nils-herrmann/clean_scirepeval huggingface dataset:
Learning from Others in the Financial Market.

Prediction problems in finance go beyond estimating the unknown parameters of a model (e.g of expected returns). This is because such a model would have to include knowledge about the market participants' propensity to change their opinions on the validity of that model. This leads to a circular situation characteristic of markets, where participants collectively create the target variables they wish to estimate. In this paper, we introduce a framework for generating expectation models and study the conditions under which they are adopted by a majority of market participants.
Observation of electroweak production of a same-sign $W$ boson pair in association with two jets in $pp$ collisions at $\sqrt{s}=13$ TeV with the ATLAS detector

This Letter presents the observation and measurement of electroweak production of a same-sign W boson pair in association with two jets using 36.1 fb − 1 of proton–proton collision data recorded at a center-of-mass energy of √ s = 13 TeV by the ATLAS detector at the Large Hadron Collider. The analysis is performed in the detector ﬁducial phase-space region, deﬁned by the presence of two same-sign leptons, electron or muon, and at least two jets with a large invariant mass and rapidity diﬀerence. A total of 122 candidate events are observed for a background expectation of 69 ± 7 events, corresponding to an observed signal signiﬁcance of 6.5 standard deviations. The measured ﬁducial signal cross section is σ ﬁd. = 2 . 89 + 0 . 51 − 0 . 48 (stat.) + 0 . 29 − 0 . 28 (syst.) fb.
The varying importance of extrinsic factors in the success of startup fundraising: competition at early-stage and networks at growth-stage

We address the issue of the factors driving startup success in raising funds. Using the popular and public startup database Crunchbase, we explicitly take into account two extrinsic characteristics of startups: the competition that the companies face, using similarity measures derived from the Word2Vec algorithm, as well as the position of investors in the investment network, pioneering the use of Graph Neural Networks (GNN), a recent deep learning technique that enables the handling of graphs as such and as a whole. We show that the different stages of fundraising, early- and growth-stage, are associated with different success factors. Our results suggest a marked relevance of startup competition for early stage while growth-stage fundraising is influenced by network features. Both of these factors tend to average out in global models, which could lead to the false impression that startup success in fundraising would mostly if not only be influenced by its intrinsic characteristics, notably those of their founders.
Labeled Graph Generative Adversarial Networks

As a new way to train generative models, generative adversarial networks (GANs) have achieved considerable success in image generation, and this framework has also recently been applied to data with graph structures. We identify the drawbacks of existing deep frameworks for generating graphs, and we propose labeled-graph generative adversarial networks (LGGAN) to train deep generative models for graph-structured data with node labels. We test the approach on various types of graph datasets, such as collections of citation networks and protein graphs. Experiment results show that our model can generate diverse labeled graphs that match the structural characteristics of the training data and outperforms all baselines in terms of quality, generality, and scalability. To further evaluate the quality of the generated graphs, we apply it to a downstream task for graph classification, and the results show that LGGAN can better capture the important aspects of the graph structure.
Data-to-text Generation with Entity Modeling

Recent approaches to data-to-text generation have shown great promise thanks to the use of large-scale datasets and the application of neural network architectures which are trained end-to-end. These models rely on representation learning to select content appropriately, structure it coherently, and verbalize it grammatically, treating entities as nothing more than vocabulary tokens. In this work we propose an entity-centric neural architecture for data-to-text generation. Our model creates entity-specific representations which are dynamically updated. Text is generated conditioned on the data input and entity memory representations using hierarchical attention at each time step. We present experiments on the RotoWire benchmark and a (five times larger) new dataset on the baseball domain which we create. Our results show that the proposed model outperforms competitive baselines in automatic and human evaluation.
Inferring Phenotypic Trait Evolution on Large Trees With Many Incomplete Measurements

Comparative biologists are often interested in inferring covariation between multiple biological traits sampled across numerous related taxa. To properly study these relationships, we must control for the shared evolutionary history of the taxa to avoid spurious inference. An additional challenge arises as obtaining a full suite of measurements becomes increasingly difficult with increasing taxa. This generally necessitates data imputation or integration, and existing control techniques typically scale poorly as the number of taxa increases. We propose an inference technique that integrates out missing measurements analytically and scales linearly with the number of taxa by using a post-order traversal algorithm under a multivariate Brownian diffusion (MBD) model to characterize trait evolution. We further exploit this technique to extend the MBD model to account for sampling error or nonheritable residual variance. We test these methods to examine mammalian life history traits, prokaryotic genomic and phenotypic traits, and HIV infection traits. We find computational efficiency increases that top two orders-of-magnitude over current best practices. While we focus on the utility of this algorithm in phylogenetic comparative methods, our approach generalizes to solve long-standing challenges in computing the likelihood for matrix-normal and multivariate normal distributions with missing data at scale.
Central Extensions of Lie Groups Preserving a Differential Form

