nils-herrmann/clean_scirepeval huggingface dataset:
Hardness of Exact Distance Queries in Sparse Graphs Through Hub Labeling

A distance labeling scheme is an assignment of bit-labels to the vertices of an undirected, unweighted graph such that the distance between any pair of vertices can be decoded solely from their labels. An important class of distance labeling schemes is that of hub labelings, where a node ν ∈ G stores its distance to the so-called hubs Sν ⊆ V, chosen so that for any u,ν ∈ V there is w ∈ Su ∩ Sv belonging to some shortest uv path. Notice that for most existing graph classes, the best distance labelling constructions existing use at some point a hub labeling scheme at least as a key building block. Our interest lies in hub labelings of sparse graphs, i.e., those with |E(G)| = O (n), for which we show a lowerbound of n 2O (√log n) for the average size of the hubsets. Additionally, we show a hub-labeling construction for sparse graphs of average size O(√n RS (n)c) for some 0 < c < 1, where RS (n) is the so-called Ruzsa-Szemerédi function, linked to structure of induced matchings in dense graphs. This implies that further improving the lower bound on hub labeling size to n over 2(log n)o(1 would require a breakthrough in the study of lower bounds on RS (n), which have resisted substantial improvement in the last 70 years. For general distance labeling of sparse graphs, we show a lowerbound of 1 over 2Θ(√log n) SumIndex (n), where SumIndex (n) is the communication complexity of the SUM-I problem over Zn. Our results suggest that the best achievable hub-label size and distance-label size in sparse graphs may be Θ(n over 2(log n)c ) for some 0 < c < 1.,
Do zealots increase or decrease the polarization in social networks?

Zealots are the vertices in a social network who do not change their opinions under social pressure and are crucial to the study of opinion dynamics on complex networks. In this article, we study the effect of zealots on the polarization dynamics of a deterministic majority rule model using the configuration model as a substrate. To this end, we propose a novel quantifier, called ‘correlated polarization’, for measuring the amount of polarization in the network when vertices can exist in two opposite states. The quantifier takes into account not only the fraction of vertices with each opinion but also how they are connected to each other. We then show that the presence of zealots does not have a fixed effect on the polarization, and can change it in positive, negative or neutral way depending upon their topological characteristics like degree, their total fraction in the network, density and degree heterogeneity of the network and the type of initial conditions of the dynamics. Our results particularly highlight the importance of the role played by the initial conditions in drifting the polarization towards lower or higher values as the total number of zealots is increased.
Continuous Ordinary Differential Equations and Transfinite Computations

We consider Continuous Ordinary Differential Equations (CODE) y'=f(y), where f is a continuous function. They are known to always have solutions for a given initial condition y(0)=y0, these solutions being possibly non unique. We restrict to our attention to a class of continuous functions, that we call greedy: they always admit unique greedy solutions, i.e. going in greedy way in some fixed direction. 
We prove that they can be seen as models of computation over the ordinals and conversely in a very strong sense. 
In particular, for such ODEs, to a greedy trajectory can be associated some ordinal corresponding to some time of computation, and conversely models of computation over the ordinals can be associated to some CODE. In particular, analyzing reachability for one or the other concept with respect to greedy trajectories has the same hardness. This also brings new perspectives on analysis in Mathematics, by providing ways to translate results for ITTMs to CODEs. This also extends some recent results about the relations between ordinary differential equations and Turing machines, and more widely with (generalized) computability theory.
Learning efficient haptic shape exploration with a rigid tactile sensor array

Haptic exploration is a key skill for both robots and humans to discriminate and handle unknown objects or to recognize familiar objects. Its active nature is evident in humans who from early on reliably acquire sophisticated sensory-motor capabilities for active exploratory touch and directed manual exploration that associates surfaces and object properties with their spatial locations. This is in stark contrast to robotics. In this field, the relative lack of good real-world interaction models—along with very restricted sensors and a scarcity of suitable training data to leverage machine learning methods—has so far rendered haptic exploration a largely underdeveloped skill. In robot vision however, deep learning approaches and an abundance of available training data have triggered huge advances. In the present work, we connect recent advances in recurrent models of visual attention with previous insights about the organisation of human haptic search behavior, exploratory procedures and haptic glances for a novel architecture that learns a generative model of haptic exploration in a simulated three-dimensional environment. This environment contains a set of rigid static objects representing a selection of one-dimensional local shape features embedded in a 3D space: an edge, a flat and a convex surface. The proposed algorithm simultaneously optimizes main perception-action loop components: feature extraction, integration of features over time, and the control strategy, while continuously acquiring data online. Inspired by the Recurrent Attention Model, we formalize the target task of haptic object identification in a reinforcement learning framework and reward the learner in the case of success only. We perform a multi-module neural network training, including a feature extractor and a recurrent neural network module aiding pose control for storing and combining sequential sensory data. The resulting haptic meta-controller for the rigid 16 × 16 tactile sensor array moving in a physics-driven simulation environment, called the Haptic Attention Model, performs a sequence of haptic glances, and outputs corresponding force measurements. The resulting method has been successfully tested with four different objects. It achieved results close to 100% while performing object contour exploration that has been optimized for its own sensor morphology.
ScispaCy: Fast and Robust Models for Biomedical Natural Language Processing

