nils-herrmann/clean_scirepeval huggingface dataset:
Theoretical foundations of the reduced relativistic gas in the cosmological perturbed context

The Reduced Relativistic Gas (RRG) is a simplified version of the ideal relativistic gas, which assumes that all particles have the same momentum magnitude. Although this is a very idealized situation, the resulting model preserves the phenomenology of Maxwell-Boltzmann distribution and, in some situations, can be described as a perfect fluid, without introducing large errors in both cosmological background and first-order perturbations. The perfect fluid description of RRG model was already used to study the warmness of dark matter, massive neutrinos and interaction of baryons and photons before recombination, showing very good agreement with previous works based on the full Einstein-Boltzmann system of equations. In order to understand these results and construct a more general and formal framework for RRG, we develop a theoretical description of first-order cosmological perturbations of RRG, based on a distribution function which encodes the simplifying assumption that all particles have the same momentum magnitude. The full set of Einstein-Boltzmann equations for RRG distribution are derived and quantities beyond the perfect fluid approximation are studied. Using RRG to describe warm dark matter, we show that, for particles with m ∼ keV, the perfect fluid approximation is valid on scales k < 10 h/Mpc, for most of the universe evolution. We also determine initial conditions for RRG in the early universe and study the evolution of potential in a toy model of universe composed only by RRG.
The scalar hexaquark uuddss: a candidate to dark matter?

It is conventionally argued that Dark Matter (DM) has a non-baryonic nature, but if we assume that DM was frozen out before primordial nucleosynthesis and could not significantly impact primordial abundances this argument may be evaded. Then a hypothetical $SU(3)$ flavor-singlet, highly symmetric, deeply bound neutral scalar hexaquark $\mathrm{S}=uuddss$, which due to its features has escaped from experimental detection so far, may be considered as a candidate for a baryonic DM. In the present work we calculate the mass and coupling constant of the scalar six-quark particle $\mathrm{S}$ by means of the QCD sum rule method. Our predictions for its mass are $ m_{S}=1180_{-26}^{+40}~\mathrm{MeV}$ ($m_s=95~\mathrm{MeV}$) and $ \widetilde {m}_{S}=1239_{-28}^{+42}~\mathrm{MeV}$ ($m_s=128~\mathrm{MeV}$). Although these values of mass would produce thermally the cosmological DM abundance, existence of this state may contradict to stability of the oxygen nuclei, which requires further thorough analysis.
GraphNAS: Graph Neural Architecture Search with Reinforcement Learning

Graph Neural Networks (GNNs) have been popularly used for analyzing non-Euclidean data such as social network data and biological data. Despite their success, the design of graph neural networks requires a lot of manual work and domain knowledge. In this paper, we propose a Graph Neural Architecture Search method (GraphNAS for short) that enables automatic search of the best graph neural architecture based on reinforcement learning. Specifically, GraphNAS first uses a recurrent network to generate variable-length strings that describe the architectures of graph neural networks, and then trains the recurrent network with reinforcement learning to maximize the expected accuracy of the generated architectures on a validation data set. Extensive experimental results on node classification tasks in both transductive and inductive learning settings demonstrate that GraphNAS can achieve consistently better performance on the Cora, Citeseer, Pubmed citation network, and protein-protein interaction network. On node classification tasks, GraphNAS can design a novel network architecture that rivals the best human-invented architecture in terms of test set accuracy.
Diverse dark matter density at sub-kiloparsec scales in Milky Way satellites: Implications for the nature of dark matter

