nils-herrmann/clean_scirepeval huggingface dataset:
Impact of Stellar Superflares on Planetary Habitability

High-energy radiation caused by exoplanetary space weather events from planet-hosting stars can play a crucial role in conditions promoting or destroying habitability in addition to the conventional factors. In this paper, we present the first quantitative impact evaluation system of stellar flares on the habitability factors with an emphasis on the impact of stellar proton events. We derive the maximum flare energy from stellar star spot sizes and examine the impacts of flare-associated ionizing radiation on CO2, H2, and N2+ O2-rich atmospheres of a number of well-characterized terrestrial type exoplanets. Our simulations based on the Particle and Heavy Ion Transport code System suggest that the estimated ground-level dose for each planet in the case of terrestrial-level atmospheric pressure (1 bar) for each exoplanet does not exceed the critical dose for complex (multicellular) life to persist, even for the planetary surface of Proxima Centauri b, Ross-128 b, and TRAPPIST-1 e. However, when we take into account the effects of the possible maximum flares from those host stars, the estimated dose reaches fatal levels at the terrestrial lowest atmospheric depth on TRAPPIST-1 e and Ross-128 b. Large fluxes of coronal X-ray and ultraviolet radiation from active stars induce high atmospheric escape rates from close-in exoplanets, suggesting that the atmospheric depth can be substantially smaller than that on Earth. In a scenario with the atmospheric thickness of one-tenth of Earth’s, the radiation dose from close-in planets including Proxima Centauri b and TRAPPIST-1 e reaches near fatal levels with annual frequency of flare occurrence from their host stars.
Using historical utility outage data to compute overall transmission grid resilience

Given increasing risk from climate-induced natural hazards, there is growing interest in the development of methods that can quantitatively measure resilience in power systems. This work quantifies resilience in electric power transmission networks in a new and comprehensive way that can represent the multiple processes of resilience. A novel aspect of this approach is the use of empirical data to develop the probability distributions that drive the computational model. This paper demonstrates the approach by measuring the impact of one potential improvement to a power system. Specifically, we measure the impact of additional distributed generation (DG) on power system resilience, and find that DG can substantially increase resilience.
Sample-Efficient Neural Architecture Search by Learning Action Space

Neural Architecture Search (NAS) has emerged as a promising technique for automatic neural network design. However, existing NAS approaches often utilize manually designed action space, which is not directly related to the performance metric to be optimized (e.g., accuracy). As a result, using manually designed action space to perform NAS often leads to sample-inefficient explorations of architectures and thus can be sub-optimal. In order to improve sample efficiency, this paper proposes Latent Action Neural Architecture Search (LaNAS) that learns the action space to recursively partition the architecture search space into regions, each with concentrated performance metrics (\emph{i.e.}, low variance). During the search phase, as different architecture search action sequences lead to regions of different performance, the search efficiency can be significantly improved by biasing towards the regions with good performance. On the largest NAS dataset NasBench-101, our experimental results demonstrated that LaNAS is 22x, 14.6x and 12.4x more sample-efficient than random search, regularized evolution, and Monte Carlo Tree Search (MCTS) respectively. When applied to the open domain, LaNAS finds an architecture that achieves SoTA 98.0% accuracy on CIFAR-10 and 75.0% top1 accuracy on ImageNet (mobile setting), after exploring only 6,000 architectures.
Predicting research trends with semantic and neural networks with an application in quantum physics

The corpus of scientific literature grows at an ever increasing speed. While this poses a severe challenge for human researchers, computer algorithms with access to a large body of knowledge could help make important contributions to science. Here, we demonstrate the development of a semantic network for quantum physics, denoted SemNet, using 750,000 scientific papers and knowledge from books and Wikipedia. We use it in conjunction with an artificial neural network for predicting future research trends. Individual scientists can use SemNet for suggesting and inspiring personalized, out-of-the-box ideas. Computer-inspired scientific ideas will play a significant role in accelerating scientific progress, and we hope that our work directly contributes to that important goal. The vast and growing number of publications in all disciplines of science cannot be comprehended by a single human researcher. As a consequence, researchers have to specialize in narrow subdisciplines, which makes it challenging to uncover scientific connections beyond the own field of research. Thus, access to structured knowledge from a large corpus of publications could help push the frontiers of science. Here, we demonstrate a method to build a semantic network from published scientific literature, which we call SemNet. We use SemNet to predict future trends in research and to inspire personalized and surprising seeds of ideas in science. We apply it in the discipline of quantum physics, which has seen an unprecedented growth of activity in recent years. In SemNet, scientific knowledge is represented as an evolving network using the content of 750,000 scientific papers published since 1919. The nodes of the network correspond to physical concepts, and links between two nodes are drawn when two concepts are concurrently studied in research articles. We identify influential and prize-winning research topics from the past inside SemNet, thus confirming that it stores useful semantic knowledge. We train a neural network using states of SemNet of the past to predict future developments in quantum physics and confirm high-quality predictions using historic data. Using network theoretical tools, we can suggest personalized, out-of-the-box ideas by identifying pairs of concepts, which have unique and extremal semantic network properties. Finally, we consider possible future developments and implications of our findings.
A Multi-Task Architecture on Relevance-based Neural Query Translation

