nils-herrmann/clean_scirepeval huggingface dataset:
Probing dynamics of boson stars by fast radio bursts and gravitational wave detection

Boson stars may consist of a new type of light singlet scalar particles with nontrivial self-interactions, and may compose a fraction of the dark matter in the Universe. In this work, we study the dynamics of boson stars with Liouville and logarithmic scalar self-interaction potentials as benchmarks. We perform a numerical analysis as well as a semi-analytic study on how the compactness and the total mass will deviate from that of the usual boson stars formed with a quartic repulsive self-interaction. We apply the recently suggested Swampland conjecture to examine whether boson stars with such benchmark potentials belong to the Landscape of a quantum gravity. Using the mass constraint on the macroscopic compact halo object (MACHO) and the cold dark matter (CDM) isocurvature mode constraint from the cosmic microwave background (CMB), we derive the allowed mass range of scalar particles which compose the boson star. We further analyze applications of the lensing of fast radio bursts (FRBs) and the gravitational wave (GW) detection to probe the presence of such boson stars and constrain the parameter space of their corresponding models. We discuss how the two types of boson star potentials can be discriminated by the FRB and GW measurements.
Latent Weights Do Not Exist: Rethinking Binarized Neural Network Optimization

Optimization of Binarized Neural Networks (BNNs) currently relies on real-valued latent weights to accumulate small update steps. In this paper, we argue that these latent weights cannot be treated analogously to weights in real-valued networks. Instead their main role is to provide inertia during training. We interpret current methods in terms of inertia and provide novel insights into the optimization of BNNs. We subsequently introduce the first optimizer specifically designed for BNNs, Binary Optimizer (Bop), and demonstrate its performance on CIFAR-10 and ImageNet. Together, the redefinition of latent weights as inertia and the introduction of Bop enable a better understanding of BNN optimization and open up the way for further improvements in training methodologies for BNNs. Code is available at: this https URL
Can Graph Neural Networks Help Logic Reasoning?

Effectively combining logic reasoning and probabilistic inference has been a long-standing goal of machine learning: the former has the ability to generalize with small training data, while the latter provides a principled framework for dealing with noisy data. However, existing methods for combining the best of both worlds are typically computationally intensive. In this paper, we focus on Markov Logic Networks and explore the use of graph neural networks (GNNs) for representing probabilistic logic inference. It is revealed from our analysis that the representation power of GNN alone is not enough for such a task. We instead propose a more expressive variant, called ExpressGNN, which can perform effective probabilistic logic inference while being able to scale to a large number of entities. We demonstrate by several benchmark datasets that ExpressGNN has the potential to advance probabilistic logic reasoning to the next stage.
Classifying Norm Conflicts using Learned Semantic Representations

While most social norms are informal, they are often formalized by companies in contracts to regulate trades of goods and services. When poorly written, contracts may contain normative conflicts resulting from opposing deontic meanings or contradict specifications. As contracts tend to be long and contain many norms, manually identifying such conflicts requires human-effort, which is time-consuming and error-prone. Automating such task benefits contract makers increasing productivity and making conflict identification more reliable. To address this problem, we introduce an approach to detect and classify norm conflicts in contracts by converting them into latent representations that preserve both syntactic and semantic information and training a model to classify norm conflicts in four conflict types. Our results reach the new state of the art when compared to a previous approach.
SP-10K: A Large-scale Evaluation Set for Selectional Preference Acquisition

Selectional Preference (SP) is a commonly observed language phenomenon and proved to be useful in many natural language processing tasks. To provide a better evaluation method for SP models, we introduce SP-10K, a large-scale evaluation set that provides human ratings for the plausibility of 10,000 SP pairs over five SP relations, covering 2,500 most frequent verbs, nouns, and adjectives in American English. Three representative SP acquisition methods based on pseudo-disambiguation are evaluated with SP-10K. To demonstrate the importance of our dataset, we investigate the relationship between SP-10K and the commonsense knowledge in ConceptNet5 and show the potential of using SP to represent the commonsense knowledge. We also use the Winograd Schema Challenge to prove that the proposed new SP relations are essential for the hard pronoun coreference resolution problem.
Strong and Simple Baselines for Multimodal Utterance Embeddings

