nils-herrmann/clean_scirepeval huggingface dataset:
Measuring Effectiveness of Video Advertisements

Advertisements are unavoidable in modern society. Times Square is notorious for its incessant display of advertisements. Its popularity is worldwide and smaller cities possess miniature versions of the display, such as Pittsburgh and its digital works in Oakland on Forbes Avenue. Tokyo's Ginza district recently rose to popularity due to its upscale shops and constant onslaught of advertisements to pedestrians. Advertisements arise in other mediums as well. For example, they help popular streaming services, such as Spotify, Hulu, and Youtube TV gather significant streams of revenue to reduce the cost of monthly subscriptions for consumers. Ads provide an additional source of money for companies and entire industries to allocate resources toward alternative business motives. They are attractive to companies and nearly unavoidable for consumers. One challenge for advertisers is examining a advertisement's effectiveness or usefulness in conveying a message to their targeted demographics. Rather than constructing a single, static image of content, a video advertisement possesses hundreds of frames of data with varying scenes, actors, objects, and complexity. Therefore, measuring effectiveness of video advertisements is important to impacting a billion-dollar industry. This paper explores the combination of human-annotated features and common video processing techniques to predict effectiveness ratings of advertisements collected from Youtube. This task is seen as a binary (effective vs. non-effective), four-way, and five-way machine learning classification task. The first findings in terms of accuracy and inference on this dataset, as well as some of the first ad research, on a small dataset are presented. Accuracies of 84\%, 65\%, and 55\% are reached on the binary, four-way, and five-way tasks respectively.
DCNN-GAN: Reconstructing Realistic Image from fMRI

Visualizing the perceptual content by analyzing human functional magnetic resonance imaging (fMRI) has been an active research area. However, due to its high dimensionality, complex dimensional structure, and small number of samples available, reconstructing realistic images from fMRI remains challenging. Recently with the development of convolutional neural network (CNN) and generative adversarial network (GAN), mapping multi-voxel fMRI data to complex, realistic images has been made possible. In this paper, we propose a model, DCNN-GAN, by combining a reconstruction network and GAN. We utilize the CNN for hierarchical feature extraction and the DCNN-GAN to reconstruct more realistic images. Extensive experiments have been conducted, showing that our method outperforms previous works, regarding reconstruction quality and computational cost.
A recent survey on the applications of genetic programming in image processing

Genetic programming (GP) has been primarily used to tackle optimization, classification, and feature selection related tasks. The widespread use of GP is due to its flexible and comprehensible tree‐type structure. Similarly, research is also gaining momentum in the field of image processing, because of its promising results over vast areas of applications ranging from medical image processing to multispectral imaging. Image processing is mainly involved in applications such as computer vision, pattern recognition, image compression, storage, and medical diagnostics. This universal nature of images and their associated algorithm, that is, complexities, gave an impetus to the exploration of GP. GP has thus been used in different ways for image processing since its inception. Many interesting GP techniques have been developed and employed in the field of image processing, and consequently, we aim to provide the research community an extensive view of these techniques. This survey thus presents the diverse applications of GP in image processing and provides useful resources for further research. In addition, the comparison of different parameters used in different applications of image processing is summarized in tabular form. Moreover, analysis of the different parameters used in image processing related tasks is carried‐out to save the time needed in the future for evaluating the parameters of GP. As more advancement is made in GP methodologies, its success in solving complex tasks, not only in image processing but also in other fields, may increase. In addition, guidelines are provided for applying GP in image processing related tasks, the pros and cons of GP techniques are discussed, and some future directions are also set.
The multilayer structure of corporate networks

Various company interactions can be described by networks, for instance the ownership networks and the board membership networks. To understand the ecosystem of companies, these interactions cannot be seen in isolation. For this purpose we construct a new multiplex network of interactions between companies in Germany and in the United Kingdom, combining ownership links, social ties through joint board directors, R&D collaborations and stock correlations in one linked multiplex dataset. We describe the features of this network and show there exists a non-trivial overlap between these different types of networks, where the different types of connections complement each other and make the overall structure more complex. This highlights that corporate control, boardroom influence and other connections have different structures and together make an even smaller corporate world than previously reported. We have a first look at the relation between company performance and location in the network structure.
Debugging Frame Semantic Role Labeling

