nils-herrmann/clean_scirepeval huggingface dataset:
Learning Graph Embedding With Adversarial Training Methods

Graph embedding aims to transfer a graph into vectors to facilitate subsequent graph-analytics tasks like link prediction and graph clustering. Most approaches on graph embedding focus on preserving the graph structure or minimizing the reconstruction errors for graph data. They have mostly overlooked the embedding distribution of the latent codes, which unfortunately may lead to inferior representation in many cases. In this article, we present a novel adversarially regularized framework for graph embedding. By employing the graph convolutional network as an encoder, our framework embeds the topological information and node content into a vector representation, from which a graph decoder is further built to reconstruct the input graph. The adversarial training principle is applied to enforce our latent codes to match a prior Gaussian or uniform distribution. Based on this framework, we derive two variants of the adversarial models, the adversarially regularized graph autoencoder (ARGA) and its variational version, and adversarially regularized variational graph autoencoder (ARVGA), to learn the graph embedding effectively. We also exploit other potential variations of ARGA and ARVGA to get a deeper understanding of our designs. Experimental results that compared 12 algorithms for link prediction and 20 algorithms for graph clustering validate our solutions.
Free Heyting algebra endomorphisms: Ruitenburg’s Theorem and beyond

Theorem says that every endomorphism f of a finitely generated free Heyting algebra is ultimately periodic if f fixes all the generators but one. More precisely, there is N ≥ 0 such that f N+2 = fN, thus the period equals 2. We give a semantic proof of this theorem, using duality techniques and bounded bisimulation ranks. By the same techniques, we tackle investigation of arbitrary endomorphisms of free algebras. We show that they are not, in general, ultimately periodic. Yet, when they are (e.g. in the case of locally finite subvarieties), the period can be explicitly bounded as function of the cardinality of the set of generators.
Generic Primitive Detection in Point Clouds Using Novel Minimal Quadric Fits

We present a novel and effective method for detecting 3D primitives in cluttered, unorganized point clouds, without axillary segmentation or type specification. We consider the quadric surfaces for encapsulating the basic building blocks of our environments - planes, spheres, ellipsoids, cones or cylinders, in a unified fashion. Moreover, quadrics allow us to model higher degree of freedom shapes, such as hyperboloids or paraboloids that could be used in non-rigid settings. We begin by contributing two novel quadric fits targeting 3D point sets that are endowed with tangent space information. Based upon the idea of aligning the quadric gradients with the surface normals, our first formulation is exact and requires as low as four oriented points. The second fit approximates the first, and reduces the computational effort. We theoretically analyze these fits with rigor, and give algebraic and geometric arguments. Next, by re-parameterizing the solution, we devise a new local Hough voting scheme on the null-space coefficients that is combined with RANSAC, reducing the complexity from <inline-formula><tex-math notation="LaTeX">$O(N^4)$</tex-math><alternatives><mml:math><mml:mrow><mml:mi>O</mml:mi><mml:mo>(</mml:mo><mml:msup><mml:mi>N</mml:mi><mml:mn>4</mml:mn></mml:msup><mml:mo>)</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="birdal-ieq1-2900309.gif"/></alternatives></inline-formula> to <inline-formula><tex-math notation="LaTeX">$O(N^3)$</tex-math><alternatives><mml:math><mml:mrow><mml:mi>O</mml:mi><mml:mo>(</mml:mo><mml:msup><mml:mi>N</mml:mi><mml:mn>3</mml:mn></mml:msup><mml:mo>)</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="birdal-ieq2-2900309.gif"/></alternatives></inline-formula> (three points). To the best of our knowledge, this is the first method capable of performing a generic cross-type multi-object primitive detection in difficult scenes without segmentation. Our extensive qualitative and quantitative results show that our method is efficient and flexible, as well as being accurate.
New constraints on sterile neutrino dark matter from NuSTAR M31 observations

