nils-herrmann/clean_scirepeval huggingface dataset:
Averaged number of prompt neutrons calculus for photo-fission of actinides

The empirical calculations of the prompt neutrons average number for photofission of the 232Th, 233U, 234U, 235U, 236U, 238U, 237Np, 239Pu and 241Am actinides has been done as a function of photon energy, mass and charge of the nuclei, which can be used to evaluate the average number for photo-fission of nuclides for which no or scarce data are available.
On the Complexity of Learning with Kernels

A well-recognized limitation of kernel learning is the requirement to handle a kernel matrix, whose size is quadratic in the number of training examples. Many methods have been proposed to reduce this computational cost, mostly by using a subset of the kernel matrix entries, or some form of low-rank matrix approximation, or a random projection method. In this paper, we study lower bounds on the error attainable by such methods as a function of the number of entries observed in the kernel matrix or the rank of an approximate kernel matrix. We show that there are kernel learning problems where no such method will lead to non-trivial computational savings. Our results also quantify how the problem difficulty depends on parameters such as the nature of the loss function, the regularization parameter, the norm of the desired predictor, and the kernel matrix rank. Our results also suggest cases where more efficient kernel learning might be possible.
A Computational Framework for Bioimaging Simulation

Using bioimaging technology, biologists have attempted to identify and document analytical interpretations that underlie biological phenomena in biological cells. Theoretical biology aims at distilling those interpretations into knowledge in the mathematical form of biochemical reaction networks and understanding how higher level functions emerge from the combined action of biomolecules. However, there still remain formidable challenges in bridging the gap between bioimaging and mathematical modeling. Generally, measurements using fluorescence microscopy systems are influenced by systematic effects that arise from stochastic nature of biological cells, the imaging apparatus, and optical physics. Such systematic effects are always present in all bioimaging systems and hinder quantitative comparison between the cell model and bioimages. Computational tools for such a comparison are still unavailable. Thus, in this work, we present a computational framework for handling the parameters of the cell models and the optical physics governing bioimaging systems. Simulation using this framework can generate digital images of cell simulation results after accounting for the systematic effects. We then demonstrate that such a framework enables comparison at the level of photon-counting units.
Using Twitter to predict football outcomes

Twitter has been proven to be a notable source for predictive modelling on various domains such as the stock market, the dissemination of diseases or sports outcomes. However, such a study has not been conducted in football (soccer) so far. The purpose of this research was to study whether data mined from Twitter can be used for this purpose. We built a set of predictive models for the outcome of football games of the English Premier League for a 3 month period based on tweets and we studied whether these models can overcome predictive models which use only historical data and simple football statistics. Moreover, combined models are constructed using both Twitter and historical data. The final results indicate that data mined from Twitter can indeed be a useful source for predicting games in the Premier League. The final Twitter-based model performs significantly better than chance when measured by Cohen's kappa and is comparable to the model that uses simple statistics and historical data. Combining both models raises the performance higher than it was achieved by each individual model. Thereby, this study provides evidence that Twitter derived features can indeed provide useful information for the prediction of football (soccer) outcomes.
Impact of credit default swaps on financial contagion

It had been believed in the conventional practice that the risk of a bank going bankrupt is lessened in a straightforward manner by transferring the risk of loan defaults. But the failure of American International Group in 2008 posed a more complex aspect of financial contagion. This study presents an extension of the asset network systemic risk model (ANWSER) to investigate whether credit default swaps mitigate or intensify the severity of financial contagion. A protection buyer bank transfers the risk of every possible debtor bank default to protection seller banks. The empirical distribution of the number of bank bankruptcies is obtained with the extended model. Systemic capital buffer ratio is calculated from the distribution. The ratio quantifies the effective loss absorbency capability of the entire financial system to force back financial contagion. The key finding is that the leverage ratio is a good estimate of a systemic capital buffer ratio as the backstop of a financial system. The risk transfer from small and medium banks to big banks in an interbank network does not mitigate the severity of financial contagion.
Ethical Artificial Intelligence

This book-length article combines several peer reviewed papers and new material to analyze the issues of ethical artificial intelligence (AI). The behavior of future AI systems can be described by mathematical equations, which are adapted to analyze possible unintended AI behaviors and ways that AI designs can avoid them. This article makes the case for utility-maximizing agents and for avoiding infinite sets in agent definitions. It shows how to avoid agent self-delusion using model-based utility functions and how to avoid agents that corrupt their reward generators (sometimes called "perverse instantiation") using utility functions that evaluate outcomes at one point in time from the perspective of humans at a different point in time. It argues that agents can avoid unintended instrumental actions (sometimes called "basic AI drives" or "instrumental goals") by accurately learning human values. This article defines a self-modeling agent framework and shows how it can avoid problems of resource limits, being predicted by other agents, and inconsistency between the agent's utility function and its definition (one version of this problem is sometimes called "motivated value selection"). This article also discusses how future AI will differ from current AI, the politics of AI, and the ultimate use of AI to help understand the nature of the universe and our place in it.
Testing GeV-Scale Dark Matter with Fixed-Target Missing Momentum Experiments

