nils-herrmann/clean_scirepeval huggingface dataset:
Verification of internal risk measure estimates

This paper concerns sequential computation of risk measures for financial data and asks how, given a risk measurement procedure, we can tell whether the answers it produces are ‘correct’. We draw the distinction between ‘external’ and ‘internal’ risk measures and concentrate on the latter, where we observe data in real time, make predictions and observe outcomes. It is argued that evaluation of such procedures is best addressed from the point of view of probability forecasting or Dawid’s theory of ‘prequential statistics’ . We introduce a concept of ‘calibration’ of a risk measure in a dynamic setting, following the precepts of Dawid’s weak and strong prequential principles, and examine its application to quantile forecasting (VaR – value at risk) and to mean estimation (applicable to CVaR – expected shortfall). The relationship between these ideas and ‘elicitability’ [24] is examined. We show in particular that VaR has special properties not shared by any other risk measure. Turning to CVaR we argue that its main deficiency is the unquantifiable tail dependence of estimators. In a final section we show that a simple data-driven feedback algorithm can produce VaR estimates on financial data that easily pass both the consistency test and a further newly-introduced statistical test for independence of a binary sequence.
Theoretical description of prompt fission neutron multiplicity and spectra

The present work concerns two successful models used today: Point by Point (PbP) and the Monte Carlo approaches. The description of the PbP model and of the extended Los Alamos model for higher energies that takes into account the secondary chains and ways is given in Chapter II. In this chapter are given also examples of PbP and most probable fragmentation approach calculations for various quantities which characterize prompt emission: multi-parametric matrices, quantities as a function of fragment mass, quantities as a function of the TKE and total average quantities, for different spontaneous and neutron induced fissioning systems. Special care was given to the TXE partition between the fully accelerated fission fragments, two partition methods used in the PbP model being discussed in details. In Chapter III is given the description of the Monte Carlo treatment included in the FIFRELIN code. Only those aspects that differ from the PbP treatment are emphasized. A special attention is given to the latest developments of the code concerning the inclusion of the energy dependent compound nucleus cross section of the inverse process of neutron evaporation from fragments. In this chapter examples of calculation with the FIFRELIN code for the case of the standard fissioning system 252Cf(SF) are given. Original results for several plutonium spontaneous fissioning systems (236,238,240,242,244Pu) and one neutron induced fissioning system (239Pu(nth,f)) obtained with both PbP and Monte Carlo treatments are given in Chapter IV. The last chapter includes an overview of the most important conclusions resulting from the intercomparison of the results obtained with both treatments in chapter IV.
Multivariate Spearman's rho for Aggregating Ranks Using Copulas

We study the problem of rank aggregation: given a set of ranked lists, we want to form a consensus ranking. Furthermore, we consider the case of extreme lists: i.e., only the rank of the best or worst elements are known. We impute missing ranks by the average value and generalise Spearman's \rho to extreme ranks. Our main contribution is the derivation of a non-parametric estimator for rank aggregation based on multivariate extensions of Spearman's \rho, which measures correlation between a set of ranked lists. Multivariate Spearman's \rho is defined using copulas, and we show that the geometric mean of normalised ranks maximises multivariate correlation. Motivated by this, we propose a weighted geometric mean approach for learning to rank which has a closed form least squares solution. When only the best or worst elements of a ranked list are known, we impute the missing ranks by the average value, allowing us to apply Spearman's \rho. Finally, we demonstrate good performance on the rank aggregation benchmarks MQ2007 and MQ2008.
Should we train scientific generalists?