We use a combined 1.2 Ms of $NuSTAR$ observations of M31 to search for X-ray lines from sterile neutrino dark matter decay. For the first time in a $NuSTAR$ analysis, we consistently take into account the signal contribution from both the focused and unfocused fields of view. We also reduce the modeling systematic uncertainty by performing spectral fits to each observation individually and statistically combining the results, instead of stacking the spectra. We find no evidence of unknown lines, and thus derive limits on the sterile neutrino parameters. Our results place stringent constraints for dark matter masses $\gtrsim 12$ keV, which reduces the available parameter space for sterile neutrino dark matter produced via neutrino mixing ($e.g.$, in the $\nu$MSM) by approximately one-third. Additional $NuSTAR$ observations, together with improved low-energy background modeling, could probe the remaining parameter space in the future. Lastly, we also report model-independent limits on generic dark matter decay rates and annihilation cross sections.
Parametrized black hole quasinormal ringdown: Decoupled equations for nonrotating black holes

Black hole solutions in general relativity are simple. The frequency spectrum of linear perturbations around these solutions (i.e., the quasinormal modes) is also simple, and therefore it is a prime target for fundamental tests of black hole spacetimes and of the underlying theory of gravity. The following technical calculations must be performed to understand the imprints of any modified gravity theory on the spectrum: 1. Identify a healthy theory; 2. Find black hole solutions within the theory; 3. Compute the equations governing linearized perturbations around the black hole spacetime; 4. Solve these equations to compute the characteristic quasinormal modes. In this work (the first of a series) we assume that the background spacetime has spherical symmetry, that the relevant physics is always close to general relativity, and that there is no coupling between the perturbation equations. Under these assumptions, we provide the general numerical solution to step 4. We provide publicly available data files such that the quasinormal modes of {\em any} spherically symmetric spacetime can be computed (in principle) to arbitrary precision once the linearized perturbation equations are known. We show that the isospectrality between the even- and odd-parity quasinormal mode spectra is fragile, and we identify the necessary conditions to preserve it. Finally, we point out that new modes can appear in the spectrum even in setups that are perturbatively close to general relativity.
Revisiting the Integrated Star Formation Law. I. Non-starbursting Galaxies

We use new and updated gas- and dust-corrected star formation rate (SFR) surface densities to revisit the integrated star formation law for local “quiescent” spiral, dwarf, and low surface brightness galaxies. Using UV-based SFRs with individual IR-based dust corrections, we find that “normal” spiral galaxies alone define a tight –ΣSFR relation described by an power law with a dispersion of (errors reflect fitting and statistical uncertainties). The SFR surface densities are only weakly correlated with H i surface densities alone, exhibiting a stronger and roughly linear correlation with H2 surface densities, similar to what is seen in spatially resolved measurements of disks. However, many dwarf galaxies lie below the star formation law defined by spirals, suggesting a low-density threshold in the integrated star formation law. We consider alternative scaling laws that better describe both spirals and dwarfs. Our improved measurement precision also allows us to determine that much of the scatter in the star formation law is intrinsic, and we search for correlations between this intrinsic scatter and secondary physical parameters. We find that dwarf galaxies exhibit second-order correlations with the total gas fraction, stellar mass surface density, and dynamical time, which may explain much of the scatter in the star formation law. Finally, we discuss various systematic uncertainties that should be kept in mind when interpreting any study of the star formation law, particularly the X(CO) conversion factor and the diameter chosen to define the star-forming disk in a galaxy.
Generalized Kasha's Scheme for Classifying Two-Dimensional Excitonic Molecular Aggregates: Temperature Dependent Absorption Peak Frequency Shift