Human language is a rich multimodal signal consisting of spoken words, facial expressions, body gestures, and vocal intonations. Learning representations for these spoken utterances is a complex research problem due to the presence of multiple heterogeneous sources of information. Recent advances in multimodal learning have followed the general trend of building more complex models that utilize various attention, memory and recurrent components. In this paper, we propose two simple but strong baselines to learn embeddings of multimodal utterances. The first baseline assumes a conditional factorization of the utterance into unimodal factors. Each unimodal factor is modeled using the simple form of a likelihood function obtained via a linear transformation of the embedding. We show that the optimal embedding can be derived in closed form by taking a weighted average of the unimodal features. In order to capture richer representations, our second baseline extends the first by factorizing into unimodal, bimodal, and trimodal factors, while retaining simplicity and efficiency during learning and inference. From a set of experiments across two tasks, we show strong performance on both supervised and semi-supervised multimodal prediction, as well as significant (10 times) speedups over neural models during inference. Overall, we believe that our strong baseline models offer new benchmarking options for future research in multimodal learning.
Extractive Summarization via Weighted Dissimilarity and Importance Aligned Key Iterative Algorithm

We present importance aligned key iterative algorithm for extractive summarization that is faster than conventional algorithms keeping its accuracy. The computational complexity of our algorithm is O($SNlogN$) to summarize original $N$ sentences into final $S$ sentences. Our algorithm maximizes the weighted dissimilarity defined by the product of importance and cosine dissimilarity so that the summary represents the document and at the same time the sentences of the summary are not similar to each other. The weighted dissimilarity is heuristically maximized by iterative greedy search and binary search to the sentences ordered by importance. We finally show a benchmark score based on summarization of customer reviews of products, which highlights the quality of our algorithm comparable to human and existing algorithms. We provide the source code of our algorithm on github this https URL .
Cold Debris Disks as Strategic Targets for the 2020s

Cold debris disks (T$<$200 K) are analogues to the dust in the Solar System's Kuiper belt--dust generated from the evaporation and collision of minor bodies perturbed by planets, our Sun, and the local interstellar medium. Scattered light from debris disks acts as both a signpost for unseen planets as well as a source of contamination for directly imaging terrestrial planets, but many details of these disks are poorly understood. We lay out a critical observational path for the study of nearby debris disks that focuses on defining an empirical relationship between scattered light and thermal emission from a disk, probing the dynamics and properties of debris disks, and directly determining the influence of planets on disks. 
We endorse the findings and recommendations published in the National Academy reports on Exoplanet Science Strategy and Astrobiology Strategy for the Search for Life in the Universe. This white paper extends and complements the material presented therein with a focus on debris disks around nearby stars. Separate complementary papers are being submitted regarding the inner warm regions of debris disks (Mennesson et al.), the modeling of debris disk evolution (Gaspar et al.), studies of dust properties (Chen et al.), and thermal emission from disks (Su et al.).
(In)Stability for the Blockchain: Deleveraging Spirals and Stablecoin Attacks

We develop a model of stable assets, including noncustodial stablecoins backed by cryptocurrencies. Such stablecoins are popular methods for bootstrapping price stability within public blockchain settings. We demonstrate fundamental results about dynamics and liquidity in stablecoin markets, demonstrate that these markets face deleveraging spirals that cause illiquidity during crises, and show that these stablecoins have `stable' and `unstable' domains. Starting from documented market behaviors, we explain actual stablecoin movements; further our results are robust to a wide range of potential behaviors. In simulations, we show that these systems are susceptible to high tail volatility and failure. Our model builds foundations for stablecoin design. Based on our results, we suggest design improvements that can improve long-term stability and suggest methods for solving pricing problems that arise in existing stablecoins. In addition to the direct risk of instability, our dynamics results suggest a profitable economic attack during extreme events that can induce volatility in the `stable' asset. This attack additionally suggests ways in which stablecoins can cause perverse incentives for miners, posing risks to blockchain consensus.
Fair Distributions from Biased Samples: A Maximum Entropy Optimization Framework

One reason for the emergence of bias in AI systems is biased data -- datasets that may not be true representations of the underlying distributions -- and may over or under-represent groups with respect to protected attributes such as gender or race. We consider the problem of correcting such biases and learning distributions that are "fair", with respect to measures such as proportional representation and statistical parity, from the given samples. Our approach is based on a novel formulation of the problem of learning a fair distribution as a maximum entropy optimization problem with a given expectation vector and a prior distribution. Technically, our main contributions are: (1) a new second-order method to compute the (dual of the) maximum entropy distribution over an exponentially-sized discrete domain that turns out to be faster than previous methods, and (2) methods to construct prior distributions and expectation vectors that provably guarantee that the learned distributions satisfy a wide class of fairness criteria. Our results also come with quantitative bounds on the total variation distance between the empirical distribution obtained from the samples and the learned fair distribution. Our experimental results include testing our approach on the COMPAS dataset and showing that the fair distributions not only improve disparate impact values but when used to train classifiers only incur a small loss of accuracy.
Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks

