nils-herrmann/clean_scirepeval huggingface dataset:
Probabilistic Graphical Model Representation in Phylogenetics

Recent years have seen a rapid expansion of the model space explored in statistical phylogenetics, emphasizing the need for new approaches to statistical model representation and software development. Clear communication and representation of the chosen model is crucial for: (i) reproducibility of an analysis, (ii) model development, and (iii) software design. Moreover, a unified, clear and understandable framework for model representation lowers the barrier for beginners and nonspecialists to grasp complex phylogenetic models, including their assumptions and parameter/variable dependencies. Graphical modeling is a unifying framework that has gained in popularity in the statistical literature in recent years. The core idea is to break complex models into conditionally independent distributions. The strength lies in the comprehensibility, flexibility, and adaptability of this formalism, and the large body of computational work based on it. Graphical models are well-suited to teach statistical models, to facilitate communication among phylogeneticists and in the development of generic software for simulation and statistical inference. Here, we provide an introduction to graphical models for phylogeneticists and extend the standard graphical model representation to the realm of phylogenetics. We introduce a new graphical model component, tree plates, to capture the changing structure of the subgraph corresponding to a phylogenetic tree. We describe a range of phylogenetic models using the graphical model framework and introduce modules to simplify the representation of standard components in large and complex models. Phylogenetic model graphs can be readily used in simulation, maximum likelihood inference, and Bayesian inference using, for example, Metropolis–Hastings or Gibbs sampling of the posterior distribution.
Genome-wide scan of 29,141 African Americans finds no evidence of selection since admixture

We scanned through the genomes of 29,141 African Americans, searching for loci where the average proportion of African ancestry deviates significantly from the genome-wide average. We failed to find any genome-wide significant deviations, and conclude that any selection in African Americans since admixture is sufficiently weak that it falls below the threshold of our power to detect it using a large sample size. These results stand in contrast to the findings of a recent study of selection in African Americans. That study, which had 15 times fewer samples, reported six loci with significant deviations. We show that the discrepancy is likely due to insufficient correction for multiple hypothesis testing in the previous study. The same study reported 14 loci that showed greater population differentiation between African Americans and Nigerian Yoruba than would be expected in the absence of natural selection. Four such loci were previously shown to be genome-wide significant and likely to be affected by selection, but we show that most of the 10 additional loci are likely to be false positives. Additionally, the most parsimonious explanation for the loci that have significant evidence of unusual differentiation in frequency between Nigerians and Africans Americans is selection in Africa prior to their forced migration to the Americas.
Fiscal Shocks and Asymmetric Effects: A Comparative Analysis

We empirically test the effects of unanticipated fiscal policy shocks on the growth rate and the cyclical component of real private output and reveal different types of asymmetries in fiscal policy implementation. The data used are quarterly U.S. observati ons over the period 1967:1 to 2011:4. In doing so, we use both a vector autoregressive and the novel support vector machines systems in order to extract the fiscal policy shocks series. The latter has never been used before in a similar macroeconomic setting. Within our research framework, in order to test the robustness of our results to alternative aggregate money supply definitions we use two alternative moentary aggregates. These are the commonly reported by central banks and policy makers simple sum monetary aggregates at the MZM level of aggregation and the alternative CFS Divisia MZM aggregate. From each of these four systems we extracted four types of shocks: a negative and a positive government spending shock and a negative and a positive government revenue shock. These eight different types of unanticipated fiscal policy shocks are next used to empirically examine their effects on the growth rate and the cyclical component of real private GNP in two sets of regressions: one that assumes only contemporaneous effects of the shocks on output and one that is augmented with four lags of each fiscal shock.
LIQUIDATION OF AN INDIVISIBLE ASSET WITH INDEPENDENT INVESTMENT

We provide an extension of the explicit solution of a mixed optimal stopping–optimal stochastic control problem introduced by Henderson and Hobson. The problem examines whether the optimal investment problem on a local martingale financial market is affected by the optimal liquidation of an independent indivisible asset. The indivisible asset process is defined by a homogeneous scalar stochastic differential equation, and the investor's preferences are defined by a general expected utility function. The value function is obtained in explicit form, and we prove the existence of an optimal stopping–investment strategy characterized as the limit of an explicit maximizing strategy. Our approach is based on the standard dynamic programming approach.
Can electoral popularity be predicted using socially generated big data?

