nils-herrmann/clean_scirepeval huggingface dataset:
Joint 3D Localization and Classification of Space Debris using a Multispectral Rotating Point Spread Function

We consider the problem of joint three-dimensional (3D) localization and material classification of unresolved space debris using a multispectral rotating point spread function (RPSF). The use of RPSF allows one to estimate the 3D locations of point sources from their rotated images acquired by a single 2D sensor array, since the amount of rotation of each source image about its x, y location depends on its axial distance z. Using multispectral images, with one RPSF per spectral band, we are able not only to localize the 3D positions of the space debris but also classify their material composition. We propose a three-stage method for achieving joint localization and classification. In stage 1, we adopt an optimization scheme for localization in which the spectral signature of each material is assumed to be uniform, which significantly improves efficiency and yields better localization results than possible with a single spectral band. In stage 2, we estimate the spectral signature and refine the localization result via an alternating approach. We process classification in the final stage. Both Poisson noise and Gaussian noise models are considered, and the implementation of each is discussed. Numerical tests using multispectral data from NASA show the efficiency of our three-stage approach and illustrate the improvement of point source localization and spectral classification from using multiple bands over a single band.
Constraining primordial black hole masses with the isotropic gamma ray background

Primordial black holes can represent all or most of the dark matter in the window $10^{17}-10^{22}\,$g. Here we present an extension of the constraints on PBHs of masses $10^{13}-10^{18}\,$g arising from the isotropic diffuse gamma ray background. Primordial black holes evaporate by emitting Hawking radiation that should not exceed the observed background. Generalizing from monochromatic distributions of Schwarzschild black holes to extended mass functions of Kerr rotating black holes, we show that the lower part of this mass window can be closed for near-extremal black holes.
Measuring and exploiting the cloud consolidation of the Web

We present measurements showing that the top one million most popular Web domains are reachable within 13ms (in the median) from a collective of just 12 cloud data centers. We explore the consequences of this Web "consolidation", focusing on its potential for speeding up the evolution of the Web. That most popular services reside in or near a small number of data centers implies that new application and transport technologies can be rapidly deployed for these Web services, without the involvement of their operators. We show how this may be achieved by orchestrating a handful of reverse proxies deployed in the same data centers, with new technologies deployed at these proxies being nearly as effective as deploying them directly to the Web servers. We present early measurements of this approach, demonstrating a >50% reduction in Web page load times for users with high latencies to Web servers. We also show that this model, using a small number of proxies, can be surprisingly competitive with extensive CDN deployments, especially in geographies with high last-mile latencies.
Bridging the Physics and Chemistry of Graphene(s): From Hückel's Aromaticity to Dirac's Cones and Topological Insulators.

By bridging graphene and benzene through a well-defined sequence of polycyclic aromatic hydrocarbons and their inherent shell structure, it is shown that graphene is actually a coherent arrangement of interwoven benzene molecules, coordinated by aromaticity, shell structure, and topology, all interrelated and microscopically realized through dynamical flipping of the atomic pz-orbitals, playing the role of pseudospins or "qubits". This renders graphene resonance structure, "resonating" between two complementary aromaticity patterns, involving 2k, k → ∞ Kekulé type of resonances, resulting in "robust electronic coherence", with a dual "molecular crystalline" nature, and two valence-conduction bands of opposite parity, driven by inversion symmetry competition, which is essentially a "molecule-versus-crystal" competition, in accordance with topological insulators and many-body theory. The "average picture" converges to the usual band structure with two aromatic π-electrons per ring, and with the fingerprints of inversion competition at the D3h-symmetric Dirac points, which for rectangular nanographene(s) appear as gapless topological edge states without real spin polarization, in contrast to opposite claims.
Globular clusters as probes of dark matter cusp-core transformations

Bursty star formation in dwarf galaxies can slowly transform a steep dark matter cusp into a constant density core. We explore the possibility that globular clusters (GCs) retain a dynamical memory of this transformation. To test this, we use the nbody6df code to simulate the dynamical evolution of GCs, including stellar evolution, orbiting in static and time-varying potentials for a Hubble time. We find that GCs orbiting within a cored dark matter halo, or within a halo that has undergone a cusp-core transformation, grow to a size that is substantially larger (Reff > 10 pc) than those in a static cusped dark matter halo. They also produce much less tidal debris. We find that the cleanest signal of an historic cusp-core transformation is the presence of large GCs with tidal debris. However, the effect is small and will be challenging to observe in real galaxies. Finally, we qualitatively compare our simulated GCs with the observed GC populations in the Fornax, NGC 6822, IKN, and Sagittarius dwarf galaxies. We find that the GCs in these dwarf galaxies are systematically larger (〈Reff〉 ≃ 7.8 pc), and have substantially more scatter in their sizes than in situ metal-rich GCs in the Milky Way and young massive star clusters forming in M83 (〈Reff〉 ≃ 2.5 pc). We show that the size, scatter, and survival of GCs in dwarf galaxies are all consistent with them having evolved in a constant density core, or a potential that has undergone a cusp-core transformation, but not in a dark matter cusp.
Unsupervised Discovery of Gendered Language through Latent-Variable Modeling

