nils-herrmann/clean_scirepeval huggingface dataset:
Subspace learning from extremely compressed measurements

We consider learning the principal subspace of a large set of vectors from an extremely small number of compressive measurements of each vector. Our theoretical results show that even a constant number of measurements per column suffices to approximate the principal subspace to arbitrary precision, provided that the number of vectors is large. This result is achieved by a simple algorithm that computes the eigenvectors of an estimate of the covariance matrix. The main insight is to exploit an averaging effect that arises from applying a different random projection to each vector. We provide a number of simulations confirming our theoretical results.
Effects of topological constraints on globular polymers.

Topological constraints can affect both equilibrium and dynamic properties of polymer systems and can play a role in the organization of chromosomes. Despite many theoretical studies, the effects of topological constraints on the equilibrium state of a single compact polymer have not been systematically studied. Here we use simulations to address this longstanding problem. We find that sufficiently long unknotted polymers differ from knotted ones in the spatial and topological states of their subchains. The unknotted globule has subchains that are mostly unknotted and form asymptotically compact RG(s)∼s1/3 crumples. However, crumples display a high fractal dimension of the surface db=2.8, forming excessive contacts and interpenetrating each other. We conclude that this topologically constrained equilibrium state resembles a conjectured crumpled globule [Grosberg et al.,  2095], but differs from its idealized hierarchy of self-similar, isolated and compact crumples.
The derivative discontinuity of the exchange-correlation functional.

The derivative discontinuity is a key concept in electronic structure theory in general and density functional theory in particular. The electronic energy of a quantum system exhibits derivative discontinuities with respect to different degrees of freedom that are a consequence of the integer nature of electrons. The classical understanding refers to the derivative discontinuity of the total energy as a function of the total number of electrons (N), but it can also manifest at constant N. Examples are shown in models including several hydrogen systems with varying numbers of electrons or nuclear charge (Z), as well as the 1-dimensional Hubbard model (1DHM). Two sides of the problem are investigated: first, the failure of currently used approximate exchange-correlation functionals in DFT and, second, the importance of the derivative discontinuity in the exact electronic structure of molecules, as revealed by full configuration interaction (FCI). Currently, all approximate functionals, including hybrids, miss the derivative discontinuity, leading to basic errors that can be seen in many ways: from the complete failure to give the total energy of H2 and H2(+), to the missing gap in Mott insulators such as stretched H2 and the thermodynamic limit of the 1DHM, or a qualitatively incorrect density in the HZ molecule with two electrons and incorrect electron transfer processes. Description of the exact particle behaviour of electrons is emphasised, which is key to many important physical processes in real systems, especially those involving electron transfer, and offers a challenge for the development of new exchange-correlation functionals.
Toward better understanding of artifacts in variant calling from high-coverage samples

Whole-genome high-coverage sequencing has been widely used for personal and cancer genomics as well as in various research areas. However, in the lack of an unbiased whole-genome truth set, the global error rate of variant calls and the leading causal artifacts still remain unclear even given the great efforts in the evaluation of variant calling methods.



We made 10 single nucleotide polymorphism and INDEL call sets with two read mappers and five variant callers, both on a haploid human genome and a diploid genome at a similar coverage. By investigating false heterozygous calls in the haploid genome, we identified the erroneous realignment in low-complexity regions and the incomplete reference genome with respect to the sample as the two major sources of errors, which press for continued improvements in these two areas. We estimated that the error rate of raw genotype calls is as high as 1 in 10-15 kb, but the error rate of post-filtered calls is reduced to 1 in 100-200 kb without significant compromise on the sensitivity.



BWA-MEM alignment and raw variant calls are available at http://bit.ly/1g8XqRt scripts and miscellaneous data at https://github.com/lh3/varcmp.



hengli@broadinstitute.org



Supplementary data are available at Bioinformatics online.
Success of Chemotherapy in Soft Matter

The success of chemotharapy in soft matter as a survival is found in the paper. Therefore, it is found the analogous tumor stretching force in soft matter; ultrasonography is performed for this tumor; restoration in soft matter with such a tumor is found; Bayes estimate of the probability of chemotherapy success is derived from the transferred chemical energy and from soft matter entropy; survival probability is juxtaposed to this probability of success.
Taxator-tk: Fast and Precise Taxonomic Assignment of Metagenomes by Approximating Evolutionary Neighborhoods

