars onto white dwarfs. While rare in the field, these binary systems can be formed dynamically at much higher rates in globular clusters. Subsequent binary hardening both increases their interaction rate and ejects them from their parent globular clusters prior to mass transfer contact. Their production in, and ejection from, globular clusters may explain their radial distribution and the absence of globular clusters at their explosion site. This model predicts a currently undiscovered high rate of CaSTs in nuclear star clusters. Alternatively, an undetermined progenitor scenario involving old, low-metallicity stars may instead hold the key to understanding CaSTs.
A Search for IceCube Events in the Direction of ANITA Neutrino Candidates

During the first three flights of the Antarctic Impulsive Transient Antenna (ANITA) experiment, the collaboration detected several neutrino candidates. Two of these candidate events were consistent with an ultra-high-energy upgoing air shower and compatible with a tau neutrino interpretation. A third neutrino candidate event was detected in a search for Askaryan radiation in the Antarctic ice, although it is also consistent with the background expectation. The inferred emergence angle of the first two events is in tension with IceCube and ANITA limits on isotropic cosmogenic neutrino fluxes. Here we test the hypothesis that these events are astrophysical in origin, possibly caused by a point source in the reconstructed direction. Given that any ultra-high-energy tau neutrino flux traversing the Earth should be accompanied by a secondary flux in the TeV–PeV range, we search for these secondary counterparts in 7 yr of IceCube data using three complementary approaches. In the absence of any significant detection, we set upper limits on the neutrino flux from potential point sources. We compare these limits to ANITA’s sensitivity in the same direction and show that an astrophysical explanation of these anomalous events under standard model assumptions is severely constrained regardless of source spectrum.
Impact of Cosmic-Ray Feedback on Accretion and Chemistry in Circumstellar Disks

We use the gas-grain chemistry code uclchem to explore the impact of cosmic-ray feedback on the chemistry of circumstellar disks. We model the attenuation and energy losses of the cosmic rays as they propagate outward from the star and also consider ionization due to stellar radiation and radionuclides. For accretion rates typical of young stars of M⊙ yr−1, we show that cosmic rays accelerated by the stellar accretion shock produce an ionization rate at the disk surface ζ ≳ 10−15 s−1, at least an order of magnitude higher than the ionization rate associated with the Galactic cosmic-ray background. The incident cosmic-ray flux enhances the disk ionization at intermediate to high surface densities (Σ > 10 g cm−2), particularly within 10 au of the star. We find that the dominant ions are C+, S+, and Mg+ in the disk surface layers, while the ion dominates at surface densities above 1.0 g cm−2. We predict the radii and column densities at which the magnetorotational instability (MRI) is active in T Tauri disks and show that ionization by cosmic-ray feedback extends the MRI-active region toward the disk midplane. However, the MRI is only active at the midplane of a minimum-mass solar nebula disk if cosmic rays propagate diffusively (ζ ∝ r−1) away from the star. The relationship between accretion, which accelerates cosmic rays, the dense accretion columns, which attenuate cosmic rays, and the MRI, which facilitates accretion, creates a cosmic-ray feedback loop that mediates accretion and may produce variable luminosity.
The Modularity of Special Cycles on Orthogonal Shimura Varieties over Totally Real Fields under the Beilinson–Bloch Conjecture

We study special cycles on a Shimura variety of orthogonal type over a totally real field of degree d associated with a quadratic form in $n+2$ variables whose signature is $(n,2)$ at e real places and $(n+2,0)$ at the remaining $d-e$ real places for $1\leq e <d$. Recently, these cycles were constructed by Kudla and Rosu–Yott, and they proved that the generating series of special cycles in the cohomology group is a Hilbert-Siegel modular form of half integral weight. We prove that, assuming the Beilinson–Bloch conjecture on the injectivity of the higher Abel–Jacobi map, the generating series of special cycles of codimension er in the Chow group is a Hilbert–Siegel modular form of genus r and weight $1+n/2$. Our result is a generalization of Kudla’s modularity conjecture, solved by Yuan–Zhang–Zhang unconditionally when $
Boundary Aware Networks for Medical Image Segmentation

