nils-herrmann/clean_scirepeval huggingface dataset:
Neural Naturalist: Generating Fine-Grained Image Comparisons

We introduce the new Birds-to-Words dataset of 41k sentences describing fine-grained differences between photographs of birds. The language collected is highly detailed, while remaining understandable to the everyday observer (e.g., “heart-shaped face,” “squat body”). Paragraph-length descriptions naturally adapt to varying levels of taxonomic and visual distance—drawn from a novel stratified sampling approach—with the appropriate level of detail. We propose a new model called Neural Naturalist that uses a joint image encoding and comparative module to generate comparative language, and evaluate the results with humans who must use the descriptions to distinguish real images. Our results indicate promising potential for neural models to explain differences in visual embedding space using natural language, as well as a concrete path for machine learning to aid citizen scientists in their effort to preserve biodiversity.
Towards Learning a Self-inverse Network for Bidirectional Image-to-image Translation

The one-to-one mapping is necessary for many bidirectional image-to-image translation applications, such as MRI image synthesis as MRI images are unique to the patient. State-of-the-art approaches for image synthesis from domain X to domain Y learn a convolutional neural network that meticulously maps between the domains. A different network is typically implemented to map along the opposite direction, from Y to X. In this paper, we explore the possibility of only wielding one network for bi-directional image synthesis. In other words, such an autonomous learning network implements a self-inverse function. A self-inverse network shares several distinct advantages: only one network instead of two, better generalization and more restricted parameter space. Most importantly, a self-inverse function guarantees a one-to-one mapping, a property that cannot be guaranteed by earlier approaches that are not self-inverse. The experiments on three datasets show that, compared with the baseline approaches that use two separate models for the image synthesis along two directions, our self-inverse network achieves better synthesis results in terms of standard metrics. Finally, our sensitivity analysis confirms the feasibility of learning a self-inverse function for the bidirectional image translation.
Taxing Dissent: The Impact of a Social Media Tax in Uganda

We examine the impact of a new tool for suppressing the expression of dissent---a daily tax on social media use. Using a synthetic control framework, we estimate that the tax reduced the number of georeferenced Twitter users in Uganda by 13 percent. The estimated treatment effects are larger for poorer and less frequent users. Despite the overall decline in Twitter use, tweets referencing collective action increased by 31 percent and observed protests increased by 47 percent. These results suggest that taxing social media use may not be an effective tool for reducing political dissent.
Search for low mass vector resonances decaying into quark-antiquark pairs in proton-proton collisions at 
s=13  TeV

A search for low mass narrow vector resonances decaying into quark-antiquark pairs is presented. The analysis is based on data collected in 2017 with the CMS detector at the LHC in proton-proton collisions at a center-of-mass energy of 13 TeV, corresponding to an integrated luminosity of 41.1 fb$^{-1}$. The results of this analysis are combined with those of an earlier analysis based on data collected at the same collision energy in 2016, corresponding to 35.9 fb$^{-1}$. Signal candidates will be recoiling against initial state radiation and are identified as energetic, large-radius jets with two pronged substructure. The invariant jet mass spectrum is probed for a potential narrow peaking signal over a smoothly falling background. No evidence for such resonances is observed within the mass range of 50-450 GeV. Upper limits at the 95% confidence level are set on the coupling of narrow resonances to quarks, as a function of the resonance mass. For masses between 50 and 300 GeV these are the most sensitive limits to date. This analysis extends the earlier search to a mass range of 300-450 GeV, which is probed for the first time with jet substructure techniques.
Gradient-Aware Model-based Policy Search

Traditional model-based reinforcement learning approaches learn a model of the environment dynamics without explicitly considering how it will be used by the agent. In the presence of misspecified model classes, this can lead to poor estimates, as some relevant available information is ignored. In this paper, we introduce a novel model-based policy search approach that exploits the knowledge of the current agent policy to learn an approximate transition model, focusing on the portions of the environment that are most relevant for policy improvement. We leverage a weighting scheme, derived from the minimization of the error on the model-based policy gradient estimator, in order to define a suitable objective function that is optimized for learning the approximate transition model. Then, we integrate this procedure into a batch policy improvement algorithm, named Gradient-Aware Model-based Policy Search (GAMPS), which iteratively learns a transition model and uses it, together with the collected trajectories, to compute the new policy parameters. Finally, we empirically validate GAMPS on benchmark domains analyzing and discussing its properties.
Structural Commutation Relations for Stochastic Labelled Graph Grammar Rule Operators