Metagenomics characterizes microbial communities by random shotgun sequencing of DNA isolated directly from an environment of interest. An essential step in computational metagenome analysis is taxonomic sequence assignment, which allows us to identify the sequenced community members and to reconstruct taxonomic bins with sequence data for the individual taxa. We describe an algorithm and the accompanying software, taxator-tk, which performs taxonomic sequence assignments by fast approximate determination of evolutionary neighbors from sequence similarities. Taxator-tk was precise in its taxonomic assignment across all ranks and taxa for a range of evolutionary distances and for short sequences. In addition to the taxonomic binning of metagenomes, it is well suited for profiling microbial communities from metagenome samples becauseit identifies bacterial, archaeal and eukaryotic community members without being affected by varying primer binding strengths, as in marker gene amplification, or copy number variations of marker genes across different taxa. Taxator-tk has an efficient, parallelized implementation that allows the assignment of 6 Gb of sequence data per day on a standard multiprocessor system with ten CPU cores and microbial RefSeq as the genomic reference data.
SPATIOTEMPORAL ORGANIZATION OF ENERGY RELEASE EVENTS IN THE QUIET SOLAR CORONA

Using data from the STEREO and SOHO spacecraft, we show that temporal organization of energy release events in the quiet solar corona is close to random, in contrast to the clustered behavior of flaring times in solar active regions. The locations of the quiet-Sun events follow the meso- and supergranulation pattern of the underling photosphere. Together with earlier reports of the scale-free event size statistics, our findings suggest that quiet solar regions responsible for bulk coronal heating operate in a driven self-organized critical state, possibly involving long-range Alfvénic interactions.
Scalable Planning and Learning for Multiagent POMDPs: Extended Version

Online, sample-based planning algorithms for POMDPs have shown great promise in scaling to problems with large state spaces, but they become intractable for large action and observation spaces. This is particularly problematic in multiagent POMDPs where the action and observation space grows exponentially with the number of agents. To combat this intractability, we propose a novel scalable approach based on sample-based planning and factored value functions that exploits structure present in many multiagent settings. This approach applies not only in the planning case, but also the Bayesian reinforcement learning setting. Experimental results show that we are able to provide high quality solutions to large multiagent planning and learning problems.
Parallel American Monte Carlo

In this paper we introduce a new algorithm for American Monte Carlo that can be used either for American-style options, callable structured products or for computing counterparty credit risk (e.g. CVA or PFE computation). Leveraging least squares regressions, the main novel feature of our algorithm is that it can be fully parallelized. Moreover, there is no need to store the paths and the payoff computation can be done forwards: this allows to price structured products with complex path and exercise dependencies. The key idea of our algorithm is to split the set of paths in several subsets which are used iteratively. We give the convergence rate of the algorithm. We illustrate our method on an American put option and compare the results with the Longstaff-Schwartz algorithm.
Spontaneous creation of the universe from nothing

An interesting idea is that the universe could be spontaneously created from nothingbut no rigorousproof has been given. In this paperwe present such a proof based on the analytic solutions of the Wheeler-DeWitt equation (WDWE). Explicit solutions of the WDWE for the special operator ordering factorp= -2 (or 4) show thatonce a small true vacuum bubble is created by quantum fluctuations of themetastable false vacuumit can expand exponentially no matter whether the bubble is closed,flat,or open.The exponential expansion will end when the bubble becomes large and thus the early universe appears.With the de Broglie-Bohm quantum trajectory theorywe show explicitly that it is the quantum potentialthat plays the role of the cosmological constant and provides the power for the exponential expansion of thetrue vacuum bubble. So it is clear that the birth of the early universe completely depends on the quantum nature of the theory.
Model-Free Discretisation-Invariant Swaps and S&P 500 Higher-Moment Risk Premia

We derive a general multivariate theory for realised characteristics of `model-free discretisation-invariant swaps', so-called because the standard no-arbitrage assumption of martingale forward prices is sufficient to derive fair-value swap rates for such characteristics which have no jump or discretisation errors. This theory underpins specific examples for swaps based on higher moments of a single log return distribution where exact replication is possible via option-implied `fundamental contracts' like the log contact. The common factors determining the S&P 500 risk premia associated with these higher-moment characteristics are investigated empirically at the daily, weekly and monthly frequencies.
Learning the Conditional Independence Structure of Stationary Time Series: A Multitask Learning Approach

We propose a method for inferring the conditional independence graph (CIG) of a high-dimensional Gaussian vector time series (discrete-time process) from a finite-length observation. By contrast to existing approaches, we do not rely on a parametric process model (such as, e.g., an autoregressive model) for the observed random process. Instead, we only require certain smoothness properties (in the Fourier domain) of the process. The proposed inference scheme works even for sample sizes much smaller than the number of scalar process components if the true underlying CIG is sufficiently sparse. A theoretical performance analysis provides sufficient conditions on the sample size such that the new method is consistent asymptotically. Some numerical experiments validate our theoretical performance analysis and demonstrate superior performance of our scheme compared to an existing (parametric) approach in case of model mismatch.
Mapping and quantifying electric and magnetic dipole luminescence at the nanoscale.

