nils-herrmann/clean_scirepeval huggingface dataset:
Principal Model Analysis Based on Partial Least Squares

Motivated by the Bagging Partial Least Squares (PLS) and Principal Component Analysis (PCA) algorithms, we propose a Principal Model Analysis (PMA) method in this paper. In the proposed PMA algorithm, the PCA and the PLS are combined. In the method, multiple PLS models are trained on sub-training sets, derived from the original training set based on the random sampling with replacement method. The regression coefficients of all the sub-PLS models are fused in a joint regression coefficient matrix. The final projection direction is then estimated by performing the PCA on the joint regression coefficient matrix. The proposed PMA method is compared with other traditional dimension reduction methods, such as PLS, Bagging PLS, Linear discriminant analysis (LDA) and PLS-LDA. Experimental results on six public datasets show that our proposed method can achieve better classification performance and is usually more stable.
Dark matter annihilation feedback in cosmological simulations – II. The influence on gas and halo structure

We present new cosmological hydrodynamic simulations that incorporate Dark Matter Annihilation Feedback (DMAF), whereby energy released from the annihilation of dark matter particles through decay channels such as photon or positron-electron pairs provide additional heating sources for local baryonic material. For annihilation rates comparable to WIMP-like particles, we find that the key influence of DMAF is to inhibit gas accretion onto halos. Such diminished gas accretion early in the lifetimes of halos results in reduced gas fractions in smaller halos, and the delayed halo formation times of larger structures, suggesting that DMAF could impact the stellar age distribution in galaxies, and morphology of dwarfs. For a dark matter particle mass of $m_\chi\sim10$~MeV, there is a `critical halo mass' of $\sim10^{13}$ M$_{\odot}$ at $z=0$, below which there are large differences when compared to $\Lambda$CDM, such as a reduction in the abundance of halo structures as large as 25 percent, reduced gas content by 50 percent and central gas densities reduced down to 10 percent within halos of mass $\sim10^{12}$ M$_{\odot}$ but with increasing effects in smaller halos. Higher dark matter particle mass models have a smaller `critical halo mass'. For a $m_\chi\sim100$~MeV model, we find differences start appearing below halo masses of $\sim10^{12}$ M$_\odot$ and a $m_\chi\gtrsim 1$~GeV model, this mass scale lies below the resolution of our simulations, though we still observe changes in the morphology of dwarf galaxies.
Effectiveness of LSTMs in Predicting Congestive Heart Failure Onset

In this paper we present a Recurrent neural networks (RNN) based architecture that achieves an AUCROC of 0.9147 for predicting the onset of Congestive Heart Failure (CHF) 15 months in advance using a 12-month observation window on a large cohort of 216,394 patients. We believe this to be the largest study in CHF onset prediction with respect to the number of CHF case patients in the cohort and the test set (3,332 CHF patients) on which the AUC metrics are reported. We explore the extent to which LSTM (Long Short Term Memory) based model, a variant of RNNs, can accurately predict the onset of CHF when compared to known linear baselines like Logistic Regression, Random Forests and deep learning based models such as Multi-Layer Perceptron and Convolutional Neural Networks. We utilize demographics, medical diagnosis and procedure data from 21,405 CHF and 194,989 control patients to as our features. We describe our feature embedding strategy for medical diagnosis codes that accommodates the sparse, irregular, longitudinal, and high-dimensional characteristics of EHR data. We empirically show that LSTMs can capture the longitudinal aspects of EHR data better than the proposed baselines. As an attempt to interpret the model, we present a temporal data analysis-based technique on false positives to attribute feature importance. A model capable of predicting the onset of congestive heart failure months in the future with this level of accuracy and precision can support efforts of practitioners to implement risk factor reduction strategies and researchers to begin to systematically evaluate interventions to potentially delay or avert development of the disease with high mortality, morbidity and significant costs.
Spectral Analysis of Iterated Rank-One Perturbations

The authors study the spectral theory of self-adjoint operators that are subject to certain types of perturbations. 
An iterative introduction of infinitely many randomly coupled rank-one perturbations is one of our settings. Spectral theoretic tools are developed to estimate the remaining absolutely continuous spectrum of the resulting random operators. Curious choices of the perturbation directions that depend on the previous realizations of the coupling parameters are assumed, and unitary intertwining operators are used. An application of our analysis shows localization of the random operator associated to the Rademacher potential. 
Obtaining fundamental bounds on the types of spectrum under rank-one perturbation, without restriction on its direction, is another main objective. This is accomplished by analyzing Borel/Cauchy transforms centrally associated with rank-one perturbation problems.
Speeding up scaled gradient projection methods using deep neural networks for inverse problems in image processing

