nils-herrmann/clean_scirepeval huggingface dataset:
In Silico Proteome Cleavage Reveals Iterative Digestion Strategy for High Sequence Coverage

In the postgenome era, biologists have sought to measure the complete complement of proteins, termed proteomics. Currently, the most effective method to measure the proteome is with shotgun, or bottom-up, proteomics, in which the proteome is digested into peptides that are identified followed by protein inference. Despite continuous improvements to all steps of the shotgun proteomics workflow, observed proteome coverage is often low; some proteins are identified by a single peptide sequence. Complete proteome sequence coverage would allow comprehensive characterization of RNA splicing variants and all posttranslational modifications, which would drastically improve the accuracy of biological models. There are many reasons for the sequence coverage deficit, but ultimately peptide length determines sequence observability. Peptides that are too short are lost because they match many protein sequences and their true origin is ambiguous. The maximum observable peptide length is determined by several analytical challenges. This paper explores computationally how peptide lengths produced from several common proteome digestion methods limit observable proteome coverage. Iterative proteome cleavage strategies are also explored. These simulations reveal that maximized proteome coverage can be achieved by use of an iterative digestion protocol involving multiple proteases and chemical cleavages that theoretically allow 92.9% proteome coverage.
Minimizing the Probability of Lifetime Ruin Under Ambiguity Aversion

We determine the optimal robust investment strategy of an individual who targets at a given rate of consumption and seeks to minimize the probability of lifetime ruin when she does not have perfect confidence in the drift of the risky asset. Using stochastic control, we characterize the value function as the unique classical solution of an associated Hamilton-Jacobi-Bellman (HJB) equation, obtain feedback forms for the optimal investment and drift distortion, and discuss their dependence on various model parameters. In analyzing the HJB equation, we establish the existence and uniqueness of viscosity solution using Perron's method, and then upgrade regularity by working with an equivalent convex problem obtained via the Cole-Hopf transformation. We show the original value function may lose convexity for a class of parameters and the Isaacs condition may fail. Numerical examples are also included to illustrate our results.
Foundation for Frequent Pattern Mining Algorithms Implementation

As with the development of the IT technologies, the amount of accumulated data is also increasing. Thus the role of data mining comes into picture. Association rule mining becomes one of the significant responsibilities of descriptive technique which can be defined as discovering meaningful patterns from large collection of data. The frequent pattern mining algorithms determine the frequent patterns from a database. Mining frequent itemset is very fundamental part of association rule mining. Many algorithms have been proposed from last many decades including majors are Apriori, Direct Hashing and Pruning, FP-Growth, ECLAT etc. The aim of this study is to analyze the existing techniques for mining frequent patterns and evaluate the performance of them by comparing Apriori and DHP algorithms in terms of candidate generation, database and transaction pruning. This creates a foundation to develop newer algorithm for frequent pattern mining.
Pion masses in two-flavor QCD with η condensation.

We investigate some aspects of two-flavor QCD with mu≠md at low energy, using the leading order chiral perturbation theory including anomaly effects. While nothing special happens at mu=0 for the fixed md≠0, the neutral pion mass becomes zero at two critical values of mu, between which the neutral pion field condenses, leading to a spontaneously CP broken phase, the so-called Dashen phase. We also show that the "topological susceptibility" in the chiral perturbation theory diverges at these two critical points. We briefly discuss a possibility that mu=0 can be defined by the vanishing the "topological susceptibility. We finally analyze the case of mu=md=m with θ=π, which is equivalent to mu=-md=-m with θ=0 by the chiral rotation. In this case, the η condensation occurs at small m, violating the CP symmetry spontaneously. Deep in the η condensation phase, three pions become Nambu-Goldstone bosons, but they show unorthodox behavior at small m that mπ2=O(m2), which, however, is shown to be consistent with the chiral Ward-Takahashi identities.
Extended Gauss–Bonnet gravities in Weyl geometry

In this paper we consider an extended Gauss–Bonnet gravity theory in arbitrary dimensions and in a space provided with a Weyl connection, which is torsion-free but non-metric-compatible, the non-metricity tensor being determined by a vector field. The action considered consists of the usual Einstein–Hilbert action plus all the terms quadratic in the curvature that reduce to the usual Gauss–Bonnet term for vanishing Weyl connection, i.e., when only the Levi-Civita part of the connection is present. We expand the action in terms of Riemannian quantities and obtain vector–tensor theories. We find that all the free parameters only appear in the kinetic term of the vector field, so two branches are possible: one with a propagating vector field and another one where the vector field does not propagate. We focus on the propagating case. We find that in four dimensions, the theory is equivalent to Einstein's gravity plus a Proca field. This field is naturally decoupled from matter, so it represents a natural dark matter candidate. Also for d = 4, we discuss a non-trivial cubic term in the curvature that can be constructed without spoiling the second-order nature of the field equations, because it leads to the vector–tensor Horndeski interaction. In arbitrary dimensions, the theory becomes more involved. We show that, even though the vector field presents kinetic interactions which do not have U 1 ?> symmetry, there are no additional propagating degrees of freedom with respect to the usual massive case. We show that, interestingly, this relies on the fact that the corresponding Stückelberg field belongs to a specific class within the general Horndeski theories. Finally, since Weyl geometries provide the natural ground on which to build scale invariant theories, we apply the usual Weyl gauging in order to make the Horndeski action locally scale invariant, and discuss new terms that can be added.
An Inequality with Applications to Structured Sparsity and Multitask Dictionary Learning

