nils-herrmann/clean_scirepeval huggingface dataset:
A first look at user activity on tinder

Mobile dating apps have become a popular means to meet potential partners. Although several exist, one recent addition stands out amongst all others. Tinder presents its users with pictures of people geographically nearby, whom they can either like or dislike based on first impressions. If two users like each other, they are allowed to initiate a conversation via the chat feature. In this paper we use a set of curated profiles to explore the behaviour of men and women in Tinder. We reveal differences between the way men and women interact with the app, highlighting the strategies employed. Women attain large numbers of matches rapidly, whilst men only slowly accumulate matches. Most notably, our results indicate that a little effort in grooming profiles, especially for male users, goes a long way in attracting attention.
Accelerating Eulerian Fluid Simulation With Convolutional Networks

Efficient simulation of the Navier-Stokes equations for fluid flow is a long standing problem in applied mathematics, for which state-of-the-art methods require large compute resources. In this work, we propose a data-driven approach that leverages the approximation power of deep-learning with the precision of standard solvers to obtain fast and highly realistic simulations. Our method solves the incompressible Euler equations using the standard operator splitting method, in which a large sparse linear system with many free parameters must be solved. We use a Convolutional Network with a highly tailored architecture, trained using a novel unsupervised learning framework to solve the linear system. We present real-time 2D and 3D simulations that outperform recently proposed data-driven methods; the obtained results are realistic and show good generalization properties.
A Real-Time Pedestrian Detector using Deep Learning for Human-Aware Navigation

This paper addresses the problem of Human-Aware Navigation (HAN), using multi camera sensors to implement a vision-based person tracking system. The main contributions of this paper are a novel and real-time Deep Learning person detection and a standardization of personal space, that can be used with any path planer. In the first stage of the approach, we propose to cascade the Aggregate Channel Features (ACF) detector with a deep Convolutional Neural Network (CNN) to achieve fast and accurate Pedestrian Detection (PD). For the personal space definition (that can be defined as constraints associated with the robot's motion), we used a mixture of asymmetric Gaussian functions, to define the cost functions associated to each constraint. Both methods were evaluated individually. The final solution (including both the proposed pedestrian detection and the personal space constraints) was tested in a typical domestic indoor scenario, in four distinct experiments. The results show that the robot is able to cope with human-aware constraints, defined after common proxemics rules.
Mining arguments from cancer documents using Natural Language Processing and ontologies

In the medical domain, the continuous stream of scientific research contains contradictory results supported by arguments and counter-arguments. As medical expertise occurs at different levels, part of the human agents have difficulties to face the huge amount of studies, but also to understand the reasons and pieces of evidences claimed by the proponents and the opponents of the debated topic. To better understand the supporting arguments for new findings related to current state of the art in the medical domain we need tools able to identify arguments in scientific papers. Our work here aims to fill the above technological gap. We rely on the well-known interleaving of domain knowledge with natural language processing. To formalise the existing medical knowledge, we rely on ontologies. To structure the argumentation model we use also the expressivity and reasoning capabilities of Description Logics. To perform argumentation mining we formalise various linguistic patterns in a rule-based language. We tested our solution against a corpus of scientific papers related to breast cancer. The run experiments show a F-measure between 0.71 and 0.86 for identifying conclusions of an argument and between 0.65 and 0.86 for identifying premises of an argument.
Radio Detection of High Energy Neutrinos

Ultra-high energy (UHE) neutrino astrophysics sits at the crossroads of particle physics, astronomy, and astrophysics. Through neutrino astrophysics, we can uniquely explore the structure and evolution of the universe at the highest energies at cosmic distances and test our understanding of particle physics at energies greater than those available at particle colliders. The detection of UHE neutrinos would shed light on the nature of the astrophysical sources that produce the highest energy particles in the universe. Astrophysical sources almost certainly produce UHE neutrinos in hadronic processes. Also, neutrinos above 10 eV should be produced through the GZK effect, where extragalactic cosmic rays above 10 eV interact with the cosmic microwave background within tens of Mpc of their source. It was first pointed out by Berezinsky and Zatsepin that this cosmogenic neutrino flux, sometimes called “BZ” neutrinos, could be observable. These neutrinos would point close to the cosmic ray production site both because of the close proximity of the interaction to the source and because the decay products are Lorentz boosted along the line of sight. The latter effect constrains the direction the most strongly (1 MeV/1 EeV = 10−12 whereas 100 Mpc/1 Gpc = 0.1). Since cosmic rays do not follow straight paths in magnetic fields and get attenuated above the GZK threshold, and high energy photons (E> 10 eV) are attenuated by the cosmic infrared background, neutrinos offer the unique ability to point to the location of the highest energy cosmic accelerators in aHere, we loosely define the UHE regime to be the energy range near 1018 − 1021 eV. A few radio experiments have sensitivities that reach to lower energies, while others have energy thresholds at higher energies.
IC at IC: IceCube can constrain the intrinsic charm of the proton

