nils-herrmann/clean_scirepeval huggingface dataset:
Protein Chemical Shift Prediction

The protein chemical shifts holds a large amount of information about the 3-dimensional structure of the protein. A number of chemical shift predictors based on the relationship between structures resolved with X-ray crystallography and the corresponding experimental chemical shifts have been developed. These empirical predictors are very accurate on X-ray structures but tends to be insensitive to small structural changes. To overcome this limitation it has been suggested to make chemical shift predictors based on quantum mechanical(QM) calculations. In this thesis the development of the QM derived chemical shift predictor Procs14 is presented. Procs14 is based on 2.35 million density functional theory(DFT) calculations on tripeptides and contains corrections for hydrogen bonding, ring current and the effect of the previous and following residue. Procs14 is capable at performing predictions for the 13CA, 13CB, 13CO, 15NH, 1HN and 1HA backbone atoms. In order to benchmark Procs14, a number of QM NMR calculations are performed on full protein structures. Of the tested empirical and QM derived predictors, Procs14 reproduced the QM chemical shifts with the highest accuracy. A comparison with the QM derived predictor CheShift-2 on X-ray structures and NMR ensembles with experimental chemical shift data, showed that Procs14 predicted the chemical shifts with the best accuracy. The predictions on the NMR ensembles exhibited the best performance. This suggests that future work might benefit from using ensemble sampling when performing simulations of protein folding with chemical shifts. Procs14 is implemented in the markov chain monte carlo protein folding framework PHAISTOS. The computational efficient implementation of Procs14 allows for rapid predictions and therefore potential use in refinement and folding of protein structures.
Event characterization in (very) asymmetric collisions

Event-by-event reconstruction of the collision geometry using some incarnation of the Glauber-model is a widely accepted method in studying heavy ion collisions. While there is no known problem with the procedure when applied to the collision of two large ions, we will argue that in very asymmetric collisions, like p(d)+A with at least one hard scattering process occuring the event geometry deduced with the simple Glauber-model may be biased.
Poker as a skill game: rational versus irrational behaviors

In many countries poker is one of the most popular card games. Although each variant of poker has its own rules, all involve the use of money to make the challenge meaningful. Nowadays, in the collective consciousness, some variants of poker are referred to as games of skill, others as gambling. A poker table can be viewed as a psychology lab, where human behavior can be observed and quantified. This work provides a preliminary analysis of the role of rationality in poker games, using a stylized version of Texas Hold'em. In particular, we compare the performance of two different kinds of players, i.e. rational versus irrational players, during a poker tournament. Results show that these behaviors (i.e. rationality and irrationality) affect both the outcomes of challenges and the way poker should be classified.
The entropic basis of collective behaviour

We identify a unique viewpoint on the collective behaviour of intelligent agents. We first develop a highly general abstract model for the possible future lives these agents may encounter as a result of their decisions. In the context of these possibilities, we show that the causal entropic principle, whereby agents follow behavioural rules that maximize their entropy over all paths through the future, predicts many of the observed features of social interactions among both human and animal groups. Our results indicate that agents are often able to maximize their future path entropy by remaining cohesive as a group and that this cohesion leads to collectively intelligent outcomes that depend strongly on the distribution of the number of possible future paths. We derive social interaction rules that are consistent with maximum entropy group behaviour for both discrete and continuous decision spaces. Our analysis further predicts that social interactions are likely to be fundamentally based on Weber's law of response to proportional stimuli, supporting many studies that find a neurological basis for this stimulus–response mechanism and providing a novel basis for the common assumption of linearly additive ‘social forces’ in simulation studies of collective behaviour.
Collapse of an ecological network in Ancient Egypt

The composition of animal communities directly impacts the stability of ecosystems. Here, we use historical information of species extinctions in Egypt over 6,000 years to reconstruct predator–prey interactions and determine to what extent observed changes in species composition influence predictions of community stability. Our study reveals that the roles of species and the stability of the community have fundamentally changed throughout the Holocene, and provides compelling evidence that local dynamic stability is informative of species persistence over time. The dynamics of ecosystem collapse are fundamental to determining how and why biological communities change through time, as well as the potential effects of extinctions on ecosystems. Here, we integrate depictions of mammals from Egyptian antiquity with direct lines of paleontological and archeological evidence to infer local extinctions and community dynamics over a 6,000-y span. The unprecedented temporal resolution of this dataset enables examination of how the tandem effects of human population growth and climate change can disrupt mammalian communities. We show that the extinctions of mammals in Egypt were nonrandom and that destabilizing changes in community composition coincided with abrupt aridification events and the attendant collapses of some complex societies. We also show that the roles of species in a community can change over time and that persistence is predicted by measures of species sensitivity, a function of local dynamic stability. To our knowledge, our study is the first high-resolution analysis of the ecological impacts of environmental change on predator–prey networks over millennial timescales and sheds light on the historical events that have shaped modern animal communities.
A Unified Approach to Time Consistency of Dynamic Risk Measures and Dynamic Performance Measures in Discrete Time