Let $M$ be a manifold with a closed, integral $(k+1)$-form $\omega$, and let $G$ be a Fr\'echet-Lie group acting on $(M,\omega)$. As a generalization of the Kostant-Souriau extension for symplectic manifolds, we consider a canonical class of central extensions of $\mathfrak{g}$ by $\mathbb{R}$, indexed by $H^{k-1}(M,\mathbb{R})^*$. We show that the image of $H_{k-1}(M,\mathbb{Z})$ in $H^{k-1}(M,\mathbb{R})^*$ corresponds to a lattice of Lie algebra extensions that integrate to smooth central extensions of $G$ by the circle group $\mathbb{T}$. The idea is to represent a class in $H_{k-1}(M,\mathbb{Z})$ by a weighted submanifold $(S,\beta)$, where $\beta$ is a closed, integral form on $S$. We use transgression of differential characters from $S$ and $ M $ to the mapping space $ C^\infty(S, M) $, and apply the Kostant-Souriau construction on $ C^\infty(S, M) $.
Zooming Cautiously: Linear-Memory Heuristic Search With Node Expansion Guarantees

We introduce and analyze two parameter-free linear-memory tree search algorithms. Under mild assumptions we prove our algorithms are guaranteed to perform only a logarithmic factor more node expansions than A* when the search space is a tree. Previously, the best guarantee for a linear-memory algorithm under similar assumptions was achieved by IDA*, which in the worst case expands quadratically more nodes than in its last iteration. Empirical results support the theory and demonstrate the practicality and robustness of our algorithms. Furthermore, they are fast and easy to implement.
Learning Word Embeddings with Domain Awareness

Word embeddings are traditionally trained on a large corpus in an unsupervised setting, with no specific design for incorporating domain knowledge. This can lead to unsatisfactory performances when training data originate from heterogeneous domains. In this paper, we propose two novel mechanisms for domain-aware word embedding training, namely domain indicator and domain attention, which integrate domain-specific knowledge into the widely used SG and CBOW models, respectively. The two methods are based on a joint learning paradigm and ensure that words in a target domain are intensively focused when trained on a source domain corpus. Qualitative and quantitative evaluation confirm the validity and effectiveness of our models. Compared to baseline methods, our method is particularly effective in near-cold-start scenarios.
Harnessing elasticity to generate self-oscillation via an electrohydrodynamic instability

Under a steady DC electric field of sufficient strength, a weakly conducting dielectric sphere in a dielectric solvent with higher conductivity can undergo spontaneous spinning (Quincke rotation) through a pitchfork bifurcation. We design an object composed of a dielectric sphere and an elastic filament. By solving an elasto-electro-hydrodynamic (EEH) problem numerically, we uncover an EEH instability exhibiting diverse dynamic responses. Varying the bending stiffness of the filament, the composite object displays three behaviours: a stationary state, undulatory swimming and steady spinning, where the swimming results from a self-oscillatory instability through a Hopf bifurcation. By conducting a linear stability analysis incorporating an elastohydrodynamic model, we theoretically predict the growth rates and critical conditions, which agree well with the numerical counterparts. We also propose a reduced model system consisting of a minimal elastic structure which reproduces the EEH instability. The elasto-viscous response of the composite structure is able to transform the pitchfork bifurcation into a Hopf bifurcation, leading to self-oscillation. Our results imply a new way of harnessing elastic media to engineer self-oscillations, and more generally, to manipulate and diversify the bifurcations and the corresponding instabilities. These ideas will be useful in designing soft, environmentally adaptive machines.
Detecting Thin Stellar Streams in External Galaxies: Resolved Stars and Integrated Light

The morphology of thin stellar streams can be used to test the nature of dark matter. It is therefore crucial to extend searches for globular cluster (GC) streams to other galaxies than the Milky Way. In this paper, we investigate the current and future prospects of detecting GC streams in external galaxies in resolved stars (e.g., with Wide Field InfraRed Survey Telescope (WFIRST)) and using integrated light (e.g., with Hyper Suprime-cam (HSC), the Large Synoptic Survey Telescope (LSST), and Euclid). In particular, we inject mock streams to data from the PAndAS M31 survey and produce simulated M31 backgrounds mimicking what WFIRST will observe in M31. Additionally, we estimate the distance limit to which GC streams will be observable. Our results demonstrate that for a 1 hr (1000 s) exposure, using conservative estimates, WFIRST should detect GC streams in resolved stars in galaxies out to distances of ∼3.5 Mpc (∼2 Mpc). This volume contains 199 (122) galaxies, of which >90% are dwarfs. With integrated light, thin streams can be resolved out to ∼100 Mpc with HSC and LSST and to ∼600 Mpc with WFIRST and Euclid. The low surface brightness of the streams (typically >30 mag arcsec−2), however, will make them difficult to detect, unless the streams originate from very young clusters. We emphasize that if the external galaxies do not host spiral arms or galactic bars, gaps in their stellar streams provide an ideal test case for evidence of interactions with dark matter subhalos. Furthermore, obtaining a large samples of thin stellar streams can help constrain the orbital structure and hence the potentials of external halos.
xBIT: an easy to use scanning tool with machine learning abilities

