nils-herrmann/clean_scirepeval huggingface dataset:
STRICTLY CONTINUOUS EXTENSION OF FUNCTIONALS WITH LINEAR GROWTH TO THE SPACE BV

The main result of this paper is a proof of the continuity of a family of integral functionals defined on the space of functions of bounded variation with respect to a topology under which smooth functions are dense. These functionals occur often in the Calculus of Variations as the extension of integral problems defined over weakly differentiable functions with linear growth, and the result in this paper sheds light on the question of what the 'correct' extension is in this context. The result is proved via a combination of Reshetnyak's Continuity Theorem and a map assigning a lifting $\mu[u]\in\mathbf{M}(\Omega\times\mathbb{R}^m;\mathbb{R}^{m\times d})$ to each $u\in BV(\Omega;\mathbb{R}^{m})$ and is valid for a large class of integrands satisfying $|f(x,y,A)|\leq C(1+|y|^{d/(d-1)}+|A|)$. In the case where $f$ exhibits $d/(d-1)$ growth in the $y$ variable, an embedding result from the theory of concentration-compactness is needed.
A Survey of Data Mining Techniques for Social Media Analysis

Social network has gained remarkable attention in the last decade. Accessing social network sites such as Twitter, Facebook LinkedIn and Google+ through the internet and the web 2.0 technologies has become more affordable. People are becoming more interested in and relying on social network for information, news and opinion of other users on diverse subject matters. The heavy reliance on social network sites causes them to generate massive data characterised by three computational issues namely; size, noise and dynamism. These issues often make social network data very complex to analyse manually, resulting in the pertinent use of computational means of analysing them. Data mining provides a wide range of techniques for detecting useful knowledge from massive datasets like trends, patterns and rules [44]. Data mining techniques are used for information retrieval, statistical modelling and machine learning. These techniques employ data pre-processing, data analysis, and data interpretation processes in the course of data analysis. This survey discusses different data mining techniques used in mining diverse aspects of the social network over decades going from the historical techniques to the up-to-date models, including our novel technique named TRCM. All the techniques covered in this survey are listed in the Table.1 including the tools employed as well as names of their authors.
Coupled mode theory of stock price formation

We develop a theory of bid and ask price dynamics where the two prices form due to interaction of buy and sell orders. In this model the two prices are represented by eigenvalues of a 2x2 price operator corresponding to "bid" and "ask" eigenstates. Matrix elements of price operator fluctuate in time which results in phase jitter for eigenstates. We show that the theory reflects very important characteristics of bid and ask dynamics and order density in the order book. Calibration examples are provided for stocks at various time scales. Lastly, this model allows to quantify and measure risk associated with spread and its fluctuations.
Low-rank Similarity Measure for Role Model Extraction

Computing meaningful clusters of nodes is crucial to analyze large networks. In this paper, we present a pairwise node similarity measure that allows to extract roles, i.e. group of nodes sharing similar flow patterns within a network. We propose a low rank iterative scheme to approximate the similarity measure for very large networks. Finally, we show that our low rank similarity score successfully extracts the different roles in random graphs and that its performances are similar to the pairwise similarity measure.
Extracting jet transport coefficient from jet quenching at RHIC and LHC

Within ﬁve diﬀerent approaches to parton propagation and energy loss in dense matter, a phenomenological study of experimental data on suppression of large p T single inclusive hadrons in heavy-ion collisions at both RHIC and LHC was carried out. The evolution of bulk medium used in the study for parton propagation was given by 2+1D or 3+1D hydrodynamic models which are also constrained by experimental data on bulk hadron spectra. Values for the jet transport parameter ˆ q at the center of the most central heavy-ion collisions are extracted or calculated within each model, with parameters for the medium properties that are constrained by experimental data on the hadron suppression factor R AA . For a quark with initial energy of 10 GeV we ﬁnd that ˆ q ≈ 1 . 2 ± 0 . 3 GeV 2 /fm at an initial time τ 0 = 0 . 6 fm/ c in Au+Au collisions at √ s = 200 GeV/n and ˆ q ≈ 1 . 9 ± 0 . 7 GeV 2 /fm in Pb+Pb collisions at √ s = 2 . 76 TeV/n. Compared to earlier studies, these represent signiﬁcant convergence on values of the extracted jet transport parameter, reﬂecting recent advances in theory and the availability of new experiment data from the LHC.
Comparative analysis of evolutionary algorithms for image enhancement