We describe an approach to detect dark matter and other invisible particles with mass below a GeV, exploiting missing energy-momentum measurements and other kinematic features of fixed-target production. In the case of an invisibly decaying MeV-GeV-scale dark photon, this approach can improve on present constraints by 2-6 orders of magnitude over the entire mass range, reaching sensitivity as low as $\epsilon^2\sim 10^{-14}$. Moreover, the approach can explore essentially all of the viable parameter space for thermal or asymmetric dark matter annihilating through the vector portal.
On the origin of near-infrared extragalactic background light anisotropy

A diffuse cosmic glow is not primordial A cumulative map of all photons ever emitted by any star or galaxy is a highly desirable historical record of the universe's evolution. For this reason, cosmologists have sought to measure this diffuse distribution of light: the extragalactic background light. Zemcov et al. sent up a rocket to measure the fluctuations in this faint background and found largescale fluctuations greater than known galaxies alone should produce (see the Perspective by Moseley). Stars tidally stripped from their host galaxies are the most likely culprit, rather than unknown primordial galaxies. Science, this issue p. 732; see also p. 696 Emission fluctuations that trace the cosmic history are most consistent with the light from intrahalo stars at low redshift.  Extragalactic background light (EBL) anisotropy traces variations in the total production of photons over cosmic history and may contain faint, extended components missed in galaxy point-source surveys. Infrared EBL fluctuations have been attributed to primordial galaxies and black holes at the epoch of reionization (EOR) or, alternately, intrahalo light (IHL) from stars tidally stripped from their parent galaxies at low redshift. We report new EBL anisotropy measurements from a specialized sounding rocket experiment at 1.1 and 1.6 micrometers. The observed fluctuations exceed the amplitude from known galaxy populations, are inconsistent with EOR galaxies and black holes, and are largely explained by IHL emission. The measured fluctuations are associated with an EBL intensity that is comparable to the background from known galaxies measured through number counts and therefore a substantial contribution to the energy contained in photons in the cosmos.
Stochastic Simulator for modeling the transition to lasing.

A Stochastic Simulator (SS) is proposed, based on a semiclassical description of the radiation-matter interaction, to obtain an efficient description of the lasing transition for devices ranging from the nanolaser to the traditional "macroscopic" laser. Steady-state predictions obtained with the SS agree both with more traditional laser modeling and with the description of phase transitions in small-sized systems, and provide additional information on fluctuations. Dynamical information can easily be obtained, with good computing time efficiency, which convincingly highlights the role of fluctuations at threshold.
Radiopurity assessment of the tracking readout for the NEXT double beta decay experiment

The ``Neutrino Experiment with a Xenon Time-Projection Chamber'' (NEXT) is intended to investigate the neutrinoless double beta decay of 136Xe, which requires a severe suppression of potential backgrounds; therefore, an extensive screening and selection process is underway to control the radiopurity levels of the materials to be used in the experimental set-up of NEXT. The detector design combines the measurement of the topological signature of the event for background discrimination with the energy resolution optimization. Separate energy and tracking readout planes are based on different sensors: photomultiplier tubes for calorimetry and silicon multi-pixel photon counters for tracking. The design of a radiopure tracking plane, in direct contact with the gas detector medium, was specially challenging since the needed components like printed circuit boards, connectors, sensors or capacitors have typically, according to available information in databases and in the literature, activities too large for experiments requiring ultra-low background conditions. Here, the radiopurity assessment of tracking readout components based on gamma-ray spectroscopy using ultra-low background germanium detectors at the Laboratorio Subterr'aneo de Canfranc (Spain) is described. According to the obtained results, radiopure enough printed circuit boards made of kapton and copper, silicon photomultipliers and other required components, fulfilling the requirement of an overall background level in the region of interest of at most 8×10−4 counts keV−1 kg−1 y−1, have been identified.
A poroelastic model coupled to a fluid network with applications in lung modelling

