nils-herrmann/clean_scirepeval huggingface dataset:
Finite-Size Dark Matter and its Effect on Small-Scale Structure.

If dark matter has a finite size that is larger than its Compton wavelength, the corresponding self-interaction cross section decreases with the velocity. We investigate the implications of this puffy dark matter for addressing the small-scale problems of the Λ cold dark matter model and show that the way the nonrelativistic cross section varies with the velocity is largely independent of the dark matter internal structure. Even in the presence of a light particle mediating self-interactions, we find that the finite-size effect may dominate the velocity dependence. We present an explicit example in the context of a QCD-like theory and discuss possible ways to differentiate puffy dark matter from the usual light-mediator scenarios. Particularly relevant for this are low-threshold direct-detection experiments and indirect signatures associated with the internal structure of dark matter.
The Influence of Social Media Writing on Online Search Behavior for Seasonal Topics: The Sociophysics Approach

Using seasonal topics as the study subject, in this study, we focus on the timing gap between social media writing and online search behavior. To conduct our analysis, we used the mathematical model of search behavior, comprising the sociophysics approach. The seasonal topics selected were cherry-blossom viewing for spring, bikinis for summer, autumn leaves for fall, and skiing for winter. We also picked up the event like Christmas and Halloween. We analyzed the influence of blogs and Twitter on search behavior and found a deviation of interest in terms of timing. We also analyzed seasonal topics in newspapers after 2010 and observed deviations in the number of searches and spikes in the number of references in newspaper magazines.
Quantum Annealing Based Binary Compressive Sensing with Matrix Uncertainty

This research has been supported by NASA grant (#NNH16ZDA001N-AIST 16-0091), NIH-NIGMS Initiative for Maximizing Student Development Grant (2 R25- GM55036), and the Google Lime scholarship. We would like to thank the D-Wave Systems management team for access to the 2000Q quantum computer at Burnaby, Canada. We also would like to thank the NSF funded Center for Hybrid Multicore Productivity Research for access support to IBM Minsky cluster.
Training with the Invisibles: Obfuscating Images to Share Safely for Learning Visual Recognition Models

High-performance visual recognition systems generally require a large collection of labeled images to train. The expensive data curation can be an obstacle for improving recognition performance. Sharing more data allows training for better models. But personal and private information in the data prevent such sharing. To promote sharing visual data for learning a recognition model, we propose to obfuscate the images so that humans are not able to recognize their detailed contents, while machines can still utilize them to train new models. We validate our approach by comprehensive experiments on three challenging visual recognition tasks; image classification, attribute classification, and facial landmark detection on several datasets including SVHN, CIFAR10, Pascal VOC 2012,  Our method successfully obfuscates the images from humans recognition, but a machine model trained with them performs within about 1% margin (up to 0.48%) of the performance of a model trained with the original, non-obfuscated data.
On polyhedral formula for Kirillov-Reshetikhin modules

We propose a method to prove a polyhedral branching formula for Kirillov-Reshetikhin modules over a quantum affine algebra. When the underlying simple Lie algebra is of exceptional type, such a formula remains mostly conjectural. We convert a polyhedral formula into an identity between two rational functions of a single variable with only simple poles at known locations. It is then sufficient to check the equalities of the residues at those poles, which are explicitly computable quantities. By following this strategy, we obtain a computer-assisted proof of a conjectural polyhedral formula in type $F_4$.
Top-assisted di-Higgs boson production motivated by baryogenesis

We study top-assisted di-Higgs production via $cg \to tH \to thh$, where $h$ is the 125 GeV scalar boson, and $H$ is the $CP$-even heavy Higgs. The context is the two Higgs doublet model without a $Z_2$ symmetry, where the extra Yukawa coupling $\rho_{tc}$ generates $tH$ production, with the extra top Yukawa $\rho_{tt} \simeq 0$ to avoid $gg \to H$ constraints. We find that discovery is possible for $m_H$ around 300 GeV or so at the LHC, but would need finite $h$-$H$ mixing angle $\cos\gamma$ to allow for finite $\lambda_{Hhh}$ coupling, and $\rho_{tc}$ also needs to be not too small. A sizable $\rho_{tc}$ could drive electroweak baryogenesis, which further motivates the search.
Dense Morphological Network: An Universal Function Approximator

