nils-herrmann/clean_scirepeval huggingface dataset:
GAMA+KiDS: Alignment of galaxies in galaxy groups and its dependence on galaxy scale

Intrinsic galaxy alignments are a source of bias for weak lensing measurements as well as a tool for understanding galaxy formation and evolution. In this work, we measure the alignment of shapes of satellite galaxies, in galaxy groups, with respect to the brightest group galaxy (BGG), as well as alignments of the BGG shape with the satellite positions, using the highly complete Galaxy And Mass Assembly (GAMA) spectroscopic survey and deep imaging from the Kilo Degree Survey. We control systematic errors with dedicated image simulations and measure accurate shapes using the DEIMOS shape measurement method. We find a significant satellite radial alignment signal, which vanishes at large separations from the BGG. We do not identify any strong trends of the signal with galaxy absolute magnitude or group mass. The alignment signal is dominated by red satellites. We also find that the outer regions of galaxies are aligned more strongly than their inner regions, by varying the radial weight employed during the shape measurement process. This behaviour is evident for both red and blue satellites. BGGs are also found to be aligned with satellite positions, with this alignment being stronger when considering the innermost satellites, using red BGGs and the shape of the outer region of the BGG. Lastly, we measure the global intrinsic alignment signal in the GAMA sample for two different radial weight functions and find no significant difference.
Dynamical Properties of Merging Galaxy Clusters from Simulated Analogs

Merging galaxy clusters may provide a unique window into the behavior of dark matter and the evolution of member galaxies. To interpret these natural collider experiments, we must account for how much time has passed since pericenter passage (TSP), the maximum relative speed of the merging subclusters, merger phase (outbound after first pericenter or returning for second pericenter), and other dynamical parameters that are not directly observable. These quantities are often inferred from staged simulations or analytical timing arguments that include neither substructure, nor large-scale structure, nor a cosmologically motivated range of impact parameters. We include all these effects by extracting dynamical parameters from analog systems in a cosmological n-body simulation, and we present constraints for 11 observed systems. The TSP and viewing angles we derive are consistent with those of staged hydrodynamical simulations, but we find lower maximum speeds. Compared to the analytical MCMAC method, we find lower TSP and viewing angles that put the separation vector closer to the plane of the sky; we attribute this to the MCMAC assumption of zero pericenter distance. We discuss potential extensions to the basic analog method, as well as complementarities between methods.
Functional Object-Oriented Network: Considering Robot's Capability in Human-Robot Collaboration

In this work, we explore human-robot collaborative planning using the \emph{functional object-oriented network} (FOON), a graphical knowledge representation for manipulations that can be performed by domestic robots. The knowledge retrieval procedure, used for acquiring the necessary steps (as a task tree) to solve a given problem, is modified to account for weights that reflect the difficulty of performing motions in a universal FOON. These weights are given as success rates, which describe the likelihood of a robot successfully completing the action(s) on its own. However, certain manipulations may be too difficult for it to perform on its own based on its own physical limitations. To make it easier for the robot, a human can assist to the minimal extent needed to perform the activity to completion by identifying those actions with low success rates for the human to do. From our experiments, it is shown that tasks can be executed successfully with the aid of the assistant. Our results show that the best task tree can be found with the adequate chance of success in completing three activities while minimizing the effort needed from the human assistant.
SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems

In the last year, new models and methods for pretraining and transfer learning have driven striking performance improvements across a range of language understanding tasks. The GLUE benchmark, introduced a little over one year ago, offers a single-number metric that summarizes progress on a diverse set of such tasks, but performance on the benchmark has recently surpassed the level of non-expert humans, suggesting limited headroom for further research. In this paper we present SuperGLUE, a new benchmark styled after GLUE with a new set of more difficult language understanding tasks, a software toolkit, and a public leaderboard. SuperGLUE is available at this http URL.
The Collective Advantage for Advancing Communications and Intelligence

The fifth-generation cellular networks (5G) have boosted the unprecedented convergence between the information world and physical world. On the other hand, empowered with the enormous amount of data and information, AI has been universally applied and pervasive AI is believed to be an integral part of 6G. Consequently, benefiting from the advancement in communication technology and AI, we boldly argue that the conditions for collective intelligence (CI) will be mature in the 6G era and CI will emerge among the widely connected beings and things. Afterward, we highlight the potential huge impact of CI on both communications and intelligence. In particular, we introduce a regular language (i.e., the information economy metalanguage) supporting the future collective communications to augment human intelligence and explain its potential applications in naming Internet information and pushing information centric networks forward. Meanwhile, we propose a stigmergy-based federated collective intelligence and demonstrate its achievement in a simulated scenario where the agents collectively work together to form a pattern through simple indirect communications. In a word, CI could advance both communications and intelligence.
Deconstructing Lottery Tickets: Zeros, Signs, and the Supermask