We describe a multi-task learning approach to train a Neural Machine Translation (NMT) model with a Relevance-based Auxiliary Task (RAT) for search query translation. The translation process for Cross-lingual Information Retrieval (CLIR) task is usually treated as a black box and it is performed as an independent step. However, an NMT model trained on sentence-level parallel data is not aware of the vocabulary distribution of the retrieval corpus. We address this problem and propose a multi-task learning architecture that achieves 16% improvement over a strong baseline on Italian-English query-document dataset. We show using both quantitative and qualitative analysis that our model generates balanced and precise translations with the regularization effect it achieves from multi-task learning paradigm.
Noisy-As-Clean: Learning Unsupervised Denoising from the Corrupted Image

Recently, supervised deep networks have achieved promising performance on image denoising, by learning image priors and noise statistics on plenty pairs of noisy and clean images. Unsupervised denoising networks are also proposed to use external noisy images for training. However, for an unseen test image, these denoising networks ignore either its particular image prior, the noise statistics, or both. That is, the networks learned from external images inherently suffer from a domain gap problem that the image priors and noise statistics are very different between the training and test images. This problem becomes more clear when dealing with the signal dependent realistic noise. To bridge this gap, in this work, we propose a novel “Noisy-As-Clean” (NAC) strategy of training unsupervised denoising networks. In NAC, the corrupted test image is taken as the “clean” target, while the inputs are simulated images consisting of the test image and similar corruptions. A simple and useful observation on our NAC is: as long as the noise is weak, it is feasible to learn an unsupervised network only with the corrupted image, approximating the optimal parameters of a supervised network learned with pairs of noisy and clean images. Experiments show that the unsupervised networks trained with our NAC strategy outperform previous networks, including supervised ones, on synthetic and realistic noise removal.
Interconnected Question Generation with Coreference Alignment and Conversation Flow Modeling

We study the problem of generating interconnected questions in question-answering style conversations. Compared with previous works which generate questions based on a single sentence (or paragraph), this setting is different in two major aspects: (1) Questions are highly conversational. Almost half of them refer back to conversation history using coreferences. (2) In a coherent conversation, questions have smooth transitions between turns. We propose an end-to-end neural model with coreference alignment and conversation flow modeling. The coreference alignment modeling explicitly aligns coreferent mentions in conversation history with corresponding pronominal references in generated questions, which makes generated questions interconnected to conversation history. The conversation flow modeling builds a coherent conversation by starting questioning on the first few sentences in a text passage and smoothly shifting the focus to later parts. Extensive experiments show that our system outperforms several baselines and can generate highly conversational questions. The code implementation is released at https://github.com/Evan-Gao/conversaional-QG.
Of Cores: A Partial-Exploration Framework for Markov Decision Processes

We introduce a framework for approximate analysis of Markov decision processes (MDP) with bounded-, unbounded-, and infinite-horizon properties. The main idea is to identify a "core" of an MDP, i.e., a subsystem where we provably remain with high probability, and to avoid computation on the less relevant rest of the state space. Although we identify the core using simulations and statistical techniques, it allows for rigorous error bounds in the analysis. Consequently, we obtain efficient analysis algorithms based on partial exploration for various settings, including the challenging case of strongly connected systems.
Generalized Ramsey Numbers: Forbidding Paths with Few Colors

Let $f(K_n, H, q)$ be the minimum number of colors needed to edge-color $K_n$ so that every copy of $H$ is colored with at least $q$ colors. Originally posed by Erdős and Shelah when $H$ is complete, the asymptotics of this extremal function have been extensively studied when $H$ is a complete graph or a complete balanced bipartite graph. Here we investigate this function for some other $H$, and in particular we determine the asymptotic behavior of $f(K_n, P_v, q)$ for almost all values of $v$ and $q$, where $P_v$ is a path on $v$ vertices.
Context-aware Embedding for Targeted Aspect-based Sentiment Analysis