Today, our more-than-ever digital lives leave significant footprints in cyberspace. Large scale collections of these socially generated footprints, often known as big data, could help us to re-investigate different aspects of our social collective behaviour in a quantitative framework. In this contribution we discuss one such possibility: the monitoring and predicting of popularity dynamics of candidates and parties through the analysis of socially generated data on the web during electoral campaigns. Such data offer considerable possibility for improving our awareness of popularity dynamics. However they also suffer from significant drawbacks in terms of representativeness and generalisability. In this paper we discuss potential ways around such problems, suggesting the nature of different political systems and contexts might lend differing levels of predictive power to certain types of data source. We offer an initial exploratory test of these ideas, focussing on two data streams, Wikipedia page views and Google search queries. On the basis of this data, we present popularity dynamics from real case examples of recent elections in three different countries.
Protein contact prediction by integrating joint evolutionary coupling analysis and supervised learning

Protein contact prediction is important for protein structure and functional study. Both evolutionary coupling (EC) analysis and supervised machine learning methods have been developed, making use of different information sources. However, contact prediction is still challenging especially for proteins without a large number of sequence homologs.



This article presents a group graphical lasso (GGL) method for contact prediction that integrates joint multi-family EC analysis and supervised learning to improve accuracy on proteins without many sequence homologs. Different from existing single-family EC analysis that uses residue coevolution information in only the target protein family, our joint EC analysis uses residue coevolution in both the target family and its related families, which may have divergent sequences but similar folds. To implement this, we model a set of related protein families using Gaussian graphical models and then coestimate their parameters by maximum-likelihood, subject to the constraint that these parameters shall be similar to some degree. Our GGL method can also integrate supervised learning methods to further improve accuracy. Experiments show that our method outperforms existing methods on proteins without thousands of sequence homologs, and that our method performs better on both conserved and family-specific contacts.



See http://raptorx.uchicago.edu/ContactMap/ for a web server implementing the method.



j3xu@ttic.edu



Supplementary data are available at Bioinformatics online.
Efficient lineage for SUM aggregate queries

AI systems typically make decisions and find patterns in data based on the computation of aggregate and specifically sum functions, expressed as queries, on data's attributes. This computation can become costly or even inefficient when these queries concern the whole or big parts of the data and especially when we are dealing with big data. New types of intelligent analytics require also the explanation of why something happened. In this paper we present a randomised algorithm that constructs a small summary of the data, called Aggregate Lineage, which can approximate well and explain all sums with large values in time that depends only on its size. The size of Aggregate Lineage is practically independent on the size of the original data. Our algorithm does not assume any knowledge on the set of sum queries to be approximated.
Sparse Allreduce: Efficient Scalable Communication for Power-Law Data

Many large datasets exhibit power-law statistics: The web graph, social networks, text data, click through data etc. Their adjacency graphs are termed natural graphs, and are known to be difficult to partition. As a consequence most distributed algorithms on these graphs are communication intensive. Many algorithms on natural graphs involve an Allreduce: a sum or average of partitioned data which is then shared back to the cluster nodes. Examples include PageRank, spectral partitioning, and many machine learning algorithms including regression, factor (topic) models, and clustering. In this paper we describe an efficient and scalable Allreduce primitive for power-law data. We point out scaling problems with existing butterfly and round-robin networks for Sparse Allreduce, and show that a hybrid approach improves on both. Furthermore, we show that Sparse Allreduce stages should be nested instead of cascaded (as in the dense case). And that the optimum throughput Allreduce network should be a butterfly of heterogeneous degree where degree decreases with depth into the network. Finally, a simple replication scheme is introduced to deal with node failures. We present experiments showing significant improvements over existing systems such as PowerGraph and Hadoop.
Representing Knowledge Base into Database for WAP and Web-based Expert System

Expert System is developed as consulting service for users spread or public requires affordable access. The Internet has become a medium for such services, but presence of mobile devices make the access becomes more widespread by utilizing mobile web and WAP (Wireless Application Protocol). Applying expert systems applications over the web and WAP requires a knowledge base representation that can be accessed simultaneously. This paper proposes single database to accommodate the knowledge representation with decision tree mapping approach. Because of the database exist, consulting application through both web and WAP can access it to provide expert system services options for more affordable for public.
Sex-biased expression of microRNAs in Drosophila melanogaster

Most animals have separate sexes. The differential expression of gene products, in particular that of gene regulators, is underlying sexual dimorphism. Analyses of sex-biased expression have focused mostly on protein-coding genes. Several lines of evidence indicate that microRNAs, a class of major gene regulators, are likely to have a significant role in sexual dimorphism. This role has not been systematically explored so far. Here, I study the sex-biased expression pattern of microRNAs in the model species Drosophila melanogaster. As with protein-coding genes, sex-biased microRNAs are associated with the reproductive function. Strikingly, contrary to protein-coding genes, male-biased microRNAs are enriched in the X chromosome, whereas female microRNAs are mostly autosomal. I propose that the chromosomal distribution is a consequence of high rates of de novo emergence, and a preference for new microRNAs to be expressed in the testis. I also suggest that demasculinization of the X chromosome may not affect microRNAs. Interestingly, female-biased microRNAs are often encoded within protein-coding genes that are also expressed in females. MicroRNAs with sex-biased expression do not preferentially target sex-biased gene transcripts. These results strongly suggest that the sex-biased expression of microRNAs is mainly a consequence of high rates of microRNA emergence in the X chromosome (male bias) or hitchhiked expression by host genes (female bias).
The Nosoi commute : a spatial perspective on the rise of BSL-4 laboratories in cities