The recent "Lottery Ticket Hypothesis" paper by Frankle & Carbin showed that a simple approach to creating sparse networks (keeping the large weights) results in models that are trainable from scratch, but only when starting from the same initial weights. The performance of these networks often exceeds the performance of the non-sparse base model, but for reasons that were not well understood. In this paper we study the three critical components of the Lottery Ticket (LT) algorithm, showing that each may be varied significantly without impacting the overall results. Ablating these factors leads to new insights for why LT networks perform as well as they do. We show why setting weights to zero is important, how signs are all you need to make the reinitialized network train, and why masking behaves like training. Finally, we discover the existence of Supermasks, masks that can be applied to an untrained, randomly initialized network to produce a model with performance far better than chance (86% on MNIST, 41% on CIFAR-10).
Uncertainty-Aware Principal Component Analysis

We present a technique to perform dimensionality reduction on data that is subject to uncertainty. Our method is a generalization of traditional principal component analysis (PCA) to multivariate probability distributions. In comparison to non-linear methods, linear dimensionality reduction techniques have the advantage that the characteristics of such probability distributions remain intact after projection. We derive a representation of the PCA sample covariance matrix that respects potential uncertainty in each of the inputs, building the mathematical foundation of our new method: uncertainty-aware PCA. In addition to the accuracy and performance gained by our approach over sampling-based strategies, our formulation allows us to perform sensitivity analysis with regard to the uncertainty in the data. For this, we propose factor traces as a novel visualization that enables to better understand the influence of uncertainty on the chosen principal components. We provide multiple examples of our technique using real-world datasets. As a special case, we show how to propagate multivariate normal distributions through PCA in closed form. Furthermore, we discuss extensions and limitations of our approach.
Matlab vs. OpenCV: A Comparative Study of Different Machine Learning Algorithms

Computing relies on executing computer algorithms coded in some programming languages. Given a particular available hardware, algorithms speed is a crucial factor. There are many scientific computing environments used to code such algorithms. Matlab is one of the most tremendously successful and widespread scientific computing environments that is rich of toolboxes, libraries, and data visualization tools. OpenCV is a (C++)-based library written primarily for Computer Vision and its related areas. This paper presents a comparative study using 20 different real datasets to compare the speed of Matlab and OpenCV for some Machine Learning algorithms. Although Matlab is more convenient in developing and data presentation, OpenCV is much faster in execution, where the speed ratio reaches more than 80 in some cases. The best of two worlds can be achieved by exploring using Matlab or similar environments to select the most successful algorithm; then, implementing the selected algorithm using OpenCV or similar environments to gain a speed factor.
Dark Matter from Scalar Field Fluctuations.

Dark matter (DM) may have its origin in a pre-big-bang epoch, the cosmic inflation. Here, we consider for the first time a broad class of scenarios where a massive free scalar field unavoidably reaches an equilibrium between its classical and quantum dynamics in a characteristic timescale during inflation and sources the DM density. The study gives the abundance and perturbation spectrum of any DM component sourced by the scalar field. We show that this class of scenarios generically predicts enhanced structure formation, allowing one to test models where DM interacts with matter only gravitationally.
Light majoron cold dark matter from topological defects and the formation of boson stars

We show that for a relatively light majoron (≪ 100 eV) non-thermal production from topological defects is an efficient production mechanism. Taking the type I seesaw as benchmark scheme, we estimate the primordial majoron abundance and determine the required parameter choices where it can account for the observed cosmological dark matter. The latter is consistent with the scale of unification. Possible direct detection of light majorons with future experiments such as PTOLEMY and the formation of boson stars from the majoron dark matter are also discussed.
Anonymized BERT: An Augmentation Approach to the Gendered Pronoun Resolution Challenge

We present our 7th place solution to the Gendered Pronoun Resolution challenge, which uses BERT without fine-tuning and a novel augmentation strategy designed for contextual embedding token-level tasks. Our method anonymizes the referent by replacing candidate names with a set of common placeholder names. Besides the usual benefits of effectively increasing training data size, this approach diversifies idiosyncratic information embedded in names. Using same set of common first names can also help the model recognize names better, shorten token length, and remove gender and regional biases associated with names. The system scored 0.1947 log loss in stage 2, where the augmentation contributed to an improvements of 0.04. Post-competition analysis shows that, when using different embedding layers, the system scores 0.1799 which would be third place.
Computing a Data Dividend

