nils-herrmann/clean_scirepeval huggingface dataset:
Cosmos QA: Machine Reading Comprehension with Contextual Commonsense Reasoning

Understanding narratives requires reading between the lines, which in turn, requires interpreting the likely causes and effects of events, even when they are not mentioned explicitly. In this paper, we introduce Cosmos QA, a large-scale dataset of 35,600 problems that require commonsense-based reading comprehension, formulated as multiple-choice questions. In stark contrast to most existing reading comprehension datasets where the questions focus on factual and literal understanding of the context paragraph, our dataset focuses on reading between the lines over a diverse collection of people’s everyday narratives, asking such questions as “what might be the possible reason of ...?", or “what would have happened if ..." that require reasoning beyond the exact text spans in the context. To establish baseline performances on Cosmos QA, we experiment with several state-of-the-art neural architectures for reading comprehension, and also propose a new architecture that improves over the competitive baselines. Experimental results demonstrate a significant gap between machine (68.4%) and human performance (94%), pointing to avenues for future research on commonsense machine comprehension. Dataset, code and leaderboard is publicly available at https://wilburone.github.io/cosmos.
Generating Classical Chinese Poems from Vernacular Chinese

Classical Chinese poetry is a jewel in the treasure house of Chinese culture. Previous poem generation models only allow users to employ keywords to interfere the meaning of generated poems, leaving the dominion of generation to the model. In this paper, we propose a novel task of generating classical Chinese poems from vernacular, which allows users to have more control over the semantic of generated poems. We adapt the approach of unsupervised machine translation (UMT) to our task. We use segmentation-based padding and reinforcement learning to address under-translation and over-translation respectively. According to experiments, our approach significantly improve the perplexity and BLEU compared with typical UMT models. Furthermore, we explored guidelines on how to write the input vernacular to generate better poems. Human evaluation showed our approach can generate high-quality poems which are comparable to amateur poems.
Publishing Community-Preserving Attributed Social Graphs with a Differential Privacy Guarantee

We present a novel method for publishing differentially private synthetic attributed graphs. Our method allows, for the first time, to publish synthetic graphs simultaneously preserving structural properties, user attributes and the community structure of the original graph. Our proposal relies on CAGM, a new community-preserving generative model for attributed graphs. We equip CAGM with efficient methods for attributed graph sampling and parameter estimation. For the latter, we introduce differentially private computation methods, which allow us to release communitypreserving synthetic attributed social graphs with a strong formal privacy guarantee. Through comprehensive experiments, we show that our new model outperforms its most relevant counterparts in synthesising differentially private attributed social graphs that preserve the community structure of the original graph, as well as degree sequences and clustering coefficients.
Resonant magnetogenesis from axions

We investigate the generation of seed magnetic field through the Chern-Simons coupling between the U(1) gauge field and an axion field that commences to oscillate at various epoch, depending on the mass scale. We address axions which begin oscillation during inflation, reheating, and also the radiation dominated era after the thermalization of the Universe. We study the resonant generation mechanisms and highlight that a small oscillation time scale with respect to that of the cosmic expansion can lead to an efficient generation of (hyper) magnetic field via resonant generation, even for 𝒪(1) coupling. In addition, we demonstrate that the generated field can be helical due to the tachyonic amplification phase prior to the onset of oscillation. Furthermore, it is shown that the parametric resonance during reheating can generate a circularly polarized (hyper) magnetic field in a void region with the present amplitude B0 =3× 10−15Gauss and the coherent length λ0 = 0.3pc without being plagued by the backreaction issue.
Deep Mesh Reconstruction From Single RGB Images via Topology Modification Networks

Reconstructing the 3D mesh of a general object from a single image is now possible thanks to the latest advances of deep learning technologies. However, due to the nontrivial difficulty of generating a feasible mesh structure, the state-of-the-art approaches often simplify the problem by learning the displacements of a template mesh that deforms it to the target surface. Though reconstructing a 3D shape with complex topology can be achieved by deforming multiple mesh patches, it remains difficult to stitch the results to ensure a high meshing quality. In this paper, we present an end-to-end single-view mesh reconstruction framework that is able to generate high-quality meshes with complex topologies from a single genus-0 template mesh. The key to our approach is a novel progressive shaping framework that alternates between mesh deformation and topology modification. While a deformation network predicts the per-vertex translations that reduce the gap between the reconstructed mesh and the ground truth, a novel topology modification network is employed to prune the error-prone faces, enabling the evolution of topology. By iterating over the two procedures, one can progressively modify the mesh topology while achieving higher reconstruction accuracy. Moreover, a boundary refinement network is designed to refine the boundary conditions to further improve the visual quality of the reconstructed mesh. Extensive experiments demonstrate that our approach outperforms the current state-of-the-art methods both qualitatively and quantitatively, especially for the shapes with complex topologies.
Robust No Arbitrage and the Solvability of Vector-Valued Utility Maximization Problems