Despite recent advances in natural language processing, many statistical models for processing text perform extremely poorly under domain shift. Processing biomedical and clinical text is a critically important application area of natural language processing, for which there are few robust, practical, publicly available models. This paper describes scispaCy, a new Python library and models for practical biomedical/scientific text processing, which heavily leverages the spaCy library. We detail the performance of two packages of models released in scispaCy and demonstrate their robustness on several tasks and datasets. Models and code are available at https://allenai.github.io/scispacy/.
World Discovery Models

As humans we are driven by a strong desire for seeking novelty in our world. Also upon observing a novel pattern we are capable of refining our understanding of the world based on the new information---humans can discover their world. The outstanding ability of the human mind for discovery has led to many breakthroughs in science, art and technology. Here we investigate the possibility of building an agent capable of discovering its world using the modern AI technology. In particular we introduce NDIGO, Neural Differential Information Gain Optimisation, a self-supervised discovery model that aims at seeking new information to construct a global view of its world from partial and noisy observations. Our experiments on some controlled 2-D navigation tasks show that NDIGO outperforms state-of-the-art information-seeking methods in terms of the quality of the learned representation. The improvement in performance is particularly significant in the presence of white or structured noise where other information-seeking methods follow the noise instead of discovering their world.
Probabilistic Neural-symbolic Models for Interpretable Visual Question Answering

We propose a new class of probabilistic neural-symbolic models, that have symbolic functional programs as a latent, stochastic variable. Instantiated in the context of visual question answering, our probabilistic formulation offers two key conceptual advantages over prior neural-symbolic models for VQA. Firstly, the programs generated by our model are more understandable while requiring lesser number of teaching examples. Secondly, we show that one can pose counterfactual scenarios to the model, to probe its beliefs on the programs that could lead to a specified answer given an image. Our results on the CLEVR and SHAPES datasets verify our hypotheses, showing that the model gets better program (and answer) prediction accuracy even in the low data regime, and allows one to probe the coherence and consistency of reasoning performed.
TeV halos are everywhere: Prospects for new discoveries

Milagro and HAWC have detected extended TeV gamma-ray emission around nearby pulsar wind nebulae (PWNe). Building on these discoveries, Linden et al.  identified a new source class -- TeV halos -- powered by the interactions of high-energy electrons and positrons that have escaped from the PWN, but which remain trapped in a larger region where diffusion is inhibited compared to the interstellar medium. Many theoretical properties of TeV halos remain mysterious, but empirical arguments suggest that they are ubiquitous. The key to progress is finding more halos. We outline prospects for new discoveries and calculate their expectations and uncertainties. We predict, using models normalized to current data, that future HAWC and CTA observations will detect in total $\sim$50--240 TeV halos, though we note that multiple systematic uncertainties still exist. Further, the existing HESS source catalog could contain $\sim$10--50 TeV halos that are presently classified as unidentified sources or PWN candidates. We quantify the importance of these detections for new probes of the evolution of TeV halos, pulsar properties, and the sources of high-energy gamma rays and cosmic rays.
Capuchin: Causal Database Repair for Algorithmic Fairness

Fairness is increasingly recognized as a critical component of machine learning systems. However, it is the underlying data on which these systems are trained that often reflect discrimination, suggesting a database repair problem. Existing treatments of fairness rely on statistical correlations that can be fooled by statistical anomalies, such as Simpson's paradox. Proposals for causality-based definitions of fairness can correctly model some of these situations, but they require specification of the underlying causal models. In this paper, we formalize the situation as a database repair problem, proving sufficient conditions for fair classifiers in terms of admissible variables as opposed to a complete causal model. We show that these conditions correctly capture subtle fairness violations. We then use these conditions as the basis for database repair algorithms that provide provable fairness guarantees about classifiers trained on their training labels. We evaluate our algorithms on real data, demonstrating improvement over the state of the art on multiple fairness metrics proposed in the literature while retaining high utility.
OpenKiwi: An Open Source Framework for Quality Estimation