Studying the ways in which language is gendered has long been an area of interest in sociolinguistics. Studies have explored, for example, the speech of male and female characters in film and the language used to describe male and female politicians. In this paper, we aim not to merely study this phenomenon qualitatively, but instead to quantify the degree to which the language used to describe men and women is different and, moreover, different in a positive or negative way. To that end, we introduce a generative latent-variable model that jointly represents adjective (or verb) choice, with its sentiment, given the natural gender of a head (or dependent) noun. We find that there are significant differences between descriptions of male and female nouns and that these differences align with common gender stereotypes: Positive adjectives used to describe women are more often related to their bodies than adjectives used to describe men.
PerspectroScope: A Window to the World of Diverse Perspectives

This work presents PerspectroScope, a web-based system which lets users query a discussion-worthy natural language claim, and extract and visualize various perspectives in support or against the claim, along with evidence supporting each perspective. The system thus lets users explore various perspectives that could touch upon aspects of the issue at hand.The system is built as a combination of retrieval engines and learned textual-entailment-like classifiers built using a few recent developments in natural language understanding. To make the system more adaptive, expand its coverage, and improve its decisions over time, our platform employs various mechanisms to get corrections from the users. PerspectroScope is available at github.com/CogComp/perspectroscope Web  Link to demo video: https://www.youtube.com/watch?v=MXBTR1Sp3Bs
Deep 2FBSDEs for Systems with Control Multiplicative Noise

We present a deep recurrent neural network architecture to solve a class of stochastic optimal control problems described by fully nonlinear Hamilton Jacobi Bellman partial differential equations. Such PDEs arise when one considers stochastic dynamics characterized by uncertainties that are additive and control multiplicative. Stochastic models with the aforementioned characteristics have been used in computational neuroscience, biology and aerospace systems and provide a more accurate representation of actuation than models with additive uncertainty. Previous literature has established the inadequacy of the linear HJB theory and and instead rely on a non-linear Feynman-Kac lemma resulting in a second order forward-backward stochastic differential equations representation. However, the proposed solutions that use this representation suffer from compounding errors and computational complexity leading to lack of scalability. In this paper, we propose a deep learning based algorithm that leverages the second order Forward-Bacward SDE representation along with importance sampling and LSTM based recurrent neural networks to not only solve such Stochastic Optimal Control problems but also overcome the problems faced by previous approaches and scales well to high dimensional systems. The resulting control algorithm is tested on three non-linear systems to demonstrate feasibility and out-performance against previous methods.
Deep Forward-Backward SDEs for Min-max Control

This paper presents a novel approach to numerically solve stochastic differential games for nonlinear systems. The proposed approach relies on the nonlinear Feynman-Kac theorem that establishes a connection between parabolic deterministic partial differential equations and forward-backward stochastic differential equations. Using this theorem the Hamilton-Jacobi-Isaacs partial differential equation associated with differential games is represented by a system of forward-backward stochastic differential equations. Numerical solution of the aforementioned system of stochastic differential equations is performed using importance sampling and a neural network with Long Short-Term Memory and Fully Connected layers. The resulting algorithm is tested on two example systems in simulation and compared against the standard risk neutral stochastic optimal control formulations.
Issues with post-hoc counterfactual explanations: a discussion

Counterfactual post-hoc interpretability approaches have been proven to be useful tools to generate explanations for the predictions of a trained blackbox classifier. However, the assumptions they make about the data and the classifier make them unreliable in many contexts. In this paper, we discuss three desirable properties and approaches to quantify them: proximity, connectedness and stability. In addition, we illustrate that there is a risk for post-hoc counterfactual approaches to not satisfy these properties.
Distribution-Free Multisample Tests Based on Optimal Matchings With Applications to Single Cell Genomics

In this article, we propose a nonparametric graphical test based on optimal matching, for assessing the equality of multiple unknown multivariate probability distributions. Our procedure pools the data from the different classes to create a graph based on the minimum non-bipartite matching, and then utilizes the number of edges connecting data points from different classes to examine the closeness between the distributions. The proposed test is exactly distribution-free (the null distribution does not depend on the distribution of the data) and can be efficiently applied to multivariate as well as non-Euclidean data, whenever the inter-point distances are well-defined. We show that the test is universally consistent, and prove a distributional limit theorem for the test statistic under general alternatives. Through simulation studies, we demonstrate its superior performance against other common and well-known multisample tests. The method is applied to single cell transcriptomics data obtained from the peripheral blood, cancer tissue, and tumor-adjacent normal tissue of human subjects with hepatocellular carcinoma and non-small-cell lung cancer. Our method unveils patterns in how biochemical metabolic pathways are altered across immune cells in a cancer setting, depending on the tissue location. All of the methods described herein are implemented in the R package multicross.
Stability of Graph Scattering Transforms