Evolutionary algorithms are metaheuristic techniques that derive inspiration from the natural process of evolution. They can efficiently solve (generate acceptable quality of solution in reasonable time) complex optimisation (NP-hard) problems. In this paper, automatic image enhancement is considered as an optimisation problem and three evolutionary algorithms (genetic algorithm, differential evolution and self organising migration algorithm) are employed to search for an optimum solution. They are used to find an optimum parameter set for an image enhancement transfer function. The aim is to maximise a fitness criterion which is a measure of image contrast and the visibility of details in the enhanced image. The enhancement results obtained using all three evolutionary algorithms are compared amongst themselves and also with the output of histogram equalisation method.
Learning Semantic Script Knowledge with Event Embeddings

Induction of common sense knowledge about prototypical sequences of events has recently received much attention. Instead of inducing this knowledge in the form of graphs, as in much of the previous work, in our method, distributed representations of event realizations are computed based on distributed representations of predicates and their arguments, and then these representations are used to predict prototypical event orderings. The parameters of the compositional process for computing the event representations and the ranking component of the model are jointly estimated from texts. We show that this approach results in a substantial boost in ordering performance with respect to previous methods.
GSI Oscillations as Laboratory for Testing of New Physics

We analyse recent experimental data on the GSI oscillations of the hydrogen-like heavy {^{142}}{\rm Pm}^{60+} ions that is a time modulation of the K-shell electron capture (EC) decay rate. We follow the mechanism of the GSI oscillations, caused by an interference of the neutrino flavour mass-eigenstates in a content of the electron neutrino. We give arguments that these experimental data show i) an existence of sterile neutrinos that is necessary for an explanation of a phase-shift, ii) an observation of CP violation, related to a phase-shift, and iii) an influence of the Quantum Zeno Effect, explaining different values of the amplitude and phase-shift for two runs of measurements with different time resolutions and different numbers of consecutive measurements.
Temporal percolation in activity-driven networks.

We study the temporal percolation properties of temporal networks by taking as a representative example the recently proposed activity-driven-network model [N. Perra et al.,  (2012)]. Building upon an analytical framework based on a mapping to hidden variables networks, we provide expressions for the percolation time Tp marking the onset of a giant connected component in the integrated network. In particular, we consider both the generating function formalism, valid for degree-uncorrelated networks, and the general case of networks with degree correlations. We discuss the different limits of the two approaches, indicating the parameter regions where the correlated threshold collapses onto the uncorrelated case. Our analytical predictions are confirmed by numerical simulations of the model. The temporal percolation concept can be fruitfully applied to study epidemic spreading on temporal networks. We show in particular how the susceptible-infected-removed model on an activity-driven network can be mapped to the percolation problem up to a time given by the spreading rate of the epidemic process. This mapping allows us to obtain additional information on this process, not available for previous approaches.
Constraining the variation in the fine-structure constant using SDSS DR7 quasi-stellar object spectra

We report a robust constrain on the possible variation of fine -structure constant,α � e 2 /~c, obtained using Oiii λλ 4959,5007 nebular emission lines from QSOs.We find a �α/α = (2.1 ± 1.6) × 10 5 based on a well selected sample of 2347 QSOs from Sloan Digital Sky Survey Data Release 8 with 0.02< z< 0.74. Our result is consistent with a non-varyingα at a level of 2 × 10 5 over approximately 7 Gyr. This is the largest sample of extragalactic objects yet used to constrain the variation ofα. While this constraint is not as stringent as those determined using many-multiplet method it is free from various systematic effects. A factor of � 4 improvement in�α/α achieved here compared to the previous study (Bahcall et al. 2004) is just consistent with what is expected based on a factor of 14 times bigger sample used here. This suggests that errors are mainly dominated by the statistical uncertainty. We also find the ratio of transition probabilities corresponding to the Oiiiλ5007 andλ4959 lines to be 2.933±0.002, in good agreement with the National Institute of Standards and Technology measurements.
The Einstein-Klein-Gordon-AdS system for general boundary conditions

We construct unique local solutions for the spherically-symmetric Einstein-Klein-Gordon-AdS system subject to a large class of initial and boundary conditions including some considered in the context of the AdS-CFT correspondence. The proof relies on estimates developed for the linear wave equation by the second author and involves a careful renormalization of the dynamical variables, including a renormalization of the well-known Hawking mass. For some of the boundary conditions considered this system is expected to exhibit rich global dynamics, including the existence of hairy black holes. The present paper furnishes a starting point for such global investigations.
Generative NeuroEvolution for Deep Learning