Attention-based neural models were employed to detect the different aspects and sentiment polarities of the same target in targeted aspect-based sentiment analysis (TABSA). However, existing methods do not specifically pre-train reasonable embeddings for targets and aspects in TABSA. This may result in targets or aspects having the same vector representations in different contexts and losing the context-dependent information. To address this problem, we propose a novel method to refine the embeddings of targets and aspects. Such pivotal embedding refinement utilizes a sparse coefficient vector to adjust the embeddings of target and aspect from the context. Hence the embeddings of targets and aspects can be refined from the highly correlative words instead of using context-independent or randomly initialized vectors. Experiment results on two benchmark datasets show that our approach yields the state-of-the-art performance in TABSA task.
NIHAO – XXII. Introducing black hole formation, accretion, and feedback into the NIHAO simulation suite

We introduce algorithms for black hole physics, i.e. black hole formation, accretion, and feedback, into the Numerical Investigation of a Hundred Astrophysical Objects (NIHAO) project of galaxy simulations. This enables us to study high mass, elliptical galaxies, where feedback from the central black hole is generally thought to have a significant effect on their evolution. We furthermore extend the NIHAO suite by 45 simulations that encompass z = 0 halo masses from 1 × 1012 to $4 \times 10^{13}\, \mathrm{M}_{\odot }$, and resimulate five galaxies from the original NIHAO sample with black hole physics, which have z = 0 halo masses from 8 × 1011 to $3 \times 10^{12}\, \mathrm{M}_{\odot }$. Now NIHAO contains 144 different galaxies and thus has the largest sample of zoom-in simulations of galaxies, spanning z = 0 halo masses from 9 × 108 to $4 \times 10^{13}\, \mathrm{M}_{\odot }$. In this paper we focus on testing the algorithms and calibrating their free parameters against the stellar mass versus halo mass relation and the black hole mass versus stellar mass relation. We also investigate the scatter of these relations, which we find is a decreasing function with time and thus in agreement with observations. For our fiducial choice of parameters we successfully quench star formation in objects above a z = 0 halo mass of $10^{12}\, \mathrm{M}_{\odot }$, thus transforming them into red and dead galaxies.
Complex collisions of ultracold molecules: A toy model

We introduce a model to study the collisions of two ultracold diatomic molecules in one dimension interacting via pairwise potentials. We present results for this system, and argue that it offers lessons for real molecular collisions in three dimensions. We analyze the distribution of the adiabatic potentials in the hyperspherical coordinate representation as well as the distribution of the four-body bound states in the adiabatic approximation (i.e. no coupling between adiabatic channels). It is found that while the adiabatic potential distribution transitions from chaotic to non-chaotic as the two molecules are separated, the four-body bound states show no visible chaos in the distribution of nearest-neighbor energy level spacing. We also study the effects of molecular properties, such as interaction strength, interaction range, and atomic mass, on the resonance density and degree of chaos in the adiabatic potentials. We numerically find that the dependence of the four-body bound state density on these parameters is captured by simple scaling laws, in agreement with previous analytic arguments, even though these arguments relied on uncontrolled approximations. This agreement suggests that similar scaling laws may also govern real molecular collisions in three dimensions.
Invariance in ecological pattern

The abundance of different species in a community often follows the log series distribution. Other ecological patterns also have simple forms. Why does the complexity and variability of ecological systems reduce to such simplicity? Common answers include maximum entropy, neutrality, and convergent outcome from different underlying biological processes. This article proposes a more general answer based on the concept of invariance, the property by which a pattern remains the same after transformation. Invariance has a long tradition in physics. For example, general relativity emphasizes the need for the equations describing the laws of physics to have the same form in all frames of reference. By bringing this unifying invariance approach into ecology, we show that the log series pattern dominates when the consequences of processes acting on abundance are invariant to the addition or multiplication of abundance by a constant. The lognormal pattern dominates when the processes acting on net species growth rate obey rotational invariance (symmetry) with respect to the summing up of the individual component processes. Recognizing how these invariances connect pattern to process leads to a synthesis of previous approaches. First, invariance provides a simpler and more fundamental maximum entropy derivation of the log series distribution. Second, invariance provides a simple derivation of the key result from neutral theory: the log series at the metacommunity scale and a clearer form of the skewed lognormal at the local community scale. The invariance expressions are easy to understand because they uniquely describe the basic underlying components that shape pattern.
Neural network identifiability for a family of sigmoidal nonlinearities

