nils-herrmann/clean_scirepeval huggingface dataset:
Constructive Discrepancy Minimization with Hereditary L2 Guarantees

In discrepancy minimization problems, we are given a family of sets $\mathcal{S} = \{S_1,\dots,S_m\}$, with each $S_i \in \mathcal{S}$ a subset of some universe $U = \{u_1,\dots,u_n\}$ of $n$ elements. The goal is to find a coloring $\chi : U \to \{-1,+1\}$ of the elements of $U$ such that each set $S \in \mathcal{S}$ is colored as evenly as possible. Two classic measures of discrepancy are $\ell_\infty$-discrepancy defined as $\textrm{disc}_\infty(\mathcal{S},\chi):=\max_{S \in \mathcal{S}} | \sum_{u_i \in S} \chi(u_i) |$ and $\ell_2$-discrepancy defined as $\textrm{disc}_2(\mathcal{S},\chi):=\sqrt{(1/|\mathcal{S}|)\sum_{S \in \mathcal{S}} \left(\sum_{u_i \in S}\chi(u_i)\right)^2}$. Breakthrough work by Bansal gave a polynomial time algorithm, based on rounding an SDP, for finding a coloring $\chi$ such that $\textrm{disc}_\infty(\mathcal{S},\chi) = O(\lg n \cdot \textrm{herdisc}_\infty(\mathcal{S}))$ where $\textrm{herdisc}_\infty(\mathcal{S})$ is the hereditary $\ell_\infty$-discrepancy of $\mathcal{S}$. We complement his work by giving a simple $O((m+n)n^2)$ time algorithm for finding a coloring $\chi$ such $\textrm{disc}_2(\mathcal{S},\chi) = O(\sqrt{\lg n} \cdot \textrm{herdisc}_2(\mathcal{S}))$ where $\textrm{herdisc}_2(\mathcal{S})$ is the hereditary $\ell_2$-discrepancy of $\mathcal{S}$. Interestingly, our algorithm avoids solving an SDP and instead relies on computing eigendecompositions of matrices. Moreover, we use our ideas to speed up the Edge-Walk algorithm by Lovett and Meka [SICOMP'15]. To prove that our algorithm has the claimed guarantees, we show new inequalities relating $\textrm{herdisc}_\infty$ and $\textrm{herdisc}_2$ to the eigenvalues of the matrix corresponding to $\mathcal{S}$. Our inequalities improve over previous work by Chazelle and Lvov, and by Matousek et al. Finally, we also implement our algorithm and show that it far outperforms random sampling.
Conditional Random Field and Deep Feature Learning for Hyperspectral Image Classification

Image classification is considered to be one of the critical tasks in hyperspectral remote sensing image processing. Recently, a convolutional neural network (CNN) has established itself as a powerful model in classification by demonstrating excellent performances. The use of a graphical model such as a conditional random field (CRF) contributes further in capturing contextual information and thus improving the classification performance. In this paper, we propose a method to classify hyperspectral images by considering both spectral and spatial information via a combined framework consisting of CNN and CRF. We use multiple spectral band groups to learn deep features using CNN, and then formulate deep CRF with CNN-based unary and pairwise potential functions to effectively extract the semantic correlations between patches consisting of 3-D data cubes. Furthermore, we introduce a deep deconvolution network that improves the final classification performance. We also introduced a new data set and experimented our proposed method on it along with several widely adopted benchmark data sets to evaluate the effectiveness of our method. By comparing our results with those from several state-of-the-art models, we show the promising potential of our method.
Classical Control, Quantum Circuits and Linear Logic in Enriched Category Theory

We describe categorical models of a circuit-based (quantum) functional programming language. We show that enriched categories play a crucial role. Following earlier work on QWire by Paykin et al., we consider both a simple first-order linear language for circuits, and a more powerful host language, such that the circuit language is embedded inside the host language. Our categorical semantics for the host language is standard, and involves cartesian closed categories and monads. We interpret the circuit language not in an ordinary category, but in a category that is enriched in the host category. We show that this structure is also related to linear/non-linear models. As an extended example, we recall an earlier result that the category of W*-algebras is dcpo-enriched, and we use this model to extend the circuit language with some recursive types.
High-Resolution Deep Convolutional Generative Adversarial Networks