Scattering transforms are non-trainable deep convolutional architectures that exploit the multi-scale resolution of a wavelet filter bank to obtain an appropriate representation of data. More importantly, they are proven invariant to translations, and stable to perturbations that are close to translations. This stability property dons the scattering transform with a robustness to small changes in the metric domain of the data. When considering network data, regular convolutions do not hold since the data domain presents an irregular structure given by the network topology. 
In this work, we extend scattering transforms to network data by using multiresolution graph wavelets, whose computation can be obtained by means of graph convolutions. Furthermore, we prove that the resulting graph scattering transforms are stable to metric perturbations of the underlying network. This renders graph scattering transforms robust to changes on the network topology, making it particularly useful for cases of transfer learning, topology estimation or time-varying graphs.
Identifying resonances of the Galactic bar in Gaia DR2: I. Clues from action space

Action space synthesizes the orbital information of stars and is well suited to analyse the rich kinematic substructure of the disc in the second Gaia data release's radial velocity sample. We revisit the strong perturbation induced in the Milky Way disc by an m = 2 bar, using test particle simulations and the actions (JR, Lz, Jz) estimated in an axisymmetric potential. These make three useful diagnostics cleanly visible. (1) We use the well-known characteristic flip from outward to inward motion at the outer Lindblad resonance (OLR; l = +1, m = 2), which occurs along the axisymmetric resonance line (ARL) in (Lz, JR), to identify in the Gaia action data three candidates for the bar’s OLR and pattern speed Ωbar: 1.85Ω0, 1.20Ω0, and 1.63Ω0 (with ∼0.1Ω0 systematic uncertainty). The Gaia data is therefore consistent with both slow and fast bar models in the literature, but disagrees with recent measurements of ∼1.45Ω0. (2) For the first time, we demonstrate that bar resonances – especially the OLR – cause a gradient in vertical action 〈Jz〉 with Lz around the ARL via ‘Jz-sorting’ of stars. This could contribute to the observed coupling of 〈vR〉 and 〈|vz|〉 in the Galactic disc. (3) We confirm prior results that the behaviour of resonant orbits is well approximated by scattering and oscillation in (Lz, JR) along a slope ΔJR/ΔLz = l/m centred on the l:m ARL. Overall, we demonstrate that axisymmetrically estimated actions are a powerful diagnostic tool even in non-axisymmetric systems.
Power Gradient Descent

The development of machine learning is promoting the search for fast and stable minimization algorithms. To this end, we suggest a change in the current gradient descent methods that should speed up the motion in flat regions and slow it down in steep directions of the function to minimize. It is based on a "power gradient", in which each component of the gradient is replaced by its versus-preserving $H$-th power, with $0<H<1$. We test three modern gradient descent methods fed by such variant and by standard gradients, finding the new version to achieve significantly better performances for the Nesterov accelerated gradient and AMSGrad. We also propose an effective new take on the ADAM algorithm, which includes power gradients with varying $H$.
Towards Inverse Reinforcement Learning for Limit Order Book Dynamics

Multi-agent learning is a promising method to simulate aggregate competitive behaviour in finance. Learning expert agents' reward functions through their external demonstrations is hence particularly relevant for subsequent design of realistic agent-based simulations. Inverse Reinforcement Learning (IRL) aims at acquiring such reward functions through inference, allowing to generalize the resulting policy to states not observed in the past. This paper investigates whether IRL can infer such rewards from agents within real financial stochastic environments: limit order books (LOB). We introduce a simple one-level LOB, where the interactions of a number of stochastic agents and an expert trading agent are modelled as a Markov decision process. We consider two cases for the expert's reward: either a simple linear function of state features; or a complex, more realistic non-linear function. Given the expert agent's demonstrations, we attempt to discover their strategy by modelling their latent reward function using linear and Gaussian process (GP) regressors from previous literature, and our own approach through Bayesian neural networks (BNN). While the three methods can learn the linear case, only the GP-based and our proposed BNN methods are able to discover the non-linear reward case. Our BNN IRL algorithm outperforms the other two approaches as the number of samples increases. These results illustrate that complex behaviours, induced by non-linear reward functions amid agent-based stochastic scenarios, can be deduced through inference, encouraging the use of inverse reinforcement learning for opponent-modelling in multi-agent systems.
Disentangling Planets from Photoelectric Instability in Gas-rich Optically Thin Dusty Disks