In this paper, we provide a flexible framework allowing for a unified study of time consistency of risk measures and performance measures (also known as acceptability indices). The proposed framework not only integrates existing forms of time consistency, but also provides a comprehensive toolbox for analysis and synthesis of the concept of time consistency in decision making. In particular, it allows for in-depth comparative analysis of (most of) the existing types of time consistency—a feat that has not been possible before and, which is done in the companion paper by the authors. In our approach, the time consistency is studied for a large class of maps that are postulated to satisfy only two properties—monotonicity and locality. We call these maps LM-measures. The time consistency is defined in terms of an update rule. The form of the update rule introduced here is novel, and is perfectly suited for developing the unifying framework that is worked out in this paper. As an illustration of the applicabi...
Schroedinger vs. Navier-Stokes

Quantum mechanics has been argued to be a coarse-graining of some underlying deterministic theory. Here we support this view by establishing a map between certain solutions of the Schroedinger equation, and the corresponding solutions of the irrotational Navier–Stokes equation for viscous fluid flow. As a physical model for the fluid itself we propose the quantum probability fluid. It turns out that the (state-dependent) viscosity of this fluid is proportional to Planck’s constant, while the volume density of entropy is proportional to Boltzmann’s constant. Stationary states have zero viscosity and a vanishing time rate of entropy density. On the other hand, the nonzero viscosity of nonstationary states provides an information-loss mechanism whereby a deterministic theory (a classical fluid governed by the Navier–Stokes equation) gives rise to an emergent theory (a quantum particle governed by the Schroedinger equation).
Variational Pseudolikelihood for Regularized Ising Inference

I propose a variational approach to maximum pseudolikelihood inference of the Ising model. The variational algorithm is more computationally efficient, and does a better job predicting out-of-sample correlations than $L_2$ regularized maximum pseudolikelihood inference as well as mean field and isolated spin pair approximations with pseudocount regularization. The key to the approach is a variational energy that regularizes the inference problem by shrinking the couplings towards zero, while still allowing some large couplings to explain strong correlations. The utility of the variational pseudolikelihood approach is illustrated by training an Ising model to represent the letters A-J using samples of letters from different computer fonts.
Semantically-Informed Syntactic Machine Translation: A Tree-Grafting Approach

We describe a unified and coherent syntactic framework for supporting a semantically-informed syntactic approach to statistical machine translation. Semantically enriched syntactic tags assigned to the target-language training texts improved translation quality. The resulting system significantly outperformed a linguistically naive baseline model (Hiero), and reached the highest scores yet reported on the NIST 2009 Urdu-English translation task. This finding supports the hypothesis (posed by many researchers in the MT community, e.g., in DARPA GALE) that both syntactic and semantic information are critical for improving translation quality—and further demonstrates that large gains can be achieved for low-resource languages with different word order than English.
Properties of the top quark

Recent measurements of top-quark properties at the LHC and the Tevatron are presented. Most recent measurements of the top quark mass have been carried out by CMS using 19.7/fb of p s = 8 TeV data including the study of the dependence on event kinematics. ATLAS uses the full Run I data at p s = 7 TeV for a ”3D” measurement that significantly reduces systematic uncertainties. D0 employs the full Run II data using the matrix element method to measure the top quark mass with significantly reduced systematic uncertainties. Many different measurements of the top quark exist to date and the most precise ones per decay channel per experiment have been combined into the first world combination with a relative precision of 0.44%. Latest updates of measurements of production asymmetries include the measurement of the tt production asymmetry by D0 employing the full Run II data set, by CMS and ATLAS (including the polarization of the top quark) employing both the full data set at p s = 7 TeV. CMS uses the full p s = 8 TeV data to measure the top quark polarization in single top production, the ratio R of the branching fractions B(t ! Wb)/B(t ! Wq) and to search for flavor changing neutral currents. The results from all these measurements agree well with their respective Standard Model expectation.
Deconvolution of High Dimensional Mixtures via Boosting, with Application to Diffusion-Weighted MRI of Human Brain