We develop a lung ventilation model based on a continuum poroelastic representation of lung parenchyma that is strongly coupled to a pipe network representation of the airway tree. The continuous system of equations is discretized using a low‐order stabilised finite element method. The framework is applied to a realistic lung anatomical model derived from computed tomography data and an artificially generated airway tree to model the conducting airway region. Numerical simulations produce physiologically realistic solutions and demonstrate the effect of airway constriction and reduced tissue elasticity on ventilation, tissue stress and alveolar pressure distribution. The key advantage of the model is the ability to provide insight into the mutual dependence between ventilation and deformation. This is essential when studying lung diseases, such as chronic obstructive pulmonary disease and pulmonary fibrosis. Thus the model can be used to form a better understanding of integrated lung mechanics in both the healthy and diseased states.
The Spaces of Data, Information, and Knowledge

We study the data space $D$ of any given data set $X$ and explain how functions and relations are defined over $D$. From $D$ and for a specific domain $\Delta$ we construct the information space $I$ of $X$ by interpreting variables, functions, and explicit relations over $D$ in $\Delta$ and by including other relations that $D$ implies under the interpretation in $\Delta$. Then from $I$ we build up the knowledge space $K$ of $X$ as the product of two spaces $K_T$ and $K_P$, where $K_T$ is obtained from $I$ by using the induction principle to generalize propositional relations to quantified relations, the deduction principle to generate new relations, and standard mechanisms to validate relations and $K_P$ is the space of specifications of methods with operational instructions which are valid in $K_T$. Through our construction of the three topological spaces the following key observation is made clear: the retrieval of information from the given data set for $\Delta$ consists essentially in mining domain objects and relations, and the discovery of knowledge from the retrieved information consists essentially in applying the induction and deduction principles to generate propositions, synthesizing and modeling the information to generate specifications of methods with operational instructions, and validating the propositions and specifications. Based on this observation, efficient approaches may be designed to discover profound knowledge automatically from simple data, as demonstrated by the result of our study in the case of geometry.
Classical and quantum behavior of the harmonic and the quartic oscillators

In a previous paper a formalism to analyze the dynamical evolution of classical and quantum probability distributions in terms of their moments was presented. Here the application of this formalism to the system of a particle moving on a potential is considered in order to derive physical implications about the classical limit of a quantum system. The complete set of harmonic potentials is considered, which includes the particle under a uniform force, as well as the harmonic and the inverse harmonic oscillators. In addition, as an example of anharmonic system, the pure quartic oscillator is analyzed. Classical and quantum moments corresponding to stationary states of these systems are analytically obtained without solving any differential equation. Finally, dynamical states are also considered in order to study the differences between their classical and quantum evolution.
Large-Margin Determinantal Point Processes

Determinantal point processes (DPPs) offer a powerful approach to modeling diversity in many applications where the goal is to select a diverse subset. We study the problem of learning the parameters (the kernel matrix) of a DPP from labeled training data. We make two contributions. First, we show how to reparameterize a DPP's kernel matrix with multiple kernel functions, thus enhancing modeling flexibility. Second, we propose a novel parameter estimation technique based on the principle of large margin separation. In contrast to the state-of-the-art method of maximum likelihood estimation, our large-margin loss function explicitly models errors in selecting the target subsets, and it can be customized to trade off different types of errors (precision vs. recall). Extensive empirical studies validate our contributions, including applications on challenging document and video summarization, where flexibility in modeling the kernel matrix and balancing different errors is indispensable.
Tree decompositions and social graphs

Recent work has established that large informatics graphs such as social and information networks have non-trivial tree-like structure when viewed at moderate size scales. Here, we present results from the first detailed empirical evaluation of the use of tree decomposition (TD) heuristics for structure identification and extraction in social graphs. Although TDs have historically been used in structural graph theory and scientific computing, we show that—even with existing TD heuristics developed for those very different areas—TD methods can identify interesting structure in a wide range of realistic informatics graphs. Our main contributions are the following: we show that TD methods can identify structures that correlate strongly with the core-periphery structure of realistic networks, even when using simple greedy heuristics; we show that the peripheral bags of these TDs correlate well with low-conductance communities (when they exist) found using local spectral computations; and we show that several types of large-scale “ground-truth” communities, defined by demographic metadata on the nodes of the network, are well-localized in the large-scale and/or peripheral structures of the TDs. Our other main contributions are the following: we provide detailed empirical results for TD heuristics on toy and synthetic networks to establish a baseline to understand better the behavior of the heuristics on more complex real-world networks; and we prove a theorem providing formal justification for the intuition that the only two impediments to low-distortion hyperbolic embedding are high tree-width and long geodesic cycles. Our results suggest future directions for improved TD heuristics that are more appropriate for realistic social graphs.
Proof Supplement - Learning Sparse Causal Models is not NP-hard (UAI2013)

