nils-herrmann/clean_scirepeval huggingface dataset:
The optimal hedging in a semi-Markov modulated market

This paper includes an original self contained proof of well-posedness of an initial-boundary value problem involving a non-local parabolic PDE which naturally arises in the study of derivative pricing in a generalized market model. We call this market model a semi-Markov modulated market. Although a wellposedness result of that problem is available in the literature, but this recent paper has a different proof. Here the existence of solution is established without invoking mild solution technique. We study the well-posedness of the initial-boundary value problem via a Volterra integral equation of second kind. The method of conditioning on stopping times was used only for showing uniqueness. Furthermore, in the present study we find an integral representation of the PDE problem which enables us to find a robust numerical scheme to compute derivative of the solution. This study paves for addressing many other interesting problems involving this new set of PDEs. Some derivations of external cash flow corresponding to an optimal strategy are presented. These quantities are extremely important when dealing with an incomplete market. Apart from these, the risk measures for discrete trading are formulated which may be of interest to the practitioners.
Unsupervised spike sorting based on discriminative subspace learning

Spike sorting is a fundamental preprocessing step for many neuroscience studies which rely on the analysis of spike trains. In this paper, we present two unsupervised spike sorting algorithms based on discriminative subspace learning. The first algorithm simultaneously learns the discriminative feature subspace and performs clustering. It uses histogram of features in the most discriminative projection to detect the number of neurons. The second algorithm performs hierarchical divisive clustering that learns a discriminative 1-dimensional subspace for clustering in each level of the hierarchy until achieving almost unimodal distribution in the subspace. The algorithms are tested on synthetic and in-vivo data, and are compared against two widely used spike sorting methods. The comparative results demonstrate that our spike sorting methods can achieve substantially higher accuracy in lower dimensional feature space, and they are highly robust to noise. Moreover, they provide significantly better cluster separability in the learned subspace than in the subspace obtained by principal component analysis or wavelet transform.
Designing Labeled Graph Classifiers by Exploiting the Rényi Entropy of the Dissimilarity Representation

Representing patterns as labeled graphs is becoming increasingly common in the broad field of computational intelligence. Accordingly, a wide repertoire of pattern recognition tools, such as classifiers and knowledge discovery procedures, are nowadays available and tested for various datasets of labeled graphs. However, the design of effective learning procedures operating in the space of labeled graphs is still a challenging problem, especially from the computational complexity viewpoint. In this paper, we present a major improvement of a general-purpose classifier for graphs, which is conceived on an interplay between dissimilarity representation, clustering, information-theoretic techniques, and evolutionary optimization algorithms. The improvement focuses on a specific key subroutine devised to compress the input data. We prove different theorems which are fundamental to the setting of the parameters controlling such a compression operation. We demonstrate the effectiveness of the resulting classifier by benchmarking the developed variants on well-known datasets of labeled graphs, considering as distinct performance indicators the classification accuracy, computing time, and parsimony in terms of structural complexity of the synthesized classification models. The results show state-of-the-art standards in terms of test set accuracy and a considerable speed-up for what concerns the computing time.
Nonconvex Statistical Optimization: Minimax-Optimal Sparse PCA in Polynomial Time

Sparse principal component analysis (PCA) involves nonconvex optimization for which the global solution is hard to obtain. To address this issue, one popular approach is convex relaxation. However, such an approach may produce suboptimal estimators due to the relaxation effect. To optimally estimate sparse principal subspaces, we propose a two-stage computational framework named "tighten after relax": Within the 'relax' stage, we approximately solve a convex relaxation of sparse PCA with early stopping to obtain a desired initial estimator; For the 'tighten' stage, we propose a novel algorithm called sparse orthogonal iteration pursuit (SOAP), which iteratively refines the initial estimator by directly solving the underlying nonconvex problem. A key concept of this two-stage framework is the basin of attraction. It represents a local region within which the `tighten' stage has desired computational and statistical guarantees. We prove that, the initial estimator obtained from the 'relax' stage falls into such a region, and hence SOAP geometrically converges to a principal subspace estimator which is minimax-optimal within a certain model class. Unlike most existing sparse PCA estimators, our approach applies to the non-spiked covariance models, and adapts to non-Gaussianity as well as dependent data settings. Moreover, through analyzing the computational complexity of the two stages, we illustrate an interesting phenomenon that larger sample size can reduce the total iteration complexity. Our framework motivates a general paradigm for solving many complex statistical problems which involve nonconvex optimization with provable guarantees.
THE MILKY WAY TOMOGRAPHY WITH SLOAN DIGITAL SKY SURVEY. V. MAPPING THE DARK MATTER HALO