Recent H5N1 influenza research has revived the debate on the storage and manipulation of 
potentially harmful pathogens. In the last two decades, new high biosafety (BSL-4) laboratories 
entered into operation, raising strong concerns from the public. The probability of an accidental 
release of a pathogen from a BSL-4 laboratory is extremely low, but the corresponding risk -- defined 
as the probability of occurrence multiplied by its impact -- could be significant depending on the 
pathogen specificities and the population potentially affected. A list of BSL-4 laboratories throughout 
the world, with their location and date of first activity, was established from publicly available 
sources. This database was used to estimate the total population living within a daily commuting 
distance of BSL-4 laboratories, and to quantify how this figure changed over time. We show that 
from 1990 to present, the population living within the commuting belt of BSL-4 laboratories 
increased by a factor of 4 to reach up to 1.8% of the world population, owing to an increase in the 
number of facilities and their installation in cities. Europe is currently hosting the largest population 
living in the direct vicinity of BSL-4 laboratories, while the recent building of new facilities in Asia 
suggests that an important increase of the population living close to BSL-4 laboratories will be 
observed in the next decades. We discuss the potential implications in term of global risk, and call for 
better pathogen-specific quantitative assessment of the risk of outbreaks resulting from the 
accidental release of potentially pandemic pathogens.
Planck data reconsidered

The authors perform an independent analysis of the Planck data and discuss the tension between the parameters (Hubble constant etc.) derived by the Planck team and those obtained from a number of other astronomical measurements. They find that a particular data set (217 GHz maps) drives this tension, which they are able to decrease, confirming the standard six parameter $\mathrm{\ensuremath{\Lambda}}\phantom{\rule{0}{0ex}}C\phantom{\rule{0}{0ex}}D\phantom{\rule{0}{0ex}}M$ model.
A Herschel (C II) Galactic plane survey II. CO-dark H2 in clouds

H i and CO large scale surveys of the Milky Way trace the diffuse atomic clouds and the dense shielded regions of molecular hydrogen clouds, respectively. However, until recently, we have not had spectrally resolved C + surveys in sufficient lines of sight to characterize the ionized and photon dominated components of the interstellar medium, in particular, the H2 gas without CO, referred to as CO-dark H2, in a large sample of interstellar clouds.  We use a sparse Galactic plane survey of the 1.9 THz (158 μm) [C ii] spectral line from the Herschel open time key programme, Galactic Observations of Terahertz C+ (GOT C+), to characterize the H2 gas without CO in a statistically significant sample of interstellar clouds.  We identify individual clouds in the inner Galaxy by fitting the [C ii] and CO isotopologue spectra along each line of sight. We then combine these spectra with those of H i and use them along with excitation models and cloud models of C + to determine the column densities and fractional mass of CO-dark H2 clouds.  We identify1804 narrow velocity [C ii] components corresponding to interstellar clouds in different categories and evolutionary states. About 840 are diffuse molecular clouds with no CO, ∼510 are transition clouds containing [C ii ]a nd 12 CO, but no 13 CO, and the remainder are dense molecular clouds containing 13 CO emission. The CO-dark H2 clouds are concentrated between Galactic radii of ∼3.5 to 7.5 kpc and the column density of the CO-dark H2 layer varies significantly from cloud to cloud with a global average of 9 × 10 20 cm −2 . These clouds contain a significant fraction by mass of CO-dark H2, that varies from ∼75% for diffuse molecular clouds to ∼20% for dense molecular clouds.  We find a significant fraction of the warm molecular ISM gas is invisible in H i and CO, but is detected in [C ii]. The fraction of CO-dark H2 is greatest in the diffuse clouds and decreases with increasing total column density, and is lowest in the massive clouds. The column densities and mass fraction of CO-dark H2 are less than predicted by models of diffuse molecular clouds using solar metallicity, which is not surprising as most of our detections are in Galactic regions where the metallicity is larger and shielding more effective. There is an overall trend towards a higher fraction of CO-dark H2 in clouds with increasing Galactic radius, consistent with lower metallicity there.
Structure of exoplanets