We introduce OpenKiwi, a Pytorch-based open source framework for translation quality estimation. OpenKiwi supports training and testing of word-level and sentence-level quality estimation systems, implementing the winning systems of the WMT 2015–18 quality estimation campaigns. We benchmark OpenKiwi on two datasets from WMT 2018 (English-German SMT and NMT), yielding state-of-the-art performance on the word-level tasks and near state-of-the-art in the sentence-level tasks.
Evidence Sentence Extraction for Machine Reading Comprehension

Remarkable success has been achieved in the last few years on some limited machine reading comprehension (MRC) tasks. However, it is still difficult to interpret the predictions of existing MRC models. In this paper, we focus on extracting evidence sentences that can explain or support the answers of multiple-choice MRC tasks, where the majority of answer options cannot be directly extracted from reference documents. Due to the lack of ground truth evidence sentence labels in most cases, we apply distant supervision to generate imperfect labels and then use them to train an evidence sentence extractor. To denoise the noisy labels, we apply a recently proposed deep probabilistic logic learning framework to incorporate both sentence-level and cross-sentence linguistic indicators for indirect supervision. We feed the extracted evidence sentences into existing MRC models and evaluate the end-to-end performance on three challenging multiple-choice MRC datasets: MultiRC, RACE, and DREAM, achieving comparable or better performance than the same models that take as input the full reference document. To the best of our knowledge, this is the first work extracting evidence sentences for multiple-choice MRC.
Perturbative Resonance in WIMP paradigm and its Cosmological Implications on Cosmic Reheating and Primordial Gravitational Wave Detection

We investigate the co-evolution of dark matter (DM) density perturbation and metric perturbation in the WIMP paradigm. Instead of adopting the thermal equilibrium assumption as conventional, we propose a simple phase of DM production for the WIMP paradigm and extend our analysis to this phase. Being free from the envelop of thermal equilibrium, an amplified perturbative resonance between DM density perturbation and scalar modes of metric perturbation takes place during the DM production phase, and consequently results in a suppression of the tensor-to-scalar ratio of metric perturbation. By specifying the cosmic background with a typical realization of cosmic reheating, we establish a new relation between DM particle mass $m_\chi$ and the tensor-to-scalar ratio $r$ in the WIMP paradigm, which also contains two reheating parameters, the reheating temperature $\Gamma_0$ and the dissipative constant $T_{R_f}$. Notably, for a sizeable parameter region of WIMP candidate and cosmic reheating, this relation predicts a smaller value of $r$ in comparing with the conventional expectation obtained by ignoring the process of DM production. Once the suppression of $r$ is measured in future observations of primordial gravitational wave in CMB, this relation can be used to constrain $m_\chi$, $T_{R_f}$ and $\Gamma_0$ in principle.
SSA-CNN: Semantic Self-Attention CNN for Pedestrian Detection

Pedestrian detection plays an important role in many applications such as autonomous driving. We propose a method that explores semantic segmentation results as self-attention cues to significantly improve the pedestrian detection performance. Specifically, a multi-task network is designed to jointly learn semantic segmentation and pedestrian detection from image datasets with weak box-wise annotations. The semantic segmentation feature maps are concatenated with corresponding convolution features maps to provide more discriminative features for pedestrian detection and pedestrian classification. By jointly learning segmentation and detection, our proposed pedestrian self-attention mechanism can effectively identify pedestrian regions and suppress backgrounds. In addition, we propose to incorporate semantic attention information from multi-scale layers into deep convolution neural network to boost pedestrian detection. Experiment results show that the proposed method achieves the best detection performance with MR of 6.27% on Caltech dataset and obtain competitive performance on CityPersons dataset while maintaining high computational efficiency.
Configuration models of random hypergraphs

Many empirical networks are intrinsically polyadic, with interactions occurring within groups of agents of arbitrary size. There are, however, few flexible null models that can support statistical inference in polyadic networks. We define a class of null random hypergraphs that hold constant both the node degree and edge dimension sequences, thereby generalizing the classical dyadic configuration model. We provide a Markov Chain Monte Carlo scheme for sampling from these models and discuss connections and distinctions between our proposed models and previous approaches. We then illustrate the application of these models through a triplet of data-analytic vignettes. We start with two classical topics in network science—triadic clustering and degree-assortativity. In each, we emphasize the importance of randomizing over hypergraph space rather than projected graph space, showing that this choice can dramatically alter both the quantitative and qualitative outcomes of statistical inference. We then define and study the edge intersection profile of a hypergraph as a measure of higher-order correlation between edges, and derive asymptotic approximations for this profile under the stub-labeled null. We close with suggestions for multiple avenues of future work. Taken as a whole, our experiments emphasize the ability of explicit, statistically grounded polyadic modelling to significantly enhance the toolbox of network data science.
The discernible and hidden effects of clonality on the genotypic and genetic states of populations: Improving our estimation of clonal rates

