nils-herrmann/clean_scirepeval huggingface dataset:
Do NLP Models Know Numbers? Probing Numeracy in Embeddings

The ability to understand and work with numbers (numeracy) is critical for many complex reasoning tasks. Currently, most NLP models treat numbers in text in the same way as other tokens—they embed them as distributed vectors. Is this enough to capture numeracy? We begin by investigating the numerical reasoning capabilities of a state-of-the-art question answering model on the DROP dataset. We find this model excels on questions that require numerical reasoning, i.e., it already captures numeracy. To understand how this capability emerges, we probe token embedding methods (e.g., BERT, GloVe) on synthetic list maximum, number decoding, and addition tasks. A surprising degree of numeracy is naturally present in standard embeddings. For example, GloVe and word2vec accurately encode magnitude for numbers up to 1,000. Furthermore, character-level embeddings are even more precise—ELMo captures numeracy the best for all pre-trained methods—but BERT, which uses sub-word units, is less exact.
Semantic Relatedness Based Re-ranker for Text Spotting

Applications such as textual entailment, plagiarism detection or document clustering rely on the notion of semantic similarity, and are usually approached with dimension reduction techniques like LDA or with embedding-based neural approaches. We present a scenario where semantic similarity is not enough, and we devise a neural approach to learn semantic relatedness. The scenario is text spotting in the wild, where a text in an image (e.g. street sign, advertisement or bus destination) must be identified and recognized. Our goal is to improve the performance of vision systems by leveraging semantic information. Our rationale is that the text to be spotted is often related to the image context in which it appears (word pairs such as Delta-airplane, or quarters-parking are not similar, but are clearly related). We show how learning a word-to-word or word-to-sentence relatedness score can improve the performance of text spotting systems up to 2.9 points, outperforming other measures in a benchmark dataset.
Efficient cosmological analysis of the SDSS/BOSS data from the Effective Field Theory of Large-Scale Structure

The precision of the cosmological data allows us to accurately approximate the predictions for cosmological observables by Taylor expanding up to a low order the dependence on the cosmological parameters around a reference cosmology. By applying this observation to the redshift-space one-loop galaxy power spectrum of the Effective Field Theory of Large-Scale Structure, we analyze the BOSS DR12 data by scanning over all the parameters of ΛCDM cosmology with massive neutrinos. We impose several sets of priors, the widest of which is just a Big Bang Nucleosynthesis prior on the current fractional energy density of baryons, Ωb h2, and a bound on the sum of neutrino masses to be less than 0.9 eV. In this case we measure the primordial amplitude of the power spectrum, As, the abundance of matter, Ωm, the Hubble parameter, H0, and the tilt of the primordial power spectrum, ns, to about 19%, 5.7%, 2.2% and 7.3% respectively, obtaining ln (1010As)=2.91± 0.19, Ωm=0.314± 0.018, H0=68.7± 1.5 km/(s Mpc) and ns=0.979± 0.071 at 68% confidence level. A public code is released with this preprint.
A Data-Center FPGA Acceleration Platform for Convolutional Neural Networks

Intensive computation is entering data centers with multiple workloads of deep learning. To balance the compute efficiency, performance, and total cost of ownership (TCO), the use of a field-programmable gate array (FPGA) with reconfigurable logic provides an acceptable acceleration capacity and is compatible with diverse computation-sensitive tasks in the cloud. In this paper, we develop an FPGA acceleration platform that leverages a unified framework architecture for general-purpose convolutional neural network (CNN) inference acceleration at a data center. To overcome the computation bound, 4,096 DSPs are assembled and shaped as supertile units (SUs) for different types of convolution, which provide up to 4.2 TOP/s 16-bit fixed-point performance at 500 MHz. The interleaved-task-dispatching method is proposed to map the computation across the SUs, and the memory bound is solved by a dispatching-assembling buffering model and broadcast caches. For various non-convolution operators, a filter processing unit is designed for general-purpose filter-like/pointwise operators. In the experiment, the performances of CNN models running on server-class CPUs, a GPU, and an FPGA are compared. The results show that our design achieves the best FPGA peak performance and a throughput at the same level as that of the state-of-the-art GPU in data centers, with more than 50 times lower latency.
Properties of Laplacian Pyramids for Extension and Denoising