Milky Way (MW) satellites reside within dark matter (DM) subhalos with a broad distribution of circular velocity profiles. This diversity is enhanced with the inclusion of ultrafaint satellites, which seemingly have very high DM densities, albeit with large systematic uncertainties. We argue that if confirmed, this large diversity in the MW satellite population poses a serious test for the structure formation theory with possible implications for the DM nature. For the cold dark matter model, the diversity might be a signature of the combined effects of subhalo tidal disruption by the MW disk and strong supernova feedback. For models with a dwarf-scale cutoff in the power spectrum, the diversity is a consequence of the lower abundance of dwarf-scale halos. This diversity is most challenging for self-interacting dark matter (SIDM) models with cross sections 
σ 
/ 
m 
χ 
≳ 
1 
  
  
cm 
2 
  
g 
− 
1 
 where subhalos have too low densities to explain the ultrafaint galaxies. We propose a novel solution to explain the diversity of MW satellites based on the gravothermal collapse of SIDM haloes. This solution requires a velocity-dependent cross section that predicts a bimodal distribution of cuspy dense (collapsed) subhaloes consistent with the ultrafaint satellites, and cored lower density subhaloes consistent with the brighter satellites.
The MineRL Competition on Sample Efficient Reinforcement Learning using Human Priors

Though deep reinforcement learning has led to breakthroughs in many difficult domains, these successes have required an ever-increasing number of samples. As state-of-the-art reinforcement learning (RL) systems require an exponentially increasing number of samples, their development is restricted to a continually shrinking segment of the AI community. Likewise, many of these systems cannot be applied to real-world problems, where environment samples are expensive. Resolution of these limitations requires new, sample-efficient methods. To facilitate research in this direction, we introduce the MineRL Competition on Sample Efficient Reinforcement Learning using Human Priors. 
The primary goal of the competition is to foster the development of algorithms which can efficiently leverage human demonstrations to drastically reduce the number of samples needed to solve complex, hierarchical, and sparse environments. To that end, we introduce: (1) the Minecraft ObtainDiamond task, a sequential decision making environment requiring long-term planning, hierarchical control, and efficient exploration methods; and (2) the MineRL-v0 dataset, a large-scale collection of over 60 million state-action pairs of human demonstrations that can be resimulated into embodied trajectories with arbitrary modifications to game state and visuals. 
Participants will compete to develop systems which solve the ObtainDiamond task with a limited number of samples from the environment simulator, Malmo. The competition is structured into two rounds in which competitors are provided several paired versions of the dataset and environment with different game textures. At the end of each round, competitors will submit containerized versions of their learning algorithms and they will then be trained/evaluated from scratch on a hold-out dataset-environment pair for a total of 4-days on a prespecified hardware platform.
Scalar-metric-affine theories: Can we get ghost-free theories from symmetry?

We reveal the existence of a certain hidden symmetry in general ghost-free scalar-tensor theories which can only be seen when generalizing the geometry of the spacetime from Riemannian. For this purpose, we study scalar-tensor theories in the metric-affine (Palatini) formalism of gravity, which we call scalar-metric-affine theories for short, where the metric and the connection are independent. We show that the projective symmetry, a local symmetry under a shift of the connection, can provide a ghost-free structure of scalar-metric-affine theories. The ghostly sector of the second-order derivative of the scalar is absorbed into the projective gauge mode when the unitary gauge can be imposed. Incidentally, the connection does not have the kinetic term in these theories, and then it is just an auxiliary field. We can thus (at least in principle) integrate the connection out and obtain a form of scalar-tensor theories in the Riemannian geometry. The projective symmetry then hides in the ghost-free scalar-tensor theories. For an explicit example, we show the relationship between the quadratic-order scalar-metric-affine theory and the quadratic U-degenerate theory. The explicit correspondence between the metric-affine (Palatini) formalism and the metric one could be also useful for analyzing phenomenology such as inflation.
Consequences of 
μ−τ
 reflection symmetry for 
3+1
 neutrino mixing

