nils-herrmann/clean_scirepeval huggingface dataset:
A General Statistic Framework for Genome-based Disease Risk Prediction

Advances of modern sensing and sequencing technologies generate a deluge of high dimensional space-temporal physiological and next-generation sequencing (NGS) data. Physiological traits are observed either as continuous random functions, or on a dense grid and referred to as function-valued traits. Both physiological and NGS data are highly correlated data with their inherent order, spacing, and functional nature which are ignored by traditional summary-based univariate and multivariate regression methods designed for quantitative genetic analysis of scalar trait and common variants. To capture morphological and dynamic features of the data and utilize their dependent structure, we propose a functional linear model (FLM) in which a trait curve is modeled as a response function, the genetic variation in a genomic region or gene is modeled as a functional predictor, and the genetic effects are modeled as a function of both time and genomic position (FLMF) for genetic analysis of function-valued trait with both GWAS and NGS data. By extensive simulations, we demonstrate that the FLMF has the correct type 1 error rates and much higher power to detect association than the existing methods. The FLMF is applied to sleep data from Starr County health studies where oxygen saturation were measured in 22,670 seconds on average for 833 individuals. We found 65 genes that were significantly associated with oxygen saturation functional trait with P-values ranging from 2.40E-06 to 2.53E-21. The results clearly demonstrate that the FLMF substantially outperforms the traditional genetic models with scalar trait.
The timing and location of dust formation in the remnant of SN 1987A

The discovery with the Herschel Space Observatory of bright far-infrared and submm emission from the ejecta of the core-collapse supernova SN 1987A has been interpreted as indicating the presence of some 0.4–0.7 M⊙ of dust. We have constructed radiative transfer models of the ejecta to fit optical to far-infrared observations from the literature at epochs between 615 d and 24 years after the explosion, to determine when and where this unexpectedly large amount of dust formed. We find that the observations by day 1153 are consistent with the presence of 3 × 10−3 M⊙ of dust. Although this is a larger amount than has previously been considered possible at this epoch, it is still very small compared to the amount present in the remnant after 24 years, and significantly higher dust masses at the earlier epochs are firmly ruled out by the observations, indicating that the majority of the dust must have formed at very late times. By 8515–9200 d after the explosion, 0.6–0.8 M⊙ of dust is present, and dust grains with radii greater than 2 μm are required to obtain a fit to the observed spectral energy distribution (SED). This suggests that the dust mass increase at late times was caused by accretion on to and coagulation of the dust grains formed at earlier epochs. These findings provide further confirmation that core-collapse supernovae can create large quantities of dust, and indicate that the reason for small dust masses being estimated in many cases is that the vast majority of the dust forms long after most supernovae have been detectable at mid-infrared wavelengths.
DUST IN THE WIND: THE ROLE OF RECENT MASS LOSS IN LONG GAMMA-RAY BURSTS

We study the late-time ( t > 0.5 ?> days) X-ray afterglows of nearby ( z < 0.5 ?> ) long gamma-ray bursts (GRBs) with Swift and identify a population of explosions with slowly decaying, super-soft (photon index Γ x > 3 ?> ) X-ray emission that is inconsistent with forward shock synchrotron radiation associated with the afterglow. These explosions also show larger-than-average intrinsic absorption ( NH x , i > 6 × 10 21 cm − 2 ?> ) and prompt γ-ray emission with extremely long duration ( T 90 > 1000 ?> s). The chance association of these three rare properties (i.e., large NH x , i ?> , super-soft Γ x ?> , and extreme duration) in the same class of explosions is statistically unlikely. We associate these properties with the turbulent mass-loss history of the progenitor star that enriched and shaped the circumburst medium. We identify a natural connection between NH x , i ?> , Γ x ?> , and T 90 ?> in these sources by suggesting that the late-time super-soft X-rays originate from radiation reprocessed by material lost to the environment by the stellar progenitor before exploding (either in the form of a dust echo or as reprocessed radiation from a long-lived GRB remnant), and that the interaction of the explosion's shock/jet with the complex medium is the source of the extremely long prompt emission. However, current observations do not allow us to exclude the possibility that super-soft X-ray emitters originate from peculiar stellar progenitors with large radii that only form in very dusty environments.
RELATIVISTIC SIMULATIONS OF BLACK HOLE–NEUTRON STAR COALESCENCE: THE JET EMERGES