The discovery of extraterrestrial neutrinos in the $\sim$ 30 TeV -- PeV energy range by IceCube provides new constraints on high energy astrophysics. An important background to the signal are the prompt neutrinos which originate from the decay of charm hadrons produced by high energy cosmic-ray particles interacting in the Earth's atmosphere. It is conventional to use the calculations of charm hadroproduction using gluon splitting $g \to c \bar c$ alone. However, QCD predicts an additional "intrinsic" component of the heavy quark distribution which arises from diagrams where heavy quarks are multiply connected to the proton's valence quarks. We estimate the prompt neutrino spectrum due to intrinsic charm. We find that the atmospheric prompt neutrino flux from intrinsic charm is comparable to those calculated using QCD computations not including intrinsic charm, once we normalize the intrinsic charm differential cross sections to the ISR and the LEBC-MPS collaboration data. In future, IceCube will constrain the intrinsic charm content of the proton and will contribute to one of the major questions in high energy physics phenomenology.
Synthetic Approach to the Singularity Problem

We try to convince the reader that the categorical version of differential geometry, called Synthetic Differential Geometry (SDG), offers valuable tools which can be applied to work with some unsolved problems of general relativity. We do this with respect to the space-time singularity problem. The essential difference between the usual differential geometry and SDG is that the latter enriches the real line by introducing infinitesimal of various kinds. Owing to this geometry acquires a tool to penetrate "infinitesimally small" parts of a given manifold. However, to make use of this tool we must switch from the category of sets to some other suitable category. We try two topoi: the topos ${\cal G}$ of germ determined ideals and the so-called Basel topos ${\cal B}$. The category of manifolds is a subcategory of both of them. In ${\cal G}$, we construct a simple model of a contracting sphere. As the sphere shrinks, its curvature increases, but when the radius of the sphere reaches infinitesimal values, the curvature becomes infinitesimal and the singularity is avoided. The topos ${\cal B}$, unlike the topos ${\cal G}$, has invertible infinitesimal and infinitely large nonstandard natural numbers. This allows us to see what happens when a function "goes through a singularity". When changing from the category of sets to another topos, one must be ready to switch from classical logic to intuitionistic logic. This is a radical step, but the logic of the universe is not obliged to conform to the logic of our brains.
On the effect of heterogeneity on flocking behavior and systemic risk

The goal of this paper is to study organized flocking behavior and systemic risk in heterogeneous mean-field interacting diffusions. We illustrate in a number of case studies the effect of heterogeneity in the behavior of systemic risk in the system, i.e., the risk that several agents default simultaneously as a result of interconnections. We also investigate the effect of heterogeneity on the “flocking behavior” of different agents, i.e., when agents with different dynamics end up following very similar paths and follow closely the mean behavior of the system. Using Laplace asymptotics, we derive an asymptotic formula for the tail of the loss distribution as the number of agents grows to infinity. This characterizes the tail of the loss distribution and the effect of the heterogeneity of the network on the tail loss probability.
Preterm Birth Prediction: Deriving Stable and Interpretable Rules from High Dimensional Data

Preterm births occur at an alarming rate of 10-15%. Preemies have a higher risk of infant mortality, developmental retardation and long-term disabilities. Predicting preterm birth is difficult, even for the most experienced clinicians. The most well-designed clinical study thus far reaches a modest sensitivity of 18.2-24.2% at specificity of 28.6-33.3%. We take a different approach by exploiting databases of normal hospital operations. We aims are twofold: (i) to derive an easy-to-use, interpretable prediction rule with quantified uncertainties, and (ii) to construct accurate classifiers for preterm birth prediction. Our approach is to automatically generate and select from hundreds (if not thousands) of possible predictors using stability-aware techniques. Derived from a large database of 15,814 women, our simplified prediction rule with only 10 items has sensitivity of 62.3% at specificity of 81.5%.
Efficient Hyperparameter Optimization for Deep Learning Algorithms Using Deterministic RBF Surrogates