From concentration inequalities for the suprema of Gaussian or Rademacher processes an inequality is derived. It is applied to sharpen existing and to derive novel bounds on the empirical Rademacher complexities of unit balls in various norms appearing in the context of structured sparsity and multitask dictionary learning or matrix factorization. A key role is played by the largest eigenvalue of the data covariance matrix.
Entropy in Dimension One

This paper completely classifies which numbers arise as the topological entropy associated to postcritically finite self-maps of the unit interval. Specifically, a positive real number h is the topological entropy of a postcritically finite self-map of the unit interval if and only if exp(h) is an algebraic integer that is at least as large as the absolute value of any of the conjugates of exp(h); that is, if exp(h) is a weak Perron number. The postcritically finite map may be chosen to be a polynomial all of whose critical points are in the interval (0,1). This paper also proves that the weak Perron numbers are precisely the numbers that arise as exp(h), where h is the topological entropy associated to ergodic train track representatives of outer automorphisms of a free group.
Dynamic Control of Modern, Network-Based Epidemic Models

In this paper we make the first steps to bridge the gap between classic control theory and modern, network-based epidemic models. In particular, we apply nonlinear model predictive control (NMPC) to a pairwise ODE model which we use to model a susceptible-infectious-susceptible (SIS) epidemic on nontrivial contact structures. While classic control of epidemics concentrates on aspects such as vaccination, quarantine, and fast diagnosis, our novel setup allows us to deliver control by altering the contact network within the population. Moreover, the ideal outcome of control is to eradicate the disease while keeping the network well connected. The paper gives a thorough and detailed numerical investigation of the impact and interaction of system and control parameters on the controllability of the system. For a certain combination of parameters, we used our method to identify the critical control bounds above which the system is controllable. We foresee that our approach can be extended to even more realistic or simulation-based models with the aim of applying these to real-world situations.
The tongue as an excitable medium

Geographic tongue (GT) is a medical condition affecting approximately 2% of the population, whereby the papillae covering the upper part of the tongue are lost due to a slowly expanding inflammation. The resultant dynamical appearance of the tongue has striking similarities with well known out-of-equilibrium phenomena observed in excitable media, such as forest fires, cardiac dynamics, chemically driven reaction-diffusion systems and morphogenesis in multicellular organisms. Here we identify GT as a novel example of excitable media dynamics and explore the evolution of the condition from a dynamical systems perspective. We focus on two characteristic aspects of GT in particular: anisotropic expansion of lesions and re-entry of the inflammation into recovering regions. Our investigation sheds light on the evolution of the inflammation and suggests a practical way to classify the severity of the condition, based on the characteristic patterns observed in GT patients.
Investigating evidence for different black hole accretion modes since redshift z ̃ 1

Chandra data in the COSMOS, AEGIS-XD and 4 Ms Chandra Deep Field South are combined with multiwavelength photometry available in those fields to determine the rest-frame U-V versus V-J colours of X-ray AGN hosts in the redshift intervals 0.1 0.6, such differences are significant at the 2σ level only for sources with Eddington ratios ≥10-3. These findings are consistent with scenarios in which diverse accretion modes are responsible for the build-up of supermassive black holes at the centres of galaxies.We compare these results with the predictions of the GALFORM semi-analytic model for the cosmological evolution of AGN and galaxies. This model postulates two black hole fuelling modes, the first is linked to star formation events and the second takes place in passive galaxies. GALFORM predicts that a substantial fraction of the black hole growth at z < 1 is associated with quiescent galaxies, in apparent conflict with the observations. Relaxing the strong assumption of the model that passive AGN hosts have zero star formation rate could bring those predictions in better agreement with the data.
Spin and parity measurement of the Λ (1405) baryon

A determination of the spin and parity of the Λ(1405) is presented using photoproduction data from the CLAS detector at Jefferson Lab. The reaction γ+p→K++Λ(1405) is analyzed in the decay channel Lambda(1405)→Σ++π, where the decay distribution to Σ+pi- and the variation of the Σ+ polarization with respect to the Λ(1405) polarization direction determines the parity. The Λ(1405) is produced, in the energy range 2.55 c.m.K+<0.9, with polarization P=0.45±0.02(stat)±0.07(syst). The analysis shows that the decays are in S wave, with the Σ+ polarized such that the Λ(1405) has spin-parity JP=1/2-, as expected by most theories.
Connecting dream networks across cultures

