nils-herrmann/clean_scirepeval huggingface dataset:
Resolved [C ii] Emission from z > 6 Quasar Host–Companion Galaxy Pairs

We report on ≈0.″35 (≈2kpc) resolution observations of the [C ii] and dust continuum emission from five z > 6 quasar host–companion galaxy pairs obtained with the Atacama Large Millimeter/submillimeter Array. The [C ii] emission is resolved in all galaxies, with physical extents of 3.2–5.4 kpc. The dust continuum is on-average 40% more compact, which results in larger [C ii] deficits in the center of the galaxies. However, the measured [C ii] deficits are fully consistent with those found at lower redshifts. Four of the galaxies show [C ii] velocity fields that are consistent with ordered rotation, while the remaining six galaxies show no clear velocity gradient. All galaxies have high (∼80−200 km s−1) velocity dispersions, consistent with the interpretation that the interstellar medium (ISM) of these high-redshift galaxies is turbulent. By fitting the galaxies with kinematic models, we estimate the dynamical mass of these systems, which ranges between (0.3 − >5.4) × 1010 M⊙. For the three closest-separation galaxy pairs, we observe dust and [C ii] emission from gas in between and surrounding the galaxies, which is an indication that tidal interactions are disturbing the gas in these systems. Although gas exchange in these tidal interactions could power luminous quasars, the existence of quasars in host galaxies without nearby companions suggests that tidal interactions are not the only viable method for fueling their active centers. These observations corroborate the assertion that accreting supermassive black holes do not substantially contribute to the [C ii] and dust continuum emission of the quasar host galaxies, and showcase the diverse ISM properties of galaxies when the universe was less than one billion years old.
Large Scale Adversarial Representation Learning

Adversarially trained generative models (GANs) have recently achieved compelling image synthesis results. But despite early successes in using GANs for unsupervised representation learning, they have since been superseded by approaches based on self-supervision. In this work we show that progress in image generation quality translates to substantially improved representation learning performance. Our approach, BigBiGAN, builds upon the state-of-the-art BigGAN model, extending it to representation learning by adding an encoder and modifying the discriminator. We extensively evaluate the representation learning and generation capabilities of these BigBiGAN models, demonstrating that these generation-based models achieve the state of the art in unsupervised representation learning on ImageNet, as well as in unconditional image generation. Pretrained BigBiGAN models -- including image generators and encoders -- are available on TensorFlow Hub (https://tfhub.dev/s?publisher=deepmind&q=bigbigan).
ACNe: Attentive Context Normalization for Robust Permutation-Equivariant Learning

Many problems in computer vision require dealing with sparse, unordered data in the form of point clouds. Permutation-equivariant networks have become a popular solution – they operate on individual data points with simple perceptrons and extract contextual information with global pooling. This can be achieved with a simple normalization of the feature maps, a global operation that is unaffected by the order. In this paper, we propose Attentive Context Normalization (ACN), a simple yet effective technique to build permutation-equivariant networks robust to outliers. Specifically, we show how to normalize the feature maps with weights that are estimated within the network, excluding outliers from this normalization. We use this mechanism to leverage two types of attention: local and global – by combining them, our method is able to find the essential data points in high-dimensional space in order to solve a given task. We demonstrate through extensive experiments that our approach, which we call Attentive Context Networks (ACNe), provides a significant leap in performance compared to the state-of-the-art on camera pose estimation, robust fitting, and point cloud classification under noise and outliers.
Clustering with JWST: Constraining galaxy host halo masses, satellite quenching efficiencies, and merger rates at z  = 4−10

Galaxy clustering measurements can be used to constrain many aspects of galaxy evolution, including galaxy host halo masses, satellite quenching efficiencies, and merger rates. We simulate JWST galaxy clustering measurements at z ∼ 4–10 by utilizing mock galaxy samples produced by an empirical model, the universemachine. We also adopt the survey footprints and typical depths of the planned joint NIRCam and NIRSpec Guaranteed Time Observation program planned for Cycle 1 to generate realistic JWST survey realizations and to model high-redshift galaxy selection completeness. We find that galaxy clustering will be measured with ≳5σ significance at z ∼ 4–10. Halo mass precisions resulting from Cycle 1 angular clustering measurements will be ∼0.2 dex for faint (−18 ≳ $\mathit {M}_{\mathrm{UV}}^{ }$ ≳ −19) galaxies at z ∼ 4–10 as well as ∼0.3 dex for bright ($\mathit {M}_{\mathrm{UV}}^{ }$ ∼ −20) galaxies at z ∼ 4–7. Dedicated spectroscopic follow-up over ∼150 arcmin2 would improve these precisions by ∼0.1 dex by removing chance projections and low-redshift contaminants. Future JWST observations will therefore provide the first constraints on the stellar–halo mass relation in the epoch of reionization and substantially clarify how this relation evolves at z > 4. We also find that ∼1000 individual satellites will be identifiable at z ∼ 4–8 with JWST, enabling strong tests of satellite quenching evolution beyond currently available data (z ≲ 2). Finally, we find that JWST observations can measure the evolution of galaxy major merger pair fractions at z ∼ 4–8 with ∼0.1–0.2 dex uncertainties. Such measurements would help determine the relative role of mergers to the build-up of stellar mass into the epoch of reionization.
A Survey of Pruning Methods for Efficient Person Re-identification Across Domains