We show how to calculate the operator algebra and the operator Lie algebra of a stochastic labelled-graph grammar. More specifically, we carry out a generic calculation of the product (and therefore the commutator) of time-evolution operators for any two labelled-graph grammar rewrite rules, where the operator corresponding to each rule is defined in terms of elementary two-state creation/annihilation operators. The resulting graph grammar algebra has the following properties: (1) The product and commutator of two such operators is a sum of such operators with integer coefficients. Thus, the algebra and the Lie algebra occurs entirely at the structural (or graph-combinatorial) level of graph grammar rules, lifted from the level of elementary creation/annihilation operators (an improvement over , Propositions 1 and 2). (2) The product of the off-diagonal (state-changing) parts of two such graph rule operators is a sum of off-diagonal graph rule operators with non-negative integer coefficients. (3) These results apply whether the semantics of a graph grammar rule leaves behind hanging edges (Theorem 1), or removes hanging edges (Theorem 2). (4) The algebra is constructive in terms of elementary two-state creation/annihilation operators (. These results are useful because dynamical transformations of labelled graphs comprise a general modeling framework, and algebraic commutators of time-evolution operators have many analytic uses including designing simulation algorithms and estimating their errors.
Span Selection Pre-training for Question Answering

BERT (Bidirectional Encoder Representations from Transformers) and related pre-trained Transformers have provided large gains across many language understanding tasks, achieving a new state-of-the-art (SOTA). BERT is pretrained on two auxiliary tasks: Masked Language Model and Next Sentence Prediction. In this paper we introduce a new pre-training task inspired by reading comprehension to better align the pre-training from memorization to understanding. Span Selection PreTraining (SSPT) poses cloze-like training instances, but rather than draw the answer from the model’s parameters, it is selected from a relevant passage. We find significant and consistent improvements over both BERT-BASE and BERT-LARGE on multiple Machine Reading Comprehension (MRC) datasets. Specifically, our proposed model has strong empirical evidence as it obtains SOTA results on Natural Questions, a new benchmark MRC dataset, outperforming BERT-LARGE by 3 F1 points on short answer prediction. We also show significant impact in HotpotQA, improving answer prediction F1 by 4 points and supporting fact prediction F1 by 1 point and outperforming the previous best system. Moreover, we show that our pre-training approach is particularly effective when training data is limited, improving the learning curve by a large amount.
AC-Teach: A Bayesian Actor-Critic Method for Policy Learning with an Ensemble of Suboptimal Teachers

The exploration mechanism used by a Deep Reinforcement Learning (RL) agent plays a key role in determining its sample efficiency. Thus, improving over random exploration is crucial to solve long-horizon tasks with sparse rewards. We propose to leverage an ensemble of partial solutions as teachers that guide the agent's exploration with action suggestions throughout training. While the setup of learning with teachers has been previously studied, our proposed approach - Actor-Critic with Teacher Ensembles (AC-Teach) - is the first to work with an ensemble of suboptimal teachers that may solve only part of the problem or contradict other each other, forming a unified algorithmic solution that is compatible with a broad range of teacher ensembles. AC-Teach leverages a probabilistic representation of the expected outcome of the teachers' and student's actions to direct exploration, reduce dithering, and adapt to the dynamically changing quality of the learner. We evaluate a variant of AC-Teach that guides the learning of a Bayesian DDPG agent on three tasks - path following, robotic pick and place, and robotic cube sweeping using a hook - and show that it improves largely on sampling efficiency over a set of baselines, both for our target scenario of unconstrained suboptimal teachers and for easier setups with optimal or single teachers. Additional results and videos at this https URL.
Yang–Baxter endomorphisms

Every unitary solution of the Yang–Baxter equation (R‐matrix) in dimension d can be viewed as a unitary element of the Cuntz algebra Od and as such defines an endomorphism of Od . These Yang–Baxter endomorphisms restrict and extend to several other C∗ ‐ and von Neumann algebras, and furthermore define a II 1 factor associated with an extremal character of the infinite braid group. This paper is devoted to a detailed study of such Yang–Baxter endomorphisms.
Super learning for daily streamflow forecasting: Large-scale demonstration and comparison with multiple machine learning algorithms

Daily streamflow forecasting through data-driven approaches is traditionally performed using a single machine learning algorithm. Existing applications are mostly restricted to examination of few case studies, not allowing accurate assessment of the predictive performance of the algorithms involved. Here we propose super learning (a type of ensemble learning) by combining 10 machine learning algorithms. We apply the proposed algorithm in one-step ahead forecasting mode. For the application, we exploit a big dataset consisting of 10-year long time series of daily streamflow, precipitation and temperature from 511 basins. The super learner improves over the performance of the linear regression algorithm by 20.06%, outperforming the "hard to beat in practice" equal weight combiner. The latter improves over the performance of the linear regression algorithm by 19.21%. The best performing individual machine learning algorithm is neural networks, which improves over the performance of the linear regression algorithm by 16.73%, followed by extremely randomized trees (16.40%), XGBoost (15.92%), loess (15.36%), random forests (12.75%), polyMARS (12.36%), MARS (4.74%), lasso (0.11%) and support vector regression (-0.45%). Based on the obtained large-scale results, we propose super learning for daily streamflow forecasting.
Self-Reinforcing Unsupervised Matching