Artificial neural networks are built on the basic operation of linear combination and non-linear activation function. Theoretically this structure can approximate any continuous function with three layer architecture. But in practice learning the parameters of such network can be hard. Also the choice of activation function can greatly impact the performance of the network. In this paper we are proposing to replace the basic linear combination operation with non-linear operations that do away with the need of additional non-linear activation function. To this end we are proposing the use of elementary morphological operations (dilation and erosion) as the basic operation in neurons. We show that these networks (Denoted as DenMo-Net) with morphological operations can approximate any smooth function requiring less number of parameters than what is necessary for normal neural networks. The results show that our network perform favorably when compared with similar structured network.
Closing the gap towards end-to-end autonomous vehicle system

Designing a driving policy for autonomous vehicles is a difficult task. Recent studies suggested an end-toend (E2E) training of a policy to predict car actuators directly from raw sensory inputs. It is appealing due to the ease of labeled data collection and since handcrafted features are avoided. Explicit drawbacks such as interpretability, safety enforcement and learning efficiency limit the practical application of the approach. In this paper, we amend the basic E2E architecture to address these shortcomings, while retaining the power of end-to-end learning. A key element in our proposed architecture is formulation of the learning problem as learning of trajectory. We also apply a Gaussian mixture model loss to contend with multi-modal data, and adopt a finance risk measure, conditional value at risk, to emphasize rare events. We analyze the effect of each concept and present driving performance in a highway scenario in the TORCS simulator. Video is available in this link: this https URL
An Active Learning Framework for Efficient Robust Policy Search

Robust Policy Search is the problem of learning policies that do not degrade in performance when subject to unseen environment model parameters. It is particularly relevant for transferring policies learned in a simulation environment to the real world. Several existing approaches involve sampling large batches of trajectories which reflect the differences in various possible environments, and then selecting some subset of these to learn robust policies, such as the ones that result in the worst performance. We propose an active learning based framework, EffAcTS, to selectively choose model parameters for this purpose so as to collect only as much data as necessary to select such a subset. We apply this framework using Linear Bandits, and experimentally validate the gains in sample efficiency and the performance of our approach on standard continuous control tasks. We also present a Multi-Task Learning perspective to the problem of Robust Policy Search, and draw connections from our proposed framework to existing work on Multi-Task Learning.
Gated-Dilated Networks for Lung Nodule Classification in CT Scans

Different types of Convolutional Neural Networks (CNNs) have been applied to detect cancerous lung nodules from computed tomography (CT) scans. However, the size of a nodule is very diverse and can range anywhere between 3 and 30 millimeters. The high variation of nodule sizes makes classifying them a difficult and challenging task. In this study, we propose a novel CNN architecture called Gated-Dilated (GD) networks to classify nodules as malignant or benign. Unlike previous studies, the GD network uses multiple dilated convolutions instead of max-poolings to capture the scale variations. Moreover, the GD network has a Context-Aware sub-network that analyzes the input features and guides the features to a suitable dilated convolution. We evaluated the proposed network on more than 1,000 CT scans from the LIDC-LDRI dataset. Our proposed network outperforms state-of-the-art baseline models including Multi-Crop, Resnet, and Densenet, with an AUC of >0.95. Compared to the baseline models, the GD network improves the classification accuracies of mid-range sized nodules. Furthermore, we observe a relationship between the size of the nodule and the attention signal generated by the Context-Aware sub-network, which validates our new network architecture.
5G Traffic Forecasting: If Verticals and Mobile Operators Cooperate

In 5G research, it is traditionally assumed that vertical industries (a.k.a verticals) set the performance requirements for the services they want to offer to mobile users, and the mobile operators alone are in charge of orchestrating their resources so as to meet such requirements. Motivated by the observation that successful orchestration requires reliable traffic predictions, in this paper we investigate the effects of having the verticals, instead of the mobile operators, performing such predictions. Leveraging a real-world, large-scale, crowd-sourced trace, we find that involving the verticals in the prediction process reduces the prediction errors and improves the quality of the resulting orchestration decisions.
A Theoretical Analysis of Deep Q-Learning

Despite the great empirical success of deep reinforcement learning, its theoretical foundation is less well understood. In this work, we make the first attempt to theoretically understand the deep Q-network (DQN) algorithm (Mnih et al., 2015) from both algorithmic and statistical perspectives. In specific, we focus on a slight simplification of DQN that fully captures its key features. Under mild assumptions, we establish the algorithmic and statistical rates of convergence for the action-value functions of the iterative policy sequence obtained by DQN. In particular, the statistical error characterizes the bias and variance that arise from approximating the action-value function using deep neural network, while the algorithmic error converges to zero at a geometric rate. As a byproduct, our analysis provides justifications for the techniques of experience replay and target network, which are crucial to the empirical success of DQN. Furthermore, as a simple extension of DQN, we propose the Minimax-DQN algorithm for zero-sum Markov game with two players. Borrowing the analysis of DQN, we also quantify the difference between the policies obtained by Minimax-DQN and the Nash equilibrium of the Markov game in terms of both the algorithmic and statistical rates of convergence.
Rethinking on Multi-Stage Networks for Human Pose Estimation