We report on an experimental technique to quantify the relative importance of electric and magnetic dipole luminescence from a single nanosource in structured environments. By attaching a Eu^{3+}-doped nanocrystal to a near-field scanning optical microscope tip, we map the branching ratios associated with two electric dipole and one magnetic dipole transitions in three dimensions on a gold stripe. The relative weights of the electric and magnetic radiative local density of states can be recovered quantitatively, based on a multilevel model. This paves the way towards the full electric and magnetic characterization of nanostructures for the control of single emitter luminescence.
Flavored Dark Matter and the Galactic Center Gamma-Ray Excess

avor-safe manner. We propose a phenomenologically viable model of bottom avored dark matter that can account for the spectral shape and normalization of the gamma-ray excess while naturally suppressing the elastic scattering cross sections probed by direct detection experiments. This model will be denitively tested with increased exposure at LUX and with data from the upcoming high-energy run of the Large Hadron Collider (LHC).
Orthogonal Rank-One Matrix Pursuit for Low Rank Matrix Completion

In this paper, we propose an efficient and scalable low rank matrix completion algorithm. The key idea is to extend the orthogonal matching pursuit method from the vector case to the matrix case. We further propose an economic version of our algorithm by introducing a novel weight updating rule to reduce the time and storage complexity. Both versions are computationally inexpensive for each matrix pursuit iteration and find satisfactory results in a few iterations. Another advantage of our proposed algorithm is that it has only one tunable parameter, which is the rank. It is easy to understand and to use by the user. This becomes especially important in large-scale learning problems. In addition, we rigorously show that both versions achieve a linear convergence rate, which is significantly better than the previous known results. We also empirically compare the proposed algorithms with several state-of-the-art matrix completion algorithms on many real-world datasets, including the large-scale recommendati...
Optimal budget allocation in social networks: Quality or seeding?

In this paper, we study a strategic model of marketing and product consumption in social networks. We consider two competing firms in a market providing two substitutable products with preset qualities. Agents choose their consumptions following a myopic best response dynamics which results in a local, linear update for the consumptions. At some point in time, firms receive a limited budget which they can use to trigger a larger consumption of their products in the network. Firms have to decide between marginally improving the quality of their products and giving free offers to a chosen set of agents in the network in order to better facilitate spreading their products. We derive a simple threshold rule for the optimal allocation of the budget and describe the resulting Nash equilibrium. It is shown that the optimal allocation of the budget depends on the entire distribution of centralities in the network, quality of products and the model parameters. In particular, we show that in a graph with a higher number of agents with centralities above a certain threshold, firms spend more budget on seeding in the optimal allocation. Furthermore, if seeding budget is nonzero for a balanced graph, it will also be nonzero for any other graph, and if seeding budget is zero for a star graph, it will be zero for any other graph too. We also show that firms allocate more budget to quality improvement when their qualities are close, in order to distance themselves from the rival firm. However, as the gap between qualities widens, competition in qualities becomes less effective and firms spend more budget on seeding.
Pressure-driven evaporative cooling in atom guides

We study steady-state evaporation in an atom guide via Monte Carlo simulations. The evaporation surface follows a specific profile as a function of longitudinal guide location. We demonstrate that the choice of evaporation profile significantly impacts the performance of the evaporation. Our simulations also demonstrate a significant performance boost in the evaporation when using a longitudinally compressed guide. We show that for a purely pressure-driven atom beam, it should be possible to reach degeneracy within a 0.5 m guide for experimentally feasible, albeit challenging, loading conditions.
Conformal mass in AdS gravity

We show that the Ashtekar-Magnon-Das mass and other conserved quantities are equivalent to the Kounterterm charges in the asymptotically anti-de Sitter space-times that satisfy the Einstein equations, if we assume the same asymptotic falloff behavior of the Weyl tensor as considered by Ashtekar, Magnon, and Das. This, therefore, implies that, in all dimensions, the conformal mass can be directly derived from the bulk action and the boundary terms, which are written in terms of the extrinsic curvature.
A stochastic maximum principle for risk-sensitive mean-field-type control

In this paper we study mean-field type control problems with risk-sensitive performance functionals. We establish a stochastic maximum principle for optimal control of stochastic differential equations of mean-field type, in which the drift and the diffusion coefficients as well as the performance functional depend not only on the state and the control but also on the mean of the distribution of the state. Our result extends to optimal control problems for non-Markovian dynamics which may be time-inconsistent in the sense that the Bellman optimality principle does not hold. For a general action space a Peng's type stochastic maximum principle is derived, specifying the necessary conditions for optimality. Two examples are carried out to illustrate the proposed risk-sensitive mean-field type under linear stochastic dynamics with exponential quadratic cost function. Explicit characterizations are given for both mean-field free and mean-field risk-sensitive models.
Applications of Algorithmic Probability to the Philosophy of Mind

