nils-herrmann/clean_scirepeval huggingface dataset:
A giant exoplanet orbiting a very-low-mass star challenges planet formation models

A small star hosts a big planet M dwarfs, the most common type of star, are low-mass objects that emit most of their faint light in the near-infrared, making it difficult to detect any orbiting exoplanets. Morales et al. have observed the nearby M dwarf GJ 3512 in the optical and near-infrared (see the Perspective by Laughlin). Periodic variations in the star's radial velocity show that it hosts a gas giant exoplanet on an eccentric orbit. The authors use simulations to show that such a large exoplanet around such a small star has implications for models of planet formation. Science, this issue p. 1441; see also p. 1382 A gas giant exoplanet orbits a low-mass star, a configuration that is difficult for planet formation models to reproduce. Surveys have shown that super-Earth and Neptune-mass exoplanets are more frequent than gas giants around low-mass stars, as predicted by the core accretion theory of planet formation. We report the discovery of a giant planet around the very-low-mass star GJ 3512, as determined by optical and near-infrared radial-velocity observations. The planet has a minimum mass of 0.46 Jupiter masses, very high for such a small host star, and an eccentric 204-day orbit. Dynamical models show that the high eccentricity is most likely due to planet-planet interactions. We use simulations to demonstrate that the GJ 3512 planetary system challenges generally accepted formation theories, and that it puts constraints on the planet accretion and migration rates. Disk instabilities may be more efficient in forming planets than previously thought.
Towards neural networks that provably know when they don't know

It has recently been shown that ReLU networks produce arbitrarily over-confident predictions far away from the training data. Thus, ReLU networks do not know when they don't know. However, this is a highly important property in safety critical applications. In the context of out-of-distribution detection (OOD) there have been a number of proposals to mitigate this problem but none of them are able to make any mathematical guarantees. In this paper we propose a new approach to OOD which overcomes both problems. Our approach can be used with ReLU networks and provides provably low confidence predictions far away from the training data as well as the first certificates for low confidence predictions in a neighborhood of an out-distribution point. In the experiments we show that state-of-the-art methods fail in this worst-case setting whereas our model can guarantee its performance while retaining state-of-the-art OOD performance.
Finiteness Properties of Pseudo-Hyperbolic Varieties

Motivated by Lang-Vojta's conjecture, we show that the set of dominant rational self-maps of an algebraic variety over a number field with only finitely many rational points in any given number field is finite by combining Amerik's theorem for dynamical systems of infinite order with properties of Prokhorov-Shramov's notion of quasi-minimal models. We also prove a similar result in the geometric setting by using again Amerik's theorem and Prokhorov-Shramov's notion of quasi-minimal model, but also Weil's regularization theorem for birational self-maps and properties of dynamical degrees. Furthermore, in the geometric setting, we obtain an analogue of Kobayashi-Ochiai's finiteness result for varieties of general type, and thereby generalize Noguchi's theorem (formerly Lang's conjecture). Our proof here relies on a deformation-theoretic result for surjective maps of normal varieties due to Hwang-Kebekus-Peternell.
Keyphrase Generation for Scientific Articles using GANs

In this paper, we present a keyphrase generation approach using conditional Generative Adversarial Networks (GAN). In our GAN model, the generator outputs a sequence of keyphrases based on the title and abstract of a scientific article. The discriminator learns to distinguish between machine-generated and human-curated keyphrases. We evaluate this approach on standard benchmark datasets. Our model achieves state-of-the-art performance in generation of abstractive keyphrases and is also comparable to the best performing extractive techniques. We also demonstrate that our method generates more diverse keyphrases and make our implementation publicly available.
Learning to Create Sentence Semantic Relation Graphs for Multi-Document Summarization