We propose a quantitative and qualitative analysis of the performances of statistical models for frame semantic structure extraction. We report on a replication study on  1.7 data and show that preprocessing toolkits play a major role in argument identification performances, observing gains similar in their order of magnitude to those reported by recent models for frame semantic parsing. We report on the robustness of a recent statistical classifier for frame semantic parsing to lexical configurations of predicate-argument structures, relying on an artificially augmented dataset generated using a rule-based algorithm combining valence pattern matching and lexical substitution. We prove that syntactic pre-processing plays a major role in the performances of statistical classifiers to argument identification, and discuss the core reasons of syntactic mismatch between dependency parsers output and FrameNet syntactic formalism. Finally, we suggest new leads for improving statistical models for frame semantic parsing, including joint syntax-semantic parsing relying on FrameNet syntactic formalism, latent classes inference via split-and-merge algorithms and neural network architectures relying on rich input representations of words.
Unsupervised Learning of Neural Networks to Explain Neural Networks

This paper presents an unsupervised method to learn a neural network, namely an explainer, to interpret a pre-trained convolutional neural network (CNN), i.e., explaining knowledge representations hidden in middle conv-layers of the CNN. Given feature maps of a certain conv-layer of the CNN, the explainer performs like an auto-encoder, which first disentangles the feature maps into object-part features and then inverts object-part features back to features of higher conv-layers of the CNN. More specifically, the explainer contains interpretable conv-layers, where each filter disentangles the representation of a specific object part from chaotic input feature maps. As a paraphrase of CNN features, the disentangled representations of object parts help people understand the logic inside the CNN. We also learn the explainer to use object-part features to reconstruct features of higher CNN layers, in order to minimize loss of information during the feature disentanglement. More crucially, we learn the explainer via network distillation without using any annotations of sample labels, object parts, or textures for supervision. We have applied our method to different types of CNNs for evaluation, and explainers have significantly boosted the interpretability of CNN features.
Applications of an Approximate Kerr-like Metric with Quadrupole

In this contribution, we calculate the light deflection, perihelium shift, time delay and gravitational redshift using an approximate metric that contains the Kerr metric and an approximaction of the Erez-Rosen spacetime. The results were obtained directly using Mathematica. The results agree with the ones presented in the literature, but they are extended until second order terms of mass, angular momentum and mass quadrupole.
Dancing with Donald: Polarity in the 2016 Presidential Election

In almost every election cycle, the validity of the United States Electoral College is brought into question. The 2016 Presidential Election again brought up the issue of a candidate winning the popular vote but not winning the Electoral College, with Hillary Clinton receiving close to three million more votes than Donald Trump. However, did the popular vote actually determine the most liked candidate in the election? In this paper, we demonstrate that different voting policies can alter which candidate is elected. Additionally, we explore the trade-offs between each of these mechanisms. Finally, we introduce two novel mechanisms with the intent of electing the least polarizing candidate.
Testing the warmness of dark matter

Dark matter (DM) as a pressureless perfect fluid provides a good fit of the standard Λ cold dark matter (ΛCDM) model to the astrophysical and cosmological data. In this paper, we investigate two extended properties of DM: a possible time dependence of the equation of state of DM via Chevallier–Polarski–Linder parametrization, wdm = wdm0 + wdm1(1 − a), and the constant non-null sound speed $\hat{c}^2_{\rm s,dm}$. We analyse these DM properties on top of the base ΛCDM model by using the data from Planck cosmic microwave background (CMB) temperature and polarization anisotropy, baryonic acoustic oscillations (BAOs), and the local value of the Hubble constant from the Hubble Space Telescope (HST). We find new and robust constraints on the extended free parameters of DM. The most tight constraints are imposed by CMB+BAO data, where the three parameters wdm0, wdm1, and $\hat{c}^2_{\rm s,dm}$ are, respectively, constrained to be less than 1.43 × 10−3, 1.44 × 10−3, and 1.79 × 10−6 at 95 per cent CL. All the extended parameters of DM show consistency with zero at 95 per cent CL, indicating no evidence beyond the CDM paradigm. We notice that the extended properties of DM significantly affect several parameters of the base ΛCDM model. In particular, in all the analyses performed here, we find significantly larger mean values of H0 and lower mean values of σ8 in comparison to the base ΛCDM model. Thus, the well-known H0 and σ8 tensions might be reconciled in the presence of extended DM parameters within the ΛCDM framework. Also, we estimate the warmness of DM particles as well as its mass scale, and find a lower bound: ∼500 eV from our analyses.
Diamond detectors for direct detection of sub-GeV dark matter

We propose using high-purity lab-grown diamond crystal for the detection of sub–giga electron volt dark matter. Diamond targets can be sensitive to both nuclear and electron recoils from dark matter scattering in the mega-electron-volt and above mass range as well as to absorption processes of dark matter with masses between sub–electron volts to tens of electron volts. Compared to other proposed semiconducting targets such as germanium and silicon, diamond detectors can probe lower dark matter masses via nuclear recoils due to the lightness of the carbon nucleus. The expected reach for electron recoils is comparable to that of germanium and silicon, with the advantage that dark counts are expected to be under better control. Via absorption processes, unconstrained QCD axion parameter space can be successfully probed in diamond for masses of order 10 eV, further demonstrating the power of our approach.
The evolution of ultra-diffuse galaxies in nearby galaxy clusters from the Kapteyn IAC WEAVE INT Clusters Survey

