nils-herrmann/clean_scirepeval huggingface dataset:
Interaction Graphs: Exponentials

This paper is the fourth of a series exposing a systematic combinatorial approach to Girard's Geometry of Interaction program. This program aims at obtaining particular realizability models for linear logic that accounts for the dynamics of cut-elimination. This fourth paper tackles the complex issue of defining exponential connectives in this framework. In order to succeed in this, we use the notion of graphings, a generalization of graphs which was defined in earlier work. We explain how we can use this framework to define a GoI for Elementary Linear Logic (ELL) with second-order quantification, a sub-system of linear logic that captures the class of elementary time computable functions.
Hamiltonian formalism for integer-valued variables and integer time steps and a possible application in quantum physics

Most classical mechanical systems are based on dynamical variables whose values are real numbers. Energy conservation is then guaranteed if the dynamical equations are phrased in terms of a Hamiltonian function, which then leads to differential equations in the time variable. If these real dynamical variables are instead replaced by integers, and also the time variable is restricted to integers, it appears to be hard to enforce energy conservation unless one can also derive a Hamiltonian formalism for that case. We here show how the Hamiltonian formalism works here, and how it may yield the usual Hamilton equations in the continuum limit. The question was motivated by the author's investigations of special quantum systems that allow for a deterministic interpretation. The 'discrete Hamiltonian formalism' appears to shed new light on these approaches.
Direct and indirect detection of dissipative dark matter

We study the constraints from direct detection and solar capture on dark matter scenarios with a subdominant dissipative component. This dissipative dark matter component in general has both a symmetric and asymmetric relic abundance. Dissipative dynamics allow this subdominant dark matter component to cool, resulting in its partial or total collapse into a smaller volume inside the halo (e.g., a dark disk) as well as a reduced thermal velocity dispersion compared to that of normal cold dark matter. We first show that these features considerably relax the limits from direct detection experiments on the couplings between standard model (SM) particles and dissipative dark matter. On the other hand, indirect detection of the annihilation of the symmetric dissipative dark matter component inside the Sun sets stringent and robust constraints on the properties of the dissipative dark matter. In particular, IceCube observations force dissipative dark matter particles with mass above 50 GeV to either have a small coupling to the SM or a low local density in the solar system, or to have a nearly asymmetric relic abundance. Possible helioseismology signals associated with purely asymmetric dissipative dark matter are discussed, with no present constraints.
Improving self-calibration.

Response calibration is the process of inferring how much the measured data depend on the signal one is interested in. It is essential for any quantitative signal estimation on the basis of the data. Here, we investigate self-calibration methods for linear signal measurements and linear dependence of the response on the calibration parameters. The common practice is to augment an external calibration solution using a known reference signal with an internal calibration on the unknown measurement signal itself. Contemporary self-calibration schemes try to find a self-consistent solution for signal and calibration by exploiting redundancies in the measurements. This can be understood in terms of maximizing the joint probability of signal and calibration. However, the full uncertainty structure of this joint probability around its maximum is thereby not taken into account by these schemes. Therefore, better schemes, in sense of minimal square error, can be designed by accounting for asymmetries in the uncertainty of signal and calibration. We argue that at least a systematic correction of the common self-calibration scheme should be applied in many measurement situations in order to properly treat uncertainties of the signal on which one calibrates. Otherwise, the calibration solutions suffer from a systematic bias, which consequently distorts the signal reconstruction. Furthermore, we argue that nonparametric, signal-to-noise filtered calibration should provide more accurate reconstructions than the common bin averages and provide a new, improved self-calibration scheme. We illustrate our findings with a simplistic numerical example.
On Gravitational Entropy of de Sitter Universe

The paper deals with the calculation of the gravitational entropy in the context of teleparallel gravity for de Sitter space-time. In such a theory it is possible to define gravitational energy and pressure; thus we use those expressions to construct the gravitational entropy. We use the temperature as a function of the cosmological constant and write the first law of thermodynamics from which we obtain the entropy. In the limit we find that the entropy is proportional to volume, for a specific temperature’s choice; we find that as well. We also identify a phase transition in de Sitter space-time by analyzing the specific heat.
Noncommutative Quantum Mechanics based on Representations of Exotic Galilei Group

Using elements of symmetry, we constructed the Noncommutative Schrödinger Equation from a representation of Exotic Galilei Group. As a consequence, we derive the Ehrenfest theorem using noncommutative coordinates. We also have showed others features of quantum mechanics in such a manifold. As an important result, we find out that a linear potential in the noncommutative Schrödinger equation is completely analogous to the ordinary case. We also worked with harmonic and anharmonic oscillators, giving corrections in the energy for each one.
Depolarized light scattering and dielectric response of a peptide dissolved in water.