Linking facts across documents is a challenging task, as the language used to express the same information in a sentence can vary significantly, which complicates the task of multi-document summarization. Consequently, existing approaches heavily rely on hand-crafted features, which are domain-dependent and hard to craft, or additional annotated data, which is costly to gather. To overcome these limitations, we present a novel method, which makes use of two types of sentence embeddings: universal embeddings, which are trained on a large unrelated corpus, and domain-specific embeddings, which are learned during training. To this end, we develop SemSentSum, a fully data-driven model able to leverage both types of sentence embeddings by building a sentence semantic relation graph. SemSentSum achieves competitive results on two types of summary, consisting of 665 bytes and 100 words. Unlike other state-of-the-art models, neither hand-crafted features nor additional annotated data are necessary, and the method is easily adaptable for other tasks. To our knowledge, we are the first to use multiple sentence embeddings for the task of multi-document summarization.
Deep Ensemble Learning for News Stance Detection

Stance detection in fake news is an important component in news veracity assessment because this process helps fact-checking by understanding stance to a central claim from different information sources. The Fake News Challenge Stage 1 (FNC-1) held in 2017 was setup for this purpose, which involves estimating the stance of a news article body relative to a given headline. This thesis starts from the error analysis for the three top-performing systems in FNC-1. Based on the analysis, a simple but tough-to-beat Multilayer Perceptron system is chosen as the baseline. Afterwards, three approaches are explored to improve baseline.The first approach explores the possibility of improving the prediction accuracy by adding extra keywords features when training a model, where keywords are converted to an indicator vector and then concatenated to the baseline features. A list of keywords is manually selected based on the error analysis, which may best reflect some characteristics of fake news titles and bodies. To make this selection process automatically, three algorithms are created based on Mutual Information (MI) theory: keywords generator based on MI stance class, MI customised class, and Pointwise MI algorithm. The second approach is based on word embedding, where word2vec model is introduced and two document similarities calculation algorithms are implemented: wor2vec cosine similarity and WMD distance. The third approach is ensemble learning. Different models are configured together with two continuous outputs combining algorithms. The 10-fold cross validation reveals that the ensemble of three neural network models trained from simple bag-of-words features gives the best performance. It is therefore selected to compete in FNC-1. After hyperparameters fine tuning, the selected deep ensemble model beats the FNC-1 winner team by a remarkable 34.25 marks under FNC-1's evaluation metric.
Data Smashing 2.0: Sequence Likelihood (SL) Divergence For Fast Time Series Comparison

Recognizing subtle historical patterns is central to modeling and forecasting problems in time series analysis. Here we introduce and develop a new approach to quantify deviations in the underlying hidden generators of observed data streams, resulting in a new efficiently computable universal metric for time series. The proposed metric is in the sense that we can compare and contrast data streams regardless of where and how they are generated and without any feature engineering step. The approach proposed in this paper is conceptually distinct from our previous work on data smashing, and vastly improves discrimination performance and computing speed. The core idea here is the generalization of the notion of KL divergence often used to compare probability distributions to a notion of divergence in time series. We call this the sequence likelihood (SL) divergence, which may be used to measure deviations within a well-defined class of discrete-valued stochastic processes. We devise efficient estimators of SL divergence from finite sample paths and subsequently formulate a universal metric useful for computing distance between time series produced by hidden stochastic generators.
Quantum Graph Neural Networks

We introduce Quantum Graph Neural Networks (QGNN), a new class of quantum neural network ansatze which are tailored to represent quantum processes which have a graph structure, and are particularly suitable to be executed on distributed quantum systems over a quantum network. Along with this general class of ansatze, we introduce further specialized architectures, namely, Quantum Graph Recurrent Neural Networks (QGRNN) and Quantum Graph Convolutional Neural Networks (QGCNN). We provide four example applications of QGNNs: learning Hamiltonian dynamics of quantum systems, learning how to create multipartite entanglement in a quantum network, unsupervised learning for spectral clustering, and supervised learning for graph isomorphism classification.
RLBench: The Robot Learning Benchmark & Learning Environment

