nils-herrmann/clean_scirepeval huggingface dataset:
REBOUNDx: a library for adding conservative and dissipative forces to otherwise symplectic N-body integrations

Symplectic methods, in particular the Wisdom–Holman map, have revolutionized our ability to model the long-term, conservative dynamics of planetary systems. However, many astrophysically important effects are dissipative. The consequences of incorporating such forces into otherwise symplectic schemes are not always clear. We show that moving to a general framework of non-commutative operators (dissipative or not) clarifies many of these questions, and that several important properties of symplectic schemes carry over to the general case. In particular, we show that explicit splitting schemes generically exploit symmetries in the applied external forces, which often strongly suppress integration errors. Furthermore, we demonstrate that so-called ‘symplectic correctors’ (which reduce energy errors by orders of magnitude at fixed computational cost) apply equally well to weakly dissipative systems and can thus be more generally thought of as ‘weak splitting correctors’. Finally, we show that previously advocated approaches of incorporating additional forces into symplectic methods work well for dissipative forces, but give qualitatively wrong answers for conservative but velocity-dependent forces like post-Newtonian corrections. We release REBOUNDx, an open-source C library for incorporating additional effects into REBOUNDN-body integrations, together with a convenient python wrapper. All effects are machine independent and we provide a binary format that interfaces with the SimulationArchive class in REBOUND to enable the sharing and reproducibility of results. Users can add effects from a list of pre-implemented astrophysical forces, or contribute new ones.
Deep learning on butterfly phenotypes tests evolution’s oldest mathematical model

Artificial intelligence validates evolution’s oldest mathematical model by learning about butterfly mimicry. Traditional anatomical analyses captured only a fraction of real phenomic information. Here, we apply deep learning to quantify total phenotypic similarity across 2468 butterfly photographs, covering 38 subspecies from the polymorphic mimicry complex of Heliconius erato and Heliconius melpomene. Euclidean phenotypic distances, calculated using a deep convolutional triplet network, demonstrate significant convergence between interspecies co-mimics. This quantitatively validates a key prediction of Müllerian mimicry theory, evolutionary biology’s oldest mathematical model. Phenotypic neighbor-joining trees are significantly correlated with wing pattern gene phylogenies, demonstrating objective, phylogenetically informative phenome capture. Comparative analyses indicate frequency-dependent mutual convergence with coevolutionary exchange of wing pattern features. Therefore, phenotypic analysis supports reciprocal coevolution, predicted by classical mimicry theory but since disputed, and reveals mutual convergence as an intrinsic generator for the unexpected diversity of Müllerian mimicry. This demonstrates that deep learning can generate phenomic spatial embeddings, which enable quantitative tests of evolutionary hypotheses previously only testable subjectively.
A Bayesian Choice Model for Eliminating Feedback Loops

Self-reinforcing feedback loops in personalization systems are typically caused by users choosing from a limited set of alternatives presented systematically based on previous choices. We propose a Bayesian choice model built on Luce axioms that explicitly accounts for users' limited exposure to alternatives. Our model is fair---it does not impose negative bias towards unpresented alternatives, and practical---preference estimates are accurately inferred upon observing a small number of interactions. It also allows efficient sampling, leading to a straightforward online presentation mechanism based on Thompson sampling. Our approach achieves low regret in learning to present upon exploration of only a small fraction of possible presentations. The proposed structure can be reused as a building block in interactive systems, e.g., recommender systems, free of feedback loops.
An Improved Compact Embedding Theorem for Degenerate Sobolev Spaces