The density and orientational relaxation of bulk water can be separately studied by depolarized light scattering (DLS) and dielectric spectroscopy (DS), respectively. Here, we ask the question of what are the leading collective modes responsible for polarization anisotropy relaxation (DLS) and dipole moment relaxation (DS) of solutions involving mostly hydrophobic solute-water interfaces. We study, by atomistic molecular dynamics simulations, the dynamics and structure of hydration water interfacing N-Acetyl-leucine-methylamide dipeptide. The DLS response of the solution is consistent with three relaxation processes: bulk water, rotations of single solutes, and collective dipole-induced-dipole polarizability of the solutes, with the time-scale of 130-200 ps. No separate DLS response of the hydration shell has been identified by our simulations. Density fluctuations of the hydration layer, which largely contribute to the response, do not produce a dynamical process distinct from bulk water. We find that the structural perturbation of the orientational distribution of hydration waters by the dipeptide solute is quite significant and propagates ∼3-5 hydration layers into the bulk. This perturbation is still below that produced by hydrated globular proteins. Despite this structural perturbation, there is little change in the orientational dynamics of the hydration layers, compared to the bulk, as probed by both single-particle orientational dynamics and collective dynamics of the dipole moment of the shells. There is a clear distinction between the perturbation of the interfacial structure by the solute-solvent interaction potential and the perturbation of the interfacial dynamics by the corresponding forces.
ABC-SG: A New Artificial Bee Colony Algorithm-Based Distance of Sequential Data Using Sigma Grams

The problem of similarity search is one of the main problems in computer science. This problem has many applications in text-retrieval, web search, computational biology, bioinformatics and others. Similarity between two data objects can be depicted using a similarity measure or a distance metric. There are numerous distance metrics in the literature, some are used for a particular data type, and others are more general. In this paper we present a new distance metric for sequential data which is based on the sum of n-grams. The novelty of our distance is that these n-grams are weighted using artificial bee colony; a recent optimization algorithm based on the collective intelligence of a swarm of bees on their search for nectar. This algorithm has been used in optimizing a large number of numerical problems. We validate the new distance experimentally.
Gravitational energy, local holography and non-equilibrium thermodynamics

We study the properties of gravitational systems in finite regions bounded by gravitational screens. We present a detailed construction of the total energy of such regions and of the energy and momentum balance equations due to the flow of matter and gravitational radiation through the screen. We establish that the gravitational screen possesses analogs of surface tension, internal energy, and viscous stress tensor, while the conservations are analogs of nonequilibrium balance equations for a viscous system. This gives a precise correspondence between gravity in finite regions and nonequilibrium thermodynamics.
Credit Portfolio Management in a Turning Rates Environment

We give a detailed account of correlations between credit sector/quality and treasury curve factors, using the robust framework of the Barclays POINT Global Risk Model. Consistent with earlier studies, we find a strong negative correlation between sector spreads and rate shifts. However, we also observe that the correlations between spreads and Treasury twists reversed recently, which is likely attributable to the Fed's ongoing quantitative easing. We also find that short-term effective durations in the banking industry are now significantly lower than historical patterns would indicate. Our findings are relevant for credit portfolio managers contemplating the impact of rising interest rates and steepening Treasury curve on corporate bond portfolios.
Finite temperature fermionic condensate and currents in topologically nontrivial spaces

We investigate the finite temperature fermionic condensate and the expectation values of the charge and current densities for a massive fermion field in a spacetime background with an arbitrary number of toroidally compactified spatial dimensions in the presence of a non-vanishing chemical potential. Periodicity conditions along compact dimensions are taken with arbitrary phases and the presence of a constant gauge field is assumed. The latter gives rise to Aharonov-Bohm-like effects on the expectation values. They are periodic functions of magnetic fluxes enclosed by compact dimensions with the period equal to the flux quantum. The current density has nonzero components along compact dimensions only. Both lowand high-temperature asymptotics of the expectation values are studied. In particular, it has been shown that at high temperatures the current density is exponentially suppressed. This behavior is in sharp contrast with the corresponding asymptotic in the case of a scalar field, where the current density linearly grows with the temperature. The features for the models in odd dimensional spacetimes are discussed. Applications are given to cylindrical and toroidal nanotubes described within the framework of effective Dirac theory for the electronic subsystem.
Gate count estimates for performing quantum chemistry on small quantum computers