We present robust constraints from the Sloan Digital Sky Survey (SDSS) on the shape and distribution of the dark matter halo within the Milky Way (MW). Using the number density distribution and kinematics of SDSS halo stars, we probe the dark matter distribution to heliocentric distances exceeding ∼10 kpc and galactocentric distances exceeding ∼20 kpc. Our analysis utilizes Jeans equations to generate two-dimensional acceleration maps throughout the volume; this approach is thoroughly tested on a cosmologically derived N-body+SPH simulation of a MW-like galaxy. We show that the known accelerations (gradients of the gravitational potential) can be successfully recovered in such a realistic system. Leveraging the baryonic gravitational potential derived by Bovy & Rix, we show that the gravitational potential implied by the SDSS observations cannot be explained, assuming Newtonian gravity, by visible matter alone: the gravitational force experienced by stars at galactocentric distances of ∼20 kpc is as much as three times stronger than what can be attributed to purely visible matter. We also show that the SDSS data provide a strong constraint on the shape of the dark matter halo potential. Within galactocentric distances of ∼20 kpc, the dark matter halo potential is well described as an oblate halo with axis ratio ; this corresponds to an axis ratio for the dark matter density distribution. Because of our precise two-dimensional measurements of the acceleration of the halo stars, we can reject several MOND models as an explanation of the observed behavior.
A Wild Bootstrap for Degenerate Kernel Tests

A wild bootstrap method for nonparametric hypothesis tests based on kernel distribution embeddings is proposed. This bootstrap method is used to construct provably consistent tests that apply to random processes, for which the naive permutation-based bootstrap fails. It applies to a large group of kernel tests based on V-statistics, which are degenerate under the null hypothesis, and non-degenerate elsewhere. To illustrate this approach, we construct a two-sample test, an instantaneous independence test and a multiple lag independence test for time series. In experiments, the wild bootstrap gives strong performance on synthetic examples, on audio data, and in performance benchmarking for the Gibbs sampler. The code is available at https://github.com/kacperChwialkowski/wildBootstrap.
Superconducting Quantum Computing Without Entanglement

In recent years, quantum computing has promised a revolution in computing performance, based on massive parallelism enabled by many entangled qubits. Josephson junction integrated circuits have emerged as the key technology to implement such a universal digital quantum computer. Indeed, prior experiments have demonstrated simple Josephson qubit configurations with quantized energy levels and long coherence times, which are a necessary prerequisite for a practical quantum computer. However, these quantized states do not directly prove the presence of entanglement or macroscopic superposition, which are essential for the superior speed of such a digital quantum computer. On the contrary, an alternative realistic foundation for quantum mechanics has recently been proposed, with coherent transitions between quantized states, but without entanglement. A new experiment is proposed that may test whether superconducting quantum circuits can exhibit quantized states without macroscopic entanglement or superposition. Specifically, a flux qubit (a bi-stable SQUID) may be configured with a resonant input line for excitation and a single-flux quantum output line for simultaneous direct measurement of quantized energy and flux states, which are incompatible measurements in standard quantum theory. Such an observation could undermine the assumptions of superposition and entanglement, bringing into question the foundation and the ultimate performance of a universal digital quantum computer.
THE NuSTAR VIEW OF NEARBY COMPTON-THICK ACTIVE GALACTIC NUCLEI: THE CASES OF NGC 424, NGC 1320, AND IC 2560

We present X-ray spectral analyses for three Seyfert 2 active galactic nuclei (AGNs), NGC 424, NGC 1320, and IC 2560, observed by NuSTAR in the 3–79 keV band. The high quality hard X-ray spectra allow detailed modeling of the Compton reflection component for the first time in these sources. Using quasi-simultaneous NuSTAR and Swift/XRT data, as well as archival XMM-Newton data, we find that all three nuclei are obscured by Compton-thick material with column densities in excess of ∼5 × 1024 cm−2, and that their X-ray spectra above 3 keV are dominated by reflection of the intrinsic continuum on Compton-thick material. Due to the very high obscuration, absorbed intrinsic continuum components are not formally required by the data in any of the sources. We constrain the intrinsic photon indices and the column density of the reflecting medium through the shape of the reflection spectra. Using archival multi-wavelength data we recover the intrinsic X-ray luminosities consistent with the broadband spectral energy distributions. Our results are consistent with the reflecting medium being an edge-on clumpy torus with a relatively large global covering factor and overall reflection efficiency of the order of 1%. Given the unambiguous confirmation of the Compton-thick nature of the sources, we investigate whether similar sources are likely to be missed by commonly used selection criteria for Compton-thick AGNs, and explore the possibility of finding their high-redshift counterparts.
Perspective: Markov models for long-timescale biomolecular dynamics.