Generative Adversarial Networks (GANs) [Goodfellow et al. 2014] convergence in a high-resolution setting with a computational constrain of GPU memory capacity has been beset with difficulty due to the known lack of convergence rate stability. In order to boost network convergence of DCGAN (Deep Convolutional Generative Adversarial Networks) [Radford et al. 2016] and achieve good-looking high-resolution results we propose a new layered network, HDCGAN, that incorporates current state-of-the-art techniques for this effect. Glasses, a mechanism to arbitrarily improve the final GAN generated results by enlarging the input size by a telescope {\zeta} is also presented. A novel bias-free dataset, Curt\'o & Zarza, containing human faces from different ethnical groups in a wide variety of illumination conditions and image resolutions is introduced. Curt\'o is enhanced with HDCGAN synthetic images, thus being the first GAN augmented dataset of faces. We conduct extensive experiments on CelebA [Liu et al. 2015], CelebA-hq [Karras et al. 2018] and Curt\'o. HDCGAN is the current state-of-the-art in synthetic image generation on CelebA achieving a MS-SSIM of 0.1978 and a FR\'ECHET Inception Distance of 8.44.
Evolving soft locomotion in aquatic and terrestrial environments: effects of material properties and environmental transitions

Designing soft robots poses considerable challenges; automated design approaches may be particularly appealing in this field, as they promise to optimize complex multimaterial machines with very little or no human intervention. Evolutionary soft robotics is concerned with the application of optimization algorithms inspired by natural evolution to let soft robots (both their morphologies and controllers) spontaneously evolve within physically realistic simulated environments, figuring out how to satisfy a set of objectives defined by human designers. In this article, a powerful evolutionary system is put in place to perform a broad investigation on the free-form evolution of simulated walking and swimming soft robots in different environments. Three sets of experiments are reported, tackling different aspects of the evolution of soft locomotion. The first two explore the effects of different material properties on the evolution of terrestrial and aquatic soft locomotion: particularly, we show how different materials lead to the evolution of different morphologies, behaviors, and energy-performance trade-offs. It is found that within our simplified physics world, stiffer robots evolve more sophisticated and effective gaits and morphologies on land, while softer ones tend to perform better in water. The third set of experiments starts investigating the effect and potential benefits of major environmental transitions (land↔water) during evolution. Results provide interesting morphological exaptation phenomena and point out a potential asymmetry between land→water and water→land transitions: while the first type of transition appears to be detrimental, the second one seems to have some beneficial effects.
MicroExpNet: An Extremely Small and Fast Model For Expression Recognition From Face Images

—This paper is aimed at creating extremely small and fast convolutional neural networks (CNN) for the problem of facial expression recognition (FER) from frontal face images. To this end, we employed the popular knowledge distillation (KD) method and identiﬁed two major shortcomings with its use: 1) a ﬁne-grained grid search is needed for tuning the temperature hyperparameter and 2) to ﬁnd the optimal size-accuracy balance, one needs to search for the ﬁnal network size (or the compression rate). On the other hand, KD is proved to be useful for model compression for the FER problem, and we discovered that its effects get more and more signiﬁcant with decreasing model size. In addition, we hypothesized that translation invariance achieved using max-pooling layers would not be useful for the FER problem as the expressions are sensitive to small, pixel-wise changes around the eye and the mouth. However, we have found an intriguing improvement in generalization when max-pooling is used. We conducted experiments on two widely- used FER datasets, CK+ and Oulu-CASIA. Our smallest model (MicroExpNet), obtained using knowledge distillation, is less than 1 MB in size and works at 1851 frames per second on an Intel i7 CPU. Despite being less accurate than the state-of-the-art, MicroExpNet still provides signiﬁcant insights for designing a microarchitecture for the FER problem.
A Quantum Walk Enhanced Grover Search Algorithm for Global Optimization

One of the significant breakthroughs in quantum computation is Grover's algorithm for unsorted database search. Recently, the applications of Grover's algorithm to solve global optimization problems have been demonstrated, where unknown optimum solutions are found by iteratively improving the threshold value for the selective phase shift operator in Grover rotation. In this paper, a hybrid approach that combines continuous-time quantum walks with Grover search is proposed so that the search is accelerated with improved threshold values. By taking advantage of the quantum tunneling effect, better threshold values can be found at the early stage of the search process so that the sharpness of probability improves. The results between the new algorithm, existing Grover search, and classical heuristic algorithms are compared.
Cantor Set Arithmetic

Every element u of can be written in the form , where x, y are elements of the Cantor set C. In particular, every real number between zero and one is the product of three elements of the Cantor set. On the other hand, the set of real numbers v that can be written in the form v = xy with x and y in C is a closed subset of with Lebesgue measure strictly between and . We also describe the structure of the quotient of C by itself, that is, the image of under the function .
Self-Supervised Vision-Based Detection of the Active Speaker as Support for Socially Aware Language Acquisition