This paper presents formulae that can solve various seemingly hopeless philosophical conundrums. We discuss the simulation argument, teleportation, mind-uploading, the rationality of utilitarianism, and the ethics of exploiting artificial general intelligence. Our approach arises from combining the essential ideas of formalisms such as algorithmic probability, the universal intelligence measure, space-time-embedded intelligence, and Hutter's observer localization. We argue that such universal models can yield the ultimate solutions, but a novel research direction would be required in order to find computationally efficient approximations thereof.
Stochastic Evolution of Stock Market Volume-Price Distributions

Using available data from the New York stock market (NYSM) we test four different bi-parametric models to fit the correspondent volume-price distributions at each $10$-minute lag: the Gamma distribution, the inverse Gamma distribution, the Weibull distribution and the log-normal distribution. The volume-price data, which measures market capitalization, appears to follow a specific statistical pattern, other than the evolution of prices measured in similar studies. We find that the inverse Gamma model gives a superior fit to the volume-price evolution than the other models. We then focus on the inverse Gamma distribution as a model for the NYSM data and analyze the evolution of the pair of distribution parameters as a stochastic process. Assuming that the evolution of these parameters is governed by coupled Langevin equations, we derive the corresponding drift and diffusion coefficients, which then provide insight for understanding the mechanisms underlying the evolution of the stock market.
FINGERPRINTS OF GALACTIC LOOP I ON THE COSMIC MICROWAVE BACKGROUND

We investigate possible imprints of galactic foreground structures such as the “radio loops” in the derived maps of the cosmic microwave background. Surprisingly, there is evidence for these not only at radio frequencies through their synchrotron radiation, but also at microwave frequencies where emission by dust dominates. This suggests the mechanism is magnetic dipole radiation from dust grains enriched by metallic iron or ferrimagnetic molecules. This new foreground we have identified is present at high galactic latitudes, and potentially dominates over the expected B-mode polarization signal due to primordial gravitational waves from inflation.
Probing the matter and dark energy sources in a viable Big Rip model of the Universe

Chevallier–Polarski–Linder (CPL) parametrization for the equation of state (EoS) of dark energy in terms of cosmic redshift or scale factor have been frequently studied in the literature. In this study, we consider cosmic time-based CPL parametrization for the EoS parameter of the effective cosmic fluid that fills the fabric of spatially flat and homogeneous Robertson–Walker (RW) spacetime in General Relativity. The model exhibits two worthy features: (i) It fits the observational data from the latest H(z) and Union 2.1 SN Ia compilations matching the success of ΛCDM model. (ii) It describes the evolution of the Universe from the matter-dominated phase to the recent accelerating phase similar to the ΛCDM model but leads to Big Rip end of the Universe contrary to the everlasting de Sitter expansion in the ΛCDM model. We investigate the matter and dark energy sources in the model, in particular, behavior of the dynamical dark energy responsible for the Big Rip end of Universe.
Dark matter on top

We consider a simplified model of fermionic dark matter which couples exclusively to the right-handed top quark via a renormalizable interaction with a color-charged scalar. We first compute the relic abundance of this type of dark matter and investigate constraints placed on the model parameter space by the latest direct detection data. We also perform a detailed analysis for the production of dark matter at the LHC for this model. We find several kinematic variables that allow for a clean signal extraction and we show that the parameter space of this model will be well probed during LHC Run-II. Finally, we investigate the possibility of detecting this type of dark matter via its annihilations into gamma rays. We compute the continuum and the line emission (which includes a possible ''Higgs in Space!'' line) and its possible discovery by future gamma-ray telescopes. We find that the annihilation spectrum has distinctive features which may distinguish it from other models.
A Tunable Terahertz Detector Based On Self Assembled Plasmonic Structure on a GaAs 2DEG

Plasmonic detectors have the potential to provide a method of rapid spectroscopy without the need of moving mirrors or gratings. Previous measurements have demonstrated frequency tunable detection based on plasmonic excitations, however these devices were either small area, polarization dependent and/or required e-beam lithography. We demonstrate that large area high sensitivity THz plasmonic detection can be achieved using self-assembly nanosphere lithography. We achieve a submicron feature size grid covering a detector area of 4 mm^2. The absorbance at the fundamental, third and 5th harmonics of plasmon resonances are observed. Measurements at 80 K show a large transmission change of 25%. We also confirmed a magneto plasmon dispersion of this device. In this paper we will discuss the frequency dependence of the radiative damping effect which affects enhanced absorption at the higher harmonics mode relative to fundamental.