A market model with d assets in discrete time is considered where trades are subject to proportional transaction costs given via bid-ask spreads, while the existence of a numeraire is not assumed. It is shown that robust no arbitrage holds if, and only if, there exists a Pareto solution for some vector-valued utility maximization problem with component-wise utility functions. Moreover, it is demonstrated that a consistent price process can be constructed from the Pareto maximizer.
The Ambiguous World of Emotion Representation

Artificial intelligence and machine learning systems have demonstrated huge improvements and human-level parity in a range of activities, including speech recognition, face recognition and speaker verification. However, these diverse tasks share a key commonality that is not true in affective computing: the ground truth information that is inferred can be unambiguously represented. This observation provides some hints as to why affective computing, despite having attracted the attention of researchers for years, may not still be considered a mature field of research. A key reason for this is the lack of a common mathematical framework to describe all the relevant elements of emotion representations. This paper proposes the AMBiguous Emotion Representation (AMBER) framework to address this deficiency. AMBER is a unified framework that explicitly describes categorical, numerical and ordinal representations of emotions, including time varying representations. In addition to explaining the core elements of AMBER, the paper also discusses how some of the commonly employed emotion representation schemes can be viewed through the AMBER framework, and concludes with a discussion of how the proposed framework can be used to reason about current and future affective computing systems.
Vector Autoregressive Moving Average Model with Scalar Moving Average

We show Vector Autoregressive Moving Average models with scalar Moving Average components could be estimated by generalized least square (GLS) for each fixed moving average polynomial. The conditional variance of the GLS model is the concentrated covariant matrix of the moving average process. Under GLS the likelihood function of these models has similar format to their VAR counterparts. Maximum likelihood estimate can be done by optimizing with gradient over the moving average parameters. These models are inexpensive generalizations of Vector Autoregressive models. We discuss a relationship between this result and the Borodin-Okounkov formula in operator theory.
A Dataset of General-Purpose Rebuttal

In Natural Language Understanding, the task of response generation is usually focused on responses to short texts, such as tweets or a turn in a dialog. Here we present a novel task of producing a critical response to a long argumentative text, and suggest a method based on general rebuttal arguments to address it. We do this in the context of the recently-suggested task of listening comprehension over argumentative content: given a speech on some specified topic, and a list of relevant arguments, the goal is to determine which of the arguments appear in the speech. The general rebuttals we describe here (in English) overcome the need for topic-specific arguments to be provided, by proving to be applicable for a large set of topics. This allows creating responses beyond the scope of topics for which specific arguments are available. All data collected during this work is freely available for research.
An Open-Source Framework for Adaptive Traffic Signal Control

Sub-optimal control policies in transportation systems negatively impact mobility, the environment and human health. Developing optimal transportation control systems at the appropriate scale can be difficult as cities' transportation systems can be large, complex and stochastic. Intersection traffic signal controllers are an important element of modern transportation infrastructure where sub-optimal control policies can incur high costs to many users. Many adaptive traffic signal controllers have been proposed by the community but research is lacking regarding their relative performance difference - which adaptive traffic signal controller is best remains an open question. This research contributes a framework for developing and evaluating different adaptive traffic signal controller models in simulation - both learning and non-learning - and demonstrates its capabilities. The framework is used to first, investigate the performance variance of the modelled adaptive traffic signal controllers with respect to their hyperparameters and second, analyze the performance differences between controllers with optimal hyperparameters. The proposed framework contains implementations of some of the most popular adaptive traffic signal controllers from the literature; Webster's, Max-pressure and Self-Organizing Traffic Lights, along with deep Q-network and deep deterministic policy gradient reinforcement learning controllers. This framework will aid researchers by accelerating their work from a common starting point, allowing them to generate results faster with less effort. All framework source code is available at this https URL.
Misinformation spreading on correlated multiplex networks