Structures in circumstellar disks such as gaps and rings are often attributed to planets. This connection has been difficult to show unequivocally, as other processes may also produce these features. In particular, a photoelectric instability (PEI) has been proposed, operating in gas-rich optically thin disks, that generates structures predicted by planet–disk interactions. We examine the question of how to disentangle the planetary effects on disk structure from the effects of the PEI. We use the Pencil Code to perform 2D global hydrodynamical models of the dynamics of gas and dust in a thin disk with and without planetary perturbers. Photoelectric heating is modeled with an equation of state where pressure is proportional to dust surface density. The drag force on grains and its backreaction on the gas are included. Analyzing the situation without PEI, we find that gas–dust interactions alter the shape of the planetary gap from the dust-free case when the local dust-to-gas ratio ε approaches unity. This result also applies to primordial disks, because dust drifting inward accumulates at the edge of the planetary gap, and any initial dust-to-gas ratio eventually achieves ε = 1 if the dust reservoir is sufficient. We find a result particular to high dust-to-gas ratio disks as well: as dust drifts inward, the dust front becomes a sharp transition, and the backreaction triggers the Rossby wave instability. When PEI is included, we find that it obscures structures induced by planets unless the planet’s mass is sufficiently large to carve a noticeable gap. Specifically, the instability generates arcs and rings of regular spacing: a planet is discernible when it carves a dust gap wider than the wavelength of the PEI.
Generalized Beta Prime Distribution: Stochastic Model of Economic Exchange and Properties of Inequality Indices

We argue that a stochastic model of economic exchange, whose steady-state distribution is a Generalized Beta Prime (also known as GB2), and some unique properties of the latter, are the reason for GB2's success in describing wealth/income distributions. We use housing sale prices as a proxy to wealth/income distribution to numerically illustrate this point. We also explore parametric limits of the distribution to do so analytically. We discuss parametric properties of the inequality indices -- Gini, Hoover, Theil T and Theil L -- vis-a-vis those of GB2 and introduce a new inequality index, which serves a similar purpose. We argue that Hoover and Theil L are more appropriate measures for distributions with power-law dependencies, especially fat tails, such as GB2.
Customizing Pareto Simulated Annealing for Multi-Objective Optimization of Control Cabinet Layout

Determining the optimal location of control cabinet components requires the exploration of a large configuration space. For real-world control cabinets it is impractical to evaluate all possible cabinet configurations. Therefore, we need to apply methods for intelligent exploration of cabinet configuration space that enable to find a near-optimal configuration without evaluation of all possible configurations. In this paper, we describe an approach for multi-objective optimization of control cabinet layout that is based on Pareto Simulated Annealing. Optimization aims at minimizing the total wire length used for interconnection of components and the heat convection within the cabinet. We simulate heat convection to study the warm air flow within the control cabinet and determine the optimal position of components that generate heat during the operation. We evaluate and demonstrate the effectiveness of our approach empirically for various control cabinet sizes and usage scenarios.
Toward Best Practices for Explainable B2B Machine Learning

To design tools and data pipelines for explainable B2B machine learning (ML) systems, we need to recognize not only the immediate audience of such tools and data, but also (1) their organizational context and (2) secondary audiences. Our learnings are based on building custom ML-based chatbots for recruitment. We believe that in the B2B context, "explainable" ML means not only a system that can "explain itself" through tools and data pipelines, but also enables its domain-expert users to explain it to other stakeholders.
Edge-Direct Visual Odometry

In this paper we propose an edge-direct visual odometry algorithm that efficiently utilizes edge pixels to find the relative pose that minimizes the photometric error between images. Prior work on exploiting edge pixels instead treats edges as features and employ various techniques to match edge lines or pixels, which adds unnecessary complexity. Direct methods typically operate on all pixel intensities, which proves to be highly redundant. In contrast our method builds on direct visual odometry methods naturally with minimal added computation. It is not only more efficient than direct dense methods since we iterate with a fraction of the pixels, but also more accurate. We achieve high accuracy and efficiency by extracting edges from only one image, and utilize robust Gauss-Newton to minimize the photometric error of these edge pixels. This simultaneously finds the edge pixels in the reference image, as well as the relative camera pose that minimizes the photometric error. We test various edge detectors, including learned edges, and determine that the optimal edge detector for this method is the Canny edge detection algorithm using automatic thresholding. We highlight key differences between our edge direct method and direct dense methods, in particular how higher levels of image pyramids can lead to significant aliasing effects and result in incorrect solution convergence. We show experimentally that reducing the photometric error of edge pixels also reduces the photometric error of all pixels, and we show through an ablation study the increase in accuracy obtained by optimizing edge pixels only. We evaluate our method on the RGB-D TUM benchmark on which we achieve state-of-the-art performance.