We study the population of ultra-diffuse galaxies (UDGs) in a set of eight nearby (z < 0.035) galaxy clusters, from the Kapteyn IAC WEAVE INT Clusters Survey. We report the discovery of 442 UDG candidates in our eight field of views, with 247 of these galaxies lying at projected distances <1R200 from their host cluster. With the aim of testing theories about their formation, we study the scaling relations of UDGs comparing with different types of galaxies, finding that in the full parameter space they behave as dwarf galaxies and their colours do not seem to correlate with their effective radii. To investigate the influence of the environment on the evolution of UDGs, we analyse their structural properties as functions of the projected clustercentric distance and the mass of their host cluster. We find no systematic trends for the stellar mass nor effective radius as function of the projected distance. However, the fraction of blue UDGs seem to be lower towards the centre of clusters, and UDGs in the inner and outer regions of clusters have different Sersic index and axial ratio distributions. Specifically, the axial ratio distributions of the outer and inner UDGs resemble the axial ratio distributions of, respectively, late-type dwarfs and dwarf ellipticals in the Fornax Cluster suggesting an environmentally driven evolution and another link between UDGs and dwarf galaxies. In general our results suggest strong similarities between UDGs and smaller dwarf galaxies in their structural parameters and their transformation within clusters.
Entangle me! A game to demonstrate the principles of quantum mechanics

We introduce a game to illustrate the principles of quantum mechanics using a qubit (or spin-first) approach, where students can experience and discover its puzzling features first-hand. Students play the role of particles and scientists. Scientists unravel underlying rules and properties by collecting and analysing data that are generated by observing particles that act according to the given rules. We show how this allows one to illustrate quantum states and their stochastic behavior under measurements as well as quantum entanglement. In addition, we use this approach to illustrate and discuss decoherence and a modern application of quantum features, namely, quantum cryptography. We have tested the game in the class and report on the results that we obtained.We introduce a game to illustrate the principles of quantum mechanics using a qubit (or spin-first) approach, where students can experience and discover its puzzling features first-hand. Students play the role of particles and scientists. Scientists unravel underlying rules and properties by collecting and analysing data that are generated by observing particles that act according to the given rules. We show how this allows one to illustrate quantum states and their stochastic behavior under measurements as well as quantum entanglement. In addition, we use this approach to illustrate and discuss decoherence and a modern application of quantum features, namely, quantum cryptography. We have tested the game in the class and report on the results that we obtained.
Striking the Right Balance With Uncertainty

Learning unbiased models on imbalanced datasets is a significant challenge. Rare classes tend to get a concentrated representation in the classification space which hampers the generalization of learned boundaries to new test examples. In this paper, we demonstrate that the Bayesian uncertainty estimates directly correlate with the rarity of classes and the difficulty level of individual samples. Subsequently, we present a novel framework for uncertainty based class imbalance learning that follows two key insights: First, classification boundaries should be extended further away from a more uncertain (rare) class to avoid over-fitting and enhance its generalization. Second, each sample should be modeled as a multi-variate Gaussian distribution with a mean vector and a covariance matrix defined by the sample's uncertainty. The learned boundaries should respect not only the individual samples but also their distribution in the feature space. Our proposed approach efficiently utilizes sample and class uncertainty information to learn robust features and more generalizable classifiers. We systematically study the class imbalance problem and derive a novel loss formulation for max-margin learning based on Bayesian uncertainty measure. The proposed method shows significant performance improvements on six benchmark datasets for face verification, attribute prediction, digit/object classification and skin lesion detection.
What Can Machine Learning Teach Us about Communications?

Rapid improvements in machine learning over the past decade are beginning to have far-reaching effects. For communications, engineers with limited domain expertise can now use off-the-shelf learning packages to design high-performance systems based on simulations. Prior to the current revolution in machine learning, the majority of communication engineers were quite aware that system parameters (such as filter coefficients) could be learned using stochastic gradient descent. It was not at all clear, however, that more complicated parts of the system architecture could be learned as well. In this paper, we discuss the application of machine-learning techniques to two communications problems and focus on what can be learned from the resulting systems. We were pleasantly surprised that the observed gains in one example have a simple explanation that only became clear in hindsight. In essence, deep learning discovered a simple and effective strategy that had not been considered earlier.
Aggregated Pairwise Classification of Statistical Shapes