We perform magnetohydrodynamic simulations in full general relativity (GRMHD) of a binary black hole–neutron star (BHNS) on a quasicircular orbit that undergoes merger. The binary mass ratio is 3:1, the black hole initial spin parameter a / m = 0.75 ?> (m is the black hole Christodoulou mass) aligned with the orbital angular momentum, and the neutron star is an irrotational Γ = 2 ?> polytrope. About two orbits prior to merger (at time t = t B ?> ), we seed the neutron star with a dynamically weak interior dipole magnetic field that extends into the stellar exterior. At t = t B ?> , the exterior has a low-density atmosphere with a constant plasma parameter β ≡ P gas / P mag ?> . Varying β at tB in the exterior from 0.1 to 0.01, we find that at a time ∼ 4000 M ∼ 100 ( M NS / 1.4 M ⊙ ) ms ?> (M is the total (ADM) mass) following the onset of accretion of tidally disrupted debris, magnetic winding above the remnant black hole poles builds up the magnetic field sufficiently to launch a mildly relativistic, collimated outflow—an incipient jet. The duration of the accretion and the lifetime of the jet is Δ t ∼ 0.5 ( M NS / 1.4 M ⊙ ) ?> s. Our simulations furnish the first explicit examples in GRMHD that show that a jet can emerge following a BHNS merger.
Eight billion asteroids in the Oort cloud

The Oort cloud is usually thought of as a collection of icy comets inhabiting the outer reaches of the Solar system, but this picture is incomplete. We use simulations of the formation of the Oort cloud to show that 4% of the small bodies in the Oort cloud should have formed within 2.5 au of the Sun, and hence be ice-free rock-iron bodies. If we assume these Oort cloud asteroids have the same size distribution as their cometary counterparts, the Large Synoptic Survey Telescope should nd roughly a dozen Oort cloud asteroids during ten years of operations. Measurement of the asteroid fraction within the Oort cloud can serve as an excellent test of the Solar system’s formation and dynamical history. Oort cloud asteroids could be of particular concern as impact hazards as their high mass density, high impact velocity, and low visibility make them both hard to detect and hard to divert or destroy. However, they should be a rare class of object, and we estimate globally catastrophic collisions should only occur about once per billion years.
Maximally Informative Hierarchical Representations of High-Dimensional Data

We consider a set of probabilistic functions of some input variables as a representation of the inputs. We present bounds on how informative a representation is about input data. We extend these bounds to hierarchical representations so that we can quantify the contribution of each layer towards capturing the information in the original data. The special form of these bounds leads to a simple, bottom-up optimization procedure to construct hierarchical representations that are also maximally informative about the data. This optimization has linear computational complexity and constant sample complexity in the number of variables. These results establish a new approach to unsupervised learning of deep representations that is both principled and practical. We demonstrate the usefulness of the approach on both synthetic and real-world data.
Fast Function to Function Regression

We analyze the problem of regression when both input covariates and output responses are functions from a nonparametric function class. Function to function regression (FFR) covers a large range of interesting applications including timeseries prediction problems, and also more general tasks like studying a mapping between two separate types of distributions. However, previous nonparametric estimators for FFR type problems scale badly computationally with the number of input/output pairs in a data-set. Given the complexity of a mapping between general functions it may be necessary to consider large datasets in order to achieve a low estimation risk. To address this issue, we develop a novel scalable nonparametric estimator, the Triple-Basis Estimator (3BE), which is capable of operating over data-sets with many instances. To the best of our knowledge, the 3BE is the first nonparametric FFR estimator that can scale to massive data-sets. We analyze the 3BE’s risk and derive an upperbound rate. Furthermore, we show an improvement of several orders of magnitude in terms of prediction speed and a reduction in error over previous estimators in various real-world datasets.
Consensus Message Passing for Layered Graphical Models

Generative models provide a powerful framework for probabilistic reasoning. However, in many domains their use has been hampered by the practical difficulties of inference. This is particularly the case in computer vision, where models of the imaging process tend to be large, loopy and layered. For this reason bottom-up conditional models have traditionally dominated in such domains. We find that widely-used, general-purpose message passing inference algorithms such as Expectation Propagation (EP) and Variational Message Passing (VMP) fail on the simplest of vision models. With these models in mind, we introduce a modification to message passing that learns to exploit their layered structure by passing consensus messages that guide inference towards good solutions. Experiments on a variety of problems show that the proposed technique leads to significantly more accurate inference results, not only when compared to standard EP and VMP, but also when compared to competitive bottom-up conditional models.
GMWB Riders in a Binomial Framework - Pricing, Hedging, and Diversification of Mortality Risk