Recent years have witnessed a substantial increase in the deep learning architectures proposed for visual recognition tasks like person re-identification, where individuals must be recognized over multiple distributed cameras. Although deep Siamese networks have greatly improved the state-of-the-art accuracy, the computational complexity of the CNNs used for feature extraction remains an issue, hindering their deployment on platforms with with limited resources, or in applications with real-time constraints. Thus, there is an obvious advantage to compressing these architectures without significantly decreasing their accuracy. This paper provides a survey of state-of-the-art pruning techniques that are suitable for compressing deep Siamese networks applied to person re-identification. These techniques are analysed according to their pruning criteria and strategy, and according to different design scenarios for exploiting pruning methods to fine-tuning networks for target applications. Experimental results obtained using Siamese networks with ResNet feature extractors, and multiple benchmarks re-identification datasets, indicate that pruning can considerably reduce network complexity while maintaining a high level of accuracy. In scenarios where pruning is performed with large pre-training or fine-tuning datasets, the number of FLOPS required by the ResNet feature extractor is reduced by half, while maintaining a comparable rank-1 accuracy (within 1\% of the original model). Pruning while training a larger CNNs can also provide a significantly better performance than fine-tuning smaller ones.
Measuring the Data Efficiency of Deep Learning Methods

In this paper, we propose a new experimental protocol and use it to benchmark the data efficiency --- performance as a function of training set size --- of two deep learning algorithms, convolutional neural networks (CNNs) and hierarchical information-preserving graph-based slow feature analysis (HiGSFA), for tasks in classification and transfer learning scenarios. The algorithms are trained on different-sized subsets of the MNIST and Omniglot data sets. HiGSFA outperforms standard CNN networks when the models are trained on 50 and 200 samples per class for MNIST classification. In other cases, the CNNs perform better. The results suggest that there are cases where greedy, locally optimal bottom-up learning is equally or more powerful than global gradient-based learning.
Strong 
CP
 problem with low-energy emergent QCD: The 4321 case

We analyze the strong CP problem and the implications for axion physics in the context of $U_1$ vector leptoquark models, recently put forward as an elegant solution to the hints of lepton flavor universality violation in B-meson decays. It is shown that in minimal gauge models containing the $U_1$ as a gauge boson, the Peccei-Quinn solution of the strong CP problem requires the introduction of two axions. Characteristic predictions for the associated axions can be deduced from the model parameter space hinted by B-physics, allowing the new axion sector to account for the dark matter of the Universe. We also provide a specific ultraviolet completion of the axion sector that connects the Peccei-Quinn mechanism to the generation of neutrino masses.
The Newton integral and the Stirling formula

We present details of logically simplest integral sufficient for deducing the Stirling asymptotic formula for n!. It is the Newton integral, defined as the difference of values of any primitive at the endpoints of the integration interval. We review in its framework in detail two derivations of the Stirling formula. The first approximates log(1)+log(2)+...+log(n) with an integral and the second uses the classical gamma function and a Fubini-type result. We mention two more integral representations of n!.
A Cognition-Affect Integrated Model of Emotion

The focus of the efforts for defining and modelling emotion is broadly shifting from classical definite marker theory to statistically context situated conceptual theory. However, the role of context processing and its interaction with the affect is still not comprehensively explored and modelled. With the help of neural decoding of functional networks, we have decoded cognitive functions for 12 different basic and complex emotion conditions. Using transfer learning in deep neural architecture, we arrived at the conclusion that the core affect is unable to provide varieties of emotions unless coupled with cortical cognitive functions such as autobiographical memory, dmn, self-referential, social, tom and salient event detection. Following our results, in this article, we present a 'cognition-affect integrated model of emotion' which includes many cortical and subcortical regions and their interactions. Our model suggests three testable hypotheses. First, affect and physiological sensations alone are inconsequential in defining or classifying emotions until integrated with the domain-general cognitive systems. Second, cognition and affect modulate each other throughout the generation of meaningful instance which is situated in the current context. And, finally, the structural and temporal hierarchies in the brain's organization and anatomical projections play an important role in emotion responses in terms of hierarchical activities and their durations. The model, along with the analytical and anatomical support, is presented. The article concludes with the future research questions.
The citation advantage of linking publications to research data