Remarkable gains in deep learning usually benefit from large-scale supervised data. Ensuring the intra-class modality diversity in training set is critical for generalization capability of cutting-edge deep models, but it burdens human with heavy manual labor on data collection and annotation. In addition, some rare or unexpected modalities are new for the current model, causing reduced performance under such emerging modalities. Inspired by the achievements in speech recognition, psychology and behavioristics, we present a practical solution, self-reinforcing unsupervised matching (SUM), to annotate the images with 2D structure-preserving property in an emerging modality by cross-modality matching. Specifically, we propose a dynamic programming algorithm, dynamic position warping (DPW), to reveal the underlying element correspondence relationship between two matrix-form data in an order-preserving fashion, and devise a local feature adapter (LoFA) to allow for cross-modality similarity measurement. On these bases, we develop a two-tier self-reinforcing learning mechanism on both feature level and image level to optimize the LoFA. The proposed SUM framework requires no any supervision in emerging modality and only one template in seen modality, providing a promising route towards incremental learning and continual learning. Extensive experimental evaluation on two proposed challenging one-template visual matching tasks demonstrate its efficiency and superiority.
The highest-speed local dark matter particles come from the Large Magellanic Cloud

Using N-body simulations of the Large Magellanic Cloud (LMC's) passage through the Milky Way (MW), tailored to reproduce observed kinematic properties of both galaxies, we show that the high-speed tail of the Solar Neighborhood dark matter distribution is overwhelmingly of LMC origin. Two populations contribute at high speeds: 1) Particles that were once bound to the LMC, and 2) MW halo particles that have been accelerated owing to the response of the halo to the recent passage of the LMC . These particles reach speeds of 700–900 km/s with respect to the Earth, near or somewhat higher that the local escape speed of the MW . The high-speed particles follow trajectories similar to the Solar reflex motion, with peak velocities reached in June. For low-mass dark matter, these high-speed particles can dominate the signal in direct-detection experiments, extending the reach of the experiments to lower mass and elastic scattering cross sections even with existing data sets. Our study shows that even non-disrupted MW satellite galaxies can leave a significant dark matter footprint in the Solar Neighborhood.
Detection and Classification of Breast Cancer Metastates Based on U-Net

This paper presents U-net based breast cancer metastases detection and classification in lymph nodes, as well as patient-level classification based on metastases detection. The whole pipeline can be divided into five steps: preprocessing and data argumentation, patch-based segmentation, post processing, slide-level classification, and patient-level classification. In order to reduce overfitting and speedup convergence, we applied batch normalization and dropout into U-Net. The final Kappa score reaches 0.902 on training data.
DaTscan SPECT Image Classification for Parkinson's Disease

Parkinson's Disease (PD) is a neurodegenerative disease that currently does not have a cure. In order to facilitate disease management and reduce the speed of symptom progression, early diagnosis is essential. The current clinical, diagnostic approach is to have radiologists perform human visual analysis of the degeneration of dopaminergic neurons in the substantia nigra region of the brain. Clinically, dopamine levels are monitored through observing dopamine transporter (DaT) activity. One method of DaT activity analysis is performed with the injection of an Iodine-123 fluoropropyl (123I-FP-CIT) tracer combined with single photon emission computerized tomography (SPECT) imaging. The tracer illustrates the region of interest in the resulting DaTscan SPECT images. Human visual analysis is slow and vulnerable to subjectivity between radiologists, so the goal was to develop an introductory implementation of a deep convolutional neural network that can objectively and accurately classify DaTscan SPECT images as Parkinson's Disease or normal. This study illustrates the approach of using a deep convolutional neural network and evaluates its performance on DaTscan SPECT image classification. The data used in this study was obtained through a database provided by the Parkinson's Progression Markers Initiative (PPMI). The deep neural network in this study utilizes the InceptionV3 architecture, 1st runner up in the 2015 ImageNet Large Scale Visual Recognition Competition (ILSVRC), as a base model. A custom, binary classifier block was added on top of this base. In order to account for the small dataset size, a ten fold cross validation was implemented to evaluate the model's performance.
A Three-Feature Model to Predict Colour Change Blindness

Change blindness is a striking shortcoming of our visual system which is exploited in the popular ‘Spot the difference’ game, as it makes us unable to notice large visual changes happening right before our eyes. Change blindness illustrates the fact that we see much less than we think we do. In this paper, we introduce a fully automated model to predict colour change blindness in cartoon images based on image complexity, change magnitude and observer experience. Using linear regression with only three parameters, the predictions of the proposed model correlate significantly with measured detection times. We also demonstrate the efficacy of the model to classify stimuli in terms of difficulty.
Magnetoelectrostatics of axionically active systems: Induced field restructuring in magnetic stars