Partial clonality is widespread across the tree of life, but most population genetic models are designed for exclusively clonal or sexual organisms. This gap hampers our understanding of the influence of clonality on evolutionary trajectories and the interpretation of population genetic data. We performed forward simulations of diploid populations at increasing rates of clonality (c), analysed their relationships with genotypic (clonal richness, R, and distribution of clonal sizes, Pareto β) and genetic (FIS and linkage disequilibrium) indices, and tested predictions of c from population genetic data through supervised machine learning. Two complementary behaviours emerged from the probability distributions of genotypic and genetic indices with increasing c. While the impact of c on R and Pareto β was easily described by simple mathematical equations, its effects on genetic indices were noticeable only at the highest levels (c > 0.95). Consequently, genotypic indices allowed reliable estimates of c, while genetic descriptors led to poorer performances when c < 0.95. These results provide clear baseline expectations for genotypic and genetic diversity and dynamics under partial clonality. Worryingly, however, the use of realistic sample sizes to acquire empirical data systematically led to gross underestimates (often of one to two orders of magnitude) of c, suggesting that many interpretations hitherto proposed in the literature, mostly based on genotypic richness, should be reappraised. We propose future avenues to derive realistic confidence intervals for c and show that, although still approximate, a supervised learning method would greatly improve the estimation of c from population genetic data.
GQA: a new dataset for compositional question answering over real-world images

We introduce GQA, a new dataset for real-world visual reasoning and compositional question answering, seeking to address key shortcomings of previous VQA datasets. We have developed a strong and robust question engine that leverages scene graph structures to create 22M diverse reasoning questions, all come with functional programs that represent their semantics. We use the programs to gain tight control over the answer distribution and present a new tunable smoothing technique to mitigate language biases. Accompanying the dataset is a suite of new metrics that evaluate essential qualities such as consistency, grounding and plausibility. An extensive analysis is performed for baselines as well as state-of-the-art models, providing fine-grained results for different question types and topologies. Whereas a blind LSTM obtains mere 42.1%, and strong VQA models achieve 54.1%, human performance tops at 89.3\%, offering ample opportunity for new research to explore. We strongly hope GQA will provide an enabling resource for the next generation of models with enhanced robustness, improved consistency, and deeper semantic understanding for images and language.
A Unifying Framework for Spectrum-Preserving Graph Sparsification and Coarsening

How might one "reduce" a graph? That is, generate a smaller graph that preserves the global structure at the expense of discarding local details? There has been extensive work on both graph sparsification (removing edges) and graph coarsening (merging nodes, often by edge contraction); however, these operations are currently treated separately. Interestingly, for a planar graph, edge deletion corresponds to edge contraction in its planar dual (and more generally, for a graphical matroid and its dual). Moreover, with respect to the dynamics induced by the graph Laplacian (e.g., diffusion), deletion and contraction are physical manifestations of two reciprocal limits: edge weights of $0$ and $\infty$, respectively. In this work, we provide a unifying framework that captures both of these operations, allowing one to simultaneously sparsify and coarsen a graph while preserving its large-scale structure. The limit of infinite edge weight is rarely considered, as many classical notions of graph similarity diverge. However, its algebraic, geometric, and physical interpretations are reflected in the Laplacian pseudoinverse $\mathbf{\mathit{L}}^{\dagger}$, which remains finite in this limit. Motivated by this insight, we provide a probabilistic algorithm that reduces graphs while preserving $\mathbf{\mathit{L}}^{\dagger}$, using an unbiased procedure that minimizes its variance. We compare our algorithm with several existing sparsification and coarsening algorithms using real-world datasets, and demonstrate that it more accurately preserves the large-scale structure.
Conservative Agency via Attainable Utility Preservation

Reward functions are easy to misspecify; although designers can make corrections after observing mistakes, an agent pursuing a misspecified reward function can irreversibly change the state of its environment. If that change precludes optimization of the correctly specified reward function, then correction is futile. For example, a robotic factory assistant could break expensive equipment due to a reward misspecification; even if the designers immediately correct the reward function, the damage is done. To mitigate this risk, we introduce an approach that balances optimization of the primary reward function with preservation of the ability to optimize auxiliary reward functions. Surprisingly, even when the auxiliary reward functions are randomly generated and therefore uninformative about the correctly specified reward function, this approach induces conservative, effective behavior.