Author(s): Chuang, Chern; Bennett, Doran IG; Caram, Justin R; Aspuru-Guzik, Alan; Bawendi, Moungi G; Cao,  We propose a generalized theoretical framework for classifying two-dimensional (2D) excitonic molecular aggregates based on an analysis of temperature dependent spectra. In addition to the monomer-aggregate absorption peak shift, which defines the conventional J- and H-aggregates, we incorporate the peak shift associated with increasing temperature as a measure to characterize the exciton band structure. First we show that there is a one-to-one correspondence between the monomer-aggregate and the T-dependent peak shifts for Kasha's well-established model of 1D aggregates, where J-aggregates exhibit further redshift upon increasing temperature and H-aggregates exhibit further blueshift. On the contrary, 2D aggregate structures are capable of supporting the two other combinations: blueshifting J-aggregates and redshifting H-aggregates, owing to their more complex exciton band structures. Secondly, using spectral lineshape theory, the T-dependent shift is associated with the relative abundance of states on each side of the bright state. We further establish that the density of states can be connected to the microscopic packing condition leading to these four classes of aggregates by separately considering the short and long-range contribution to the excitonic couplings. In particular the T-dependent shift is shown to be an unambiguous signature for the sign of net short-range couplings: Aggregates with net negative (positive) short-range couplings redshift (blueshift) with increasing temperature. Lastly, comparison with experiments shows that our theory can be utilized to quantitatively account for the observed but previously unexplained T-dependent absorption lineshapes. Thus, our work provides a firm ground for elucidating the structure-function relationships for molecular aggregates and is fully compatible with existing experimental and theoretical structure characterization tools.
Rotational Light Curves of Jupiter from Ultraviolet to Mid-infrared and Implications for Brown Dwarfs and Exoplanets

Rotational modulations are observed on brown dwarfs and directly imaged exoplanets, but the underlying mechanism is not well understood. Here we analyze Jupiter’s rotational light curves at 12 wavelengths from the ultraviolet (UV) to the mid-infrared (mid-IR). The peak-to-peak amplitudes of Jupiter’s light curves range from subpercent to 4% at most wavelengths, but the amplitude exceeds 20% at 5 μm, a wavelength sensing Jupiter’s deep troposphere. Jupiter’s rotational modulations are primarily caused by discrete patterns in the cloudless belts instead of the cloudy zones. The light-curve amplitude is controlled by the sizes and brightness contrasts of the Great Red Spot (GRS), expansions of the North Equatorial Belt (NEB), patchy clouds in the North Temperate Belt (NTB), and a train of hot spots in the NEB. In reflection, the contrast is controlled by upper tropospheric and stratospheric hazes, clouds, and chromophores in the clouds. In thermal emission, the small rotational variability is caused by the spatial distribution of temperature and opacities of gas and aerosols; the large variation is caused by the NH3 cloud holes and thin-thick clouds. The methane-band light curves exhibit opposite-shape behavior compared with the UV and visible wavelengths, caused by a wavelength-dependent brightness change of the GRS. Light-curve evolution is induced by periodic events in the belts and longitudinal drifting of the GRS and patchy clouds in the NTB. This study suggests several interesting mechanisms related to distributions of temperature, gas, hazes, and clouds for understanding the observed rotational modulations on brown dwarfs and exoplanets.
Optimal Decision-Making in Mixed-Agent Partially Observable Stochastic Environments via Reinforcement Learning

Optimal decision making with limited or no information in stochastic environments where multiple agents interact is a challenging topic in the realm of artificial intelligence. Reinforcement learning (RL) is a popular approach for arriving at optimal strategies by predicating stimuli, such as the reward for following a strategy, on experience. RL is heavily explored in the single-agent context, but is a nascent concept in multiagent problems. To this end, I propose several principled model-free and partially model-based reinforcement learning approaches for several multiagent settings. In the realm of normative reinforcement learning, I introduce scalable extensions to Monte Carlo exploring starts for partially observable Markov Decision Processes (POMDP), dubbed MCES-P, where I expand the theory and algorithm to the multiagent setting. I first examine MCES-P with probably approximately correct (PAC) bounds in the context of multiagent setting, showing MCESP+PAC holds in the presence of other agents. I then propose a more sample-efficient methodology for antagonistic settings, MCESIP+PAC. For cooperative settings, I extend MCES-P to the Multiagent POMDP, dubbed MCESMP+PAC. I then explore the use of reinforcement learning as a methodology in searching for optima in realistic and latent model environments. First, I explore a parameterized Q-learning approach in modeling humans learning to reason in an uncertain, multiagent environment. Next, I propose an implementation of MCES-P, along with image segmentation, to create an adaptive team-based reinforcement learning technique to positively identify the presence of phenotypically-expressed water and pathogen stress in crop fields.
Projective Decomposition and Matrix Equivalence up to Scale