We analyze the Laplacian pyramids algorithm of Rabin and Coifman for extending and denoising a function sampled on a discrete set of points. We provide mild conditions under which the algorithm converges, and prove stability bounds on the extended function. We also consider the iterative application of truncated Laplacian pyramids kernels for denoising signals by non-local means.
The Star Formation History and Dynamics of the Ultra-diffuse Galaxy Dragonfly 44 in MOND and MOG

The observed line-of-sight velocity dispersion σlos of the ultra-diffuse galaxy Dragonfly 44 (DF44) requires a Newtonian dynamical mass-to-light ratio of solar units. This is well outside the acceptable limits of our stellar population synthesis (SPS) models, which we construct using the integrated galactic initial mass function (IGIMF) theory. Assuming DF44 is in isolation and using Jeans analysis, we calculate σlos profiles of DF44 in Milgromian dynamics (MOND) and modified gravity (MOG) theories without invoking dark matter. Comparing with the observed kinematics, the best-fitting MOND model has and a constant orbital anisotropy of . In MOG, we first fix its two theoretical parameters α and μ based on previous fits to the observed rotation curve data of The HI Nearby Galaxy Survey (THINGS). The DF44 σlos profile is best fit with , larger than plausible SPS values. MOG produces a σlos profile for DF44 with acceptable Mdyn/LI and isotropic orbits if α and μ are allowed to vary. MOND with the canonical a0 can explain DF44 at the 2.40σ confidence level (1.66%) if considering both its observed kinematics and typical star formation histories in an IGIMF context. However, MOG is ruled out at 5.49σ (P-value of 4.07 × 10−8) if its free parameters are fixed at the highest values consistent with THINGS data.
Are Milky-Way dwarf-spheroidal galaxies dark-matter free?

We have found that the high velocity dispersions of dwarf spheroidal galaxies (dSphs) can be well explained by Milky Way (MW) tidal shocks, which reproduce precisely the gravitational acceleration previously attributed to dark matter (DM). Here we summarize the main results of Hammer et al. (2019) who studied the main scaling relations of dSphs and show how dark-matter free galaxies in departure from equilibrium reproduce them well, while they appear to be challenging for the DM model. These results are consistent with our most recent knowledge about dSph past histories, including their orbits, their past star formation history and their progenitors, which are likely tiny dwarf irregular galaxies.
Constraining dark-matter ensembles with supernova data

The constraints on non-minimal dark sectors involving ensembles of unstable dark-matter species are well established and quite stringent in cases in which these species decay to visible-sector particles. However, in cases in which these ensembles decay exclusively to other, lighter dark-sector states, the corresponding constraints are less well established. In this paper, we investigate how information about the expansion rate of the universe at low redshifts gleaned from observations of Type Ia supernovae can be used to constrain ensembles of unstable particles which decay primarily into dark radiation.
The ALMA Survey of 70 μm Dark High-mass Clumps in Early Stages (ASHES). I. Pilot Survey: Clump Fragmentation

The ALMA Survey of 70 μm dark High-mass clumps in Early Stages (ASHES) is designed to systematically characterize the earliest stages and constrain theories of high-mass star formation. Twelve massive (>500 ), cold (≤15 K), 3.6–70 μm dark prestellar clump candidates, embedded in infrared dark clouds, were carefully selected in the pilot survey to be observed with the Atacama Large Millimeter/submillimeter Array (ALMA). We have mosaicked each clump (∼1 arcmin2) in continuum and line emission with the 12 m, 7 m, and Total Power (TP) arrays at 224 GHz (1.34 mm), resulting in ∼1.″2 resolution (∼4800 au, at the average source distance). As the first paper in the series, we concentrate on the continuum emission to reveal clump fragmentation. We detect 294 cores, from which 84 (29%) are categorized as protostellar based on outflow activity or “warm core” line emission. The remaining 210 (71%) are considered prestellar core candidates. The number of detected cores is independent of the mass sensitivity range of the observations and, on average, more massive clumps tend to form more cores. We find a large population of low-mass (<1 ) cores and no high-mass (>30 ) prestellar cores (maximum mass 11 ). From the prestellar core mass function, we derive a power-law index of 1.17 ± 0.10, which is slightly shallower than Salpeter. We used the minimum spanning tree (MST) technique to characterize the separation between cores and their spatial distribution, and to derive mass segregation ratios. While there is a range of core masses and separations detected in the sample, the mean separation and mass per clump are well explained by thermal Jeans fragmentation and are inconsistent with turbulent Jeans fragmentation. Core spatial distribution is well described by hierarchical subclustering rather than centrally peaked clustering. There is no conclusive evidence of mass segregation. We test several theoretical conditions and conclude that overall, competitive accretion and global hierarchical collapse scenarios are favored over the turbulent core accretion scenario.
Cosmic dissonance: are new physics or systematics behind a short sound horizon?