Planets around other stars, or exoplanets, are now known to be common in our galaxy. Exoplanets span a much wider range of physical conditions than the planets in our solar system, and include extremely puffy gas giants to compact rocky planets that can have densities as high as that of iron. The diversity of exoplanets allows us to place the planets of our solar system in a broader perspective, and invites further research in the nascent field of comparative exoplanetology. Here we review the continuum of detected planets, from the gas giants, which are composed mostly of hydrogen, to smaller worlds where water may provide more of their mass, to rock-iron worlds in some ways similar to Earth. The hundreds of exoplanets that have been discovered in the past two decades offer a new perspective on planetary structure. Instead of being the archetypal examples of planets, those of our solar system are merely possible outcomes of planetary system formation and evolution, and conceivably not even especially common outcomes (although this remains an open question). Here, we review the diverse range of interior structures that are both known and speculated to exist in exoplanetary systems—from mostly degenerate objects that are more than 10× as massive as Jupiter, to intermediate-mass Neptune-like objects with large cores and moderate hydrogen/helium envelopes, to rocky objects with roughly the mass of Earth.
Composite strongly interacting dark matter

It has been suggested that cold dark matter (CDM) has difficulties in explaining tentative evidence for noncuspy halo profiles in small galaxies, and the low velocity dispersions observed in the largest Milky Way satellites ("too big to fail" problem). Strongly self-interacting dark matter has been noted as a robust solution to these problems. The elastic cross sections required are much larger than predicted by generic CDM models, but could naturally be of the right size if dark matter is composite. We explore in a general way the constraints on models where strongly interacting CDM is in the form of dark "atoms" or "molecules," or bound states of a confining gauge interaction ("hadrons"). These constraints include considerations of relic density, direct detection, big bang nucleosynthesis, the cosmic microwave background, and LHC data.
Density of Saturated Nuclear Matter at Large $N_{c}$ and Heavy Quark Mass Limits

We exhibit the existence of stable, saturated nuclear matter in the large $N_{c}$ and heavy quark mass limits of QCD. In this limit, baryons (with the same spin flavor structure) interact at leading order in $N_{c}$ via a repulsive interaction due to the Pauli exclusion principle and at subleading order in $1/N_c$ via the exchange of glueballs. Assuming that the lightest glueball is a scalar, which implies that the subleading baryon interaction is attractive, we find that nuclear matter saturates since the subleading attractive interaction is longer ranged than the leading order repulsive one. We find that the saturated matter is in the form of a crystal with either a face-centered cubic or a hexagonal-close-packed symmetry with baryon densities of $\mathcal{O}((\, \tilde{\alpha}_{s} m_q (\ln (N_{c}m_{q}\Lambda_{\textrm{QCD}}^{-1}))^{-1})^3 )$. Remarkably, the leading order expression for the density of saturated nuclear matter is independent of the lighest glueball mass and scalar-glueball-baryon coupling in the extreme large $N_{c}$ limit or heavy quark limit (or both), which we define precisely in this work.
Market Impact Paradoxes

The market impact (MI) of Volume Weighted Average Price (VWAP) orders is a convex function of a trading rate, but most empirical estimates of transaction cost are concave functions. How is this possible? We show that isochronic (constant trading time) MI is slightly convex, and isochoric (constant trading volume) MI is concave. We suggest a model that fits all trading regimes and guarantees no-dynamic-arbitrage.
Multi-mJ, kHz, 2.1-μm OPCPA for high-flux soft X-ray high-harmonic radiation

We report on a multi-mJ 2.1-μm OPCPA system operating at a 1-kHz repetition rate, pumped by a picosecond cryogenic Yb:YAG pump laser, and the phase-matched high-flux high-harmonic soft X-ray generation.
Geometry of quantum evolution for mixed quantum states

The geometric formulation of quantum mechanics is a very interesting field of research which has many applications in the emerging field of quantum computation and quantum information, such as schemes for optimal quantum computers. In this work we discuss a geometric formulation of mixed quantum states represented by density operators. Our formulation is based on principal fiber bundles and purifications of quantum states. In our construction, the Riemannian metric and symplectic form on the total space are induced from the real and imaginary parts of the Hilbert–Schmidt Hermitian inner product, and we define a mechanical connection in terms of a locked inertia tensor and moment map. We also discuss some applications of our geometric framework.
Unsupervised learning of depth and motion

We present a model for the joint estimation of disparity and motion. The model is based on learning about the interrelations between images from multiple cameras, multiple frames in a video, or the combination of both. We show that learning depth and motion cues, as well as their combinations, from data is possible within a single type of architecture and a single type of learning algorithm, by using biologically inspired "complex cell" like units, which encode correlations between the pixels across image pairs. Our experimental results show that the learning of depth and motion makes it possible to achieve state-of-the-art performance in 3-D activity analysis, and to outperform existing hand-engineered 3-D motion features by a very large margin.