Recently, neural network based approaches have achieved significant improvement for solving large, complex, graph-structured problems. However, their bottlenecks still need to be addressed, and the advantages of multi-scale information and deep architectures have not been sufficiently exploited. In this paper, we theoretically analyze how existing Graph Convolutional Networks (GCNs) have limited expressive power due to the constraint of the activation functions and their architectures. We generalize spectral graph convolution and deep GCN in block Krylov subspace forms and devise two architectures, both with the potential to be scaled deeper but each making use of the multi-scale information in different ways. We further show that the equivalence of these two architectures can be established under certain conditions. On several node classification tasks, with or without the help of validation, the two new architectures achieve better performance compared to many state-of-the-art methods.
Pseudo-Goldstone dark matter confronts cosmic ray and collider anomalies

Persistent excesses in the spectra of gamma rays from the galactic center and cosmic ray antiprotons can be explained by dark matter of mass $50\sim 65$\,GeV annihilating into $b$ quarks, but this is typically hard to reconcile with direct detection constraints. We resolve this tension using a simple class of models, where dark matter is a pseudo-Nambu-Goldstone boson, having naturally momentum-suppressed couplings to nuclei. Exploring the parameter space of the model, we find that it can explain the cosmic ray anomalies while remaining compatible with constraints from the relic abundance and annihilation in dwarf spheroidal galaxies. In certain regions of parameter space, the Higgs-dark matter coupling can help stabilize the Higgs potential up to the Planck scale. The scalar partner of the dark matter is an extra Higgs boson, that can explain a tentative diphoton excess observed by CMS, and an excess of $b\bar b$ signal from LEP, if its mass is $\sim 96$\,GeV and the model is extended to include a heavy scalar quark. This extended model predicts a monochromatic gamma-ray line near 64 GeV, at a level close to current experimental sensitivity, from dark matter annihilations in the galaxy.
Syntax-Infused Variational Autoencoder for Text Generation

We present a syntax-infused variational autoencoder (SIVAE), that integrates sentences with their syntactic trees to improve the grammar of generated sentences. Distinct from existing VAE-based text generative models, SIVAE contains two separate latent spaces, for sentences and syntactic trees. The evidence lower bound objective is redesigned correspondingly, by optimizing a joint distribution that accommodates two encoders and two decoders. SIVAE works with long short-term memory architectures to simultaneously generate sentences and syntactic trees. Two versions of SIVAE are proposed: one captures the dependencies between the latent variables through a conditional prior network, and the other treats the latent variables independently such that syntactically-controlled sentence generation can be performed. Experimental results demonstrate the generative superiority of SIVAE on both reconstruction and targeted syntactic evaluations. Finally, we show that the proposed models can be used for unsupervised paraphrasing given different syntactic tree templates.
An Uncertainty-Driven GCN Refinement Strategy for Organ Segmentation

Organ segmentation is an important pre-processing step in many computer assisted intervention and computer assisted diagnosis methods. In recent years, CNNs have dominated the state of the art in this task. Organ segmentation scenarios present a challenging environment for these methods due to high variability in shape, similarity with background, etc. This leads to the generation of false negative and false positive regions in the output segmentation. In this context, the uncertainty analysis of the model can provide us with useful information about potentially misclassified elements. In this work we propose a method based on uncertainty analysis and graph convolutional networks as a post-processing step for segmentation. For this, we employ the uncertainty levels of the CNN to formulate a semi-supervised graph learning problem that is solved by training a GCN on the low uncertainty elements. Finally, we evaluate the full graph on the trained GCN to get the refined segmentation. We compare our framework with CRF on a graph-like data representation as refinement strategy.
Large-Scale Multi-Label Text Classification on EU Legislation

We consider Large-Scale Multi-Label Text Classification (LMTC) in the legal domain. We release a new dataset of 57k legislative documents from EUR-LEX, annotated with ∼4.3k EUROVOC labels, which is suitable for LMTC, few- and zero-shot learning. Experimenting with several neural classifiers, we show that BIGRUs with label-wise attention perform better than other current state of the art methods. Domain-specific WORD2VEC and context-sensitive ELMO embeddings further improve performance. We also find that considering only particular zones of the documents is sufficient. This allows us to bypass BERT’s maximum text length limit and fine-tune BERT, obtaining the best results in all but zero-shot learning cases.
Independent cosmological constraints from high-z H ii galaxies