Many species dream, yet there remain many open research questions in the study of dreams. The symbolism of dreams and their interpretation is present in cultures throughout history. Analysis of online data sources for dream interpretation using network science leads to understanding symbolism in dreams and their associated meaning. In this study, we introduce dream interpretation networks for English, Chinese and Arabic that represent different cultures from various parts of the world. We analyze communities in these networks, finding that symbols within a community are semantically related. The central nodes in communities give insight about cultures and symbols in dreams. The community structure of different networks highlights cultural similarities and differences. Interconnections between different networks are also identified by translating symbols from different languages into English. Structural correlations across networks point out relationships between cultures. Similarities between network communities are also investigated by analysis of sentiment in symbol interpretations. We find that interpretations within a community tend to have similar sentiment. Furthermore, we cluster communities based on their sentiment, yielding three main categories of positive, negative, and neutral dream symbols.
Feature and Variable Selection in Classification

The amount of information in the form of features and variables avail- able to machine learning algorithms is ever increasing. This can lead to classifiers that are prone to overfitting in high dimensions, high di- mensional models do not lend themselves to interpretable results, and the CPU and memory resources necessary to run on high-dimensional datasets severly limit the applications of the approaches. Variable and feature selection aim to remedy this by finding a subset of features that in some way captures the information provided best. In this paper we present the general methodology and highlight some specific approaches.
DETECTION OF AN UNIDENTIFIED EMISSION LINE IN THE STACKED X-RAY SPECTRUM OF GALAXY CLUSTERS

We detect a weak unidentified emission line at E = (3.55–3.57) ± 0.03 keV in a stacked XMM-Newton spectrum of 73 galaxy clusters spanning a redshift range 0.01–0.35. When the full sample is divided into three subsamples (Perseus, Centaurus+Ophiuchus+Coma, and all others), the line is seen at >3σ statistical significance in all three independent MOS spectra and the PN “all others” spectrum. It is also detected in the Chandra spectra of the Perseus Cluster. However, it is very weak and located within 50–110 eV of several known lines. The detection is at the limit of the current instrument capabilities. We argue that there should be no atomic transitions in thermal plasma at this energy. An intriguing possibility is the decay of sterile neutrino, a long-sought dark matter particle candidate. Assuming that all dark matter is in sterile neutrinos with ms = 2E = 7.1 keV, our detection corresponds to a neutrino decay rate consistent with previous upper limits. However, based on the cluster masses and distances, the line in Perseus is much brighter than expected in this model, significantly deviating from other subsamples. This appears to be because of an anomalously bright line at E = 3.62 keV in Perseus, which could be an Ar xvii dielectronic recombination line, although its emissivity would have to be 30 times the expected value and physically difficult to understand. Another alternative is the above anomaly in the Ar line combined with the nearby 3.51 keV K line also exceeding expectation by a factor of 10–20. Confirmation with Astro-H will be critical to determine the nature of this new line.
Predicting crowd behavior with big public data

With public information becoming widely accessible and shared on today's web, greater insights are possible into crowd actions by citizens and non-state actors such as large protests and cyber activism. We present efforts to predict the occurrence, specific timeframe, and location of such actions before they occur based on public data collected from over 300,000 open content web sources in 7 languages, from all over the world, ranging from mainstream news to government publications to blogs and social media. Using natural language processing, event information is extracted from content such as type of event, what entities are involved and in what role, sentiment and tone, and the occurrence time range of the event discussed. Statements made on Twitter about a future date from the time of posting prove particularly indicative. We consider in particular the case of the 2013 Egyptian coup d'état. The study validates and quantifies the common intuition that data on social media (beyond mainstream news sources) are able to predict major events.
Interacting vacuum energy in the dark sector

We analyse three cosmological scenarios with interaction in the dark sector, which are particular cases of a general expression for the energy flux from vacuum to matter. In the first case the interaction leads to a transition from an unstable de Sitter phase to a radiation dominated universe, avoiding in this way the initial singularity. In the second case the interaction gives rise to a slow-roll power-law inflation. Finally, the third scenario is a concordance model for the late-time universe, with the vacuum term decaying into cold dark matter. We identify the physics behind these forms of interaction and show that they can be described as particular types of the modified Chaplygin gas.
On dark degeneracy and interacting models