xBIT is a tool for performing parameter scans in beyond the Standard Model theories. It's written in Python and fully open source. The main purpose of xBIT is to provide an easy to use tool to help phenomenologists with their daily task: exploring the parameter space of new models. It was developed under the impression of the SARAH/SPheno framework, but should be use-able with other tools as well that use the SLHA format to transfer data. It also supports by default MicrOmegas for dark matter calculations, HiggsBounds and HiggsSignals for checking the Higgs properties, and Vevacious for testing the vacuum stability. Classes for other tools can be added if necessary. In order to improve the efficiency of the parameter scans, the recently proposed 'Machine Learning Scan' approach is included. For this purpose, xBIT uses pyTorch to deal with artificial neural networks.
Latent feature disentanglement for 3D meshes

Generative modeling of 3D shapes has become an important problem due to its relevance to many applications across Computer Vision, Graphics, and VR. In this paper we build upon recently introduced 3D mesh-convolutional Variational AutoEncoders which have shown great promise for learning rich representations of deformable 3D shapes. We introduce a supervised generative 3D mesh model that disentangles the latent shape representation into independent generative factors. Our extensive experimental analysis shows that learning an explicitly disentangled representation can both improve random shape generation as well as successfully address downstream tasks such as pose and shape transfer, shape-invariant temporal synchronization, and pose-invariant shape matching.
Equalized odds postprocessing under imperfect group information

Most approaches aiming to ensure a model's fairness with respect to a protected attribute (such as gender or race) assume to know the true value of the attribute for every data point. In this paper, we ask to what extent fairness interventions can be effective even when only imperfect information about the protected attribute is available. In particular, we study the prominent equalized odds postprocessing method of Hardt et al. (2016) under a perturbation of the attribute. We identify conditions on the perturbation that guarantee that the bias of a classifier is reduced even by running equalized odds with the perturbed attribute. We also study the error of the resulting classifier. We empirically observe that under our identified conditions most often the error does not suffer from a perturbation of the protected attribute. For a special case, we formally prove this observation to be true.
Adaptive Nonparametric Variational Autoencoder

Clustering is used to find structure in unlabeled data by grouping similar objects together. Cluster analysis depends on the definition of similarity in the feature space. In this paper, we propose an Adaptive Nonparametric Variational Autoencoder (AdapVAE) to perform end-to-end feature learning from raw data jointly with cluster membership learning through a Nonparametric Bayesian modeling framework with deep neural networks. It has the advantage of avoiding pre-definition of similarity or feature engineering. Our model relaxes the constraint of fixing the number of clusters in advance by assigning a Dirichlet Process prior on the latent representation in a low-dimensional feature space. It can adaptively detect novel clusters when new data arrives based on a learned model from historical data in an online unsupervised learning setting. We develop a joint online variational inference algorithm to learn feature representations and cluster assignments via iteratively optimizing the evidence lower bound (ELBO). Our experimental results demonstrate the capacity of our modelling framework to learn the number of clusters automatically using data, the flexibility to detect novel clusters with emerging data adaptively, the ability of high quality reconstruction and generation of samples without supervised information and the improvement over state-of-the-art end-to-end clustering methods in terms of accuracy on both image and text corpora benchmark datasets.
On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement Dataset

Learning meaningful and compact representations with disentangled semantic aspects is considered to be of key importance in representation learning. Since real-world data is notoriously costly to collect, many recent state-of-the-art disentanglement models have heavily relied on synthetic toy data-sets. In this paper, we propose a novel data-set which consists of over one million images of physical 3D objects with seven factors of variation, such as object color, shape, size and position. In order to be able to control all the factors of variation precisely, we built an experimental platform where the objects are being moved by a robotic arm. In addition, we provide two more datasets which consist of simulations of the experimental setup. These datasets provide for the first time the possibility to systematically investigate how well different disentanglement methods perform on real data in comparison to simulation, and how simulated data can be leveraged to build better representations of the real world. We provide a first experimental study of these questions and our results indicate that learned models transfer poorly, but that model and hyperparameter selection is an effective means of transferring information to the real world.
Assessing Incrementality in Sequence-to-Sequence Models