Diffusion-weighted magnetic resonance imaging (DWI) and fiber tractography are the only methods to measure the structure of the white matter in the living human brain. The diffusion signal has been modelled as the combined contribution from many individual fascicles of nerve fibers passing through each location in the white matter. Typically, this is done via basis pursuit, but estimation of the exact directions is limited due to discretization [1, 2]. The difficulties inherent in modeling DWI data are shared by many other problems involving fitting non-parametric mixture models. Ekanadaham et al.  proposed an approach, continuous basis pursuit, to overcome discretization error in the 1-dimensional case (e.g., spike-sorting). Here, we propose a more general algorithm that fits mixture models of any dimensionality without discretization. Our algorithm uses the principles of L2-boost [4], together with refitting of the weights and pruning of the parameters. The addition of these steps to L2-boost both accelerates the algorithm and assures its accuracy. We refer to the resulting algorithm as elastic basis pursuit, or EBP, since it expands and contracts the active set of kernels as needed. We show that in contrast to existing approaches to fitting mixtures, our boosting framework (1) enables the selection of the optimal bias-variance tradeoff along the solution path, and (2) scales with high-dimensional problems. In simulations of DWI, we find that EBP yields better parameter estimates than a non-negative least squares (NNLS) approach, or the standard model used in DWI, the tensor model, which serves as the basis for diffusion tensor imaging (DTI) . We demonstrate the utility of the method in DWI data acquired in parts of the brain containing crossings of multiple fascicles of nerve fibers.
Bayesian Inference for Tumor Subclones Accounting for Sequencing and Structural Variants

Tumor samples are heterogeneous. They consist of different subclones that are characterized by differences in DNA nucleotide sequences and copy numbers on multiple loci. Heterogeneity can be measured through the identification of the subclonal copy number and sequence at a selected set of loci. Understanding that the accurate identification of variant allele fractions greatly depends on a precise determination of copy numbers, we develop a Bayesian feature allocation model for jointly calling subclonal copy numbers and the corresponding allele sequences for the same loci. The proposed method utilizes three random matrices, L, Z and w to represent subclonal copy numbers (L), numbers of subclonal variant alleles (Z) and cellular fractions of subclones in samples (w), respectively. The unknown number of subclones implies a random number of columns for these matrices. We use next-generation sequencing data to estimate the subclonal structures through inference on these three matrices. Using simulation studies and a real data analysis, we demonstrate how posterior inference on the subclonal structure is enhanced with the joint modeling of both structure and sequencing variants on subclonal genomes. Software is available at this http URL
Heterogeneous Metric Learning with Content-Based Regularization for Software Artifact Retrieval

The problem of software artifact retrieval has the goal to effectively locate software artifacts, such as a piece of source code, in a large code repository. This problem has been traditionally addressed through the textual query. In other words, information retrieval techniques will be exploited based on the textual similarity between queries and textual representation of software artifacts, which is generated by collecting words from comments, identifiers, and descriptions of programs. However, in addition to these semantic information, there are rich information embedded in source codes themselves. These source codes, if analyzed properly, can be a rich source for enhancing the efforts of software artifact retrieval. To this end, in this paper, we develop a feature extraction method on source codes. Specifically, this method can capture both the inherent information in the source codes and the semantic information hidden in the comments, descriptions, and identifiers of the source codes. Moreover, we design a heterogeneous metric learning approach, which allows to integrate code features and text features into the same latent semantic space. This, in turn, can help to measure the artifact similarity by exploiting the joint power of both code and text features. Finally, extensive experiments on real-world data show that the proposed method can help to improve the performances of software artifact retrieval with a significant margin.
Nuclear matter from effective quark-quark interaction.

We study neutron matter and symmetric nuclear matter with the quark-meson model for the two-nucleon interaction. The Bethe-Bruckner-Goldstone many-body theory is used to describe the correlations up to the three hole-line approximation with no extra parameters. At variance with other nonrelativistic realistic interactions, the three hole-line contribution turns out to be non-negligible and to have a substantial saturation effect. The saturation point of nuclear matter, the compressibility, the symmetry energy, and its slope are within the phenomenological constraints. Since the interaction also reproduces fairly well the properties of the three-nucleon system, these results indicate that the explicit introduction of the quark degrees of freedom within the considered constituent quark model is expected to reduce the role of three-body forces.
Inferring epigenetic dynamics from kin correlations