I examine the topic of training scientific generalists. To focus the discussion, I propose the creation of a new graduate program, analogous in structure to existing MD/PhD programs, aimed at training a critical mass of scientific researchers with substantial intellectual breadth. In addition to completing the normal requirements for a PhD, students would undergo an intense, several year training period designed to expose them to the core vocabulary of multiple subjects at the graduate level. After providing some historical and philosophical context for this proposal, I outline how such a program could be implemented with little institutional overhead by existing research universities. Finally, I discuss alternative possibilities for training generalists by taking advantage of contemporary developments in online learning and open science.
Proton and Neutron Momentum Distributions in A = 3 Asymmetric Nuclei

A proposal approved by the Jefferson Lab (JLab) PAC to study the proton-to-neutron momentum distribution ratio in A=3 nuclei via (e,e'p) scattering off 3He and 3H mirror nuclei. The experiment will measure the 3H(e,e'p) and 3He(e,e'p) cross-sections and cross-section ratios at Q2 = 2 and xB>1 kinematics, over a missing momentum range of 0 - 450 MeV/c. The experiment was approved in 2014 at part of the JLab Hall-A Tritium run period for a total run time of 12 days.
Casimir effect in a quantum space–time

We apply quantum field theory in quantum space–time techniques to study the Casimir effect for large spherical shells. As background we use the recently constructed exact quantum solution for spherically symmetric vacuum space–time in loop quantum gravity. All calculations are finite and one recovers the usual results without the need for regularization or renormalization. This is an example of how loop quantum gravity provides a natural resolution to the infinities of quantum field theories.
A Black-Hole Primer: Particles, Waves, Critical Phenomena and Superradiant Instabilities

These notes were prepared for a lecture on black holes delivered at the DPG Physics School "General Relativity @ 99" (Physikzentrum Bad Honnef, Germany, September 2014). The common thread of the lecture is the relation between geodesic stability and black-hole perturbations in the geometric optics limit. Chapter 1 establishes notation and discusses a common misconception on Michell's "Newtonian black holes". Chapters 2 and 3 deal with particle dynamics and wave dynamics in black-hole spacetimes, respectively. All calculations should be simple enough that they can be done with pen and paper. Chapter 4 builds on this introduction to discuss two exciting topics in current research: critical phenomena in black-hole mergers and the black-hole bomb instability.
Graph-Sparse LDA: A Topic Model with Structured Sparsity

Topic modeling is a powerful tool for uncovering latent structure in many domains, including medicine, finance, and vision. The goals for the model vary depending on the application: sometimes the discovered topics are used for prediction or another downstream task. In other cases, the content of the topic may be of intrinsic scientific interest. Unfortunately, even when one uses modern sparse techniques, discovered topics are often difficult to interpret due to the high dimensionality of the underlying space. To improve topic interpretability, we introduce Graph-Sparse LDA, a hierarchical topic model that uses knowledge of relationships between words (e.g., as encoded by an ontology). In our model, topics are summarized by a few latent concept-words from the underlying graph that explain the observed words. Graph-Sparse LDA recovers sparse, interpretable summaries on two real-world biomedical datasets while matching state-of-the-art prediction performance.
A thought to illustrate the uncertainty principle on the base of Otto-Wiener's experiment

In this short paper a new thought experiment has been introduced to illustrate the famous Heisenberg's uncertainty principle based on Otto-Wiener's experiment (1890) associated with standing light waves. This illustration is quite easy as well as far more realizing than all other thought experiments generally found in textbooks. In this work seeding of quantum nature of light has been done with the Otto-Wiener's experiment. May be this new thought experiment will help the students to understand the Heisenberg's principle in a better way and also enhance their interest of learning quantum mechanics from the beginning of their course.
Reactive Strategies: The Establishment of Cooperation

Cooperation is usually represented as a Prisoner's Dilemma game. Although individual self-interest may not favour cooperation, cooperation can evolve if, for example, players interact multiple times adjusting their behaviour accordingly to opponent's previous action. To analyze population dynamics, replicator equation has been widely used under several versions. Although it is usually stated that a strategy called Generous-tit-for-tat is the winner within the reactive strategies set, here we show that this result depends on replicator's version and on the number of available strategies, stemming from the fact that a dynamics system is also defined by the number of available strategies and not only by the model version. Using computer simulations and analytical arguments, we show that Generous-tit-for-tat victory is found only if the number of strategies available is not too large, with defection winning otherwise.
Vacuum energy sequestering and cosmic dynamics