Efforts to make research results open and reproducible are increasingly reflected by journal policies encouraging or mandating authors to provide data availability statements. As a consequence of this, there has been a strong uptake of data availability statements in recent literature. Nevertheless, it is still unclear what proportion of these statements actually contain well-formed links to data, for example via a URL or permanent identifier, and if there is an added value in providing such links. We consider 531, 889 journal articles published by PLOS and BMC, develop an automatic system for labelling their data availability statements according to four categories based on their content and the type of data availability they display, and finally analyze the citation advantage of different statement categories via regression. We find that, following mandated publisher policies, data availability statements become very common. In 2018 93.7% of 21,793 PLOS articles and 88.2% of 31,956 BMC articles had data availability statements. Data availability statements containing a link to data in a repository—rather than being available on request or included as supporting information files—are a fraction of the total. In 2017 and 2018, 20.8% of PLOS publications and 12.2% of BMC publications provided DAS containing a link to data in a repository. We also find an association between articles that include statements that link to data in a repository and up to 25.36% (± 1.07%) higher citation impact on average, using a citation prediction model. We discuss the potential implications of these results for authors (researchers) and journal publishers who make the effort of sharing their data in repositories. All our data and code are made available in order to reproduce and extend our results.
Functorial Approach to Graph and Hypergraph Theory

We provide a new approach to categorical graph and hypergraph theory by using categorical syntax and semantics. For each monoid $M$ and action on a set $X$, there is an associated presheaf topos of $(X,M)$-graphs where each object can be interpreted as a generalized uniform hypergraph where each edge has cardinality $\#X$ incident vertices (including multiplicity) and where the monoid informs what type of cohesivity the edges possess. One distinguishing feature of $(X,M)$-graphs is the presence of unfixed edges. We prove that unfixed edges are a necessary feature of a category of graphs or uniform hypergraphs if one wants exponentials and effective equivalence relations to exist in the category. The main advantage of separating syntax (the $(X,M)$-graph theories) from semantics (the categories of $(X,) is the ability to interpret the theory in any cocomplete category. This interpetation functor then yields a nerve-realization adjunction and allows us to transfer structure between the category of $(X,M)$-graphs and the receptive cocomplete category.
Data-Centric Mixed-Variable Bayesian Optimization For Materials Design

Materials design can be cast as an optimization problem with the goal of achieving desired properties, by varying material composition, microstructure morphology, and processing conditions. Existence of both qualitative and quantitative material design variables leads to disjointed regions in property space, making the search for optimal design challenging. Limited availability of experimental data and the high cost of simulations magnify the challenge. This situation calls for design methodologies that can extract useful information from existing data and guide the search for optimal designs efficiently. To this end, we present a data-centric, mixed-variable Bayesian Optimization framework that integrates data from literature, experiments, and simulations for knowledge discovery and computational materials design. Our framework pivots around the Latent Variable Gaussian Process (LVGP), a novel Gaussian Process technique which projects qualitative variables on a continuous latent space for covariance formulation, as the surrogate model to quantify “lack of data” uncertainty. Expected improvement, an acquisition criterion that balances exploration and exploitation, helps navigate a complex, nonlinear design space to locate the optimum design. The proposed framework is tested through a case study which seeks to concurrently identify the optimal composition and morphology for insulating polymer nanocomposites. We also present an extension of mixed-variable Bayesian Optimization for multiple objectives to identify the Pareto Frontier within tens of iterations. These findings project Bayesian Optimization as a powerful tool for design of engineered material systems.
Transfer Learning for Risk Classification of Social Media Posts: Model Evaluation Study

Mental illness affects a significant portion of the worldwide population. Online mental health forums can provide a supportive environment for those afflicted and also generate a large amount of data that can be mined to predict mental health states using machine learning methods.  This study aimed to benchmark multiple methods of text feature representation for social media posts and compare their downstream use with automated machine learning (AutoML) tools. We tested on datasets that contain posts labeled for perceived suicide risk or moderator attention in the context of self-harm. Specifically, we assessed the ability of the methods to prioritize posts that a moderator would identify for immediate response.  We used 1588 labeled posts from the Computational Linguistics and Clinical Psychology (CLPsych) 2017 shared task collected from the Reachout.com forum. Posts were represented using lexicon-based tools, including Valence Aware Dictionary and sEntiment Reasoner, Empath, and Linguistic Inquiry and Word Count, and also using pretrained artificial neural network models, including DeepMoji, Universal Sentence Encoder, and Generative Pretrained Transformer-1 (GPT-1). We used Tree-based Optimization Tool and Auto-Sklearn as AutoML tools to generate classifiers to triage the posts.  The top-performing system used features derived from the GPT-1 model, which was fine-tuned on over 150,000 unlabeled posts from Reachout.com. Our top system had a macroaveraged F1 score of 0.572, providing a new state-of-the-art result on the CLPsych 2017 task. This was achieved without additional information from metadata or preceding posts. Error analyses revealed that this top system often misses expressions of hopelessness. In addition, we have presented visualizations that aid in the understanding of the learned classifiers.  In this study, we found that transfer learning is an effective strategy for predicting risk with relatively little labeled data and noted that fine-tuning of pretrained language models provides further gains when large amounts of unlabeled text are available.
Explaining Predictions from Tree-based Boosting Ensembles