Cosmological background observations cannot fix the dark energy equation of state, which is related to a degeneracy in the definition of the dark sector components. Here we show that this degeneracy can be broken at perturbation level by imposing two observational properties on dark matter. First, dark matter is defined as the clustering component we observe in large scale structures. This definition is meaningful only if dark energy is unperturbed, which is achieved if we additionally assume, as a second condition, that dark matter is cold, i.e. non-relativistic. As a consequence, dark energy models with equation-of-state parameter −1 ⩽ ω < 0 are reduced to two observationally distinguishable classes with ω = −1, equally competitive when tested against observations. The first comprises the ΛCDM model with constant dark energy density. The second consists of interacting models with an energy flux from dark energy to dark matter.
Universal Matrix Completion

The problem of low-rank matrix completion has recently generated a lot of interest leading to several results that offer exact solutions to the problem. However, in order to do so, these methods make assumptions that can be quite restrictive in practice. More specifically, the methods assume that: a) the observed indices are sampled uniformly at random, and b) for every new matrix, the observed indices are sampled afresh. In this work, we address these issues by providing a universal recovery guarantee for matrix completion that works for a variety of sampling schemes. In particular, we show that if the set of sampled indices come from the edges of a bipartite graph with large spectral gap (i.e. gap between the first and the second singular value), then the nuclear norm minimization based method exactly recovers all low-rank matrices that satisfy certain incoherence properties. Moreover, we also show that under certain stricter incoherence conditions, $O(nr^2)$ uniformly sampled entries are enough to recover any rank-$r$ $n\times n$ matrix, in contrast to the $O(nr\log n)$ sample complexity required by other matrix completion algorithms as well as existing analyses of the nuclear norm method.
Modeling sequential data using higher-order relational features and predictive training

Bi-linear feature learning models, like the gated autoencoder, were proposed as a way to model relationships between frames in a video. By minimizing reconstruction error of one frame, given the previous frame, these models learn “mapping units” that encode the transformations inherent in a sequence, and thereby learn to encode motion. In this work we extend bi-linear models by introducing “higher order mapping units” that allow us to encode transformations between frames and transformations between transformations. We show that this makes it possible to encode temporal structure that is more complex and longer-range than the structure captured within standard bi-linear models. We also show that a natural way to train the model is by replacing the commonly used reconstruction objective with a prediction objective which forces the model to correctly predict the evolution of the input multiple steps into the future. Learning can be achieved by back-propagating the multi-step prediction through time. We test the model on various temporal prediction tasks, and show that higher-order mappings and predictive training both yield a significant improvement over bi-linear models in terms of prediction accuracy.
Model-independent superhedging under portfolio constraints

In a discrete-time market, we study model-independent superhedging where the semi-static superhedging portfolio consists of three parts: static positions in liquidly traded vanilla calls, static positions in other tradable, yet possibly less liquid, exotic options, and a dynamic trading strategy in risky assets under certain constraints. By considering the limit order book of each tradable exotic option and employing the Monge–Kantorovich theory of optimal transport we establish a general superhedging duality, which admits a natural connection to convex risk measures. With the aid of this duality, we derive a model-independent version of the fundamental theorem of asset pricing. The notion “finite optimal arbitrage profit”, weaker than no-arbitrage, is also introduced. It is worth noting that our method covers a large class of delta and gamma constraints.
Identification and Correction of Sample Mix-Ups in Expression Genetic Data: A Case Study

In a mouse intercross with more than 500 animals and genome-wide gene expression data on six tissues, we identified a high proportion (18%) of sample mix-ups in the genotype data. Local expression quantitative trait loci (eQTL; genetic loci influencing gene expression) with extremely large effect were used to form a classifier to predict an individual’s eQTL genotype based on expression data alone. By considering multiple eQTL and their related transcripts, we identified numerous individuals whose predicted eQTL genotypes (based on their expression data) did not match their observed genotypes, and then went on to identify other individuals whose genotypes did match the predicted eQTL genotypes. The concordance of predictions across six tissues indicated that the problem was due to mix-ups in the genotypes (although we further identified a small number of sample mix-ups in each of the six panels of gene expression microarrays). Consideration of the plate positions of the DNA samples indicated a number of off-by-one and off-by-two errors, likely the result of pipetting errors. Such sample mix-ups can be a problem in any genetic study, but eQTL data allow us to identify, and even correct, such problems. Our methods have been implemented in an R package, R/lineup.
On Zeroth-Order Stochastic Convex Optimization via Random Walks

We propose a method for zeroth order stochastic convex optimization that attains the suboptimality rate of $\tilde{\mathcal{O}}(n^{7}T^{-1/2})$ after $T$ queries for a convex bounded function $f:{\mathbb R}^n\to{\mathbb R}$. The method is based on a random walk (the \emph{Ball Walk}) on the epigraph of the function. The randomized approach circumvents the problem of gradient estimation, and appears to be less sensitive to noisy function evaluations compared to noiseless zeroth order methods.