This short note investigates the compact embedding of degenerate matrix weighted Sobolev spaces into weighted Lebesgue spaces. The Sobolev spaces explored are defined as the abstract completion of Lipschitz functions in a bounded domain $\Omega$ with respect to the norm: $$\|f\|_{QH^{1,p}(v,\mu;\Omega)} = \|f\|_{L^p_v(\Omega)} + \|\nabla f\|_{\mathcal{L}^p_Q(\mu;\Omega)}$$ where the weight $v$ is comparable to a power of the pointwise operator norm of the matrix valued function $Q=Q(x)$ in $\Omega$. Following our main theorem, we give an explicit application where degeneracy is controlled through an ellipticity condition of the form $$w(x)|\xi|^p \leq \left(\xi\cdot Q(x)\xi\right)^{p/2}\leq \tau(x)|\xi|^p$$ for a pair of $p$-admissible weights $w\leq \tau$ in $\Omega$. We also give explicit examples demonstrating the sharpness of our hypotheses.
SenseBERT: Driving Some Sense into BERT

The ability to learn from large unlabeled corpora has allowed neural language models to advance the frontier in natural language understanding. However, existing self-supervision techniques operate at the word form level, which serves as a surrogate for the underlying semantic content. This paper proposes a method to employ weak-supervision directly at the word sense level. Our model, named SenseBERT, is pre-trained to predict not only the masked words but also their WordNet supersenses. Accordingly, we attain a lexical-semantic level language model, without the use of human annotation. SenseBERT achieves significantly improved lexical understanding, as we demonstrate by experimenting on SemEval Word Sense Disambiguation, and by attaining a state of the art result on the ‘Word in Context’ task.
PHYRE: A New Benchmark for Physical Reasoning

Understanding and reasoning about physics is an important ability of intelligent agents. We develop the PHYRE benchmark for physical reasoning that contains a set of simple classical mechanics puzzles in a 2D physical environment. The benchmark is designed to encourage the development of learning algorithms that are sample-efficient and generalize well across puzzles. We test several modern learning algorithms on PHYRE and find that these algorithms fall short in solving the puzzles efficiently. We expect that PHYRE will encourage the development of novel sample-efficient agents that learn efficient but useful models of physics. For code and to play PHYRE for yourself, please visit this https URL.
Distributionally Robust Optimization: A Review

The concepts of risk-aversion, chance-constrained optimization, and robust optimization have developed significantly over the last decade. Statistical learning community has also witnessed a rapid theoretical and applied growth by relying on these concepts. A modeling framework, called distributionally robust optimization (DRO), has recently received significant attention in both the operations research and statistical learning communities. This paper surveys main concepts and contributions to DRO, and its relationships with robust optimization, risk-aversion, chance-constrained optimization, and function regularization.
Further searches of the Higgs scalar sector at the ESS

Recent decades have witnessed remarkable confirmations of the Standard Model (SM) describing the Electro-Weak and Strong Interactions. The Higgs boson was observed at CERN-LHC at 7-8 TeV and 13 TeV. The HL-LHC, a major luminosity upgrade has been recently approved. The HL-LHC may be already an early "Higgs factory", however only capable to perform Ho related measurements with rather large uncertainties because of persisting backgrounds and uncertainties. New projects using leptons rather than hadrons should be investigated Ho has a spin zero and its coupling is proportional to the square of the lepton mass, greatly enhancing the production from pairs of muons. A mu+mu- Collider may operate at a much higher magnetic field respect to the e+e- Collider and it has a smaller radius, easily fitting within one existing European site. However muons are unstable particles: they must be produced in sufficient amounts from pions of a proton beam, cooled and quickly accelerated to the required energies. The scenario is here primarily concentrated to further developments of the European Spallation Source (ESS) under construction in the Lund site as the most intense future source of spallation neutrons. As a initial part of the program, muon cooling should be experimentally demonstrated with the much cheaper and simpler Initial Cooling Experiment.
Are quantitative features of lung nodules reproducible at different CT acquisition and reconstruction parameters?