This paper presents a self-supervised method for visual detection of the active speaker in a multiperson spoken interaction scenario. Active speaker detection is a fundamental prerequisite for any artificial cognitive system attempting to acquire language in social settings. The proposed method is intended to complement the acoustic detection of the active speaker, thus improving the system robustness in noisy conditions. The method can detect an arbitrary number of possibly overlapping active speakers based exclusively on visual information about their face. Furthermore, the method does not rely on external annotations, thus complying with cognitive development. Instead, the method uses information from the auditory modality to support learning in the visual domain. This paper reports an extensive evaluation of the proposed method using a large multiperson face-to-face interaction data set. The results show good performance in a speaker dependent setting. However, in a speaker independent setting the proposed method yields a significantly lower performance. We believe that the proposed method represents an essential component of any artificial cognitive system or robotic platform engaging in social interactions.
Stacked Kernel Network

Kernel methods are powerful tools to capture nonlinear patterns behind data. They implicitly learn high (even infinite) dimensional nonlinear features in the Reproducing Kernel Hilbert Space (RKHS) while making the computation tractable by leveraging the kernel trick. Classic kernel methods learn a single layer of nonlinear features, whose representational power may be limited. Motivated by recent success of deep neural networks (DNNs) that learn multi-layer hierarchical representations, we propose a Stacked Kernel Network (SKN) that learns a hierarchy of RKHS-based nonlinear features. SKN interleaves several layers of nonlinear transformations (from a linear space to a RKHS) and linear transformations (from a RKHS to a linear space). Similar to DNNs, a SKN is composed of multiple layers of hidden units, but each parameterized by a RKHS function rather than a finite-dimensional vector. We propose three ways to represent the RKHS functions in SKN: (1)nonparametric representation, (2)parametric representation and (3)random Fourier feature representation. Furthermore, we expand SKN into CNN architecture called Stacked Kernel Convolutional Network (SKCN). SKCN learning a hierarchy of RKHS-based nonlinear features by convolutional operation with each filter also parameterized by a RKHS function rather than a finite-dimensional matrix in CNN, which is suitable for image inputs. Experiments on various datasets demonstrate the effectiveness of SKN and SKCN, which outperform the competitive methods.
Ryu-Takayanagi formula for symmetric random tensor networks

We consider the special case of random tensor networks (RTNs) endowed with gauge symmetry constraints on each tensor. We compute the R\'enyi entropy for such states and recover the Ryu-Takayanagi (RT) formula in the large-bond regime. The result provides first of all an interesting new extension of the existing derivations of the RT formula for RTNs. Moreover, this extension of the RTN formalism brings it in direct relation with (tensorial) group field theories (and spin networks), and thus provides new tools for realizing the tensor network/geometry duality in the context of background-independent quantum gravity, and for importing quantum gravity tools into tensor network research.
Efficient reduction of nondeterministic automata with application to language inclusion testing

We present efficient algorithms to reduce the size of nondeterministic Buchi word automata (NBA) and nondeterministic finite word automata (NFA), while retaining their languages. Additionally, we describe methods to solve PSPACE-complete automata problems like language universality, equivalence, and inclusion for much larger instances than was previously possible ($\ge 1000$ states instead of 10-100). This can be used to scale up applications of automata in formal verification tools and decision procedures for logical theories. The algorithms are based on new techniques for removing transitions (pruning) and adding transitions (saturation), as well as extensions of classic quotienting of the state space. These techniques use criteria based on combinations of backward and forward trace inclusions and simulation relations. Since trace inclusion relations are themselves PSPACE-complete, we introduce lookahead simulations as good polynomial time computable approximations thereof. Extensive experiments show that the average-case time complexity of our algorithms scales slightly above quadratically. (The space complexity is worst-case quadratic.) The size reduction of the automata depends very much on the class of instances, but our algorithm consistently reduces the size far more than all previous techniques. We tested our algorithms on NBA derived from LTL-formulae, NBA derived from mutual exclusion protocols and many classes of random NBA and NFA, and compared their performance to the well-known automata tool GOAL.
Training Convolutional Neural Networks with Limited Training Data for Ear Recognition in the Wild

Identity recognition from ear images is an active field of research within the biometric community. The ability to capture ear images from a distance and in a covert manner makes ear recognition technology an appealing choice for surveillance and security applications as well as related application domains. In contrast to other biometric modalities, where large datasets captured in uncontrolled settings are readily available, datasets of ear images are still limited in size and mostly of laboratory-like quality. As a consequence, ear recognition technology has not benefited yet from advances in deep learning and convolutionalneural networks (CNNs) and is still lacking behind other modalities that experienced significant performance gains owing to deep recognition technology. In this paper we address this problem and aim at building a CNNbased ear recognition model. We explore different strategies towards model training with limited amounts of training data and show that by selecting an appropriate model architecture, using aggressive data augmentation and selective learning on existing (pre-trained) models, we are able to learn an effective CNN·based model using a little more than 1300training images. The result of our work is the first CNN·based approach to ear recognition that is also made publicly available to the research community. With our model we are able to improve on the rank one recognition rate of the previous state-of-the-art by more than 25% on a challenging dataset of ear images captured from the web (a.k.a, in the wild).
SSD-6D: Making RGB-Based 3D Detection and 6D Pose Estimation Great Again