Persistent tension between low-redshift observations and the cosmic microwave background radiation (CMB), in terms of two fundamental distance scales set by the sound horizon rd and the Hubble constant H0, suggests new physics beyond the Standard Model, departures from concordance cosmology, or residual systematics.
 The role of different probe combinations must be assessed, as well as of different physical models that can alter the expansion history of the Universe and the inferred cosmological parameters.
Methods. We examined recently updated distance calibrations from Cepheids, gravitational lensing time-delay observations, and the tip of the red giant branch. Calibrating the baryon acoustic oscillations and type Ia supernovae with combinations of the distance indicators, we obtained a joint and self-consistent measurement of H0 and rd at low redshift, independent of cosmological models and CMB inference. In an attempt to alleviate the tension between late-time and CMB-based measurements, we considered four extensions of the standard ΛCDM model.
Results. The sound horizon from our different measurements is rd = (137 ± 3stat. ± 2syst.) Mpc based on absolute distance calibration from gravitational lensing and the cosmic distance ladder. Depending on the adopted distance indicators, the combined tension in H0 and rd ranges between 2.3 and 5.1 σ, and it is independent of changes to the low-redshift expansion history. We find that modifications of ΛCDM that change the physics after recombination fail to provide a solution to the problem, for the reason that they only resolve the tension in H0, while the tension in rd remains unchanged. Pre-recombination extensions (with early dark energy or the effective number of neutrinos Neff = 3.24 ± 0.16) are allowed by the data, unless the calibration from Cepheids is included.
Conclusions. Results from time-delay lenses are consistent with those from distance-ladder calibrations and point to a discrepancy between absolute distance scales measured from the CMB (assuming the standard cosmological model) and late-time observations. New proposals to resolve this tension should be examined with respect to reconciling not only the Hubble constant but also the sound horizon derived from the CMB and other cosmological probes.
Higgs portal to dark QED

We introduce a light dark photon $A_\mu^\prime$ to the minimal Higgs portal model, by coupling the Higgs boson to "dark QED" containing fermionic dark matter, which gives rise to rich and interesting collider phenomenology. There are two prominent features in such a simple extension -- the Higgs boson could have decays into the long-lived dark photon through the "mono-$A^\prime$ channel," or into multiple collimated leptons via "darkonium," depending on the mixing parameter of the $A_\mu^\prime$ with the visible photon. We initiate a study on the possibility of probing the parameter space of the model in both the energy and the lifetime frontiers at the Large Hadron Collider.
CLASH-VLT: Enhancement of (O/H) in z  =  0.35 RX J2248−4431 cluster galaxies