Different cells in a clonal population can be in different phenotypic states, which persist for a few generations before switching to another state. Dynamics of switching between these states determines the extent of correlations between the phenotypes of related cells. Here we demonstrate—using ideas from statistical physics—that it is possible to infer simple stochastic dynamics along lineages from an instantaneous measurement of phenotypic correlations in a cell population with defined genealogy. The approach is validated using experimental observations on Pseudomonas aeruginosa colonies. Populations of isogenic embryonic stem cells or clonal bacteria often exhibit extensive phenotypic heterogeneity that arises from intrinsic stochastic dynamics of cells. The phenotypic state of a cell can be transmitted epigenetically in cell division, leading to correlations in the states of cells related by descent. The extent of these correlations is determined by the rates of transitions between the phenotypic states. Therefore, a snapshot of the phenotypes of a collection of cells with known genealogical structure contains information on phenotypic dynamics. Here, we use a model of phenotypic dynamics on a genealogical tree to define an inference method that allows extraction of an approximate probabilistic description of the dynamics from observed phenotype correlations as a function of the degree of kinship. The approach is tested and validated on the example of Pyoverdine dynamics in Pseudomonas aeruginosa colonies. Interestingly, we find that correlations among pairs and triples of distant relatives have a simple but nontrivial structure indicating that observed phenotypic dynamics on the genealogical tree is approximately conformal—a symmetry characteristic of critical behavior in physical systems. The proposed inference method is sufficiently general to be applied in any system where lineage information is available.
Nucleon resonances in the $\pi^- p \to K^0 \Lambda$ reaction near threshold

We investigate the two-body reaction $\pi^- p \to K^{0} \Lambda$ within the effective Lagrangian approach and the isobar model. In addition to the "background" contributions from $t$-channel $K^*$ exchange, $u$-channel $\Sigma(1192)$ and $\Sigma^*(1385)$ exchanges, and $s$-channel nucleon pole terms, the contributions from the nucleon resonances $N^*(1535)$, $N^*(1650)$, and $N^*(1720)$ are investigated. It is shown that the inclusion of these nucleon resonances contributions leads to a good description of the experimental total and differential cross sections data at low energy region.
Causal Graph Justifications of Logic Programs*

In this work we propose a multi-valued extension of logic programs under the stable models semantics where each true atom in a model is associated with a set of justifications. These justifications are expressed in terms of causal graphs formed by rule labels and edges that represent their application ordering. For positive programs, we show that the causal justifications obtained for a given atom have a direct correspondence to (relevant) syntactic proofs of that atom using the program rules involved in the graphs. The most interesting contribution is that this causal information is obtained in a purely semantic way, by algebraic operations (product, sum and application) on a lattice of causal values whose ordering relation expresses when a justification is stronger than another. Finally, for programs with negation, we define the concept of causal stable model by introducing an analogous transformation to Gelfond and Lifschitz's program reduct. As a result, default negation behaves as “absence of proof” and no justification is derived from negative literals, something that turns out convenient for elaboration tolerance, as we explain with a running example.
Electron Structure: Shape, Size and GPDs in QED

The shape of the electron is studied using lowest-order perturbation theory. Quantities used to probe the structure of the proton: form factors, generalized parton distributions, transverse densities, Wigner distributions and the angular momentum content are computed for the electronphoton component of the electron wave function. The influence of longitudinally polarized photons, demanded by the need for infrared regularization via a non-zero photon mass, is included. The appropriate value of the photon mass depends on experimental conditions, and consequently the size of the electron (as defined by the slope of its Dirac form factor) bound in a hydrogen atom is found to be about four times larger than when the electron is free. The shape of the electron, as determined from the transverse density and generalized parton distributions is shown to not be round, and the free electron is shown to be far less round than the bound electron. An electron distribution function (analogous to the quark distribution function) is defined, and that of the bound electron is shown to be suppressed compared to that of the free electron. If the relative transverse momentum of the virtual electron and photon is large compared with the electron mass, the virtual electron and photon each carry nearly the total angular momentum of the physical electron (1/2), with the orbital angular momentum being nearly (-1/2). Including the non-zero photon mass leads to the suppression of end-point contributions to form factors. Implications for proton structure and color transparency are discussed.
Modified gravity models and the central cusp of dark matter haloes in galaxies

The N-body dark matter (DM) simulations point that DM density profiles, e.g. the Navarro Frenk White (NFW) halo, should be cuspy in its centre, but observations disfavour this kind of DM profile. Here we consider whether the observed rotation curves close to the galactic centre can favour modified gravity models in comparison to the NFW halo, and how to quantify such difference. Two explicit modified gravity models are considered, Modified Newtonian Dynamics (MOND) and a more recent approach renormalization group effects in general relativity (RGGR). It is also the purpose of this work to significantly extend the sample on which RGGR has been tested in comparison to other approaches. By analysing 62 galaxies from five samples, we find that (i) there is a radius, given by half the disc scale length, below which RGGR and MOND can match the data about as well or better than NFW, albeit the formers have fewer free parameters; (ii) considering the complete rotation curve data, RGGR could achieve fits with better agreement than MOND, and almost as good as a NFW halo with two free parameters (NFW and RGGR have, respectively, two and one more free parameters than MOND).