We investigate the consequences of $\mu-\tau$ reflection symmetry in presence of a light sterile neutrino for the $3+1$ neutrino mixing scheme. We discuss the implications of total $\mu-\tau$ reflection symmetry as well partial $\mu-\tau$ reflection symmetry. For the total $\mu-\tau$ reflection symmetry we find values of $\theta_{23}$ and $\delta$ remains confined near $\pi/4$ and $\pm \pi/2$ respectively. The current allowed region for $\theta_{23}$ and $\delta$ in case of inverted hierarchy lies outside the area preferred by the total $\mu-\tau$ reflection symmetry. However, interesting predictions on the neutrino mixing angles and Dirac CP violating phases are obtained considering partial $\mu-\tau$ reflection symmetry. We obtain predictive correlations between the neutrino mixing angle $\theta_{23}$ and Dirac CP phase $\delta$ and study the testability of these correlations at the future long baseline experiment DUNE. We find that while the imposition of $\mu-\tau$ reflection symmetry in the first column admit both normal and inverted neutrino mass hierarchy, demanding $\mu-\tau$ reflection symmetry for the second column excludes the inverted hierarchy. Interestingly, the sterile mixing angle $\theta_{34}$ gets tightly constrained considering the $\mu-\tau$ reflection symmetry in the fourth column. We also study consequences of $\mu-\tau$ reflection symmetry for the Majorana phases and neutrinoless double beta decay.
Two variable fragment of Term Modal Logic

Term modal logics (TML) are modal logics with unboundedly many modalities, with quantification over modal indices, so that we can have formulas of the form $\exists y. \forall x. (\Box_x P(x,y) \supset\Diamond_y P(y,x))$. Like First order modal logic, TML is also "notoriously" undecidable, in the sense that even very simple fragments are undecidable. In this paper, we show the decidability of one interesting fragment, that of two variable TML. This is in contrast to two-variable First order modal logic, which is undecidable.
Primordial black hole tower: Dark matter, earth-mass, and LIGO black holes

We investigate a possibility of primordial black hole (PBH) formation with a hierarchical mass spectrum in multiple phases of inflation. As an example, we find that one can simultaneously realize a mass spectrum which has recently attracted a lot of attention: stellar-mass PBHs ($\sim\mathcal{O}(10)M_\odot$) as a possible source of binary black holes detected by LIGO/Virgo collaboration, asteroid-mass ($\sim\mathcal{O}(10^{-12})M_\odot$) as a main component of dark matter, and earth-mass ($\sim\mathcal{O}(10^{-5})M_\odot$) as a source of ultrashort-timescale events in Optical Gravitational Lensing Experiment microlensing data. The recent refined de Sitter swampland conjecture may support such a multi-phase inflationary scenario with hierarchical mass PBHs as a transition signal of each inflationary phase.
Mind the Gap: Is the Too Big to Fail Problem Resolved?

The faintness of satellite systems in galaxy groups has contributed to the widely discussed “missing satellite” and “too big to fail” issues. Using techniques based on Tremaine & Richstone, we show that there is no problem with the luminosity function computed from modern codes per se, but that the gap between first and second-brightest systems is too big given the luminosity function, and the same large gap found in modern, large-scale baryonic ΛCDM simulations such as EAGLE and IllustrisTNG, is even greater in dark-matter-only simulations. Additionally, this is most likely due to gravitationally induced merging caused by classical dynamical friction. Quantitatively, the gap is larger in the computed simulations than in the randomized ones by 1.79 ± 1.04, 1.51 ± 0.93, 3.43 ± 1.44, and 3.33 ± 1.35 mag in the EAGLE, IllustrisTNG, and dark-matter-only simulations of EAGLE and IllustrisTNG, respectively. Furthermore, the anomalous gaps in the simulated systems are even larger than those in the real data by over half a magnitude and are still larger in the dark-matter-only simulations. Briefly stated, ΛCDM does not have a problem with an absence of “too big to fail” galaxies. Statistically significant large gaps between first and second-brightest galaxies are to be expected.
Low-Memory Neural Network Training: A Technical Report