Quality data is a fundamental contributor to success in statistics and machine learning. If a statistical assessment or machine learning leads to decisions that create value, data contributors may want a share of that value. This paper presents methods to assess the value of individual data samples, and of sets of samples, to apportion value among different data contributors. We use Shapley values for individual samples and Owen values for combined samples, and show that these values can be computed in polynomial time in spite of their definitions having numbers of terms that are exponential in the number of samples.
Distributional Semantics and Linguistic Theory

Distributional semantics provides multidimensional, graded, empirically induced word representations that successfully capture many aspects of meaning in natural languages, as shown by a large body of research in computational linguistics; yet, its impact in theoretical linguistics has so far been limited. This review provides a critical discussion of the literature on distributional semantics, with an emphasis on methods and results that are relevant for theoretical linguistics, in three areas: semantic change, polysemy and composition, and the grammar–semantics interface (specifically, the interface of semantics with syntax and with derivational morphology). The goal of this review is to foster greater cross-fertilization of theoretical and computational approaches to language as a means to advance our collective knowledge of how it works.
Poly-encoders: Transformer Architectures and Pre-training Strategies for Fast and Accurate Multi-sentence Scoring

The use of deep pre-trained bidirectional transformers has led to remarkable progress in a number of applications (Devlin et al., 2018). For tasks that make pairwise comparisons between sequences, matching a given input with a corresponding label, two approaches are common: Cross-encoders performing full self-attention over the pair and Bi-encoders encoding the pair separately. The former often performs better, but is too slow for practical use. In this work, we develop a new transformer architecture, the Poly-encoder, that learns global rather than token level self-attention features. We perform a detailed comparison of all three approaches, including what pre-training and fine-tuning strategies work best. We show our models achieve state-of-the-art results on three existing tasks; that Poly-encoders are faster than Cross-encoders and more accurate than Bi-encoders; and that the best results are obtained by pre-training on large datasets similar to the downstream tasks.
Moment-based analysis of biochemical feedback circuits in a population of chemically interacting cells

Cells utilize chemical communication to exchange information and coordinate their behavior in noisy environments. Depending on the scenario, communication can reduce variability and shape a collective response, or amplify variability to generate distinct phenotypic subpopulations. Here we use a moment-based approach to study how cell-cell communication affects noise in biochemical networks that arises from both intrinsic and extrinsic sources. Based on a recently proposed model reduction technique, we derive a system of differential equations that captures lower-order moments of a population of cells, which communicate by secreting and sensing a diffusing molecule. Importantly, the number of equations that we obtain in this way is independent of the number of considered cells such that the method scales to arbitrary population sizes. Based on this approach, we analyze how cell-cell communication affects noise in several biochemical circuits.
Adversarial Examples Are Not Bugs, They Are Features

Adversarial examples have attracted significant attention in machine learning, but the reasons for their existence and pervasiveness remain unclear. We demonstrate that adversarial examples can be directly attributed to the presence of non-robust features: features derived from patterns in the data distribution that are highly predictive, yet brittle and incomprehensible to humans. After capturing these features within a theoretical framework, we establish their widespread existence in standard datasets. Finally, we present a simple setting where we can rigorously tie the phenomena we observe in practice to a misalignment between the (human-specified) notion of robustness and the inherent geometry of the data.
Hyper Wide Field Imaging of the Local Group Dwarf Irregular Galaxy IC 1613: An Extended Component of Metal-poor Stars

Stellar halos offer fossil evidence for hierarchical structure formation. Since halo assembly is predicted to be scale-free, stellar halos around low-mass galaxies constrain properties such as star formation in the accreted subhalos and the formation of dwarf galaxies. However, few observational searches for stellar halos in dwarfs exist. Here we present gi photometry of resolved stars in isolated Local Group dwarf irregular galaxy IC 1613 (M⋆ ∼ 108 M⊙). These Subaru/Hyper Suprime-Cam observations are the widest and deepest of IC 1613 to date. We measure surface density profiles of young main-sequence, intermediate to old red giant branch, and ancient horizontal branch stars outside of 12′ (∼2.6 kpc; 2.5 half-light radii) from the IC 1613 center. All of the populations extend to ∼24′ (∼5.2 kpc; 5 half-light radii), with the older populations best fit by a broken exponential in these outer regions. Comparison with earlier studies sensitive to IC 1613's inner regions shows that the density of old stellar populations steepens substantially with distance from the center; we trace the g-band effective surface brightness to an extremely faint limit of ∼33.7 mag arcsec−2. Conversely, the distribution of younger stars follows a single, shallow exponential profile in the outer regions, demonstrating different formation channels for the younger and older components of IC 1613. The outermost, intermediate-age and old stars have properties consistent with those expected for accreted stellar halos, though future observational and theoretical work is needed to definitively distinguish this scenario from other possibilities.
New Horizon: On the Origin of the Stellar Disk and Spheroid of Field Galaxies at z = 0.7