Consistency and duplicability in Computed Tomography (CT) output is essential to quantitative imaging for lung cancer detection and monitoring. This study of CT-detected lung nodules investigated the reproducibility of volume-, density-, and texture-based features (outcome variables) over routine ranges of radiation dose, reconstruction kernel, and slice thickness. CT raw data of 23 nodules were reconstructed using 320 acquisition/reconstruction conditions (combinations of 4 doses, 10 kernels, and 8 thicknesses). Scans at 12.5%, 25%, and 50% of protocol dose were simulated; reduced-dose and full-dose data were reconstructed using conventional filtered back-projection and iterative-reconstruction kernels at a range of thicknesses (0.6–5.0 mm). Full-dose/B50f kernel reconstructions underwent expert segmentation for reference Region-Of-Interest (ROI) and nodule volume per thickness; each ROI was applied to 40 corresponding images (combinations of 4 doses and 10 kernels). Typical texture analysis metrics (including 5 histogram features, 13 Gray Level Co-occurrence Matrix, 5 Run Length Matrix, 2 Neighboring Gray-Level Dependence Matrix, and 3 Neighborhood Gray-Tone Difference Matrix) were computed per ROI. Reconstruction conditions resulting in no significant change in volume, density, or texture metrics were identified as “compatible pairs” for a given outcome variable. Our results indicate that as thickness increases, volumetric reproducibility decreases, while reproducibility of histogram- and texture-based features across different acquisition and reconstruction parameters improves. To achieve concomitant reproducibility of volumetric and radiomic results across studies, balanced standardization of the imaging acquisition parameters is required.
The Hubble Legacy Field GOODS-S Photometric Catalog

This manuscript describes the public release of the Hubble Legacy Fields (HLF) project photometric catalog for the extended GOODS-South region from the Hubble Space Telescope (HST) archival program AR-13252. The analysis is based on the version 2.0 HLF data release that now includes all ultraviolet (UV) imaging, combining three major UV surveys. The HLF data combines over a decade worth of 7475 exposures taken in 2635 orbits totaling 6.3 Ms with the HST Advanced Camera for Surveys Wide Field Channel (ACS/WFC) and the Wide Field Camera 3 (WFC3) UVIS/IR Channels in the greater GOODS-S extragalactic field, covering all major observational efforts (e.g., GOODS, GEMS, CANDELS, ERS, UVUDF, and many other programs; see Illingworth et al.). The HLF GOODS-S catalogs include photometry in 13 bandpasses from the UV (WFC3/UVIS F225W, F275W, and F336W filters), optical (ACS/WFC F435W, F606W, F775W, F814W and F850LP filters), to near-infrared (WFC3/IR F098M, F105W, F125W, F140W and F160W filters). Such a data set makes it possible to construct the spectral energy distributions (SEDs) of objects over a wide wavelength range from high-resolution mosaics that are largely contiguous. Here, we describe a photometric analysis of 186,474 objects in the HST imaging at wavelengths 0.2–1.6 μm. We detect objects from an ultra-deep image combining the PSF-homogenized and noise-equalized F850LP, F125W, F140W, and F160W images, including Gaia astrometric corrections. SEDs were determined by carefully taking the effects of the point-spread function in each observation into account. All of the data presented herein are available through the HLF website (https://archive.stsci.edu/prepds/hlf/).
Einstein-Vlasov calculations of structure formation

We study the dynamics of small inhomogeneities in an expanding universe collapsing to form bound structures using full solutions of the Einstein-Vlasov (N-body) equations. We compare these to standard Newtonian N-body solutions using quantities defined with respect to fiducial observers in order to bound relativistic effects. We focus on simplified initial conditions containing a limited range of length scales, but vary the inhomogeneities from small magnitude, where the Newtonian and general-relativistic calculations agree quite well, to large magnitude, where the background metric receives an order one correction. For large inhomogeneities, we find that the collapse of overdensities tends to happen faster in Newtonian calculations relative to fully general-relativistic ones. Even in this extreme regime, the differences in the spacetime evolution outside the regions of large gravitational potential and velocity are small. For standard cosmological values, we corroborate the robustness of Newtonian N-body simulations to model large scale perturbations and the related cosmic variance in the local expansion rate.
The Formation of Ultra-Diffuse Galaxies from Passive Evolution in the RomulusC Galaxy Cluster Simulation