Since their inception, encoder-decoder models have successfully been applied to a wide array of problems in computational linguistics. The most recent successes are predominantly due to the use of different variations of attention mechanisms, but their cognitive plausibility is questionable. In particular, because past representations can be revisited at any point in time, attention-centric methods seem to lack an incentive to build up incrementally more informative representations of incoming sentences. This way of processing stands in stark contrast with the way in which humans are believed to process language: continuously and rapidly integrating new information as it is encountered. In this work, we propose three novel metrics to assess the behavior of RNNs with and without an attention mechanism and identify key differences in the way the different model types process sentences.
PyramNet: Point Cloud Pyramid Attention Network and Graph Embedding Module for Classification and Segmentation

With the tide of artificial intelligence, we try to apply deep learning to understand 3D data. Point cloud is an important 3D data structure, which can accurately and directly reflect the real world. In this paper, we propose a simple and effective network, which is named PyramNet, suites for point cloud object classification and semantic segmentation in 3D scene. We design two new operators: Graph Embedding Module(GEM) and Pyramid Attention Network(PAN). Specifically, GEM projects point cloud onto the graph and practices the covariance matrix to explore the relationship between points, so as to improve the local feature expression ability of the model. PAN assigns some strong semantic features to each point to retain fine geometric features as much as possible. Furthermore, we provide extensive evaluation and analysis for the effectiveness of PyramNet. Empirically, we evaluate our model on ModelNet40, ShapeNet and S3DIS.
A first-year research experience: The Freshman Project in Physics at Loyola University Chicago

Undergraduate research has become an essential mode of engaging and retaining students in physics. At Loyola University Chicago, first-year physics students have been participating in the Freshman Projects program for over twenty years, which has coincided with a period of significant growth for our department. In this paper, we describe how the Freshman Projects program has played an important role in advancing undergraduate research at Loyola, and the profound impact it has made on our program. We conclude with suggestions for adoption of similar programs at other institutions.
Clustering Degree-Corrected Stochastic Block Model with Outliers

For the degree corrected stochastic block model in the presence of arbitrary or even adversarial outliers, we develop a convex-optimization-based clustering algorithm that includes a penalization term depending on the positive deviation of a node from the expected number of edges to other inliers. We prove that under mild conditions, this method achieves exact recovery of the underlying clusters. Our synthetic experiments show that our algorithm performs well on heterogeneous networks, and in particular those with Pareto degree distributions, for which outliers have a broad range of possible degrees that may enhance their adversarial power. We also demonstrate that our method allows for recovery with significantly lower error rates compared to existing algorithms.
Invoice Financing of Supply Chains with Blockchain technology and Artificial Intelligence

Supply chains lend themselves to blockchain technology, but certain challenges remain, especially around invoice financing. For example, the further a supplier is removed from the final consumer product, the more difficult it is to get their invoices financed. Moreover, for competitive reasons, retailers and manufacturers do not want to disclose their supply chains. However, upstream suppliers need to prove that they are part of a `stable' supply chain to get their invoices financed, which presents the upstream suppliers with huge, and often unsurmountable, obstacles to get the necessary finance to fulfil the next order, or to expand their business. Using a fictitious supply chain use case, which is based on a real world use case, we demonstrate how these challenges have the potential to be solved by combining more advanced and specialised blockchain technologies with other technologies such as Artificial Intelligence. We describe how atomic crosschain functionality can be utilised across private blockchains to retrieve the information required for an invoice financier to make informed decisions under uncertainty, and consider the effect this decision has on the overall stability of the supply chain.
An optimal transport problem with backward martingale constraints motivated by insider trading

We study a single-period optimal transport problem on $\mathbb{R}^2$ with a covariance-type cost function $c(x,y) = (x_1-y_1)(x_2-y_2)$ and a backward martingale constraint. We show that a transport plan $\gamma$ is optimal if and only if there is a maximal monotone set $G$ that supports the $x$-marginal of $\gamma$ and such that $c(x,y) = \min_{z\in G}c(z,y)$ for every $(x,y)$ in the support of $\gamma$. We obtain sharp regularity conditions for the uniqueness of an optimal plan and for its representation in terms of a map. Our study is motivated by a variant of the classical Kyle model of insider trading from Rochet and Vila (1994).
Optimal Transport Relaxations with Application to Wasserstein GANs

We propose a family of relaxations of the optimal transport problem which regularize the problem by introducing an additional minimization step over a small region around one of the underlying transporting measures. The type of regularization that we obtain is related to smoothing techniques studied in the optimization literature. When using our approach to estimate optimal transport costs based on empirical measures, we obtain statistical learning bounds which are useful to guide the amount of regularization, while maintaining good generalization properties. To illustrate the computational advantages of our regularization approach, we apply our method to training Wasserstein GANs. We obtain running time improvements, relative to current benchmarks, with no deterioration in testing performance (via FID). The running time improvement occurs because our new optimality-based threshold criterion reduces the number of expensive iterates of the generating networks, while increasing the number of actor-critic iterations.