As quantum computing technology improves and quantum computers with a small but non-trivial number of N > 100 qubits appear feasible in the near future the question of possible applications of small quantum computers gains importance. One frequently mentioned application is Feynman's original proposal of simulating quantum systems, and in particular the electronic structure of molecules and materials. In this paper, we analyze the computational requirements for one of the standard algorithms to perform quantum chemistry on a quantum computer. We focus on the quantum resources required to find the ground state of a molecule twice as large as what current classical computers can solve exactly. We find that while such a problem requires about a ten-fold increase in the number of qubits over current technology, the required increase in the number of gates that can be coherently executed is many orders of magnitude larger. This suggests that for quantum computation to become useful for quantum chemistry problems, drastic algorithmic improvements will be needed.
Ribosome profiling reveals post-transcriptional buffering of divergent gene expression in yeast.

Understanding the patterns and causes of phenotypic divergence is a central goal in evolutionary biology. Much work has shown that mRNA abundance is highly variable between closely related species. However, the extent and mechanisms of post-transcriptional gene regulatory evolution are largely unknown. Here we used ribosome profiling to compare transcript abundance and translation efficiency in two closely related yeast species (S. cerevisiae and S. paradoxus). By comparing translation regulatory divergence to interspecies differences in mRNA sequence features, we show that differences in transcript leaders and codon bias substantially contribute to divergent translation. Globally, we find that translation regulatory divergence often buffers species differences in mRNA abundance, such that ribosome occupancy is more conserved than transcript abundance. We used allele-specific ribosome profiling in interspecies hybrids to compare the relative contributions of cis- and trans-regulatory divergence to species differences in mRNA abundance and translation efficiency. The mode of gene regulatory divergence differs for these processes, as trans-regulatory changes play a greater role in divergent mRNA abundance than in divergent translation efficiency. Strikingly, most genes with aberrant transcript abundance in F1 hybrids (either over- or underexpressed compared to both parent species) did not exhibit aberrant ribosome occupancy. Our results show that interspecies differences in translation contribute substantially to the evolution of gene expression. Compensatory differences in transcript abundance and translation efficiency may increase the robustness of gene regulation.
Interfaces with internal structures in generalized rock-paper-scissors models.

In this work we investigate the development of stable dynamical structures along interfaces separating domains belonging to enemy partnerships in the context of cyclic predator-prey models with an even number of species N≥8. We use both stochastic and field theory simulations in one and two spatial dimensions, as well as analytical arguments, to describe the association at the interfaces of mutually neutral individuals belonging to enemy partnerships and to probe their role in the development of the dynamical structures at the interfaces. We identify an interesting behavior associated with the symmetric or asymmetric evolution of the interface profiles depending on whether N/2 is odd or even, respectively. We also show that the macroscopic evolution of the interface network is not very sensitive to the internal structure of the interfaces. Although this work focuses on cyclic predator-prey models with an even number of species, we argue that the results are expected to be quite generic in the context of spatial stochastic May-Leonard models.
Optimal Trading Strategies as Measures of Market Disequilibrium

For classification of the high frequency trading quantities, waiting times, price increments within and between sessions are referred to as the a-, b-, and c-increments. Statistics of the a-b-c-increments are computed for the Time & Sales records posted by the Chicago Mercantile Exchange Group for the futures traded on Globex. The Weibull, Kumaraswamy, Riemann and Hurwitz Zeta, parabolic, Zipf-Mandelbrot distributions are tested for the a- and b-increments. A discrete version of the Fisher-Tippett distribution is suggested for approximating the extreme b-increments. Kolmogorov and Uspenskii classification of stochastic, typical, and chaotic random sequences is reviewed with regard to the futures price limits. Non-parametric L1 and log-likelihood tests are applied to check dependencies between the a- and b-increments. The maximum profit strategies and optimal trading elements are suggested as measures of frequency and magnitude of the market offers and disequilibrium. Empirical cumulative distribution functions of optimal profits are reported. A few classical papers are reviewed with more details in order to trace the origin and foundation of modern finance.
Les Houches Lectures on Physics Beyond the Standard Model of Cosmology

In these Lectures, I review various extensions of the Lambda-Cold Dark Matter model, characterized by additional light degrees of freedom in the dark sector. In order to reproduce the successful phenomenology of GR in the solar system, these fields must effectively decouple from matter on solar system/laboratory scales. This is achieved through screening mechanisms, which rely on the interplay between self-interactions and coupling to matter to suppress deviations from standard gravity. The manifestation of the new degrees of freedom depends sensitively on their environment, which in turn leads to striking experimental signatures.
Generalised Eisenhart lift of the Toda chain