Conventional optimization based methods have utilized forward models with image priors to solve inverse problems in image processing. Recently, deep neural networks (DNN) have been investigated to significantly improve the image quality of the solution for inverse problems. Most DNN based inverse problems have focused on using data-driven image priors with massive amount of data. However, these methods often do not inherit nice properties of conventional approaches using theoretically well-grounded optimization algorithms such as monotone, global convergence. Here we investigate another possibility of using DNN for inverse problems in image processing. We propose methods to use DNNs to seamlessly speed up convergence rates of conventional optimization based methods. Our DNN-incorporated scaled gradient projection methods, without breaking theoretical properties, significantly improved convergence speed over state-of-the-art conventional optimization methods such as ISTA or FISTA in practice for inverse problems such as image inpainting, compressive image recovery with partial Fourier samples, image deblurring, and medical image reconstruction with sparse-view projections.
Theoretical analysis on Noise2Noise using Stein's Unbiased Risk Estimator for Gaussian denoising: Towards unsupervised training with clipped noisy images

Recently, Noise2Noise has been proposed for unsupervised training of deep neural networks in image restoration problems including denoising Gaussian noise. However, it does not work well for truncated noise with non-zero mean. Here, we perform theoretical analysis on Noise2Noise for the limited case of Gaussian noise removal using Stein’s Unbiased Risk Estimator (SURE). We extend SURE to deal with a pair of noise realizations to directly compare with Noise2Noise. Then, we show that Noise2Noise with Gaussian noise is a special case of our newly extended SURE with a pair of uncorrelated noise realizations. Lastly, we propose a compensation method for clipped Gaussian noise to approximately follow Normal distribution and show how this compensation method can be used for SURE based unsupervised denoiser training. We also show that our theoretical analysis provides insights on how to use Noise2Noise for clipped Gaussian noise.
Schur algebras for the alternating group and Koszul duality

We introduce the alternating Schur algebra $AS_F(n,d)$ as the commutant of the action of the alternating group $A_d$ on the $d$-fold tensor power of an $n$-dimensional $F$-vector space. When $F$ has characteristic different from $2$, we give a basis of $AS_F(n,d)$ in terms of bipartite graphs, and a graphical interpretation of the structure constants. We introduce the abstract Koszul duality functor on modules for the even part of any $\mathbf Z/2\mathbf Z$-graded algebra. The algebra $AS_F(n,d)$ is $\mathbf Z/2\mathbf Z$-graded, having the classical Schur algebra $S_F(n,d)$ as its even part. This leads to an approach to Koszul duality for $S_F(n,d)$-modules that is amenable to combinatorial methods. We characterize the category of $AS_F(n,d)$-modules in terms of $S_F(n,d)$-modules and their Koszul duals. We use the graphical basis of $AS_F(n,d)$ to study the dependence of the behavior of derived Koszul duality on $n$ and $d$.
Intertwining operators between one-dimensional homogeneous quantum walks

The subject of this paper is a kind of dynamical systems called quantum walks. We study one-dimensional homogeneous analytic quantum walks U. We explain how to identify the space of all the uniform intertwining operators between these walks. We can also determine whether U can be realized by a (not necessarily homogeneous) continuous-time uniform quantum walk on Z. Several examples of quantum walks, which can not be realized by continuous-time uniform quantum walks, are presented. The 4-state Grover walk is one of them. Before stating the main theorems, we clarify the definition of one-dimensional quantum walks. For the first half of this paper, we study basic properties of one-dimensional quantum walks, which are not necessarily homogeneous. An equivalence relation between quantum walks called similarity is also introduced. This allows us to manipulate quantum walks in a flexible manner.
Geometry and holonomy of indecomposable cones

We study the geometry and holonomy of semi-Riemannian, time-like metric cones that are indecomposable, i.e., which do not admit a local decomposition into a semi-Riemannian product. This includes irreducible cones, for which the holonomy can be classified, as well as non irreducible cones. The latter admit a parallel distribution of null $k$-planes, and we study the cases $k=1$ and $k=2$ in detail. In these cases, i.e., when the cone admits a distribution of parallel null tangent lines or planes, we give structure theorems about the base manifold. Moreover, in the case $k=1$ and when the base manifold is Lorentzian, we derive a description of the cone holonomy. This result is obtained by a computation of certain cocycles of indecomposable subalgebras in $\mathfrak{so}(1,n-1)$.
Gravitational waves in 
F(R)
 gravity: Scalar waves and the chameleon mechanism

We discuss the scalar mode of gravitational waves emerging in the context of $F(R)$ gravity by taking into account the chameleon mechanism. Assuming a toy model with a specific matter distribution to reproduce the environment of detection experiment by a ground-based gravitational wave observatory, we find that chameleon mechanism remarkably suppresses the scalar wave in the atmosphere of Earth, compared with the tensor modes of the gravitational waves. We also discuss the possibility to detect and constrain scalar waves by the current gravitational observatories and advocate a necessity of the future space-based observations.
Advances on CNN-Based Super-Resolution of Sentinel-2 Images