This article contains detailed proofs and additional examples related to the UAI-2013 submission `Learning Sparse Causal Models is not NP-hard'. It describes the FCI+ algorithm: a method for sound and complete causal model discovery in the presence of latent confounders and/or selection bias, that has worst case polynomial complexity of order $N^{2(k+1)}$ in the number of independence tests, for sparse graphs over $N$ nodes, bounded by node degree $k$. The algorithm is an adaptation of the well-known FCI algorithm by (Spirtes et al., 2000) that is also sound and complete, but has worst case complexity exponential in $N$.
General Relativity as an Extended Canonical Gauge Theory

It is widely accepted that the fundamental geometrical law of nature should follow from an action principle. The particular subset of transformations of a system's dynamical variables that maintain the form of the action principle comprises the group of canonical transformations. In the context of canonical field theory, the adjective "extended" signifies that not only the fields but also the space-time geometry is subject to transformation. Thus, in order to be physical, the transition to another, possibly noninertial frame of reference must necessarily constitute an extended canonical transformation that defines the general mapping of the connection coefficients, hence the quantities that determine the space-time curvature and torsion of the respective reference frame. The canonical transformation formalism defines simultaneously the transformation rules for the conjugates of the connection coefficients and for the Hamiltonian. As will be shown, this yields unambiguously a particular Hamiltonian that is form-invariant under the canonical transformation of the connection coefficients and thus satisfies the general principle of relativity. This Hamiltonian turns out to be a quadratic function of the curvature tensor. Its Legendre-transformed counterpart then establishes a unique Lagrangian description of the dynamics of space-time that is not postulated but derived from basic principles, namely the action principle and the general principle of relativity. Moreover, the resulting theory satisfies the principle of scale invariance and is renormalizable.
On Stochastic Orders and its applications : Policy limits and Deductibles

This paper focuses on stochastic orders and its applications : policy limits and deductibles. Further, many applications and some examples are given : comparison of two families of copulas, individual and collective risk model, reinsurance contracts and dependent portfolios increase risk. More precisely, we propose a new model for insurance risks while we give some properties. To this end, we obtain the ordering of the optimal allocation of policy limits and deductibles for this model.
General Smile Asymptotics with Bounded Maturity

We provide explicit conditions on the distribution of risk-neutral log-returns which yield sharp asymptotic estimates on the implied volatility smile. We allow for a variety of asymptotic regimes, including both small maturity (with arbitrary strike) and extreme strike (with arbitrary bounded maturity), extending previous work of Benaim and Friz 2009), 1-12]. We present applications to popular models, including Carr-Wu finite moment logstable model, Merton's jump diffusion model and Heston's model.
Nonminimal scalar-tensor theories

We compute the spectrum of scalar models with a general coupling to the scalar curvature. We find that the perturbative states of these theories are given by two massive spin-0 modes in addition to one massless spin-2 state. This latter mode can be identified with the standard graviton field. Indeed, we are able to define an Einstein frame, where the dynamics of the massless spin-2 graviton is the one associated with the Einstein-Hilbert action. We also explore the interactions of all these degrees of freedom in the mentioned frame, since part of the coupling structure can be anticipated by geometrical arguments.
Rotating black holes in a draining bathtub: superradiant scattering of gravity waves

In a draining rotating fluid flow background, surface perturbations behave as a scalar field on a rotating effective black hole spacetime. We propose a new model for the background flow which takes into account the varying depth of the water. Numerical integration of the associated Klein-Gordon equation using accessible experimental parameters shows that gravity waves in an appropriate frequency range are amplified through the mechanism of superradiance. Our numerical results suggest that the observation of this phenomenon in a common fluid mechanical system is within experimental reach. Unlike the case of wave scattering around Kerr black holes, which depends only on one dimensionless background parameter (the ratio a/M between the specific angular momentum and the mass of the black hole), our system depends on two dimensionless background parameters, namely the normalized angular velocity and surface gravity at the effective black hole horizon.
Stochastic variational inference for hidden Markov models

Variational inference algorithms have proven successful for Bayesian analysis in large data settings, with recent advances using stochastic variational inference (SVI). However, such methods have largely been studied in independent or exchangeable data settings. We develop an SVI algorithm to learn the parameters of hidden Markov models (HMMs) in a time-dependent data setting. The challenge in applying stochastic optimization in this setting arises from dependencies in the chain, which must be broken to consider minibatches of observations. We propose an algorithm that harnesses the memory decay of the chain to adaptively bound errors arising from edge effects. We demonstrate the effectiveness of our algorithm on synthetic experiments and a large genomics dataset where a batch algorithm is computationally infeasible.