Existing pose estimation approaches fall into two categories: single-stage and multi-stage methods. While multi-stage methods are seemingly more suited for the task, their performance in current practice is not as good as single-stage methods. This work studies this issue. We argue that the current multi-stage methods' unsatisfactory performance comes from the insufficiency in various design choices. We propose several improvements, including the single-stage module design, cross stage feature aggregation, and coarse-to-fine supervision. The resulting method establishes the new state-of-the-art on both MS COCO and MPII Human Pose dataset, justifying the effectiveness of a multi-stage architecture. The source code is publicly available for further research.
Sterile Neutrino. A short introduction

This is a pedagogical introduction to the main concepts of the sterile neutrino a hypothetical particle, coined to resolve some anomalies in neutrino data and retain consistency with observed widths of the W and Z bosons. We briefly review existing anomalies and the oscillation parameters that best describe these data. We discuss in more detail how sterile neutrinos can be observed, as well as the consequences of its possible existence. In particular, we pay attention to a possible loss of coherence in a model of neutrino oscillations with sterile neutrinos, where this effect might be of a major importance with respect to the 3ν model. The current status of searches for a sterile neutrino state is also briefly reviewed.
Complementary reinforcement learning towards explainable agents

Reinforcement learning (RL) algorithms allow agents to learn skills and strategies to perform complex tasks without detailed instructions or expensive labelled training examples. That is, RL agents can learn, as we learn. Given the importance of learning in our intelligence, RL has been thought to be one of key components to general artificial intelligence, and recent breakthroughs in deep reinforcement learning suggest that neural networks (NN) are natural platforms for RL agents. However, despite the efficiency and versatility of NN-based RL agents, their decision-making remains incomprehensible, reducing their utilities. To deploy RL into a wider range of applications, it is imperative to develop explainable NN-based RL agents. Here, we propose a method to derive a secondary comprehensible agent from a NN-based RL agent, whose decision-makings are based on simple rules. Our empirical evaluation of this secondary agent's performance supports the possibility of building a comprehensible and transparent agent using a NN-based RL agent.
Methodological provisions for conducting empirical research of the availability and implementation of the consumers socially responsible intentions

Social responsibility of consumers is one of the main conditions for the recoupment of enterprises expenses associated with the implementation of social and ethical marketing tasks. Therefore, the enterprises, which plan to act on terms of social and ethical marketing, should monitor the social responsibility of consumers in the relevant markets. At the same time, special attention should be paid to the analysis of factors that prevent consumers from implementing their socially responsible intentions in the regions with a low level of social activity of consumers. The purpose of the article is to develop methodological guidelines that determine the tasks and directions of conducting empirical studies aimed at assessing the gap between the socially responsible intentions of consumers and the actual implementation of these intentions, as well as to identify the causes of this gap. An empirical survey of the sampled consumers in Kharkiv was carried out in terms of the proposed methodological provisions. It revealed a rather high level of respondents' willingness to support socially responsible enterprises and a rather low level of implementation of these intentions due to the lack of consumers awareness. To test the proposed methodological guidelines, an empirical study of the consumers social responsibility was conducted in 2017 on a sample of students and professors of the Semen Kuznets Kharkiv National University of Economics (120 people). Questioning of the respondents was carried out using the Google Forms. The finding allowed to make conclusion for existence of a high level of respondents' willingness to support socially responsible and socially active enterprises. However, the study also revealed the existence of a significant gap between the intentions and actions of consumers, caused by the lack of awareness.
Twist-2 operators induced Dark Matter Interactions

We study the effective interactions of the fermionic, scalar and vector dark matter (DM) with leptons and neutral electroweak gauge Bosons induced by the higher dimensional effective twist-2 tensor operators. We constrain these lepto-philic, $\tau^\pm$-philic and $U(1)_Y$ gauge Boson B-philic effective interactions of DM with the visible world from the WMAP and Planck data. The thermally averaged indirect DM pair annihilation cross-section and the DM-electron direct-detection cross-section for such DM candidates are observed to be consistent with the respective experimental data. Constraining coefficients of the effective operators from the low energy LEP data for the DM $\le$ 80 GeV, we further study their sensitivities in the pair production of such DM $\ge$ 50 GeV in association with mono-photon at the proposed ILC and perform the $\chi^2$ analysis to obtain the 99.73\% C.L. acceptance contours in the $m_{\rm DM}-\Lambda_{\rm eff}$ plane from the two dimensional differential distributions of the kinematic observables. We observe that ILC has rich potential to probe the contribution of such effective operators.
Accounting for incompleteness due to transit multiplicity inKeplerplanet occurrence rates