We present a challenging new benchmark and learning-environment for robot learning: RLBench. The benchmark features 100 completely unique, hand-designed tasks, ranging in difficulty from simple target reaching and door opening to longer multi-stage tasks, such as opening an oven and placing a tray in it. We provide an array of both proprioceptive observations and visual observations, which include rgb, depth, and segmentation masks from an over-the-shoulder stereo camera and an eye-in-hand monocular camera. Uniquely, each task comes with an infinite supply of demos through the use of motion planners operating on a series of waypoints given during task creation time; enabling an exciting flurry of demonstration-based learning possibilities. RLBench has been designed with scalability in mind; new tasks, along with their motion-planned demos, can be easily created and then verified by a series of tools, allowing users to submit their own tasks to the RLBench task repository. This large-scale benchmark aims to accelerate progress in a number of vision-guided manipulation research areas, including: reinforcement learning, imitation learning, multi-task learning, geometric computer vision, and in particular, few-shot learning. With the benchmark's breadth of tasks and demonstrations, we propose the first large-scale few-shot challenge in robotics. We hope that the scale and diversity of RLBench offers unparalleled research opportunities in the robot learning community and beyond. Benchmarking code and videos can be found at https://sites.google.com/view/rlbench.
Box splines, tensor product multiplicities and the volume function

We study the relationship between the tensor product multiplicities of a compact semisimple Lie algebra $\mathfrak{g}$ and a special function $\mathcal{J}$ associated to $\mathfrak{g}$, called the volume function, which arises in symplectic geometry and random matrix theory. Building on box spline deconvolution formulae of Dahmen-Micchelli and De Concini-Procesi-Vergne, we develop new techniques for computing the multiplicities from $\mathcal{J}$, answering a question posed by Coquereaux and Zuber. In particular, we derive an explicit algebraic formula for a large class of Littlewood-Richardson coefficients in terms of $\mathcal{J}$. We also give analogous results for weight multiplicities, and we show a number of further identities relating the tensor product multiplicities, the volume function and the box spline. To illustrate these ideas, we give new proofs of some known theorems.
Attention Forcing for Sequence-to-sequence Model Training

Auto-regressive sequence-to-sequence models with attention mechanism have achieved state-of-the-art performance in many tasks such as machine translation and speech synthesis. These models can be difficult to train. The standard approach, teacher forcing, guides a model with reference output history during training. The problem is that the model is unlikely to recover from its mistakes during inference, where the reference output is replaced by generated output. Several approaches deal with this problem, largely by guiding the model with generated output history. To make training stable, these approaches often require a heuristic schedule or an auxiliary classifier. This paper introduces attention forcing, which guides the model with generated output history and reference attention. This approach can train the model to recover from its mistakes, in a stable fashion, without the need for a schedule or a classifier. In addition, it allows the model to generate output sequences aligned with the references, which can be important for cascaded systems like many speech synthesis systems. Experiments on speech synthesis show that attention forcing yields significant performance gain. Experiments on machine translation show that for tasks where various re-orderings of the output are valid, guiding the model with generated output history is challenging, while guiding the model with reference attention is beneficial.
Eigenvalues and dynamical degrees of self-maps on abelian varieties

LetX be a smooth projective variety over an algebraically closed field, and f : X → X a surjective self-morphism of X . The i-th cohomological dynamical degree χi(f) is defined as the spectral radius of the pullback f∗ on the étale cohomology group H ét(X,Ql) and the k-th numerical dynamical degree λk(f) as the spectral radius of the pullback f∗ on the vector space Nk(X)R of real algebraic cycles of codimension k on X modulo numerical equivalence. Truong conjectured that χ2k(f) = λk(f) for all 0 ≤ k ≤ dimX as a generalization of Weil’s Riemann hypothesis. We prove this conjecture in the case of abelian varieties. In the course of the proof, we also obtain a new parity result on the eigenvalues of self-maps of abelian varieties in prime characteristic, which is of independent interest.
DCTD: Deep Conditional Target Densities for Accurate Regression