Molecular dynamics simulations have the potential to provide atomic-level detail and insight to important questions in chemical physics that cannot be observed in typical experiments. However, simply generating a long trajectory is insufficient, as researchers must be able to transform the data in a simulation trajectory into specific scientific insights. Although this analysis step has often been taken for granted, it deserves further attention as large-scale simulations become increasingly routine. In this perspective, we discuss the application of Markov models to the analysis of large-scale biomolecular simulations. We draw attention to recent improvements in the construction of these models as well as several important open issues. In addition, we highlight recent theoretical advances that pave the way for a new generation of models of molecular kinetics.
The Nonperturbative Structure of Hadrons

In this thesis we explore a diverse array of issues that strike at the inherently nonperturbative structure of hadrons at momenta below the QCD confinement scale. In so doing, we mainly seek a better control over the partonic substructure of strongly-interacting matter, especially as this relates to the nonperturbative effects that both motivate and complicate experiments --- particularly DIS; among others, such considerations entail sub-leading corrections in $Q^2$, dynamical higher twist effects, and hadron mass corrections. We also present novel calculations of several examples of flavor symmetry violation, which also originates in the long-distance properties of QCD at low energy. Moreover, we outline a recently developed model, framed as a hadronic effective theory amenable to QCD global analysis, which provides new insights into the possibility of nonperturbative heavy quarks in the nucleon. This model can be extended to the scale of the lighter mesons, and we assess the accessibility of the structure function of the interacting pion in the resulting framework.
Towards Decision Support Technology Platform for Modular Systems

The survey methodological paper addresses a glance to a general decision support platform technology for modular systems (modular/composite alterantives/solutions) in various applied domains. The decision support platform consists of seven basic combinatorial engineering frameworks (system synthesis, system modeling, evaluation, detection of bottleneck, improvement/extension, multistage design, combinatorial evolution and forecasting). The decision support platform is based on decision support procedures (e.g., multicriteria selection/sorting, clustering), combinatorial optimization problems (e.g., knapsack, multiple choice problem, clique, assignment/allocation, covering, spanning trees), and their combinations. The following is described: (1) general scheme of the decision support platform technology; (2) brief descriptions of modular (composite) systems (or composite alternatives); (3) trends in moving from chocie/selection of alternatives to processing of composite alternatives which correspond to hierarchical modular products/systems; (4) scheme of resource requirements (i.e., human, information-computer); and (5) basic combinatorial engineering frameworks and their applications in various domains.
Hierarchical Causality in Financial Economics

Hierarchical analysis is considered and a multilevel model is presented in order to explore causality, chance and complexity in financial economics. A coupled system of models is used to describe multilevel interactions, consistent with market data: the lowest level is occupied by agents generating the prices of individual traded assets; the next level entails aggregation of stocks into markets; the third level combines shared risk factors with information variables and bottom-up, agent-generated structure, consistent with conditions for no-arbitrage pricing theory; the fourth level describes market factors which originate in the greater economy and the highest levels are described by regulated market structure and the customs and ethics which define the nature of acceptable transactions. A mechanism for emergence or innovation is considered and causal sources are discussed in terms of five causation classes.
Kernel-Based Information Criterion

This paper introduces Kernel-based Information Criterion (KIC) for model selection in regression analysis. The kernel-based complexity measure in KIC efficiently computes the interdependency between parameters of the model using a novel variable-wise variance and yields selection of better, more robust regressors. Experimental results show superior performance on both simulated and real data sets compared to Leave-One-Out Cross-Validation (LOOCV), kernel-based Information Complexity (ICOMP), and maximum log of marginal likelihood in Gaussian Process Regression (GPR).
Eccentricity evolution in hierarchical triple systems with eccentric outer binaries

We develop a technique for estimating the inner eccentricity in hierarchical triple systems, with the inner orbit being initially circular, while the outer one is eccentric. We consider coplanar systems with well-separated components and comparable masses. The derivation of short-period terms is based on an expansion of the rate of change of the Runge-Lenz vector. Then, the short-period terms are combined with secular terms, obtained by means of canonical perturbation theory. The validity of the theoretical equations is tested by numerical integrations of the full equations of motion.
Thoughts on the Vacuum Energy in the Quantum N-Portrait

An application of the quantum N-portrait to the Universe is discussed, wherein the space-time geometry is understood as a Bose-Einstein condensate of N soft gravitons. If near or at the critical point of a quantum phase transition, indications are found that the vacuum energy is partly suppressed by 1/N, as being due to quanta not in the condensate state. Time evolution decreases this suppression, which might have implications for cosmic expansion.
Duality theory for portfolio optimisation under transaction costs