In the framework of Einstein-Maxwell-axion theory we consider a static field configuration in the outer zone of a magnetic star. We assume that this field configuration is formed by the following interacting quartet: a spherically symmetric gravitational field of the Reissner-Nordstr\"om type, pseudoscalar field, associated with an axionic dark matter, strong intrinsic magnetic field, and axionically induced electric field. Based on the analysis of the solutions to the master equations of the model, we show that the structure of the formed field configuration reveals the following triple effect. First, if the strong magnetic field of the star has the dipole component, the axionic halo around the magnetic star with spherically symmetric gravitational field is no longer spheroidal, and the distortion is induced by the axion-photon coupling; second, due to the distortion of the axionic halo the magnetic field is no longer pure dipolar, and the induced quadruple, octupole, etc., components appear thus redistributing the magnetic energy between the components with more steep radial profiles than the dipolar one; third, the interaction of the axionic and magnetic fields produces the electric field, which inherits their multipole structure. We attract attention to possible applications of the predicted field restructuring to the theory of axionic rotating magnetars.
Self-Teaching Networks

We propose self-teaching networks to improve the generalization capacity of deep neural networks. The idea is to generate soft supervision labels using the output layer for training the lower layers of the network. During the network training, we seek an auxiliary loss that drives the lower layer to mimic the behavior of the output layer. The connection between the two network layers through the auxiliary loss can help the gradient flow, which works similar to the residual networks. Furthermore, the auxiliary loss also works as a regularizer, which improves the generalization capacity of the network. We evaluated the self-teaching network with deep recurrent neural networks on speech recognition tasks, where we trained the acoustic model using 30 thousand hours of data. We tested the acoustic model using data collected from 4 scenarios. We show that the self-teaching network can achieve consistent improvements and outperform existing methods such as label smoothing and confidence penalization.
MLOD: A multi-view 3D object detection based on robust feature fusion method

This paper presents Multi-view Labelling Object Detector (MLOD). The detector takes an RGB image and a LIDAR point cloud as input and follows the two-stage object detection framework  . A Region Proposal Network (RPN) generates 3D proposals in a Bird’s Eye View (BEV) projection of the point cloud. The second stage projects the 3D proposal bounding boxes to the image and BEV feature maps and sends the corresponding map crops to a detection header for classification and bounding-box regression. Unlike other multi-view based methods, the cropped image features are not directly fed to the detection header, but masked by the depth information to filter out parts outside 3D bounding boxes. The fusion of image and BEV features is challenging, as they are derived from different perspectives. We introduce a novel detection header, which provides detection results not just from fusion layer, but also from each sensor channel. Hence the object detector can be trained on data labelled in different views to avoid the degeneration of feature extractors. MLOD achieves state-of-the-art performance on the KITTI 3D object detection benchmark. Most importantly, the evaluation shows that the new header architecture is effective in preventing image feature extractor degeneration.
OncoNetExplainer: Explainable Predictions of Cancer Types Based on Gene Expression Data

The discovery of important biomarkers is a significant step towards understanding the molecular mechanisms of carcinogenesis; enabling accurate diagnosis for, and prognosis of, a certain cancer type. Before recommending any diagnosis, genomics data such as gene expressions (GE) and clinical outcomes need to be analyzed. However, complex nature, high dimensionality, and heterogeneity in genomics data make the overall analysis challenging. Convolutional neural networks (CNN) have shown tremendous success in solving such problems. However, neural network models are perceived mostly as 'black box' methods because of their not well-understood internal functioning. However, interpretability is important to provide insights on why a given cancer case has a certain type. Besides, finding the most important biomarkers can help in recommending more accurate treatments and drug repositioning. Moreover, the 'right to explanation' of the EU GDPR gives patients the right to know why and how an algorithm made a diagnosis decision. Hence, in this paper, we propose a new approach called OncoNetExplainer to make explainable predictions of cancer types based on GE data. We used genomics data about 9,074 cancer patients covering 33 different cancer types from the Pan-Cancer Atlas on which we trained CNN and VGG16 networks using guided-gradient class activation maps++ (GradCAM++). Further, we generate class-specific heat maps to identify significant biomarkers and computed feature importance in terms of mean absolute impact to rank top genes across all the cancer types. Quantitative and qualitative analyses show that both models exhibit high confidence at predicting the cancer types correctly giving an average precision of 96.25%. To provide comparisons with the baselines, we identified top genes, and cancer-specific driver genes using gradient boosted trees and SHapley Additive exPlanations (SHAP). Finally, our findings were validated with the annotations provided by the TumorPortal.