We explicitly compute the dynamics of closed homogeneous and isotropic universes permeated by a single perfect fluid with a constant equation-of-state parameter $w$ in the context of a recent reformulation of general relativity, proposed by Kaloper and Padilla  (2014)], which prevents the vacuum energy from acting as a gravitational source. This is done using an iterative algorithm, taking as an initial guess the background cosmological evolution obtained using standard general relativity in the absence of a cosmological constant. We show that, in general, the impact of the vacuum energy sequestering mechanism on the dynamics of the universe is significant, except for the $w=1/3$ case where the results are identical to those obtained in the context of general relativity with a null cosmological constant. We also show that there are well-behaved models in general relativity that do not have a well-behaved counterpart in the vacuum energy sequestering paradigm studied in this paper, highlighting the specific case of a quintessence scalar field with a linear potential.
SUPPRESSION OF STAR FORMATION IN NGC 1266

NGC 1266 is a nearby lenticular galaxy that harbors a massive outflow of molecular gas powered by the mechanical energy of an active galactic nucleus (AGN). It has been speculated that such outflows hinder star formation (SF) in their host galaxies, providing a form of feedback to the process of galaxy formation. Previous studies, however, indicated that only jets from extremely rare, high-power quasars or radio galaxies could impart significant feedback on their hosts. Here we present detailed observations of the gas and dust continuum of NGC 1266 at millimeter wavelengths. Our observations show that molecular gas is being driven out of the nuclear region at  yr−1, of which the vast majority cannot escape the nucleus. Only 2 M☉ yr−1 is actually capable of escaping the galaxy. Most of the molecular gas that remains is very inefficient at forming stars. The far-infrared emission is dominated by an ultra-compact (≲ 50 pc) source that could either be powered by an AGN or by an ultra-compact starburst. The ratio of the SF surface density (ΣSFR) to the gas surface density () indicates that SF is suppressed by a factor of ≈50 compared to normal star-forming galaxies if all gas is forming stars, and ≈150 for the outskirt (98%) dense molecular gas if the central region is powered by an ultra-compact starburst. The AGN-driven bulk outflow could account for this extreme suppression by hindering the fragmentation and gravitational collapse necessary to form stars through a process of turbulent injection. This result suggests that even relatively common, low-power AGNs are able to alter the evolution of their host galaxies as their black holes grow onto the M–σ relation.
Impurity-Induced Bound States in Superconductors with Spin-Orbit Coupling.

We study the effect of strong spin-orbit coupling (SOC) on bound states induced by impurities in superconductors. The presence of SOC breaks the SU(2)-spin symmetry and causes the superconducting order parameter to have generically both singlet (s-wave) and triplet (p-wave) components. We find that in the presence of SOC the spectrum of Yu-Shiba-Rusinov (YSR) states is qualitatively different in s-wave and p-wave superconductors, a fact that can be used to identify the superconducting pairing symmetry of the host system. We also predict that, in the presence of SOC, the spectrum of the impurity-induced bound states depends on the orientation of the magnetic moment S of the impurity and, in particular, that by changing the orientation of S, the fermion-parity of the lowest energy bound state can be tuned. We then study the case of a dimer of magnetic impurities and show that, in this case, the YSR spectrum for a p-wave superconductor is qualitatively very different from the one for an s-wave superconductor even in the limit of vanishing SOC.
Visual appearance of wireframe objects in special relativity

The visual appearance of a moving object in special relativity can be constructed in a straightforward manner when representing the surface of the object, or at least a wire frame model of it, as a point cloud. The apparent position of each individual point is then found by intersecting its worldline with the observerʼs backward light cone. In this paper, we present a complete derivation of the apparent position of a point and some more complex geometric objects for general parameter settings (configurations). We implemented our results in python and asymptote and used these tools to generate scripts that create the figures in this paper. These scripts are directly applicable in an undergraduate course to special relativity and can also serve as the basis for student projects with the aim to study more complex sceneries.
Non-parametric Bayesian learning with deep learning structure and its applications in wireless networks