Memory is increasingly often the bottleneck when training neural network models. Despite this, techniques to lower the overall memory requirements of training have been less widely studied compared to the extensive literature on reducing the memory requirements of inference. In this paper we study a fundamental question: How much memory is actually needed to train a neural network? To answer this question, we profile the overall memory usage of training on two representative deep learning benchmarks -- the WideResNet model for image classification and the DynamicConv Transformer model for machine translation -- and comprehensively evaluate four standard techniques for reducing the training memory requirements: (1) imposing sparsity on the model, (2) using low precision, (3) microbatching, and (4) gradient checkpointing. We explore how each of these techniques in isolation affects both the peak memory usage of training and the quality of the end model, and explore the memory, accuracy, and computation tradeoffs incurred when combining these techniques. Using appropriate combinations of these techniques, we show that it is possible to the reduce the memory required to train a WideResNet-28-2 on CIFAR-10 by up to 60.7x with a 0.4% loss in accuracy, and reduce the memory required to train a DynamicConv model on IWSLT'14 German to English translation by up to 8.7x with a BLEU score drop of 0.15.
γ-ray and ν Searches for Dark-Matter Subhalos in the Milky Way with a Baryonic Potential

The distribution of dark-matter (DM) subhalos in our galaxy remains disputed, leading to varying γ -ray and ν flux predictions from their annihilation or decay. In this work, we study how, in the inner galaxy, subhalo tidal disruption from the galactic baryonic potential impacts these signals. Based on state-of-the art modeling of this effect from numerical simulations and semi-analytical results, updated subhalo spatial distributions are derived and included in the CLUMPY code. The latter is used to produce a thousand realizations of the γ -ray and ν sky. Compared to predictions based on DM only, we conclude a decrease of the flux of the brightest subhalo by a factor of 2 to 7 for annihilating DM and no impact on decaying DM: the discovery prospects or limits subhalos can set on DM candidates are affected by the same factor. This study also provides probability density functions for the distance, mass, and angular distribution of the brightest subhalo, among which the mass may hint at its nature: it is most likely a dwarf spheroidal galaxy in the case of strong tidal effects from the baryonic potential, whereas it is lighter and possibly a dark halo for DM only or less pronounced tidal effects.
DeepSurvival: Pedestrian Wait Time Estimation in Mixed Traffic Conditions Using Deep Survival Analysis

Pedestrian’s road crossing behaviour is one of the important aspects of urban dynamics that will be affected by the introduction of autonomous vehicles. In this study we introduce DeepSurvival, a novel framework for estimating pedestrian’s waiting time at unsignalized mid-block crosswalks in mixed traffic conditions. We exploit the strengths of deep learning in capturing the nonlinearities in the data and develop a cox proportional hazard model with a deep neural network as the log-risk function. An embedded feature selection algorithm for reducing data dimensionality and enhancing the interpretability of the network is also developed. We test our framework on a dataset collected from 160 participants using an immersive virtual reality environment. Validation results showed that with a C-index of 0.64 our proposed framework outperformed the standard cox proportional hazard-based model with a C-index of 0.58.
On Learning to Prove

In this paper, we consider the problem of learning a first-order theorem prover that uses a representation of beliefs in mathematical claims to construct proofs. The inspiration for doing so comes from the practices of human mathematicians where "plausible reasoning" is applied in addition to deductive reasoning to find proofs. 
Towards this end, we introduce a representation of beliefs that assigns probabilities to the exhaustive and mutually exclusive first-order possibilities found in Hintikka's theory of distributive normal forms. The representation supports Bayesian update, induces a distribution on statements that does not enforce that logically equivalent statements are assigned the same probability, and suggests an embedding of statements into an associated Hilbert space. 
We then examine conjecturing as model selection and an alternating-turn game of determining consistency. The game is amenable (in principle) to self-play training to learn beliefs and derive a prover that is complete when logical omniscience is attained and sound when beliefs are reasonable. The representation has super-exponential space requirements as a function of quantifier depth so the ideas in this paper should be taken as theoretical. We will comment on how abstractions can be used to control the space requirements at the cost of completeness.
Mechanics-Aware Modeling of Cloth Appearance