Gas-phase metallicities offer insight into the chemical evolution of galaxies as they reflect the recycling of gas through star formation and galactic inflows and outflows. Environmental effects such as star-formation quenching mechanisms play an important role in shaping the evolution of galaxies. Clusters of galaxies at z <  0.5 are expected to be the sites where environmental effects can be clearly observed with present-day telescopes.
Methods. We explored the Frontier Fields cluster RX J2248−443 at z = 0.348 with VIMOS/VLT spectroscopy from CLASH-VLT, which covers a central region corresponding to almost 2 virial radii. The fluxes of [OII] λ3727, Hβ, [OIII] λ5007, Hα and [NII] λ6584 emission lines were measured allowing the derivation of (O/H) gas metallicities, star formation rates based on extinction-corrected Hα fluxes, and contamination from active galactic nuclei. We compared our sample of cluster galaxies to a population of field galaxies at similar redshifts.
Results. We use the location of galaxies in projected phase-space to distinguish between cluster and field galaxies. Both populations follow the star-forming sequence in the diagnostic diagrams, which allow the ionising sources in a galaxy to be disentangled, with only a low number of galaxies classified as Seyfert II. Both field and cluster galaxies follow the “main sequence” of star-forming galaxies, with no substantial difference observed between the two populations. In the mass–metallicity (MZ) plane, both high-mass field and cluster galaxies show comparable (O/H)s to the local SDSS MZ relation, with an offset of low-mass galaxies (log(M/M⊙) < 9.2) towards higher metallicities. While both the metallicities of “accreted” (R <  R500) and “infalling” (R >  R500) cluster members are comparable at all masses, the cluster galaxies from the “mass complete” bin (which is the intermediate mass bin in this study: 9.2 <  log(M/M⊙) < 10.2), show more enhanced metallicities than their field counterparts by a factor of 0.065 dex with a ∼1.8σ significance. The intermediate-mass field galaxies are in accordance with the expected (O/H)s from the fundamental metallicity relation, while the cluster members deviate strongly from the model predictions, namely by a factor of ∼0.12 dex. The results of this work are in accordance with studies of other clusters at z <  0.5 and favour the scenario in which the hot halo gas of low- and intermediate-mass cluster galaxies is removed due to ram pressure stripping, leading to an increase in their gas-phase metallicity.
Ultraviolet freeze-in and non-standard cosmologies

A notable feature of UV freeze-in is that the relic density is strongly dependent on the highest temperatures of the thermal bath, and a common assumption is that the relevant “highest temperature” should be the reheating temperature after inflation TRH. However, the temperature of the thermal bath can be significantly higher in certain scenarios, reaching a value denoted Tmax, a fact which is only apparent away from the instantaneous decay approximation. Interestingly, it has been shown that if the operators are of sufficiently high mass dimension then the dark matter abundance can be enhanced by a “boost factor” depending on (Tmax/TRH) relative to naive estimates assuming instantaneous reheating. We highlight here that in non-standard cosmological histories the critical mass dimension of the operator above at which the instantaneous decay approximation breaks down, and the exponent of the boost factor, depend on the equation of state ω prior to reheating. We highlight four examples in which the dark matter abundance receives a significant enhancement in the context of gravitino dark matter, the moduli portal, the Higgs portal, and the spin-2 portal (as might arise in bimetric gravity models). We comment on the transition from kination domination to radiation domination as a motivated example of non-standard cosmologies.
Explaining the ANITA events by a Le−Lτ gauge model

The ANITA experiment has registered two anomalous events that can be interpreted as ντ or ν̄τ with a very high energy of 𝒪(0.6) EeV emerging from deep inside the Earth. At such high energies, the Earth is opaque to neutrinos so the emergence of these neutrinos at such large zenith angles is a mystery. In our paper, we present a model that explains the two anomalous events through a Le −Lτ gauge interaction involving two new Weyl fermions charged under the new gauge symmetry. We find that, as a bonus of the model, the lighter Weyl fermion can be a dark matter component. We discuss how the ANITA observation can be reconciled with the IceCube and Auger upper bounds. We also demonstrate how this model can be tested in future by collider experiments.
Temperature Discontinuity at an Evaporating Water Interface

Evaporative mass flux is governed by interfacial state of liquid and vapor phases. For closely similar pressures and mass fluxes of liquid water into its own vapor, discontinuity between interfacial liquid and vapor temperatures in the range of 0.14-28 K is reported. This controversial discontinuity has resulted in an obstacle on understanding and theoretical modeling of evaporation. Here, through study of vapor transport by Boltzmann transport equation solved through Direct Simulation Monte Carlo Method, we demonstrated that the measured discontinuities were strongly affected by boundary condition on the vapor side of the interface and do not reflect the interfacial state. The temperature discontinuity across the evaporating interface is < 0.1 K for all these studies. To accurately capture the interfacial state, the vapor heat flux should be suppressed.
Verifying Reachability Properties in Markov Chains via Incremental Induction