We present a novel method for detecting 3D model instances and estimating their 6D poses from RGB data in a single shot. To this end, we extend the popular SSD paradigm to cover the full 6D pose space and train on synthetic model data only. Our approach competes or surpasses current state-of-the-art methods that leverage RGBD data on multiple challenging datasets. Furthermore, our method produces these results at around 10Hz, which is many times faster than the related methods. For the sake of reproducibility, we make our trained networks and detection code publicly available.
Intelligent Notification Systems: A Survey of the State of the Art and Research Challenges

Notifications provide a unique mechanism for increasing the effectiveness of real-time information delivery systems. However, notifications that demand users' attention at inopportune moments are more likely to have adverse effects and might become a cause of potential disruption rather than proving beneficial to users. In order to address these challenges a variety of intelligent notification mechanisms based on monitoring and learning users' behavior have been proposed. The goal of such mechanisms is maximizing users' receptivity to the delivered information by automatically inferring the right time and the right context for sending a certain type of information. 
This article provides an overview of the current state of the art in the area of intelligent notification mechanisms that relies on the awareness of users' context and preferences. More specifically, we first present a survey of studies focusing on understanding and modeling users' interruptibility and receptivity to notifications from desktops and mobile devices. Then, we discuss the existing challenges and opportunities in developing mechanisms for intelligent notification systems in a variety of application scenarios.
Gradual Tuning: a better way of Fine Tuning the parameters of a Deep Neural Network

In this paper we present an alternative strategy for fine-tuning the parameters of a network. We named the technique Gradual Tuning. Once trained on a first task, the network is fine-tuned on a second task by modifying a progressively larger set of the network's parameters. We test Gradual Tuning on different transfer learning tasks, using networks of different sizes trained with different regularization techniques. The result shows that compared to the usual fine tuning, our approach significantly reduces catastrophic forgetting of the initial task, while still retaining comparable if not better performance on the new task.
Social Media, Money, and Politics: Campaign Finance in the 2016 US Congressional Cycle

With social media penetration deepening among both citizens and political figures, there is a pressing need to understand whether and how political use of major platforms is electorally influential. Particularly, the literature focused on campaign usage is thin and often describe the engagement strategies of politicians or attempt to quantify the impact of social media engagement on political learning, participation, or voting. Few have considered implications for campaign fundraising despite its recognized importance in American politics. This paper is the first to quantify a financial payoff for social media campaigning. Drawing on candidate-level data from Facebook and Twitter, Google Trends, Wikipedia page views, and Federal Election Commission (FEC) donation records, we analyze the relationship between the topic and volume of social media content and campaign funds received by all 108 candidates in the 2016 US Senate general elections. By applying an unsupervised learning approach to identify themes in candidate content across the platforms, we find that more frequent posting overall and of issue-related content are associated with higher donation income when controlling for incumbency, state population, and information-seeking about a candidate, though campaigning-related content has a stronger effect than the latter when the number rather than value of donations is considered.
Inferring users' preferences through leveraging their social relationships

Recommender systems, inferring users' preferences from their historical activities and personal profiles, have been an enormous success in the last several years. Most of the existing works are based on the similarities of users, objects or both that derived from their purchases records in the online shopping platforms. Such approaches, however, are facing bottlenecks when the known information is limited. The extreme case is how to recommend products to new users, namely the so-called cold-start problem. The rise of the online social networks gives us a chance to break the glass ceiling. Birds of a feather flock together. Close friends may have similar hidden pattern of selecting products and the advices from friends are more trustworthy. In this paper, we integrate the individual's social relationships into recommender systems and propose a new method, called Social Mass Diffusion (SMD), based on a mass diffusion process in the combined network of users' social network and user-item bipartite network. The results show that the SMD algorithm can achieve higher recommendation accuracy than the Mass Diffusion (MD) purely on the bipartite network. Especially, the improvement is striking for small degree users. Moreover, SMD provides a good solution to the cold-start problem. The recommendation accuracy for new users significantly higher than that of the conventional popularity-based algorithm. These results may shed some light on the new designs of better personalized recommender systems and information services.