Micro-appearance models have brought unprecedented fidelity and details to cloth rendering. Yet, these models neglect fabric mechanics: when a piece of cloth interacts with the environment, its yarn and fiber arrangement usually changes in response to external contact and tension forces. Since subtle changes of a fabric's microstructures can greatly affect its macroscopic appearance, mechanics-driven appearance variation of fabrics has been a phenomenon that remains to be captured. We introduce a mechanics-aware model that adapts the microstructures of cloth yarns in a physics-based manner. Our technique works on two distinct physical scales: using physics-based simulations of individual yarns, we capture the rearrangement of yarn-level structures in response to external forces. These yarn structures are further enriched to obtain appearance-driving fiber-level details. The cross-scale enrichment is made practical through a new parameter fitting algorithm for simulation, an augmented procedural yarn model coupled with a custom-design regression neural network. We train the network using a dataset generated by joint simulations at both the yarn and the fiber levels. Through several examples, we demonstrate that our model is capable of synthesizing photorealistic cloth appearance in a mechanically plausible way.
Towards Explainable Anticancer Compound Sensitivity Prediction via Multimodal Attention-based Convolutional Encoders

In line with recent advances in neural drug design and sensitivity prediction, we propose a novel architecture for interpretable prediction of anticancer compound sensitivity using a multimodal attention-based convolutional encoder. Our model is based on the three key pillars of drug sensitivity: compounds' structure in the form of a SMILES sequence, gene expression profiles of tumors and prior knowledge on intracellular interactions from protein-protein interaction networks. We demonstrate that our multiscale convolutional attention-based encoder significantly outperforms a baseline model trained on Morgan fingerprints, a selection of encoders based on SMILES as well as previously reported state of the art for multimodal drug sensitivity prediction (R2 = 0.86 and RMSE = 0.89). Moreover, the explainability of our approach is demonstrated by a thorough analysis of the attention weights. We show that the attended genes significantly enrich apoptotic processes and that the drug attention is strongly correlated with a standard chemical structure similarity index. Finally, we report a case study of two receptor tyrosine kinase (RTK) inhibitors acting on a leukemia cell line, showcasing the ability of the model to focus on informative genes and submolecular regions of the two compounds. The demonstrated generalizability and the interpretability of our model testify its potential for in-silico prediction of anticancer compound efficacy on unseen cancer cells, positioning it as a valid solution for the development of personalized therapies as well as for the evaluation of candidate compounds in de novo drug design.
Deductive Proof of Ethereum Smart Contracts Using Why3

A bug or error is a common problem that any software or computer program may encounter. It can occur from badly writing the program, a typing error or bad memory management. However, errors can become a significant issue if the unsafe program is used for critical systems. Therefore, formal methods for these kinds of systems are greatly required. In this paper, we use a formal language that performs deductive verification on an Ethereum Blockchain application based on smart contracts, which are self-executing digital contracts. Blockchain systems manipulate cryptocurrency and transaction information. Therefore , if a bug occurs in the blockchain, serious consequences such as a loss of money can happen. Thus, the aim of this paper is to propose a language dedicated to deductive verification, called Why3, as a new language for writing formal and verified smart contracts, thereby avoiding attacks exploiting such contract execution vulnerabilities. We first write a Why3 smart contracts program; next we formulate specifications to be proved as absence of RunTime Error properties and functional properties, then we verify the behavior of the program using the Why3 system. Finally we compile the Why3 contracts to the Ethereum Virtual Machine (EVM). Moreover, we give a set of generic mathematical statements that allows verifying functional properties suited to any type of smart contracts holding cryptocurrency, showing that Why3 can be a suitable language to write smart contracts. To illustrate our approach, we describe its application to a realistic industrial use case.