We study the origins of 122 ultra-diffuse galaxies (UDGs) in the RomulusC zoom-in cosmological simulation of a galaxy cluster (M$_{200} = 1.15 \times 10^{14}$ M$_{\odot}$), one of the only such simulations capable of resolving the evolution and structure of dwarf galaxies (M$_{\star} < 10^9$ M$_{\odot}$). We find broad agreement with observed cluster UDGs and predict that they are not separate from the overall cluster dwarf population. UDGs in cluster environments form primarily due to the quenching of star formation from ram pressure stripping and the subsequent passive evolution of their stellar population which results in very low surface brightness dwarf galaxies. We predict that there is little difference between UDGs and non-UDGs in terms of their dark matter halo masses and spins, their $z = 0$ colors, nor their evolution over time. UDGs are typically larger dwarf galaxies well before in-fall into the cluster and have had their star formation quenched for longer, typically due to entering the cluster earlier. We find that in most respects cluster UDG and non-UDGs alike are similar to isolated dwarf galaxies, although they are typically larger in size. This is due, in part, to the fact that cluster dwarf galaxies grow from higher angular momentum gas compared to isolated dwarf galaxies.
Improving Multi-Word Entity Recognition for Biomedical Texts

Biomedical Named Entity Recognition (BioNER) is a crucial step for analyzing Biomedical texts, which aims at extracting biomedical named entities from a given text. Different supervised machine learning algorithms have been applied for BioNER by various researchers. The main requirement of these approaches is an annotated dataset used for learning the parameters of machine learning algorithms. Segment Representation (SR) models comprise of different tag sets used for representing the annotated data, such as IOB2, IOE2 and IOBES. In this paper, we propose an extension of IOBES model to improve the performance of BioNER. The proposed SR model, FROBES, improves the representation of multi-word entities. We used Bidirectional Long Short-Term Memory (BiLSTM) network; an instance of Recurrent Neural Networks (RNN), to design a baseline system for BioNER and evaluated the new SR model on two datasets, i2b2/VA 2010 challenge dataset and JNLPBA 2004 shared task dataset. The proposed SR model outperforms other models for multi-word entities with length greater than two. Further, the outputs of different SR models have been combined using majority voting ensemble method which outperforms the baseline models performance.
Signatures of Dipolar Dark Matter on Indirect Detection

In this work we study the annihilation of fermionic dark matter, considering it as a neutral particle with non vanishing magnetic (M) and electric (D) dipole moments. Effective cross section of the process χχ→ γγ is computed starting from a general form of the coupling χχγ in the framework of an extension of the Standard Model. By taking into account annihilation of dark matter pairs into mono-energetic photons, we found that for masses of O(10), an electric dipole moment ∼ 10−16 e cm is required to satisfy the current relic density inferences. Additionally, in order to pin down models viable to describe the physics of dark matter in the early Universe, we also constrain our model according to recent measurements of the temperature anisotropies of the cosmic microwave background radiation, and report constraints to the electric and magnetic dipole moments for a range of masses within our model.
Convergence of Gradient Methods on Bilinear Zero-Sum Games

Min-max formulations have attracted great attention in the ML community due to the rise of deep generative models and adversarial methods, and understanding the dynamics of (stochastic) gradient algorithms for solving such formulations has been a grand challenge. As a first step, we restrict to bilinear zero-sum games and give a systematic analysis of popular gradient updates, for both simultaneous and alternating versions. We provide exact conditions for their convergence and find the optimal parameter setup and convergence rates. In particular, our results offer formal evidence that alternating updates converge "better" than simultaneous ones.
Threshold effects in heavy quarkonium spectroscopy and decays