A data matrix may be seen simply as a means of organizing observations into rows ( e.g., by measured object) and into columns ( e.g., by measured variable) so that the observations can be analyzed with mathematical tools. As a mathematical object, a matrix defines a linear mapping between points representing weighted combinations of its rows (the row vector space) and points representing weighted combinations of its columns (the column vector space). From this perspective, a data matrix defines a relationship between the information that labels its rows and the information that labels its columns, and numerical methods are used to analyze this relationship. A first step is to normalize the data, transforming each observation from scales convenient for measurement to a common scale, on which addition and multiplication can meaningfully combine the different observations. For example, z-transformation rescales every variable to the same scale, standardized variation from an expected value, but ignores scale differences between measured objects. Here we develop the concepts and properties of projective decomposition, which applies the same normalization strategy to both rows and columns by separating the matrix into row- and column-scaling factors and a scale-normalized matrix. We show that different scalings of the same scale-normalized matrix form an equivalence class, and call the scale-normalized, canonical member of the class its scale-invariant form that preserves all pairwise relative ratios. Projective decomposition therefore provides a means of normalizing the broad class of ratio-scale data, in which relative ratios are of primary interest, onto a common scale without altering the ratios of interest, and simultaneously accounting for scale effects for both organizations of the matrix values. Both of these properties distinguish it from z-transformation.
Sheaves: A Topological Approach to Big Data

This document develops general concepts useful for extracting knowledge embedded in large graphs or datasets that have pair-wise relationships, such as cause-effect-type relations. Almost no underlying assumptions are made, other than that the data can be presented in terms of pair-wise relationships between objects/events. This assumption is used to mine for patterns in the dataset, defining a reduced graph or dataset that boils-down or concentrates information into a more compact form. The resulting extracted structure or set of patterns are manifestly symbolic in nature, as they capture and encode the graph structure of the dataset in terms of a (generative) grammar. This structure is identified as having the formal mathematical structure of a sheaf. In essence, this paper introduces the basic concepts of sheaf theory into the domain of graphical datasets.
Supplementary Material: AVA-ActiveSpeaker: An Audio-Visual Dataset for Active Speaker Detection

Active speaker detection is an important component in video analysis algorithms for applications such as speaker diarization, video re-targeting for meetings, speech enhancement, and human-robot interaction. The absence of a large, carefully labeled audio-visual active speaker dataset has limited algorithm evaluation in terms of data diversity, environments, and accuracy. In this paper, we present the AVA Active Speaker detection dataset (AVA-ActiveSpeaker) which has been publicly released to facilitate algorithm development and comparison. It contains temporally labeled face tracks in video, where each face instance is labeled as speaking or not, and whether the speech is audible. This dataset contains about 3.65 million human labeled frames spanning 38.5 hours. We also introduce a state-of-the-art approach for real-time active speaker detection and compare several variants. This evaluation clearly demonstrates a significant gain due to audio-visual modeling and temporal integration over multiple frames.
Graph Neural Networks With Convolutional ARMA Filters