Automatically searching for optimal hyperparameter configurations is of crucial importance for applying deep learning algorithms in practice. Recently, Bayesian optimization has been proposed for optimizing hyperparameters of various machine learning algorithms. Those methods adopt probabilistic surrogate models like Gaussian processes to approximate and minimize the validation error function of hyperparameter values. However, probabilistic surrogates require accurate estimates of sufficient statistics (e.g., covariance) of the error distribution and thus need many function evaluations with a sizeable number of hyperparameters. This makes them inefficient for optimizing hyperparameters of deep learning algorithms, which are highly expensive to evaluate. In this work, we propose a new deterministic and efficient hyperparameter optimization method that employs radial basis functions as error surrogates. The proposed mixed integer algorithm, called HORD, searches the surrogate for the most promising hyperparameter values through dynamic coordinate search and requires many fewer function evaluations. HORD does well in low dimensions but it is exceptionally better in higher dimensions. Extensive evaluations on MNIST and CIFAR-10 for four deep neural networks demonstrate HORD significantly outperforms the well-established Bayesian optimization methods such as GP, SMAC, and TPE. For instance, on average, HORD is more than 6 times faster than GP-EI in obtaining the best configuration of 19 hyperparameters.
Local Feature Detectors, Descriptors, and Image Representations: A Survey

With the advances in both stable interest region detectors and robust and distinctive descriptors, local feature-based image or object retrieval has become a popular research topic. %All of the local feature-based image retrieval system involves two important processes: local feature extraction and image representation. The other key technology for image retrieval systems is image representation such as the bag-of-visual words (BoVW), Fisher vector, or Vector of Locally Aggregated Descriptors (VLAD) framework. In this paper, we review local features and image representations for image retrieval. Because many and many methods are proposed in this area, these methods are grouped into several classes and summarized. In addition, recent deep learning-based approaches for image retrieval are briefly reviewed.
Topology of Complex Networks and Performance Limitations of Community Detection Algorithms

One of the most widely studied problems in the analysis of complex networks is the detection of community structures. Many algorithms have been proposed to find communities but the quest to find the best algorithm is still on. More often than not, researchers focus on developing fast and accurate algorithms that can be generically applied to networks from various domains. As the topology of networks changes with respect to domains, community detection algorithms fail to accommodate these changes to detect communities. In this paper, we attempt to highlight this problem by studying networks with different topologies and evaluate the performance of community detection algorithms in the light of these topological changes. To generate networks with different topologies, we used the well-known Lancichinetti–Fortunato–Radicchi (LFR) model, and we also propose a new model named Naïve Scale-Free Clustering to avoid any bias that can be introduced by the underlying network generation model. Results reveal several limitations of the current popular network clustering algorithms failing to correctly find communities. This suggests the need to revisit the design of current clustering algorithms in order to improve their performances.
Early Methods for Detecting Adversarial Images

Many machine learning classifiers are vulnerable to adversarial perturbations. An adversarial perturbation modifies an input to change a classifier's prediction without causing the input to seem substantially different to human perception. We deploy three methods to detect adversarial images. Adversaries trying to bypass our detectors must make the adversarial image less pathological or they will fail trying. Our best detection method reveals that adversarial images place abnormal emphasis on the lower-ranked principal components from PCA. Other detectors and a colorful saliency map are in an appendix.
Semi-Automated SVG Programming via Direct Manipulation

Direct manipulation interfaces provide intuitive and interactive features to a broad range of users, but they often exhibit two limitations: the built-in features cannot possibly cover all use cases, and the internal representation of the content is not readily exposed. We believe that if direct manipulation interfaces were to (a) use general-purpose programs as the representation format, and (b) expose those programs to the user, then experts could customize these systems in powerful new ways and non-experts could enjoy some of the benefits of programmable systems. In recent work, we presented a prototype SVG editor called Sketch-n-Sketch that offered a step towards this vision. In that system, the user wrote a program in a general-purpose lambda-calculus to generate a graphic design and could then directly manipulate the output to indirectly change design parameters (i.e. constant literals) in the program in real-time during the manipulation. Unfortunately, the burden of programming the desired relationships rested entirely on the user. In this paper, we design and implement new features for Sketch-n-Sketch that assist in the programming process itself. Like typical direct manipulation systems, our extended Sketch-n-Sketch now provides GUI-based tools for drawing shapes, relating shapes to each other, and grouping shapes together. Unlike typical systems, however, each tool carries out the user's intention by transforming their general-purpose program. This novel, semi-automated programming workflow allows the user to rapidly create high-level, reusable abstractions in the program while at the same time retaining direct manipulation capabilities. In future work, our approach may be extended with more graphic design features or realized for other application domains.
Can Peripheral Representations Improve Clutter Metrics on Complex Scenes?