An important goal for the machine learning (ML) community is to create approaches that can learn solutions with human-level capability. One domain where humans have held a significant advantage is visual processing. A significant approach to addressing this gap has been machine learning approaches that are inspired from the natural systems, such as artificial neural networks (ANNs), evolutionary computation (EC), and generative and developmental systems (GDS). Research into deep learning has demonstrated that such architectures can achieve performance competitive with humans on some visual tasks; however, these systems have been primarily trained through supervised and unsupervised learning algorithms. Alternatively, research is showing that evolution may have a significant role in the development of visual systems. Thus this paper investigates the role neuro-evolution (NE) can take in deep learning. In particular, the Hypercube-based NeuroEvolution of Augmenting Topologies is a NE approach that can effectively learn large neural structures by training an indirect encoding that compresses the ANN weight pattern as a function of geometry. The results show that HyperNEAT struggles with performing image classification by itself, but can be effective in training a feature extractor that other ML approaches can learn from. Thus NeuroEvolution combined with other ML methods provides an intriguing area of research that can replicate the processes in nature.
Aspects of Jamming in Two-Dimensional Frictionless Systems

In this work we provide an overview of jamming transitions in two dimensional systems focusing on the limit of frictionless particle interactions in the absence of thermal fluctuations. We first discuss jamming in systems with short range repulsive interactions, where the onset of jamming occurs at a critical packing density and where certain quantities show a divergence indicative of critical behavior. We describe how aspects of the dynamics change as the jamming density is approached and how these dynamics can be explored using externally driven probes. Different particle shapes can produce jamming densities much lower than those observed for disk-shaped particles, and we show how jamming exhibits fragility for some shapes while for other shapes this is absent. Next we describe the effects of long range interactions and jamming behavior in systems such as charged colloids, vortices in type-II superconductors, and dislocations. We consider the effect of adding obstacles to frictionless jamming systems and discuss connections between this type of jamming and systems that exhibit depinning transitions. Finally, we discuss open questions such as whether the jamming transition in all these different systems can be described by the same or a small subset of universal behaviors, as well as future directions for studies of jamming transitions in two dimensional systems, such as jamming in self-driven or active matter systems.
Activation-triggered subunit exchange between CaMKII holoenzymes facilitates the spread of kinase activity

The activation of the dodecameric Ca2+/calmodulin dependent kinase II (CaMKII) holoenzyme is critical for memory formation. We now report that CaMKII has a remarkable property, which is that activation of the holoenzyme triggers the exchange of subunits between holoenzymes, including unactivated ones, enabling the calcium-independent phosphorylation of new subunits. We show, using a single-molecule TIRF microscopy technique, that the exchange process is triggered by the activation of CaMKII, and that exchange is modulated by phosphorylation of two residues in the calmodulin-binding segment, Thr 305 and Thr 306. Based on these results, and on the analysis of molecular dynamics simulations, we suggest that the phosphorylated regulatory segment of CaMKII interacts with the central hub of the holoenzyme and weakens its integrity, thereby promoting exchange. Our results have implications for an earlier idea that subunit exchange in CaMKII may have relevance for information storage resulting from brief coincident stimuli during neuronal signaling.
Missing Value Imputation with Unsupervised Backpropagation

Many data mining and data analysis techniques operate on dense matrices or complete tables of data. Real‐world data sets, however, often contain unknown values. Even many classification algorithms that are designed to operate with missing values still exhibit deteriorated accuracy. One approach to handling missing values is to fill in (impute) the missing values. In this article, we present a technique for unsupervised learning called unsupervised backpropagation (UBP), which trains a multilayer perceptron to fit to the manifold sampled by a set of observed point vectors. We evaluate UBP with the task of imputing missing values in data sets and show that UBP is able to predict missing values with significantly lower sum of squared error than other collaborative filtering and imputation techniques. We also demonstrate with 24 data sets and nine supervised learning algorithms that classification accuracy is usually higher when randomly withheld values are imputed using UBP, rather than with other methods.
Continuous Learning: Engineering Super Features With Feature Algebras

In this paper we consider a problem of searching a space of predictive models for a given training data set. We propose an iterative procedure for deriving a sequence of improving models and a corresponding sequence of sets of non-linear features on the original input space. After a finite number of iterations N, the non-linear features become 2^N -degree polynomials on the original space. We show that in a limit of an infinite number of iterations derived non-linear features must form an associative algebra: a product of two features is equal to a linear combination of features from the same feature space for any given input point. Because each iteration consists of solving a series of convex problems that contain all previous solutions, the likelihood of the models in the sequence is increasing with each iteration while the dimension of the model parameter space is set to a limited controlled value.
Approximated Infomax Early Stopping: Revisiting Gaussian RBMs on Natural Images