Popular graph neural networks implement convolution operations on graphs based on polynomial spectral filters. In this paper, we propose a novel graph convolutional layer inspired by the auto-regressive moving average (ARMA) filter that, compared to polynomial ones, provides a more flexible frequency response, is more robust to noise, and better captures the global graph structure. We propose a graph neural network implementation of the ARMA filter with a recursive and distributed formulation, obtaining a convolutional layer that is efficient to train, localized in the node space, and can be transferred to new graphs at test time. We perform a spectral analysis to study the filtering effect of the proposed ARMA layer and report experiments on four downstream tasks: semi-supervised node classification, graph signal classification, graph classification, and graph regression. Results show that the proposed ARMA layer brings significant improvements over graph neural networks based on polynomial filters.
Learning to Remember More with Less Memorization

Memory-augmented neural networks consisting of a neural controller and an external memory have shown potentials in long-term sequential learning. Current RAM-like memory models maintain memory accessing every timesteps, thus they do not effectively leverage the short-term memory held in the controller. We hypothesize that this scheme of writing is suboptimal in memory utilization and introduces redundant computation. To validate our hypothesis, we derive a theoretical bound on the amount of information stored in a RAM-like system and formulate an optimization problem that maximizes the bound. The proposed solution dubbed Uniform Writing is proved to be optimal under the assumption of equal timestep contributions. To relax this assumption, we introduce modifications to the original solution, resulting in a solution termed Cached Uniform Writing. This method aims to balance between maximizing memorization and forgetting via overwriting mechanisms. Through an extensive set of experiments, we empirically demonstrate the advantages of our solutions over other recurrent architectures, claiming the state-of-the-arts in various sequential modeling tasks.
Viscosity independent diffusion mediated by death and reproduction in biofilms

Bacterial biofilms, surface-attached communities of cells, are in some respects similar to colloidal solids; both are densely packed with non-zero yield stresses. However, unlike non-living materials, bacteria reproduce and die, breaking mechanical equilibrium and inducing collective dynamic responses. We report experiments and theory investigating the motion of immotile Vibrio cholerae, which can kill each other and reproduce in biofilms. We vary viscosity by using bacterial variants that secrete different amounts of extracellular matrix polymers, but are otherwise identical. Unlike thermally-driven diffusion, in which diffusivity decreases with increased viscosity, we find that cellular motion mediated by death and reproduction is independent of viscosity over timescales relevant to bacterial reproduction. To understand this surprising result, we use two separate modeling approaches. First we perform explicitly mechanical simulations of one-dimensional chains of Voigt-Kelvin elements that can die and reproduce. Next, we perform an independent statistical approach, modeling Brownian motion with the classic Langevin equation under an effective temperature that depends on cellular division rate. The diffusion of cells in both approaches agrees quite well, supporting a kinetic interpretation for the effective temperature used here and developed in previous work. As the viscoelastic behavior of biofilms is believed to play a large role in their anomalous biological properties, such as antibiotic resistance, the independence of cellular diffusive motion --- important for biofilm growth and remodeling --- on viscoelastic properties likely holds ecological, medical, and industrial relevance.
Hierarchical Reinforcement Learning via Advantage-Weighted Information Maximization

Real-world tasks are often highly structured. Hierarchical reinforcement learning (HRL) has attracted research interest as an approach for leveraging the hierarchical structure of a given task in reinforcement learning (RL). However, identifying the hierarchical policy structure that enhances the performance of RL is not a trivial task. In this paper, we propose an HRL method that learns a latent variable of a hierarchical policy using mutual information maximization. Our approach can be interpreted as a way to learn a discrete and latent representation of the state-action space. To learn option policies that correspond to modes of the advantage function, we introduce advantage-weighted importance sampling. In our HRL method, the gating policy learns to select option policies based on an option-value function, and these option policies are optimized based on the deterministic policy gradient method. This framework is derived by leveraging the analogy between a monolithic policy in standard RL and a hierarchical policy in HRL by using a deterministic option policy. Experimental results indicate that our HRL approach can learn a diversity of options and that it can enhance the performance of RL in continuous control tasks.