The numerous expanding online social networks offer fast channels for misinformation spreading, which could have a serious impact on socioeconomic systems. Researchers across multiple areas have paid attention to this issue with a view of addressing it. However, no systematical theoretical study has been performed to date on observing misinformation spreading on correlated multiplex networks. In this study, we propose a multiplex network-based misinformation spreading model, considering the fact that each individual can obtain misinformation from multiple platforms. Subsequently, we develop a heterogeneous edge-based compartmental theory to comprehend the spreading dynamics of our proposed model. In addition, we establish an analytical method based on stability analysis to obtain the misinformation outbreak threshold. On the basis of these theories, we finally analyze the influence of different dynamical and structural parameters on the misinformation spreading dynamics. Results show that the misinformation outbreak size R(∞) grows continuously with the effective transmission probability β once β exceeds a certain value, that is, the outbreak threshold βc. Large average degrees, strong degree heterogeneity, or positive interlayer correlation will reduce βc, accelerating the outbreak of misinformation. Besides, increasing the degree heterogeneity or a more positive interlayer correlation will enlarge (reduce) R(∞) for small (large) values of β. Our systematic theoretical analysis results agree well with the numerical simulation results. Our proposed model and accurate theoretical analysis will serve as a useful framework to understand and predict the spreading dynamics of misinformation on multiplex networks and thereby pave the way to address this serious issue.
Constraining the properties of gaseous halos via cross-correlations of upcoming galaxy surveys and thermal Sunyaev-Zel’dovich maps

The thermal Sunyaev-Zel'dovich (tSZ) effect induces a Compton-$y$ distortion in cosmic microwave background (CMB) temperature maps that is sensitive to a line of sight integral of the ionized gas pressure. By correlating the positions of galaxies with maps of the Compton-$y$ distortion, one can probe baryonic feedback processes and study the thermodynamic properties of a significant fraction of the gas in the Universe. Using a model fitting approach, we forecast how well future galaxy and CMB surveys will be able to measure these correlations, and show that powerful constraints on halo pressure profiles can be obtained. Our forecasts are focused on correlations between galaxies and halos identified by the upcoming Dark Energy Spectroscopic Instrument survey and tSZ maps from the Simons Observatory and CMB-S4 experiments, but have general applicability to other surveys, such as the Large Synoptic Survey Telescope. We include prescriptions for observational systematics, such as halo miscentering and halo mass bias, demonstrating several important degeneracies with pressure profile parameters. Assuming modest priors on these systematics, we find that measurements of halo-$y$ and galaxy-$y$ correlations with future surveys will yield tight constraints on the pressure profiles of group-scale dark matter halos, and enable current feedback models to either be confirmed or ruled out.
Stability of the cut locus and a central limit theorem for Fréchet means of Riemannian manifolds

We obtain a Central Limit Theorem for closed Riemannian manifolds, clarifying along the way the geometric meaning of some of the hypotheses in Bhattacharya and Lin's Omnibus Central Limit Theorem for Frechet means. We obtain our CLT assuming certain stability hypothesis for the cut locus, which always holds when the manifold is compact but may not be satisfied in the non-compact case.
Big data analytics for manufacturing internet of things: opportunities, challenges and enabling technologies

Data analytics in massive manufacturing data can extract huge business values while can also result in research challenges due to the heterogeneous data types, enormous volume and real-time velocity of manufacturing data. This paper provides an overview on big data analytics in manufacturing Internet of Things (MIoT). This paper first starts with a discussion on necessities and challenges of big data analytics in manufacturing data of MIoT. Then, the enabling technologies of big data analytics of manufacturing data are surveyed and discussed. Moreover, this paper also outlines the future directions in this promising area.
Observation of a Charge-Neutral Muon-Polaron Complex in Antiferromagnetic 
Cr2O3

We report a comprehensive muon spin rotation ($\mu$SR) study of the prototypical magnetoelectric antiferromagnet Cr$_2$O$_3$. We find the positively charged muon ($\mu^+$) occupies several distinct interstitial sites, and displays a rich dynamic behavior involving local hopping, thermally activated site transitions and the formation of a charge-neutral complex composed of a muon and an electron polaron. The discovery of such a complex has implications for the interpretation of $\mu$SR spectra in a wide range of magnetic oxides, and opens a route to study the dopant characteristics of interstitial hydrogen impurities in such materials. We address implications arising from implanting a $\mu^+$ into a linear magnetoelectric, and discuss the challenges of observing a local magnetoelectric effect generated by the charge of the muon.
Transfer Learning Between Related Tasks Using Expected Label Proportions