While deep learning-based classification is generally addressed using standardized approaches, a wide variety of techniques are employed for regression. In computer vision, one particularly popular such technique is that of confidencebased regression, which entails predicting a confidence value for each input-target pair (x, y). While this approach has demonstrated impressive results, it requires important task-dependent design choices, and the predicted confidences often lack a natural probabilistic meaning. We address these issues by proposing Deep Conditional Target Densities (DCTD), a novel and general regression method with a clear probabilistic interpretation. DCTD models the conditional target density p(y|x) by using a neural network to directly predict the un-normalized density from (x, y). This model of p(y|x) is trained by minimizing the associated negative log-likelihood, approximated using Monte Carlo sampling. We perform comprehensive experiments on four computer vision regression tasks. Our approach outperforms direct regression, as well as other probabilistic and confidence-based methods. Notably, our regression model achieves a 1.9% AP improvement over Faster-RCNN for object detection on the COCO dataset, and sets a new state-ofthe-art on visual tracking when applied for bounding box regression.
Multilepton signatures for scalar dark matter searches in coannihilation scenario

We revisit the scalar singlet dark matter (DM) scenario with a pair of dark lepton partners which form a vectorlike Dirac fermionic doublet. The extra doublet couples with the Standard Model (SM) leptonic doublet and the scalar singlet via a non-SM-like Yukawa structure. As a result, (i) since the extra fermionic states interact with other dark sector particles as well as the SM via gauge and Yukawa interactions, it gives rise to new DM annihilation processes including pair annihilation as well as coannihilation channels, and (ii) such a Yukawa structure opens up new production channels for leptonic final states giving much enhancement in cross sections to search for dark matter in the LHC. Using suitable kinematic observables, we train a {\em boosted decision tree} (BDT) classifier to separate enhanced but still feeble light leptonic signals from the background in an effective manner. On the other hand, the same technique is applied to study $\tau$-tagged jets in the search for DM signals.
ExpertoCoder: Capturing Divergent Brain Regions Using Mixture of Regression Experts

fMRI semantic category understanding using linguistic encoding models attempts to learn a forward mapping that relates stimuli to the corresponding brain activation. Classical encoding models use linear multivariate methods to predict brain activation (all the voxels) given the stimulus. However, these methods mainly assume multiple regions as one vast uniform region or several independent regions, ignoring connections among them. In this paper, we present a mixture of experts model for predicting brain activity patterns. Given a new stimulus, the model predicts the entire brain activation as a weighted linear combination of activation of multiple experts. We argue that each expert captures activity patterns related to a particular region of interest (ROI) in the human brain. Thus, the utility of the proposed model is twofold. It not only accurately predicts the brain activation for a given stimulus, but it also reveals the level of activation of individual brain regions. Results of our experiments highlight the importance of the proposed model for predicting brain activation. This study also helps in understanding which of the brain regions get activated together, given a certain kind of stimulus. Importantly, we suggest that the mixture of regression experts (MoRE) framework successfully combines the two principles of organization of function in the brain, namely that of specialization and integration.
DBRec: Dual-Bridging Recommendation via Discovering Latent Groups

In recommender systems, the user-item interaction data is usually sparse and not sufficient for learning comprehensive user/item representations for recommendation. To address this problem, we propose a novel dual-bridging recommendation model (DBRec). DBRec performs latent user/item group discovery simultaneously with collaborative filtering, and interacts group information with users/items for bridging similar users/items. Therefore, a user's preference over an unobserved item, in DBRec, can be bridged by the users within the same group who have rated the item, or the user-rated items that share the same group with the unobserved item. In addition, we propose to jointly learn user-user group (item-item group) hierarchies, so that we can effectively discover latent groups and learn compact user/item representations. We jointly integrate collaborative filtering, latent group discovering and hierarchical modelling into a unified framework, so that all the model parameters can be learned toward the optimization of the objective function. We validate the effectiveness of the proposed model with two real datasets, and demonstrate its advantage over the state-of-the-art recommendation models with extensive experiments.
Thermal Regulation and the Star-forming Main Sequence