This paper addresses the following question of neural network identifiability: Does the input–output map realized by a feed-forward neural network with respect to a given nonlinearity uniquely specify the network architecture, weights, and biases? The existing literature on the subject (Sussman in Neural Netw 5(4):589–593, 1992; Albertini et al. in Artificial neural networks for speech and vision, 1993; Fefferman in Rev Mat Iberoam 10(3):507–555, 1994) suggests that the answer should be yes, up to certain symmetries induced by the nonlinearity, and provided that the networks under consideration satisfy certain “genericity conditions.” The results in Sussman (1992) and Albertini et al. (1993) apply to networks with a single hidden layer and in Fefferman (1994) the networks need to be fully connected. In an effort to answer the identifiability question in greater generality, we derive necessary genericity conditions for the identifiability of neural networks of arbitrary depth and connectivity with an arbitrary nonlinearity. Moreover, we construct a family of nonlinearities for which these genericity conditions areminimal, i.e., both necessary and sufficient. This family is large enough to approximate many commonly encountered nonlinearities to within arbitrary precision in the uniform norm.
Redshift-space distortions of the H i 21-cm intensity mapping signal due to the internal motions within galaxies

The H i 21-cm intensity mapping signal experiences redshift-space distortions due to the motion of the galaxies which contain the H i as well as the motions of the H i gas within the galaxies. A detailed modelling is essential if this signal is to be used for precision cosmology. Considering dark-matter-only simulations where the H i is assumed to reside in galaxies which are associated with haloes, in this work we introduce a technique to incorporate the H i motions within the galaxies. This is achieved through a line profile which accounts for both the rotational and random (thermal and turbulent) motions of the H i within galaxies. The functional form of the double-horned line profiles used here is motivated by observations of z = 0 spiral galaxies. Analyzing the simulated 21-cm power spectrum over the redshift range 1 ≤ z ≤ 6 we find that the H i motions within galaxies make a significant contribution that is manifested as an enhancement in the Finger of God (FoG) effect which can be modelled reasonably well through a Lorentzian damping profile with a single free parameter σp. The value of σp is significantly enhanced if motions within the galaxies are included. This is particularly important at z > 3 where σp is dominated by the internal motions and a measurement of the FoG effect here could provide a handle on the line profiles of high-redshift galaxies.
How Much Information Does the Sodium Doublet Encode? Retrieval Analysis of Non-LTE Sodium Lines at Low and High Spectral Resolutions

Motivated by both ground- and space-based detections of the sodium doublet in the transmission spectra of exoplanetary atmospheres, we revisit the theory and interpretation of sodium lines in non-local thermodynamic equilibrium (NLTE), where collisions are not efficient enough to maintain a Boltzmann distribution for the excited and ground states of the sodium atom. We consider non-Boltzmann distributions that account for the ineffectiveness of collisions. We analyze the sodium doublet in transmission spectra measured at low (HAT-P-1b, HAT-P-12b, HD 189733b, WASP-6b, WASP-17b, and WASP-39b) and high (WASP-49b) spectral resolutions. Nested-sampling retrievals performed on low-resolution optical/visible transmission spectra are unable to break the normalization degeneracy if the spectral continuum is associated with Rayleigh scattering by small cloud particles. Using mock retrievals, we demonstrate that unnormalized ground-based, high-resolution spectra centered on the sodium doublet alone are unable to precisely inform us about the pressure levels probed by the transit chord and hence to identify the region (i.e., thermosphere, exosphere) of the atmosphere being probed. Retrievals performed on the HARPS transmission spectrum of WASP-49b support this conclusion. Generally, we are unable to distinguish between LTE versus NLTE interpretations of the sodium doublet based on the computed Bayesian evidence with the implication that LTE interpretations tend to underestimate the temperature probed by the transit chord. With the current low-resolution data, the sodium line shapes are consistent with Voigt profiles without the need for sub-Lorentzian wings. The retrieved sodium abundances are consistent with being subsolar to solar.
Topological spin excitations in Harper-Heisenberg spin chains

Many-body spin systems represent a paradigmatic platform for the realization of emergent states of matter in a strongly interacting regime. Spin models are commonly studied in one-dimensional periodic chains, whose lattice constant is on the order of the interatomic distance. However, in cold atomic setups or functionalized twisted van der Waals heterostructures, long-range modulations of the spin physics can be engineered. Here we show that such superlattice modulations in a many-body spin Hamiltonian can give rise to observable topological boundary modes in the excitation spectrum of the spin chain. In the case of an XY spin-$1/2$ chain, these boundary modes stem from a mathematical correspondence with the chiral edge modes of a two-dimensional quantum Hall state. Our results show that the addition of many-body interactions does not close some of the topological gaps in the excitation spectrum, and the topological boundary modes visibly persist in the isotropic Heisenberg limit. These observations carry through when the spin moment is increased and a large-spin limit of the phenomenon is established. Our results show that such spin superlattices provide a promising route to observe many-body topological boundary effects in cold atomic setups and functionalized twisted van der Waals materials.