Deep learning systems thrive on abundance of labeled training data but such data is not always available, calling for alternative methods of supervision. One such method is expectation regularization (XR) (Mann and McCallum, 2007), where models are trained based on expected label proportions. We propose a novel application of the XR framework for transfer learning between related tasks, where knowing the labels of task A provides an estimation of the label proportion of task B. We then use a model trained for A to label a large corpus, and use this corpus with an XR loss to train a model for task B. To make the XR framework applicable to large-scale deep-learning setups, we propose a stochastic batched approximation procedure. We demonstrate the approach on the task of Aspect-based Sentiment classification, where we effectively use a sentence-level sentiment predictor to train accurate aspect-based predictor. The method improves upon fully supervised neural system trained on aspect-level data, and is also cumulative with LM-based pretraining, as we demonstrate by improving a BERT-based Aspect-based Sentiment model.
A Discriminative Neural Model for Cross-Lingual Word Alignment

We introduce a novel discriminative word alignment model, which we integrate into a Transformer-based machine translation model. In experiments based on a small number of labeled examples (∼1.7K–5K sentences) we evaluate its performance intrinsically on both English-Chinese and English-Arabic alignment, where we achieve major improvements over unsupervised baselines (11–27 F1). We evaluate the model extrinsically on data projection for Chinese NER, showing that our alignments lead to higher performance when used to project NER tags from English to Chinese. Finally, we perform an ablation analysis and an annotation experiment that jointly support the utility and feasibility of future manual alignment elicitation.
Topics to Avoid: Demoting Latent Confounds in Text Classification

Despite impressive performance on many text classification tasks, deep neural networks tend to learn frequent superficial patterns that are specific to the training data and do not always generalize well. In this work, we observe this limitation with respect to the task of native language identification. We find that standard text classifiers which perform well on the test set end up learning topical features which are confounds of the prediction task (e.g., if the input text mentions Sweden, the classifier predicts that the author’s native language is Swedish). We propose a method that represents the latent topical confounds and a model which “unlearns” confounding features by predicting both the label of the input text and the confound; but we train the two predictors adversarially in an alternating fashion to learn a text representation that predicts the correct label but is less prone to using information about the confound. We show that this model generalizes better and learns features that are indicative of the writing style rather than the content.
General COHERENT constraints on neutrino nonstandard interactions

We present the results of a systematic study of the constraints on neutrino neutral-current non-standard interactions (NSI) that can be obtained from the analysis of the COHERENT spectral and temporal data. First, we consider the general case in which all the ten relevant neutral-current NSI parameters are considered as free. We show that they are very weakly constrained by the COHERENT data because of possible cancellations between the up and down quark contributions. However, the up-down average parameters are relatively well constrained and the strongest constraints are obtained for an appropriate linear combination of up and down NSI parameters. We also consider the case in which there are only NSI with either up or down quarks, and we show that the LMA-Dark fit of solar neutrino data is excluded at $5.6\sigma$ and $7.2\sigma$ respectively, for NSI with up and down quark. We finally present the tight constraints that can be obtained on each NSI parameter if it is the dominant one, assuming that the effects of the others are negligible.
Bound Dark Matter (BDM) towards solving the small scale structure problem

Cosmological observations such as structure formation, cosmic microwave background, and cosmic distance ladder set tight constraints to the amount and nature of dark matter (DM) . In particular structure formation strongly constraints not only the amount of energy density but also the time when DM became non-relativistic, anr. Standard cold and thermic warm DM particles have a smooth transition from being relativistic at high energies to a non-relativistic regime since the mass of these particles is constant while the velocity redshifts with the expansion of the universe. However, here we explore the possibility that the DM particle acquires a non-perturbative mass at a phase transition scale and a scale factor ac, e.g. the mass of protons and neutrons is due to the binding energy of QCD through a non-perturbative process. This transition acquired a more fundamental meaning for the Bound Dark Matter (BDM) model because they describe a particle getting its mass through a non-perturbative process. These BDM particles may go from being relativistic to non-relativistic at ac, which implies an abrupt transition of the velocity of the particles vc at that time, affecting the equation of state and the cosmological evolution. Here we study the cosmological impact of the values of vc and the scale transition ac and they by reducing the free-streaming scale and therefore the small scale structure. Using CMB Plank, Supernovae SNIa, and baryon acoustic oscillation data, we constrain the valid region in the parameter space ac − vc putting upper bounds to ac but not restricting vc. For instance, the transition must be ac < 2.66 × 10−6 for 0vc = . We also find that the free-streaming and the Jeans mass of the dark matter particle is highly influenced by the velocity vc, for example, a 3 keV WDM have a Jeans mass of Mfs = 1.97 × 107 M⊙/h3 but an equivalent BDM with the same anr but an abrupt transition in the velocity, vc ∼ 0 would have a Jeans mass of Mfs = 2.08×104 M⊙/h3 which significantly changes the large scale structure formation.