We argue that the interplay between cosmic rays, the initial mass function (IMF), and star formation plays a crucial role in regulating the star-forming “main sequence.” To explore these phenomena we develop a toy model for galaxy evolution in which star formation is regulated by a combination of a temperature-dependent IMF and heating due to starlight, cosmic rays, and (at very high redshift) the cosmic microwave background. This produces an attractor, near-equilibrium solution which is consistent with observations of the star-forming main sequence over a broad redshift range. Additional solutions to the same equations may correspond to other observed phases of galaxy evolution, including quiescent galaxies. This model makes several falsifiable predictions, including higher metallicities and dust masses than anticipated at high redshift and isotopic abundances in the Milky Way. It also predicts that stellar mass-to-light ratios are lower than produced using a Milky Way–derived IMF, such that inferences of stellar masses and star formation rates for high redshift galaxies are overestimated. In some cases, this may also transform inferred dark matter profiles from core-like to cusp-like.
Pseudo-rotations and Steenrod squares revisited

In this note we prove that if a closed monotone symplectic manifold admits a Hamiltonian pseudo-rotation, which may be degenerate, then the quantum Steenrod square of the cohomology class Poincare dual to the point must be deformed. This result gives restrictions on the existence of pseudo-rotations, implying a form of uniruledness by pseudo-holomorphic spheres, and generalizes a recent result of the author. The new component in the proof consists in an elementary calculation with capped periodic orbits.
Crowdsourcing via Pairwise Co-occurrences: Identifiability and Algorithms

The data deluge comes with high demands for data labeling. Crowdsourcing (or, more generally, ensemble learning) techniques aim to produce accurate labels via integrating noisy, non-expert labeling from annotators. The classic Dawid-Skene estimator and its accompanying expectation maximization (EM) algorithm have been widely used, but the theoretical properties are not fully understood. Tensor methods were proposed to guarantee identification of the Dawid-Skene model, but the sample complexity is a hurdle for applying such approaches---since the tensor methods hinge on the availability of third-order statistics that are hard to reliably estimate given limited data. In this paper, we propose a framework using pairwise co-occurrences of the annotator responses, which naturally admits lower sample complexity. We show that the approach can identify the Dawid-Skene model under realistic conditions. We propose an algebraic algorithm reminiscent of convex geometry-based structured matrix factorization to solve the model identification problem efficiently, and an identifiability-enhanced algorithm for handling more challenging and critical scenarios. Experiments show that the proposed algorithms outperform the state-of-art algorithms under a variety of scenarios.
Model Pruning Enables Efficient Federated Learning on Edge Devices

Federated learning (FL) allows model training from local data collected by edge/mobile devices while preserving data privacy, which has wide applicability to image and vision applications. A challenge is that client devices in FL usually have much more limited computation and communication resources compared to servers in a data center. To overcome this challenge, we propose PruneFL--a novel FL approach with adaptive and distributed parameter pruning, which adapts the model size during FL to reduce both communication and computation overhead and minimize the overall training time, while maintaining a similar accuracy as the original model. PruneFL includes initial pruning at a selected client and further pruning as part of the FL process. The model size is adapted during this process, which includes maximizing the approximate empirical risk reduction divided by the time of one FL round. Our experiments with various datasets on edge devices (e.g., Raspberry Pi) show that: 1) we significantly reduce the training time compared to conventional FL and various other pruning-based methods and 2) the pruned model with automatically determined size converges to an accuracy that is very similar to the original model, and it is also a lottery ticket of the original model.