There is a scalability gap between probabilistic and non-probabilistic verification. Probabilistic model checking tools are based either on explicit engines or on (Multi-Terminal) Binary Decision Diagrams. These structures are complemented in areas of non-probabilistic verification by more scalable techniques, such as IC3. We present a symbolic probabilistic model checking algorithm based on IC3-like incremental construction of inductive clauses to partition the state space, interleaved with incremental construction of a system of linear inequalities. This paper compares our implementation to standard quantitative verification alternatives: our experiments show that our algorithm is a step to more scalable symbolic verification of rare events in finite-state Markov chains.
Strategic Formation and Reliability of Supply Chain Networks

We study the incentives that independent self-interested agents have in forming a resilient supply chain network in the face of disruptions and competition. Competing suppliers are subject to yield uncertainty and congestion. Competing retailers make sourcing decisions based on price and reliability. Under yield uncertainty only, retailers---benefiting from supply variance---concentrate their links on a single supplier, counter to the idea that they should mitigate yield uncertainty by multi-sourcing. When congestion is added, the resulting networks resemble bipartite expanders known to be resilient, thus, providing the first example of endogenously formed resilient supply chains.
Cardiac MRI Image Segmentation for Left Ventricle and Right Ventricle using Deep Learning

The goal of this project is to use magnetic resonance imaging (MRI) data to provide an end-to-end analytics pipeline for left and right ventricle (LV and RV) segmentation. Another aim of the project is to find a model that would be generalizable across medical imaging datasets. We utilized a variety of models, datasets, and tests to determine which one is well suited to this purpose. Specifically, we implemented three models (2-D U-Net, 3-D U-Net, and DenseNet), and evaluated them on four datasets (Automated Cardiac Diagnosis Challenge, MICCAI 2009 LV, Sunnybrook Cardiac Data, MICCAI 2012 RV). While maintaining a consistent preprocessing strategy, we tested the performance of each model when trained on data from the same dataset as the test data, and when trained on data from a different dataset than the test dataset. Data augmentation was also used to increase the adaptability of the models. The results were compared to determine performance and generalizability.
Heterogeneity-Aware Asynchronous Decentralized Training

Distributed deep learning training usually adopts All-Reduce as the synchronization mechanism for data parallel algorithms due to its high performance in homogeneous environment. However, its performance is bounded by the slowest worker among all workers, and is significantly slower in heterogeneous situations. AD-PSGD, a newly proposed synchronization method which provides numerically fast convergence and heterogeneity tolerance, suffers from deadlock issues and high synchronization overhead. Is it possible to get the best of both worlds - designing a distributed training method that has both high performance as All-Reduce in homogeneous environment and good heterogeneity tolerance as AD-PSGD? In this paper, we propose Ripples, a high-performance heterogeneity-aware asynchronous decentralized training approach. We achieve the above goal with intensive synchronization optimization, emphasizing the interplay between algorithm and system implementation. To reduce synchronization cost, we propose a novel communication primitive Partial All-Reduce that allows a large group of workers to synchronize quickly. To reduce synchronization conflict, we propose static group scheduling in homogeneous environment and simple techniques (Group Buffer and Group Division) to avoid conflicts with slightly reduced randomness. Our experiments show that in homogeneous environment, Ripples is 1.1 times faster than the state-of-the-art implementation of All-Reduce, 5.1 times faster than Parameter Server and 4.3 times faster than AD-PSGD. In a heterogeneous setting, Ripples shows 2 times speedup over All-Reduce, and still obtains 3 times speedup over the Parameter Server baseline.
Optimizing Through Learned Errors for Accurate Sports Field Registration

We propose an optimization-based framework to register sports field templates onto broadcast videos. For accurate registration we go beyond the prevalent feed-forward paradigm. Instead, we propose to train a deep network that regresses the registration error, and then register images by finding the registration parameters that minimize the regressed error. We demonstrate the effectiveness of our method by applying it to real-world sports broadcast videos, outperforming the state of the art. We further apply our method on a synthetic toy example and demonstrate that our method brings significant gains even when the problem is simplified and unlimited training data is