Previous studies have proposed image-based clutter measures that correlate with human search times and/or eye movements. However, most models do not take into account the fact that the effects of clutter interact with the foveated nature of the human visual system: visual clutter further from the fovea has an increasing detrimental influence on perception. Here, we introduce a new foveated clutter model to predict the detrimental effects in target search utilizing a forced fixation search task. We use Feature Congestion (Rosenholtz et al.) as our non foveated clutter model, and we stack a peripheral architecture on top of Feature Congestion for our foveated model. We introduce the Peripheral Integration Feature Congestion (PIFC) coefficient, as a fundamental ingredient of our model that modulates clutter as a non-linear gain contingent on eccentricity. We finally show that Foveated Feature Congestion (FFC) clutter scores r(44) = -0.82 correlate better with target detection (hit rate) than regular Feature Congestion r(44) = -0.19 in forced fixation search. Thus, our model allows us to enrich clutter perception research by computing fixation specific clutter maps. A toolbox for creating peripheral architectures: Piranhas: Peripheral Architectures for Natural, Hybrid and Artificial Systems will be made available.
Photo Filter Recommendation by Category-Aware Aesthetic Learning

Nowadays, social media has become a popular platform for the public to share photos. To make photos more visually appealing, users usually apply filters on their photos without domain knowledge. However, due to the growing number of filter types, it becomes a major issue for users to choose the best filter type. For this purpose, filter recommendation for photo aesthetics takes an important role in image quality ranking problems. In these years, several works have declared that convolutional neural networks (CNNs) outperform traditional methods in image aesthetic categorization, which classifies images into high or low quality. Most of them do not consider the effect on filtered images; hence, we propose a novel image aesthetic learning for filter recommendation. Instead of binarizing image quality, we adjust the state-of-the-art CNN architectures and design a pairwise loss function to learn the embedded aesthetic responses in hidden layers for filtered images. Based on our pilot study, we observe image categories (e.g., portrait, landscape, food) will affect user preference on filter selection. We further integrate category classification into our proposed aesthetic-oriented models. To the best of our knowledge, there is no public dataset for aesthetic judgment with filtered images. We create a new dataset called filter aesthetic comparison dataset (FACD). It is the first dataset containing 28 160 filtered images and 42 240 user preference labels. We conduct experiments on the collected FACD for filter recommendation, and the results show that our proposed category-aware aesthetic learning outperforms aesthetic classification methods (e.g., 12% relative improvement).
Consistency of option prices under bid–ask spreads

Given a finite set of European call option prices on a single underlying, we want to know when there is a market model that is consistent with these prices. In contrast to previous studies, we allow models where the underlying trades at a bid–ask spread. The main question then is how large (in terms of a deterministic bound) this spread must be to explain the given prices. We fully solve this problem in the case of a single maturity, and give several partial results for multiple maturities. For the latter, our main mathematical tool is a recent result on approximation by peacocks.
A framework for the construction of generative models for mesoscale structure in multilayer networks

Multilayer networks allow one to represent diverse and coupled connectivity patterns --- e.g., time-dependence, multiple subsystems, or both --- that arise in many applications and which are difficult or awkward to incorporate into standard network representations. In the study of multilayer networks, it is important to investigate mesoscale (i.e., intermediate-scale) structures, such as dense sets of nodes known as communities, to discover network features that are not apparent at the microscale or the macroscale. The ill-defined nature of mesoscale structure and its ubiquity in empirical networks make it crucial to develop generative models that can produce the features that one encounters in empirical networks. Key purposes of such generative models include generating synthetic networks with empirical properties of interest, benchmarking mesoscale-detection methods and algorithms, and inferring structure in empirical multilayer networks. In this paper, we introduce a framework for the construction of generative models for mesoscale structures in multilayer networks. Our framework provides a standardized set of generative models, together with an associated set of principles from which they are derived, for studies of mesoscale structures in multilayer networks. It unifies and generalizes many existing models for mesoscale structures in fully-ordered (e.g., temporal) and unordered (e.g., multiplex) multilayer networks. One can also use it to construct generative models for mesoscale structures in partially-ordered multilayer networks (e.g., networks that are both temporal and multiplex). Our framework has the ability to produce many features of empirical multilayer networks, and it explicitly incorporates a user-specified dependency structure between layers.
Monte Carlo study of Dirac semimetals phase diagram

In this paper the phase diagram of Dirac semimetals is studied within lattice Monte-Carlo simulation. In particular, we concentrate on the dynamical chiral symmetry breaking which results in semimetal/insulator transition. Using numerical simulation we determined the values of the critical coupling constant of the semimetal/insulator transition for different values of the anisotropy of the Fermi velocity. This measurement allowed us to draw tentative phase diagram for Dirac semimetals. It turns out that within the Dirac model with Coulomb interaction both Na$_3$Bi and Cd$_3$As$_2$ known experimentally to be Dirac semimetals would lie deeply in the insulating region of the phase diagram. It probably shows a decisive role of screening of the interelectron interaction in real materials, similar to the situation in graphene.