The classification of shapes is of great interest in diverse areas ranging from medical imaging to computer vision and beyond. While many statistical frameworks have been developed for the classification problem, most are strongly tied to early formulations of the problem - with an object to be classified described as a vector in a relatively low-dimensional Euclidean space. Statistical shape data have two main properties that suggest a need for a novel approach: (i) shapes are inherently infinite dimensional with strong dependence among the positions of nearby points, and (ii) shape space is not Euclidean, but is fundamentally curved. To accommodate these features of the data, we work with the square-root velocity function of the curves to provide a useful formal description of the shape, pass to tangent spaces of the manifold of shapes at different projection points which effectively separate shapes for pairwise classification in the training data, and use principal components within these tangent spaces to reduce dimensionality. We illustrate the impact of the projection point and choice of subspace on the misclassification rate with a novel method of combining pairwise classifiers.
Mathematical model of gender bias and homophily in professional hierarchies

Women have become better represented in business, academia, and government over time, yet a dearth of women at the highest levels of leadership remains. Sociologists have attributed the leaky progression of women through professional hierarchies to various cultural and psychological factors, such as self-segregation and bias. Here, we present a minimal mathematical model that reveals the relative role that bias and homophily (self-seeking) may play in the ascension of women through professional hierarchies. Unlike previous models, our novel model predicts that gender parity is not inevitable, and deliberate intervention may be required to achieve gender balance in several fields. To validate the model, we analyze a new database of gender fractionation over time for 16 professional hierarchies. We quantify the degree of homophily and bias in each professional hierarchy, and we propose specific interventions to achieve gender parity more quickly.
Classification of a black hole spin out of its shadow using support vector machines

We use Support Vector Machines (SVMs) to classify the spin of a black hole. The SVMs are trained and tested with a catalog of numerically generated images of black holes, assuming disk and spherical matter models with monochromatic emission with wavelength of 4mm. We determine the accuracy of the SVM to classify the spin in terms of the image resolution, for which we consider three resolutions of $16^2,~32^2$ and $64^2$ pixels. Our approach is applied to the specific mass of the Supermassive Black Hole (SMBH) at the center of the Milky Way. Our findings are that when the distribution is a thin disk, the accuracy in the classification resists even the coarsest resolution with accuracy over 90\%, whereas for the spherical distribution it drops below 80\% for low and intermediate resolutions. The results show how the distribution of matter, the angle of vision and the image resolution influence the ease to determine the black hole spin.
Single Deep Counterfactual Regret Minimization

Counterfactual Regret Minimization (CFR) is the most successful algorithm for finding approximate Nash equilibria in imperfect information games. However, CFR's reliance on full game-tree traversals limits its scalability. For this reason, the game's state- and action-space is often abstracted (i.e. simplified) for CFR, and the resulting strategy is then translated back to the full game, which requires extensive expert-knowledge and often converges to highly exploitable policies. A recently proposed method, Deep CFR, applies deep learning directly to CFR, allowing the agent to intrinsically abstract and generalize over the state-space from samples, without requiring expert knowledge. In this paper, we introduce Single Deep CFR (SD-CFR), a simplified variant of Deep CFR that has a lower overall approximation error by avoiding the training of an average strategy network. We show that SD-CFR is more attractive from a theoretical perspective and empirically outperforms Deep CFR with respect to exploitability and one-on-one play in poker.
Partial Order on the set of Boolean Regulatory Functions

Logical models have been successfully used to describe regulatory and signaling networks without requiring quantitative data. However, existing data is insufficient to adequately define a unique model, rendering the parametrization of a given model a difficult task. 
Here, we focus on the characterization of the set of Boolean functions compatible with a given regulatory structure, i.e. the set of all monotone nondegenerate Boolean functions. We then propose an original set of rules to locally explore the direct neighboring functions of any function in this set, without explicitly generating the whole set. Also, we provide relationships between the regulatory functions and their corresponding dynamics. 
Finally, we illustrate the usefulness of this approach by revisiting Probabilistic Boolean Networks with the model of T helper cell differentiation from Mendoza & Xenarios.
Just-in-Time Index Compilation

Creating or modifying a primary index is a time-consuming process, as the index typically needs to be rebuilt from scratch. In this paper, we explore a more graceful "just-in-time" approach to index reorganization, where small changes are dynamically applied in the background. To enable this type of reorganization, we formalize a composable organizational grammar, expressive enough to capture instances of not only existing index structures, but arbitrary hybrids as well. We introduce an algebra of rewrite rules for such structures, and a framework for defining and optimizing policies for just-in-time rewriting. Our experimental analysis shows that the resulting index structure is flexible enough to adapt to a variety of performance goals, while also remaining competitive with existing structures like the C++ standard template library map.