We construct a binomial model for a guaranteed minimum withdrawal benefit (GMWB) rider to a variable annuity (VA) under optimal policyholder behaviour. The binomial model results in explicitly formulated perfect hedging strategies funded using only periodic fee income. We consider the separate perspectives of the insurer and policyholder and introduce a unifying relationship. Decompositions of the VA and GMWB contract into term-certain payments and options representing the guarantee and early surrender features are extended to the binomial framework. We incorporate an approximation algorithm for Asian options that significantly improves efficiency of the binomial model while retaining accuracy. Several numerical examples are provided which illustrate both the accuracy and the tractability of the binomial model. We extend the binomial model to include policy holder mortality and death benefits. Pricing, hedging, and the decompositions of the contract are extended to incorporate mortality risk. We prove limiting results for the hedging strategies and demonstrate mortality risk diversification. Numerical examples are provided which illustrate the effectiveness of hedging and the diversification of mortality risk under capacity constraints with finite pools.
Attosecond timing in optical-to-electrical conversion

The most frequency-stable sources of electromagnetic radiation are produced optically, and optical frequency combs provide the means for high-fidelity frequency transfer across hundreds of terahertz and into the microwave domain. A critical step in this photonic-based synthesis of microwave signals is the optical-to-electrical conversion process. Here, we show that attosecond (as) timing stability can be preserved across the opto-electronic interface of a photodiode, despite an intrinsic temporal response that is more than six orders of magnitude slower. The excess timing noise in the photodetection of a periodic train of ultrashort optical pulses behaves as flicker noise (1/f) with amplitude of 4  as/√Hz at 1 Hz offset. The corresponding fractional frequency fluctuations are 1.4×10−17 at 1 s and 5.5×10−20 at 1000 s. These results demonstrate that direct photodetection, as part of frequency-comb-based microwave synthesis, can support the timing performance of the best optical frequency standards, and thereby opens the possibility for generating microwave signals with significantly better stability than any existing source.
Structure of hypernuclei in relativistic approaches

We review the relativistic mean-field approach to various aspects of hypernuclei. It was Brockmann and Weise who first applied this approach to hypernuclei. At that time, it had been already observed experimentally that the spin-orbit splittings in hypernuclei are significantly smaller than that in ordinary nuclei. The relativistic approach was suitable for a discussion of spin-orbit splittings in hypernuclei, as the spin-orbit interaction is naturally emerged with the relativistic framework. Since this seminal work, many developments have been done and the relativistic approach has been applied both to the ground state and to excited states of hypernuclei. We summarize those developments in the Chapter. We shall concentrate on $\Lambda$-hypernuclei and do not discuss $\Sigma$ hypernuclei, since the theoretical treatment is rather similar to each other.
Peierls-Nabarro barrier and protein loop propagation.

When a self-localized quasiparticle excitation propagates along a discrete one-dimensional lattice, it becomes subject to a dissipation that converts the kinetic energy into lattice vibrations. Eventually the kinetic energy no longer enables the excitation to cross over the minimum energy barrier between neighboring sites, and the excitation becomes localized within a lattice cell. In the case of a protein, the lattice structure consists of the C(α) backbone. The self-localized quasiparticle excitation is the elemental building block of loops. It can be modeled by a kink that solves a variant of the discrete nonlinear Schrödinger equation. We study the propagation of such a kink in the case of the protein G related albumin-binding domain, using the united residue coarse-grained molecular-dynamics force field. We estimate the height of the energy barriers that the kink needs to cross over in order to propagate along the backbone lattice. We analyze how these barriers give rise to both stresses and reliefs, which control the kink movement. For this, we deform a natively folded protein structure by parallel translating the kink along the backbone away from its native position. We release the transposed kink, and we follow how it propagates along the backbone toward the native location. We observe that the dissipative forces that are exerted on the kink by the various energy barriers have a pivotal role in determining how a protein folds toward its native state.
Scaling laws for convection with temperature-dependent viscosity and grain-damage

Numerical experiments of convection with grain-damage are used to develop scaling laws for convective heat flow, mantle velocity, and plate velocity across the stagnant lid and plate-tectonic regimes. Three main cases are presented in order of increasing complexity: a simple case wherein viscosity is only dependent on grainsize, a case where viscosity depends on temperature and grainsize, and finally a case where viscosity is temperature and grainsize sensitive, and the grain-growth (or healing) is also temperature sensitive. In all cases, convection with grain-damage scales differently than Newtonian convection due to the effects of grain-damage. For the fully realistic case, numerical results show stagnant lid convection, fully mobilized convection that resembles the temperature-independent viscosity case, and partially mobile or transitional convection, depending on damage to healing ratio, Rayleigh number, and the activation energies for viscosity and healing. Applying our scaling laws for the fully realistic case to Earth and Venus we demonstrate that increasing surface temperature dramatically decreases plate speed and heat flow, essentially shutting down plate tectonics, due to increased healing in lithospheric shear zones, as proposed previously. Contrary to many previous studies, the transitional regime between the stagnant lid and fully mobilized regimes is large, and the transition from stagnant lid to mobile convection is gradual and continuous. Thus planets could exhibit a full range of surface mobility, as opposed to the bimodal distribution of fully mobile lid planets and stagnant lid planets that is typically assumed.
Learning graphical models from the Glauber dynamics