Understanding how "black-box" models arrive at their predictions has sparked significant interest from both within and outside the AI community. Our work focuses on doing this by generating local explanations about individual predictions for tree-based ensembles, specifically Gradient Boosting Decision Trees (GBDTs). Given a correctly predicted instance in the training set, we wish to generate a counterfactual explanation for this instance, that is, the minimal perturbation of this instance such that the prediction flips to the opposite class. Most existing methods for counterfactual explanations are (1) model-agnostic, so they do not take into account the structure of the original model, and/or (2) involve building a surrogate model on top of the original model, which is not guaranteed to represent the original model accurately. There exists a method specifically for random forests; we wish to extend this method for GBDTs. This involves accounting for (1) the sequential dependency between trees and (2) training on the negative gradients instead of the original labels.
Structure fusion based on graph convolutional networks for semi-supervised classification

Suffering from the multi-view data diversity and complexity, most of the existing graph convolutional networks focus on the networks’ architecture construction or the salient graph structure preservation for node classification in citation networks and usually ignore capturing the complete graph structure of nodes for enhancing classification performance. To mine the more complete distribution structure from multi-graph structures of multi-view data with the consideration of their specificity and the commonality, we propose structure fusion based on graph convolutional networks (SF-GCN) for improving the performance of node classification in a semi-supervised way. SF-GCN can not only exploit the special characteristic of each view datum by spectral embedding preserving multi-graph structures, but also explore the common style of multi-view data by the distance metric between multi-graph structures. Suppose the linear relationship between multi-graph structures; we can construct the optimization function of the structure fusion model by balancing the specificity loss and the commonality loss. By solving this function, we can simultaneously obtain the fusion spectral embedding from the multi-view data and the fusion structure as the adjacent matrix to input graph convolutional networks for node classification in a semi-supervised way. Furthermore, we generalize the structure fusion to structure diffusion propagation and present structure propagation fusion based on graph convolutional networks (SPF-GCN) for utilizing these structure interactions. Experiments demonstrate that the performance of SPF-GCN outperforms that of the state-of-the-art methods on three challenging datasets, which are Cora, Citeseer, and Pubmed in citation networks.
Domain-Specific Language to Encode Induction Heuristics

Proof assistants, such as Isabelle/HOL, offer tools to facilitate inductive theorem proving. Isabelle experts know how to use these tools effectively; however, they did not have a systematic way to encode their expertise. To address this problem, we present our domain-specific language, LiFtEr. LiFtEr allows experienced Isabelle users to encode their induction heuristics in a style independent of any problem domain. LiFtEr's interpreter mechanically checks if a given application of induction tool matches the heuristics specified by experienced users, thus systematically transferring experienced users' expertise to new Isabelle users.
QuPWM: Feature Extraction Method for MEG Epileptic Spike Detection

Research reported in this publication was supported by King Abdullah University of Science and Technology (KAUST) 
in collaboration with King Abdulaziz City for Science and Technology (KACST) and King Saud University (KSU). 
Funding: This research project has been funded by King Abdullah University of Science and Technology (KAUST) Base Research Fund (BAS/1/1627-01-01), in collaboration with King Abdulaziz City for Science and Technology (KACST) and King Saud University (KSU).
A Road-map Towards Explainable Question Answering A Solution for Information Pollution

The increasing rate of information pollution on the Web requires novel solutions to tackle that.  Answering (QA) interfaces are simplified and user-friendly interfaces to access information on the Web. However, similar to other AI applications, they are black boxes which do not manifest the details of the learning or reasoning steps for augmenting an answer. The Explainable Question Answering (XQA) system can alleviate the pain of information pollution where it provides transparency to the underlying computational model and exposes an interface enabling the end-user to access and validate provenance, validity, context, circulation, interpretation, and feedbacks of information. This position paper sheds light on the core concepts, expectations, and challenges in favor of the following questions (i) What is an XQA system?, (ii) Why do we need XQA?, (iii) When do we need XQA? (iv) How to represent the explanations? (iv) How to evaluate XQA systems?