We present new high spectral resolution observations of 15 high-z ($1.3 \leq$ z $\leq 2.5$) HII Galaxies (HIIG) obtained with MOSFIRE at the Keck Observatory. These data, combined with already published data for another 31 high-z and 107 z $\leq 0.15$ HIIG, are used to obtain new independent cosmological results using the distance estimator based on the established correlation between the Balmer emission line velocity dispersion and luminosity for HIIG. Our results are in excellent agreement with the latest cosmological concordance model ($\Lambda$CDM) published results. From our analysis, we find a value for the mass density parameter of $\Omega_m=0.290^{+0.056}_{-0.069}$ (stat). For a flat Universe we constrain the plane $\lbrace\Omega_m;w_0\rbrace = \lbrace 0.280^{+0.130}_{-0.100} ; -1.12^{+0.58}_{-0.32}\rbrace $ (stat). The joint likelihood analysis of HIIG with other complementary cosmic probes (Cosmic Microwave Background and Baryon Acoustic Oscillations) provides tighter constraints for the parameter space of the Equation of State of Dark Energy that are also in excellent agreement with those of similar analyses using Type Ia Supernovae instead as the geometrical probe.
Exploring low-energy neutrino physics with the Coherent Neutrino Nucleus Interaction Experiment

The Coherent Neutrino-Nucleus Interaction Experiment (CONNIE) uses low-noise fully depleted charge-coupled devices (CCDs) with the goal of measuring low-energy recoils from coherent elastic scattering (CE$\nu$NS) of reactor antineutrinos with silicon nuclei and testing nonstandard neutrino interactions (NSI). We report here the first results of the detector array deployed in 2016, considering an active mass 47.6 g (8 CCDs), which is operating at a distance of 30 m from the core of the Angra 2 nuclear reactor, with a thermal power of 3.8 GW. A search for neutrino events is performed by comparing data collected with reactor on (2.1 kg-day) and reactor off (1.6 kg-day). The results show no excess in the reactor-on data, reaching the world record sensitivity down to recoil energies of about 1 keV (0.1 keV electron-equivalent). A 95% confidence level limit for new physics is established at an event rate of 40 times the one expected from the standard model at this energy scale. The results presented here provide a new window to low-energy neutrino physics, allowing one to explore for the first time the energies accessible through the low threshold of CCDs. They will lead to new constrains on NSI from the CE$\nu$NS of antineutrinos from nuclear reactors.
Microlensing Optical Depth and Event Rate toward the Galactic Bulge from 8 yr of OGLE-IV Observations

The number and properties of observed gravitational microlensing events depend on the distribution and kinematics of stars and other compact objects along the line of sight. In particular, precise measurements of the microlensing optical depth and event rate toward the Galactic bulge enable strict tests of competing models of the Milky Way. Previous estimates, based on samples of up to a few hundred events, gave larger values than expected from the Galactic models and were difficult to reconcile with other constraints on the Galactic structure. Here we used long-term photometric observations of the Galactic bulge by the Optical Gravitational Lensing Experiment (OGLE) to select a homogeneous sample of 8000 gravitational microlensing events. We created the largest and most accurate microlensing optical depth and event rate maps of the Galactic bulge. The new maps ease the tension between the previous measurements and Galactic models. They are consistent with some earlier calculations based on bright stars and are systematically ∼30% smaller than the other estimates based on “all-source” samples of microlensing events. The difference is caused by the careful estimation of the source star population. The new maps agree well with predictions based on the Besançon model of the Galaxy. Apart from testing the Milky Way models, our maps may have numerous other applications, such as the measurement of the initial mass function or constraining the dark matter content in the Milky Way center. The new maps will also inform the planning of future space-based microlensing experiments by revising the expected number of events.
Probing High-Energy Interactions of Atmospheric and Astrophysical Neutrinos

Astrophysical and atmospheric neutrinos are important probes of the powerful accelerators that produce cosmic rays with EeV energies. Understanding these accelerators is a key goal of neutrino observatories, along with searches for neutrinos from supernovae, from dark matter annihilation, and other astrophysics topics. Here, we discuss how neutrino observatories like IceCube and future facilities like KM3NeT and IceCube-Gen2 can study the properties of high-energy (above 1TeV) neutrino interactions. This is far higher than is accessible at man-made accelerators, where the highest energy neutrino beam reached only 500GeV. In contrast, neutrino observatories have observed events with energies above 5PeV — 10,000 times higher in energy — and future large observatories may probe neutrinos with energies up to 1020 eV. These data have implications for both standard model measurements, such as of low Bjorken-x parton distributions and gluon shadowing, and also for searches for beyond-standard-model physics. This chapter will review the existing techniques and results, and discuss future prospects.
Nail Polish Try-On: Realtime Semantic Segmentation of Small Objects for Native and Browser Smartphone AR Applications

We provide a system for semantic segmentation of small objects that enables nail polish try-on AR applications to run client-side in realtime in native and web mobile applications. By adjusting input resolution and neural network depth, our model design enables a smooth trade-off of performance and runtime, with the highest performance setting achieving~\num{94.5} mIoU at 29.8ms runtime in native applications on an iPad Pro. We also provide a postprocessing and rendering algorithm for nail polish try-on, which integrates with our semantic segmentation and fingernail base-tip direction predictions.