Thanks to their temporal-spatial coverage and free access, Sentinel-2 images are very interesting for the community. However, a relatively coarse spatial resolution, compared to that of state-of-the-art commercial products, motivates the study of super-resolution techniques to mitigate such a limitation. Specifically, thirtheen bands are sensed simultaneously but at different spatial resolutions: 10, 20, and 60 meters depending on the spectral location. Here, building upon our previous convolutional neural network (CNN) based method , we propose an improved CNN solution to super-resolve the 20-m resolution bands benefiting spatial details conveyed by the accompanying 10-m spectral bands.
Model Selection for Simulator-based Statistical Models: A Kernel Approach

We propose a novel approach to model selection for simulator-based statistical models. The proposed approach defines a mixture of candidate models, and then iteratively updates the weight coefficients for those models as well as the parameters in each model simultaneously; this is done by recursively applying Bayes' rule, using the recently proposed kernel recursive ABC algorithm. The practical advantage of the method is that it can be used even when a modeler lacks appropriate prior knowledge about the parameters in each model. We demonstrate the effectiveness of the proposed approach with a number of experiments, including model selection for dynamical systems in ecology and epidemiology.
DoPAMINE: Double-sided Masked CNN for Pixel Adaptive Multiplicative Noise Despeckling

We propose DoPAMINE, a new neural network based multiplicative noise despeckling algorithm. Our algorithm is inspired by Neural AIDE (N-AIDE), which is a recently proposed neural adaptive image denoiser. While the original NAIDE was designed for the additive noise case, we show that the same framework, i.e., adaptively learning a network for pixel-wise affine denoisers by minimizing an unbiased estimate of MSE, can be applied to the multiplicative noise case as well. Moreover, we derive a double-sided masked CNN architecture which can control the variance of the activation values in each layer and converge fast to high denoising performance during supervised training. In the experimental results, we show our DoPAMINE possesses high adaptivity via fine-tuning the network parameters based on the given noisy image and achieves significantly better despeckling results compared to SAR-DRN, a state-of-the-art CNN-based algorithm.
Crowdsourcing open citations with CROCI - An analysis of the current status of open citations, and a proposal

In this paper, we analyse the current availability of open citations data in one particular dataset, namely COCI (the OpenCitations Index of Crossref open DOI-to-DOI citations; this http URL) provided by OpenCitations. The results of these analyses show a persistent gap in the coverage of the currently available open citation data. In order to address this specific issue, we propose a strategy whereby the community (e.g. scholars and publishers) can directly involve themselves in crowdsourcing open citations, by uploading their citation data via the OpenCitations infrastructure into our new index, CROCI, the Crowdsourced Open Citations Index.
Predict Globally, Correct Locally: Parallel-in-Time Optimal Control of Neural Networks

The links between optimal control of dynamical systems and neural networks have proved beneficial both from a theoretical and from a practical point of view. Several researchers have exploited these links to investigate the stability of different neural network architectures and develop memory efficient training algorithms. We also adopt the dynamical systems view of neural networks, but our aim is different from earlier works. We exploit the links between dynamical systems, optimal control, and neural networks to develop a novel distributed optimization algorithm. The proposed algorithm addresses the most significant obstacle for distributed algorithms for neural network optimization: the network weights cannot be updated until the forward propagation of the data, and backward propagation of the gradients are complete. Using the dynamical systems point of view, we interpret the layers of a (residual) neural network as the discretized dynamics of a dynamical system and exploit the relationship between the co-states (adjoints) of the optimal control problem and backpropagation. We then develop a parallel-in-time method that updates the parameters of the network without waiting for the forward or back propagation algorithms to complete in full. We establish the convergence of the proposed algorithm. Preliminary numerical results suggest that the algorithm is competitive and more efficient than the state-of-the-art.
Probing quantum gravity at low energies

Planck stars form when a collapsing shell of matter within a black hole reaches the Planck density, roughly equivalent to the mass being compressed into a volumetric size near that of the proton, and rebounds outwards. These planck stars have been considered as accounting for both fast radio bursts and short gamma ray bursts, whilst offering a comparatively low energy perspective onto quantum gravity. The observation of such an event would require black hole masses much smaller than a solar mass, which could be provided by primordial black hole dark matter models. We discuss the low energy isotropic background emissions produced by decaying primordial black holes at all epochs and derive constraints from the spectrum of the extragalactic background light. We find that, in order to avoid exceeding known extragalactic background light emissions, we must restrict the total energy emitted at low frequencies by a planck star exploding in the present epoch to be less than $10^{13}$ erg or restrict the primordial black hole population far below any existing limits. This casts doubt on whether exploding planck stars could actually account for fast radio bursts, as they are speculated to in the literature.
MORPH: An Adaptive Framework for Efficient and Byzantine Fault-Tolerant SDN Control Plane