We investigate the role that planet detection order plays in the Kepler planet detection pipeline. The Kepler pipeline typically detects planets in order of descending signal strength (MES). We find that the detectability of transits experiences an additional $5.5\%$ and $15.9\%$ efficiency loss, for periods $ 200$ days respectively, when detected after the strongest signal transit in a multiple-planet system. We provide a method for determining the transit probability for multiple-planet systems by marginalizing over the empirical Kepler dataset. Furthermore, because detection efficiency appears to be a function of detection order, we discuss the sorting statistics that affect the radius and period distributions of each detection order. Our occurrence rate dataset includes radius measurement updates from the California Kepler Survey (CKS), Gaia DR2, and asteroseismology. Our population model is consistent with the results of Burke et al. (2015), but now includes an improved estimate of the multiplicity distribution. From our obtained model parameters, we find that only $4.0\pm4.6\%$ of solar-like GK dwarfs harbor one planet. This excess is smaller than prior studies and can be well modeled with a modified Poisson distribution, suggesting that the Kepler Dichotomy can be accounted for by including the effects of multiplicity on detection efficiency. Using our modified Poisson model we expect the average number of planets is $5.86\pm0.18$ planets per GK dwarf within the radius and period parameter space of Kepler.
Initial wave function of the universe is arbitrary

We consider quantization of the gravity-scalar field system in the minisuperspace approximation. It turns out that in the gauge fixed deparametrized theory where the scale factor plays the role of time, the Hamiltonian can be uniquely defined without any ordering ambiguity as the square root of a self-adjoint operator. Moreover, the Hamiltonian degenerates to zero and the Schrödinger equation becomes well behaved as the scale factor vanishes. Therefore, there is no technical or physical obstruction for the initial wave function of the universe to be an arbitrary vector in the Hilbert space, which demonstrates the severeness of the initial condition problem in quantum cosmology.
Augmentation Scheme for Dealing with Imbalanced Network Traffic Classification Using Deep Learning

One of the most important tasks in network management is identifying different types of traffic flows. As a result, a type of management service, called Network Traffic Classifier (NTC), has been introduced. One type of NTCs that has gained huge attention in recent years applies deep learning on packets in order to classify flows. Internet is an imbalanced environment i.e., some classes of applications are a lot more populated than others e.g., HTTP. Additionally, one of the challenges in deep learning methods is that they do not perform well in imbalanced environments in terms of evaluation metrics such as precision, recall, and $\mathrm{F_1}$ measure. In order to solve this problem, we recommend the use of augmentation methods to balance the dataset. In this paper, we propose a novel data augmentation approach based on the use of Long Short Term Memory (LSTM) networks for generating traffic flow patterns and Kernel Density Estimation (KDE) for replicating the numerical features of each class. First, we use the LSTM network in order to learn and generate the sequence of packets in a flow for classes with less population. Then, we complete the features of the sequence with generating random values based on the distribution of a certain feature, which will be estimated using KDE. Finally, we compare the training of a Convolutional Recurrent Neural Network (CRNN) in large-scale imbalanced, sampled, and augmented datasets. The contribution of our augmentation scheme is then evaluated on all of the datasets through measurements of precision, recall, and F1 measure for every class of application. The results demonstrate that our scheme is well suited for network traffic flow datasets and improves the performance of deep learning algorithms when it comes to above-mentioned metrics.
Smart Information Spreading for Opinion Maximization in Social Networks

The goal of opinion maximization is to maximize the positive view towards a product, an ideology or any entity among the individuals in social networks. So far, opinion maximization is mainly studied as finding a set of influential nodes for fast content dissemination in a social network. In this paper, we propose a novel approach to solve the problem, where opinion maximization is achieved through efficient information spreading. In our model, multiple sources inject information continuously into the network, while the regular nodes with heterogeneous social learning abilities spread the information to their acquaintances through gossip mechanism. One of the sources employs smart information spreading and the rest spread information randomly. We model the social interactions and evolution of opinions as a dynamic Bayesian network (DBN), using which the opinion maximization is formulated as a sequential decision problem. Since the problem is intractable, we develop multiple variants of centralized and decentralized algorithms to obtain approximate solutions. Through simulations in synthetic and real-world networks, we demonstrate two key results: 1) the proposed methods perform better than random spreading by a large margin, and 2) even though the smart source (that spreads the desired content) is unfavorably located in the network, it can outperform the contending random sources located at favorable positions.