The Toda chain of nearest neighbour interacting particles on a line can be described both in terms of geodesic motion on a manifold with one extra dimension, the Eisenhart lift, or in terms of geodesic motion in a symmetric space with several extra dimensions. We examine the relationship between these two realisations and discover that the symmetric space is a generalised, multi-particle Eisenhart lift of the original problem that reduces to the standard Eisenhart lift. Such generalised Eisenhart lift acts as an inverse Kaluza-Klein reduction, promoting coupling constants to momenta in higher dimension. In particular, isometries of the generalised lift metric correspond to energy preserving transformations that mix coordinates and coupling constants. A by-product of the analysis is that the lift of the Toda Lax pair can be used to construct higher rank Killing tensors for both the standard and generalised lift metrics.
Probabilistic models of genetic variation in structured populations applied to global human studies

Modern population genetics studies typically involve genome-wide genotyping of individuals from a diverse network of ancestries. An important problem is how to formulate and estimate probabilistic models of observed genotypes that account for complex population structure. The most prominent work on this problem has focused on estimating a model of admixture proportions of ancestral populations for each individual. Here, we instead focus on modeling variation of the genotypes without requiring a higher-level admixture interpretation.  We formulate two general probabilistic models, and we propose computationally efficient algorithms to estimate them. First, we show how principal component analysis can be utilized to estimate a general model that includes the well-known Pritchard–Stephens–Donnelly admixture model as a special case. Noting some drawbacks of this approach, we introduce a new ‘logistic factor analysis’ framework that seeks to directly model the logit transformation of probabilities underlying observed genotypes in terms of latent variables that capture population structure. We demonstrate these advances on data from the Human Genome Diversity Panel and 1000 Genomes Project, where we are able to identify SNPs that are highly differentiated with respect to structure while making minimal modeling assumptions.  A Bioconductor R package called lfa is available at http://www.bioconductor.org/packages/release/bioc/html/lfa.html.  jstorey@princeton.edu Supplementary information: Supplementary data are available at Bioinformatics online.
The False Premises and Promises of Bitcoin

Designed to compete with fiat currencies, bitcoin proposes it is a crypto-currency alternative. Bitcoin makes a number of false claims, including: solving the double-spending problem is a good thing; bitcoin can be a reserve currency for banking; hoarding equals saving, and that we should believe bitcoin can expand by deflation to become a global transactional currency supply. Bitcoin's developers combine technical implementation proficiency with ignorance of currency and banking fundamentals. This has resulted in a failed attempt to change finance. A set of recommendations to change finance are provided in the Afterword: Investment/venture banking for the masses; Venture banking to bring back what investment banks once were; Open-outcry exchange for all CDS contracts; Attempting to develop CDS type contracts on investments in startup and existing enterprises; and Improving the connection between startup tech/ideas, business organization and investment.
Learning about Social Learning in MOOCs: From Statistical Analysis to Generative Model

We study user behavior in the courses offered by a major massive online open course (MOOC) provider during the summer of 2013. Since social learning is a key element of scalable education on MOOC and is done via online discussion forums, our main focus is on understanding forum activities. Two salient features of these activities drive our research: (1) high decline rate: for each course studied, the volume of discussion declined continuously throughout the duration of the course; (2) high-volume, noisy discussions: at least 30 percent of the courses produced new threads at rates that are infeasible for students or teaching staff to read through. Further, a substantial portion of these discussions are not directly course-related. In our analysis, we investigate factors that are associated with the decline of activity on MOOC forums, and we find effective strategies to classify threads and rank their relevance. Specifically, we first use linear regression models to analyze the forum activity count data over time, and make a number of observations; for instance, the teaching staff's active participation in the discussions is correlated with an increase in the discussion volume but does not slow down the decline rate. We then propose a unified generative model for the discussion threads, which allows us both to choose efficient thread classifiers and to design an effective algorithm for ranking thread relevance. Further, our algorithm is compared against two baselines using human evaluation from Amazon Mechanical Turk.
bartMachine: Machine Learning with Bayesian Additive Regression Trees

We present a new package in R implementing Bayesian additive regression trees (BART). The package introduces many new features for data analysis using BART such as variable selection, interaction detection, model diagnostic plots, incorporation of missing data and the ability to save trees for future prediction. It is significantly faster than the current R implementation, parallelized, and capable of handling both large sample sizes and high-dimensional data.