We pursue an early stopping technique that helps Gaussian Restricted Boltzmann Machines (GRBMs) to gain good natural image representations in terms of overcompleteness and data fitting. GRBMs are widely considered as an unsuitable model for natural images because they gain non-overcomplete representations which include uniform filters that do not represent useful image features. We have recently found that GRBMs once gain and subsequently lose useful filters during their training, contrary to this common perspective. We attribute this phenomenon to a tradeoff between overcompleteness of GRBM representations and data fitting. To gain GRBM representations that are overcomplete and fit data well, we propose a measure for GRBM representation quality, approximated mutual information, and an early stopping technique based on this measure. The proposed method boosts performance of classifiers trained on GRBM representations.
Fundamental Symmetry Tests with Nucleons, Nuclei, and Atoms: A Snowmass Report

Present and prospective fundamental symmetry tests with nucleons, nuclei and atoms are probing for possible new physics at the TeV scale and beyond. These ongoing and proposed table-top as well as accelerator-based experiments are thus a vital component of the Intensity Frontier. At the same time, these tests provide increasingly sophisticated probes of long-distance strong interactions that are responsible for the structure of nucleons and nuclei. In this community report, some of the most compelling opportunities with nucleons, nuclei and atoms are summarized, drawing largely on input received from the nuclear and atomic physics communities. In particular, this report includes many contributions submitted to two recent Intensity Frontier Workshops.
Codebook-Based Audio Feature Representation for Music Information Retrieval

Digital music has become prolific in the web in recent decades. Automated recommendation systems are essential for users to discover music they love and for artists to reach appropriate audience. When manual annotations and user preference data is lacking (e.g. for new artists) these systems must rely on content based methods. Besides powerful machine learning tools for classification and retrieval, a key component for successful recommendation is the audio content representation. Good representations should capture informative musical patterns in the audio signal of songs. These representations should be concise, to enable efficient (low storage, easy indexing, fast search) management of huge music repositories, and should also be easy and fast to compute, to enable real-time interaction with a user supplying new songs to the system. Before designing new audio features, we explore the usage of traditional local features, while adding a stage of encoding with a pre-computed codebook and a stage of pooling to get compact vectorial representations. We experiment with different encoding methods, namely the LASSO, vector quantization (VQ) and cosine similarity (CS). We evaluate the representations' quality in two music information retrieval applications: query-by-tag and query-by-example. Our results show that concise representations can be used for successful performance in both applications. We recommend using top- τ VQ encoding, which consistently performs well in both applications, and requires much less computation time than the LASSO.
Solitons in gauge theories: Existence and dependence on the charge

In this paper we review recent results on the existence of non-topological solitons in classical relativistic nonlinear field theories. We follow the Coleman approach, which is based on the existence of two conservation laws, energy and charge. In particular we show that under mild assumptions on the nonlinear term it is possible to prove the existence of solitons for a set of admissible charges. This set has been studied for the nonlinear Klein–Gordon equation, and in this paper we state new results in this direction for the Klein–Gordon–Maxwell system.
The global existence of Yang-Mills fields on curved space-times

This is an introductory chapter in a series in which we take a systematic study of the Yang-Mills equations on curved space-times. In this first, we provide standard material that consists in writing the proof of the global existence of Yang-Mills fields on arbitrary curved space-times using the Klainerman-Rodnianski parametrix combined with suitable Gr\"onwall type inequalities. While the Chru\'sciel-Shatah argument requires a simultaneous control of the $L^{\infty}_{loc}$ and the $H^{2}_{loc}$ norms of the Yang-Mills curvature, we can get away by controlling only the $H^{1}_{loc}$ norm instead, and write a new gauge independent proof on arbitrary, fixed, sufficiently smooth, globally hyperbolic, curved 4-dimensional Lorentzian manifolds. This manuscript is written in an expository way in order to provide notes to Master's level students willing to learn mathematical General Relativity.
Robust upper limit on the neutron single-particle energy of the $i_{13/2}$ orbit

The upper limit of the neutron i13/2 single-particle energy (εi13/2) is estimated in the random quasiparticle ensemble with single-particle energies. The εi13/2 distributions under constraints from 134, Te and 136, Xe spectra demonstrate that the i13/2 single-particle state can be physically mixed with the f7/2 ⊗ 3 ` configuration only if εi13/2 < 3 MeV. Thus, our ensemble calculation suggests a robust upper limit of 3 MeV.
Vulnerability and power on networks

Inspired by socio-political scenarios, like dictatorships, in which a minority of people exercise control over a majority of weakly interconnected individuals, we propose vulnerability and power measures defined on groups of actors of networks. We establish an unexpected connection between network vulnerability and graph regularizability. We use the Shapley value of coalition games to introduce fresh notions of vulnerability and power at node level defined in terms of the corresponding measures at group level. We investigate the computational complexity of computing the defined measures, both at group and node levels, and provide effective methods to quantify them. Finally we test vulnerability and power on both artificial and real networks.