The origin of the disk and spheroid of galaxies has been a key open question in understanding their morphology. Using the high-resolution cosmological simulation New Horizon, we explore kinematically decomposed disk and spheroidal components of 144 field galaxies with masses greater than at z = 0.7. The origins of stellar particles are classified according to their birthplace (in situ or ex situ) and their orbits at birth. Before disk settling, stars form mainly through chaotic mergers between protogalaxies and become part of the spheroidal component. When disk settling starts, we find that more massive galaxies begin to form disk stars from earlier epochs; massive galaxies commence to develop their disks at z ∼ 1–2, while low-mass galaxies do after z ∼ 1. The formation of disks is affected by accretion as well, as mergers can trigger gas turbulence or induce misaligned gas infall that hinders galaxies from forming corotating disk stars. The importance of accreted stars is greater in more massive galaxies, especially in developing massive spheroids. A significant fraction of the spheroids come from the disk stars that are perturbed, and this becomes more important at lower redshifts. Some (∼12.5%) of our massive galaxies develop counter-rotating disks from the gas infall misaligned with the existing disk plane, which can last for more than a gigayear until they become the dominant component and flip the angular momentum of the galaxy in the opposite direction. The final disk-to-total ratio of a galaxy needs to be understood in relation to its stellar mass and accretion history. We quantify the significance of the stars with different origins and provide them as guiding values.
Scalable Detection of Offensive and Non-compliant Content / Logo in Product Images

In e-commerce, product content, especially product images have a significant influence on a customer’s journey from product discovery to evaluation and finally, purchase decision. Since many e-commerce retailers sell items from other third-party marketplace sellers besides their own, the content published by both internal and external content creators needs to be monitored and enriched, wherever possible. Despite guidelines and warnings, product listings that contain offensive and non-compliant images continue to enter catalogs. Offensive and non-compliant content can include a wide range of objects, logos, and banners conveying violent, sexually explicit, racist, or promotional messages. Such images can severely damage the customer experience, lead to legal issues, and erode the company brand. In this paper, we present a computer vision driven offensive and non-compliant image detection system for extremely large image datasets. This paper delves into the unique challenges of applying deep learning to real-world product image data from retail world. We demonstrate how we resolve a number of technical challenges such as lack of training data, severe class imbalance, fine-grained class definitions etc. using a number of practical yet unique technical strategies. Our system combines state-of-the-art image classification and object detection techniques with budgeted crowd-sourcing to develop a solution customized for a massive, diverse, and constantly evolving product catalog.
Are Graph Neural Networks Miscalibrated?

Graph Neural Networks (GNNs) have proven to be successful in many classification tasks, outperforming previous state-of-the-art methods in terms of accuracy. However, accuracy alone is not enough for high-stakes decision making. Decision makers want to know the likelihood that a specific GNN prediction is correct. For this purpose, obtaining calibrated models is essential. In this work, we perform an empirical evaluation of the calibration of state-of-the-art GNNs on multiple datasets. Our experiments show that GNNs can be calibrated in some datasets but also badly miscalibrated in others, and that state-of-the-art calibration methods are helpful but do not fix the problem.
Design Space Exploration as Quantified Satisfaction

We propose novel algorithms for design and design space exploration. The designs computed by these algorithms are compositions of function types specified in component libraries. Our algorithms reduce the design problem to quantified satisfiability and use advanced solvers to find solutions that represent useful systems. The algorithms we present in this paper are sound and complete and are guaranteed to discover correct designs of optimal size, if they exist. We apply our method to the design of Boolean systems and discover new and more optimal classical and quantum circuits for common arithmetic functions such as addition and multiplication. The performance of our algorithms is evaluated through extensive experimentation. We have first created a benchmark consisting of specifications of scalable synthetic digital circuits and real-world mirochips. We have then generated multiple circuits functionally equivalent to the ones in the benchmark. The quantified satisfiability method shows more than four orders of magnitude speed-up, compared to a generate and test method that enumerates all non-isomorphic circuit topologies. Our approach generalizes circuit optimization. It uses arbitrary component libraries and has applications to areas such as digital circuit design, diagnostics, abductive reasoning, test vector generation, and combinatorial optimization.
Joule-Thomson expansion of the regular(Bardeen)-AdS black hole

In this paper, we study Joule-Thomson expansion for Hayward-AdS black hole in the extended phase space, and obtain a Joule-Thomson expansion formula for the black hole. We plot the inversion and isenthalpic curves in the T-P plane, and determine the cooling-heating regions. The intersection points of the isenthalpic and inversion curves are exactly the inversion points discriminating the heating process from the cooling one.