We consider the problem of portfolio optimisation with general cadlag price processes in the presence of proportional transaction costs. In this context, we develop a general duality theory. In particular, we prove the existence of a dual optimiser as well as a shadow price process in an appropriate generalised sense. This shadow price is defined by means of a "sandwiched" process consisting of a predictable and an optional strong supermartingale, and pertains to all strategies that remain solvent under transaction costs. We provide examples showing that, in the general setting we study, the shadow price processes have to be of such a generalised form.
PMCE: efficient inference of expressive models of cancer evolution with high prognostic power

Driver (epi)genomic alterations underlie the positive selection of cancer subpopulations, which promotes drug resistance and relapse. Even though substantial heterogeneity is witnessed in most cancer types, mutation accumulation patterns can be regularly found and can be exploited to reconstruct predictive models of cancer evolution. Yet, available methods cannot infer logical formulas connecting events to represent alternative evolutionary routes or convergent evolution.  We introduce PMCE, an expressive framework that leverages mutational profiles from crosssectional sequencing data to infer probabilistic graphical models of cancer evolution including arbitrary logical formulas, and which outperforms the state-of-the-art in terms of accuracy and robustness to noise, on simulations. The application of PMCE to 7866 samples from the TCGA database allows us to identify a highly significant correlation between the predicted evolutionary paths and the overall survival in 7 tumor types, proving that our approach can effectively stratify cancer patients in reliable risk groups. Availability PMCE is freely available at https://github.com/BIMIB-DISCo/PMCE, in addition to the code to replicate all the analyses presented in the manuscript. Contacts daniele.ramazzotti@unimib.it, alex.graudenzi@ibfm.cnr.it.
Formation, habitability, and detection of extrasolar moons.

The diversity and quantity of moons in the Solar System suggest a manifold population of natural satellites exist around extrasolar planets. Of peculiar interest from an astrobiological perspective, the number of sizable moons in the stellar habitable zones may outnumber planets in these circumstellar regions. With technological and theoretical methods now allowing for the detection of sub-Earth-sized extrasolar planets, the first detection of an extrasolar moon appears feasible. In this review, we summarize formation channels of massive exomoons that are potentially detectable with current or near-future instruments. We discuss the orbital effects that govern exomoon evolution, we present a framework to characterize an exomoon's stellar plus planetary illumination as well as its tidal heating, and we address the techniques that have been proposed to search for exomoons. Most notably, we show that natural satellites in the range of 0.1-0.5 Earth mass (i) are potentially habitable, (ii) can form within the circumplanetary debris and gas disk or via capture from a binary, and (iii) are detectable with current technology.
Theoretical electron energy loss spectroscopy of isolated graphene

A thorough understanding of the electronic structure is a necessary first step for the design of nanoelectronics, chemical/bio‐sensors, electrocatalysts and nanoplasmonics using graphene. As such, theoretical spectroscopic techniques to describe collective excitations of graphene are of fundamental importance. Starting from density functional theory (DFT), linear response time dependent DFT in frequency‐reciprocal space within the random phase approximation (TDDFT‐RPA) is used to describe the loss function −ℑ{ϵ−1(q,ω)} for isolated graphene. To ensure any spurious interactions between layers are removed, both radial cutoff of the Coulomb kernel, and extra vacuum directly at the TDDFT‐RPA level are employed. A combination of both methods is found to provide a correct description of the electron energy loss spectra of isolated graphene, at a significant reduction in computational cost compared to standard methods.
Bridging the Semantic Gap between RDF and SPARQL using Completeness Statements

RDF data is often treated as incomplete, following the Open-World Assumption. On the other hand, SPARQL, the standard query language over RDF, usually follows the Closed-World Assumption, assuming RDF data to be complete. This gives rise to a semantic gap between RDF and SPARQL. In this paper, we address how to close the semantic gap between RDF and SPARQL in terms of certain answers and possible answers using completeness statements.
Borromean structures in medium-heavy nuclei

Borromean nuclear cluster structures are expected at the corresponding drip lines. We locate the regions in the nuclear chart with the most promising constituents, it being protons and α particles and investigate in details the properties of the possible Borromean two-α systems in medium-heavy nuclei. We find in all cases that the α particles are located at the surface of the core nucleus as dictated by Coulomb and centrifugal barriers. The two lowest three-body bound states resemble a slightly contracted 8 Be nucleus outside the core. The next two excited states have more complex structures but with strong components of linear configurations with the core in the middle. α-removal cross sections would be enhanced with specific signatures for these two different types of structures. The even-even Borromean two-α nucleus, 142 Ba, is specifically investigated and predicted to have 134 Te-α-α structure in its ground state and low-lying spectrum.