In this paper we consider the problem of learning undirected graphical models from data generated according to the Glauber dynamics. The Glauber dynamics is a Markov chain that sequentially updates individual nodes (variables) in a graphical model and it is frequently used to sample from the stationary distribution (to which it converges given sufficient time). Additionally, the Glauber dynamics is a natural dynamical model in a variety of settings. This work deviates from the standard formulation of graphical model learning in the literature, where one assumes access to i.i.d. samples from the distribution. Much of the research on graphical model learning has been directed towards finding algorithms with low computational cost. As the main result of this work, we establish that the problem of reconstructing binary pairwise graphical models is computationally tractable when we observe the Glauber dynamics. Specifically, we show that a binary pairwise graphical model on p nodes with maximum degree d can be learned in time f(d)p3 log p, for a function f(d), using nearly the information-theoretic minimum possible number of samples. There is no known algorithm of comparable efficiency for learning arbitrary binary pairwise models from i.i.d. samples.
Non-convex Robust PCA

We propose a new provable method for robust PCA, where the task is to recover a low-rank matrix, which is corrupted with sparse perturbations. Our method consists of simple alternating projections onto the set of low rank and sparse matrices with intermediate de-noising steps. We prove correct recovery of the low rank and sparse components under tight recovery conditions, which match those for the state-of-art convex relaxation techniques. Our method is extremely simple to implement and has low computational complexity. For a $m \times n$ input matrix (say m \geq n), our method has O(r^2 mn\log(1/\epsilon)) running time, where $r$ is the rank of the low-rank component and $\epsilon$ is the accuracy. In contrast, the convex relaxation methods have a running time O(mn^2/\epsilon), which is not scalable to large problem instances. Our running time nearly matches that of the usual PCA (i.e. non robust), which is O(rmn\log (1/\epsilon)). Thus, we achieve ``best of both the worlds'', viz low computational complexity and provable recovery for robust PCA. Our analysis represents one of the few instances of global convergence guarantees for non-convex methods.
Trend Filtering on Graphs

We introduce a family of adaptive estimators on graphs, based on penalizing the $\ell_1$ norm of discrete graph differences. This generalizes the idea of trend filtering [Kim et al. (2009), Tibshirani (2014)], used for univariate nonparametric regression, to graphs. Analogous to the univariate case, graph trend filtering exhibits a level of local adaptivity unmatched by the usual $\ell_2$-based graph smoothers. It is also defined by a convex minimization problem that is readily solved (e.g., by fast ADMM or Newton algorithms). We demonstrate the merits of graph trend filtering through examples and theory.
Phenomenology of dark matter via a bimetric extension of general relativity

We propose a relativistic model of dark matter reproducing at once the concordance cosmological model $\Lambda$-Cold-Dark-Matter ($\Lambda$-CDM) at cosmological scales, and the phenomenology of the modified Newtonian dynamics (MOND) at galactic scales. To achieve this we postulate a non-standard form of dark matter, consisting of two different species of particles coupled to gravity via a bimetric extension of general relativity, and linked together through an internal vector field (a "graviphoton") generated by the mass of these particles. We prove that this dark matter behaves like ordinary cold dark matter at the level of first order cosmological perturbation, while a pure cosmological constant plays the role of dark energy. The MOND equation emerges in the non-relativistic limit through a mechanism of gravitational polarization of the dark matter medium in the gravitational field of ordinary matter. Finally we show that the model is viable in the solar system as it predicts the same parametrized post-Newtonian parameters as general relativity.
Anomaly Detection Framework Using Rule Extraction for Efficient Intrusion Detection

Huge datasets in cyber security, such as network trac logs, can be analyzed using machine learning and data mining methods. However, the amount of collected data is increasing, which makes analysis more dicult. Many machine learning methods have not been designed for big datasets, and consequently are slow and dicult to understand. We address the issue of ecient network trac classication by creating an intrusion detection framework that applies dimensionality reduction and conjunctive rule extraction. The system can perform unsupervised anomaly detection and use this information to create conjunctive rules that classify huge amounts of trac in real time. We test the implemented system with the widely used KDD Cup 99 dataset and real-world network logs to conrm that the performance is satisfactory. This system is transparent and does not work like a black box, making it intuitive for domain experts, such as network administrators.
Local Properties of Measures in Quantum Field Theory and Cosmology

We show that measure theoretical results concerning the Ashtekar{Lewandowski measure in the space of generalized connections have direct analogues in the context of the Bohr compactification of the line and associated Haar measure. We present also a cha- racterization of the support of the measure associated with the canonical quantization of the free massive scalar field, following closely well known analogous results concerning the Euclidean path integral measure.