Fully convolutional neural networks (CNNs) have proven to be effective at representing and classifying textural information, thus transforming image intensity into output class masks that achieve semantic image segmentation. In medical image analysis, however, expert manual segmentation often relies on the boundaries of anatomical structures of interest. We propose boundary aware CNNs for medical image segmentation. Our networks are designed to account for organ boundary information, both by providing a special network edge branch and edge-aware loss terms, and they are trainable end-to-end. We validate their effectiveness on the task of brain tumor segmentation using the BraTS 2018 dataset. Our experiments reveal that our approach yields more accurate segmentation results, which makes it promising for more extensive application to medical image segmentation.
DUAL-GLOW: Conditional Flow-Based Generative Model for Modality Transfer

Positron emission tomography (PET) imaging is an imaging modality for diagnosing a number of neurological diseases. In contrast to Magnetic Resonance Imaging (MRI), PET is costly and involves injecting a radioactive substance into the patient. Motivated by developments in modality transfer in vision, we study the generation of certain types of PET images from MRI data. We derive new flow-based generative models which we show perform well in this small sample size regime (much smaller than dataset sizes available in standard vision tasks). Our formulation, DUAL-GLOW, is based on two invertible networks and a relation network that maps the latent spaces to each other. We discuss how given the prior distribution, learning the conditional distribution of PET given the MRI image reduces to obtaining the conditional distribution between the two latent codes w.r.t. the two image types. We also extend our framework to leverage ``side'' information (or attributes) when available. By controlling the PET generation through ``conditioning'' on age, our model is also able to capture brain FDG-PET (hypometabolism) changes, as a function of age. We present experiments on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset with 826 subjects, and obtain good performance in PET image synthesis, qualitatively and quantitatively better than recent works.
Dynamic Scheduling of MPI-based Distributed Deep Learning Training Jobs

There is a general trend towards solving problems suited to deep learning with more complex deep learning architectures trained on larger training sets. This requires longer compute times and greater data parallelization or model parallelization. Both data and model parallelism have been historically faster in parameter server architectures, but data parallelism is starting to be faster in ring architectures due to algorithmic improvements. In this paper, we analyze the math behind ring architectures and make an informed adaptation of dynamic scheduling to ring architectures. To do so, we formulate a non-convex, non-linear, NP-hard integer programming problem and a new efficient doubling heuristic for its solution. We build upon Horovod: an open source ring architecture framework over TensorFlow. We show that Horovod jobs have a low cost to stop and restart and that stopping and restarting ring architecture jobs leads to faster completion times. These two facts make dynamic scheduling of ring architecture jobs feasible. Lastly, we simulate a scheduler using these runs and show a more than halving of average job time on some workload patterns.
Emergence of moderate opinions as a consequence of group pressure

In this work, we study a continuous opinion dynamics model considering 3-agent interactions and group pressure. Agents interact in a fully-connected population, and two parameters govern the dynamics: the agents’ convictions  see text] that are homogeneous in the population, and the group pressure . Stochastic parameters also drive the interactions. Our analytical and numerical results indicate that the model undergoes symmetry-breaking transitions at distinct critical points  see text] for any value of  see text], i.e. the transition can be suppressed for sufficiently high group pressure. Such transition separates two phases: for any  see text], the order parameter  see text] is identically null ( see text], a symmetric, absorbing phase), while for  see text], we have  see text], i.e. a symmetry-broken phase (ferromagnetic). The numerical simulations also reveal that the increase of group pressure leads to a wider distribution of opinions, decreasing the extremism in the population.
Populating Web Scale Knowledge Graphs using Distantly Supervised Relation Extraction and Validation

In this paper, we propose a fully automated system to extend knowledge graphs using external information from web-scale corpora. The designed system leverages a deep-learning-based technology for relation extraction that can be trained by a distantly supervised approach. In addition, the system uses a deep learning approach for knowledge base completion by utilizing the global structure information of the induced KG to further refine the confidence of the newly discovered relations. The designed system does not require any effort for adaptation to new languages and domains as it does not use any hand-labeled data, NLP analytics, and inference rules. Our experiments, performed on a popular academic benchmark, demonstrate that the suggested system boosts the performance of relation extraction by a wide margin, reporting error reductions of 50%, resulting in relative improvement of up to 100%. Furthermore, a web-scale experiment conducted to extend DBPedia with knowledge from Common Crawl shows that our system is not only scalable but also does not require any adaptation cost, while yielding a substantial accuracy gain.