The possible importance of threshold effects in heavy quarkonium spectroscopy is discussed. The starting point is the calculation of the spectrum of heavy quarkonium-like states with self-energy/threshold corrections. Two different approaches are compared: I) The Unquenched Quark Model (UQM); II) A novel coupled-channel model, based on the UQM formalism. The latter provides a possible solution to the long-standing problem of convergence in UQM calculations; it also makes it possible to distinguish between states which are almost pure quarkonia and exotic states, characterized by non-negligible threshold (or continuum) components in their wave functions. The UQM-based coupled-channel model is used to study the $\chi_{\rm c}(2P)$ and $\chi_{\rm b}(3P)$ multiplets: $\chi_{\rm c}(2P)$'s are described as charmonium-like states with non-negligible molecular-type components in their wave functions, $\chi_{\rm b}(3P)$'s as almost pure bottomonia. Other possible applications of the UQM and the UQM-based coupled-channel model formalisms to the calculation of other observables, like the strong decay amplitudes, are also discussed.
Automated Classification of Plasma Regions Using 3D Particle Energy Distributions

We investigate the properties of the ion sky maps produced by the Dual Ion Spectrometers (DIS) from the Fast Plasma Investigation (FPI). We have trained a convolutional neural network classifier to predict four regions crossed by the Magnetospheric Multiscale Mission (MMS) on the dayside magnetosphere: solar wind, ion foreshock, magnetosheath, and magnetopause using solely DIS spectrograms. The accuracy of the classifier is >98 %. We use the classifier to detect mixed plasma regions, in particular to find the bow shock regions. A similar approach can be used to identify the magnetopause crossings and reveal regions prone to magnetic reconnection. Data processing through the trained classifier is fast and efficient and thus can be used for classification for the whole MMS database.
Video Compression With Rate-Distortion Autoencoders

In this paper we present a a deep generative model for lossy video compression. We employ a model that consists of a 3D autoencoder with a discrete latent space and an autoregressive prior used for entropy coding. Both autoencoder and prior are trained jointly to minimize a rate-distortion loss, which is closely related to the ELBO used in variational autoencoders. Despite its simplicity, we find that our method outperforms the state-of-the-art learned video compression networks based on motion compensation or interpolation. We systematically evaluate various design choices, such as the use of frame-based or spatio-temporal autoencoders, and the type of autoregressive prior. In addition, we present three extensions of the basic method that demonstrate the benefits over classical approaches to compression. First, we introduce \emph{semantic compression}, where the model is trained to allocate more bits to objects of interest. Second, we study \emph{adaptive compression}, where the model is adapted to a domain with limited variability, \eg videos taken from an autonomous car, to achieve superior compression on that domain. Finally, we introduce \emph{multimodal compression}, where we demonstrate the effectiveness of our model in joint compression of multiple modalities captured by non-standard imaging sensors, such as quad cameras. We believe that this opens up novel video compression applications, which have not been feasible with classical codecs.
Semi-Supervised Semantic Segmentation With High- and Low-Level Consistency

The ability to understand visual information from limited labeled data is an important aspect of machine learning. While image-level classification has been extensively studied in a semi-supervised setting, dense pixel-level classification with limited data has only drawn attention recently. In this work, we propose an approach for semi-supervised semantic segmentation that learns from limited pixel-wise annotated samples while exploiting additional annotation-free images. The proposed approach relies on adversarial training with a feature matching loss to learn from unlabeled images. It uses two network branches that link semi-supervised classification with semi-supervised segmentation including self-training. The dual-branch approach reduces both the low-level and the high-level artifacts typical when training with few labels. The approach attains significant improvement over existing methods, especially when trained with very few labeled samples. On several standard benchmarks—PASCAL VOC 2012, PASCAL-Context, and Cityscapes—the approach achieves new state-of-the-art in semi-supervised learning.