Current approaches to tackle the single point of failure in SDN entail a distributed operation of SDN controller instances. Their state synchronization process is reliant on the assumption of a correct decision-making in the controllers. Successful introduction of SDN in the critical infrastructure networks also requires catering to the issue of <italic>unavailable</italic>, <italic>unreliable</italic> (e.g. buggy), and <italic>malicious</italic> controller failures. We propose MORPH, a framework tolerant to unavailability and Byzantine failures, which distinguishes and localizes faulty controller instances and appropriately reconfigures the control plane. Our controller-switch connection assignment leverages the awareness of the source of failure to optimize the number of active controllers and minimize the controller and switch reconfiguration delays. The proposed re-assignment executes dynamically after each successful failure identification. We require <inline-formula> <tex-math notation="LaTeX">$2F_{M}+F_{A}+1$ </tex-math></inline-formula> controllers to tolerate <inline-formula> <tex-math notation="LaTeX">$F_{M}$ </tex-math></inline-formula> malicious and <inline-formula> <tex-math notation="LaTeX">$F_{A}$ </tex-math></inline-formula> availability-induced failures. After a successful detection of <inline-formula> <tex-math notation="LaTeX">$F_{M}$ </tex-math></inline-formula> malicious controllers, MORPH reconfigures the control plane to require a <italic>single</italic> controller message to forward the system state. Moreover, we outline and present a solution to the practical correctness issues related to the <italic>statefulness</italic> of the distributed SDN controller applications, previously ignored in the literature. We base our performance analysis on a resource-aware routing application, deployed in an emulated testbed comprising up to 16 controllers and up to 34 switches, so to tolerate up to 5 unique Byzantine and additional 5 availability-induced controller failures (a total of 10 unique controller failures). We quantify and highlight the dynamic decrease in the packet and CPU load and the response time after each successful failure detection.
Unsupervised Data Uncertainty Learning in Visual Retrieval Systems

We introduce an unsupervised formulation to estimate heteroscedastic uncertainty in retrieval systems. We propose an extension to triplet loss that models data uncertainty for each input. Besides improving performance, our formulation models local noise in the embedding space. It quantifies input uncertainty and thus enhances interpretability of the system. This helps identify noisy observations in query and search databases. Evaluation on both image and video retrieval applications highlight the utility of our approach. We highlight our efficiency in modeling local noise using two real-world datasets: Clothing1M and Honda Driving datasets. Qualitative results illustrate our ability in identifying confusing scenarios in various domains. Uncertainty learning also enables data cleaning by detecting noisy training labels.
Dual-task agent for run-time classification and killing of malicious processes

Malicious software (malware) is one of the key vectors for cyber criminal activity. New malware samples appear every minute. These new samples are distinct from previous examples because the precise file content is new though the software behaviour may not be new. For this reason, static detection methods perform poorly by comparison with methods using behavioural data. Behavioural analysis, however, is typically conducted in a sandboxed or emulated environment. The sandbox takes several minutes to analyse the file, whilst static detection takes seconds. Some malware behaves one way in a sandbox and differently on a target endpoint, risking the sample being be misclassified. 
Run-time malware analysis examines software behaviour as it executes on the target endpoint. This eliminates the time delay caused by sandbox analysis and ensures that the behaviour monitored is identical to the behaviour on the target endpoint.Malicious software is capable of causing damage within seconds of delivery, only an automated response is capable of acting quickly enough to mitigate its impact. 
Previous run-time detection research has not considered real damage prevention to the endpoint.This paper proposes an agent for earlier run-time detection and killing of malware than has previously been presented. The agent uses a dual-task recurrent neural network trained both to maximise classification accuracy and to exercise caution in killing processes, as the latter action is irreversible. Real-time testing of the model found that it was able to detect 90% of fast-acting ransomware (ransomware which begins encryption within 30 seconds of launching) and reduce the number of files encrypted in the first 30 seconds by 50%
Two-vierbein gravity action from the gauge theory of the conformal group

We study the gravity action built from two gauge fields corresponding to the generators of the conformal group. Starting with the action from which one can obtain Einstein gravity and conformal gravity upon imposing suitable constraints, we keep two independent gauge fields and integrate out the field corresponding to the generator of Lorentz transformations. We identify the two gauge fields with two vierbeins and perturb them around an Anti--de Sitter space. This gives the linearized equations that differ from both, Einstein gravity and conformal gravity linearized equations. We also study the linearized equations for one gauge field perturbed around the flat space and one around zero, and the case when the gauge fields are proportional to each other.