In this paper, we present an infinite hierarchical non-parametric Bayesian model to extract the hidden factors over observed data, where the number of hidden factors for each layer is unknown and can be potentially infinite. Moreover, the number of layers can also be infinite. We construct the model structure that allows continuous values for the hidden factors and weights, which makes the model suitable for various applications. We use the Metropolis-Hastings method to infer the model structure. Then the performance of the algorithm is evaluated by the experiments. Simulation results show that the model fits the underlying structure of simulated data.
Robust Topological Feature Extraction for Mapping of Environments using Bio-Inspired Sensor Networks

In this paper, we exploit minimal sensing information gathered from biologically inspired sensor networks to perform exploration and mapping in an unknown environment. A probabilistic motion model of mobile sensing nodes, inspired by motion characteristics of cockroaches, is utilized to extract weak encounter information in order to build a topological representation of the environment. 
Neighbor to neighbor interactions among the nodes are exploited to build point clouds representing spatial features of the manifold characterizing the environment based on the sampled data. 
To extract dominant features from sampled data, topological data analysis is used to produce persistence intervals for features, to be used for topological mapping. In order to improve robustness characteristics of the sampled data with respect to outliers, density based subsampling algorithms are employed. Moreover, a robust scale-invariant classification algorithm for persistence diagrams is proposed to provide a quantitative representation of desired features in the data. Furthermore, various strategies for defining encounter metrics with different degrees of information regarding agents' motion are suggested to enhance the precision of the estimation and classification performance of the topological method.
Global Value Trees

The fragmentation of production across countries has become an important feature of the globalization in recent decades and is often conceptualized by the term “global value chains” (GVCs). When empirically investigating the GVCs, previous studies are mainly interested in knowing how global the GVCs are rather than how the GVCs look like. From a complex networks perspective, we use the World Input-Output Database (WIOD) to study the evolution of the global production system. We find that the industry-level GVCs are indeed not chain-like but are better characterized by the tree topology. Hence, we compute the global value trees (GVTs) for all the industries available in the WIOD. Moreover, we compute an industry importance measure based on the GVTs and compare it with other network centrality measures. Finally, we discuss some future applications of the GVTs.
Mini-Batch Semi-Stochastic Gradient Descent in the Proximal Setting

We propose mS2GD: a method incorporating a mini-batching scheme for improving the theoretical complexity and practical performance of semi-stochastic gradient descent (S2GD). We consider the problem of minimizing a strongly convex function represented as the sum of an average of a large number of smooth convex functions, and a simple nonsmooth convex regularizer. Our method first performs a deterministic step (computation of the gradient of the objective function at the starting point), followed by a large number of stochastic steps. The process is repeated a few times with the last iterate becoming the new starting point. The novelty of our method is in introduction of mini-batching into the computation of stochastic steps. In each step, instead of choosing a single function, we sample b functions, compute their gradients, and compute the direction based on this. We analyze the complexity of the method and show that it benefits from two speedup effects. First, we prove that as long as b is below a certain threshold, we can reach any predefined accuracy with less overall work than without mini-batching. Second, our mini-batching scheme admits a simple parallel implementation, and hence is suitable for further acceleration by parallelization.
Variational Bayes for Merging Noisy Databases

Bayesian entity resolution merges together multiple, noisy databases and returns the minimal collection of unique individuals represented, together with their true, latent record values. Bayesian methods allow flexible generative models that share power across databases as well as principled quantification of uncertainty for queries of the final, resolved database. However, existing Bayesian methods for entity resolution use Markov monte Carlo method (MCMC) approximations and are too slow to run on modern databases containing millions or billions of records. Instead, we propose applying variational approximations to allow scalable Bayesian inference in these models. We derive a coordinate-ascent approximation for mean-field variational Bayes, qualitatively compare our algorithm to existing methods, note unique challenges for inference that arise from the expected distribution of cluster sizes in entity resolution, and discuss directions for future work in this domain.
Convex Optimization in Julia

This paper describes Convex1, a convex optimization modeling framework in Julia. Convex translates problems from a user-friendly functional language into an abstract syntax tree describing the problem. This concise representation of the global structure of the problem allows Convex to infer whether the problem complies with the rules of disciplined convex programming (DCP), and to pass the problem to a suitable solver. These operations are carried out in Julia using multiple dispatch, which dramatically reduces the time required to verify DCP compliance and to parse a problem into conic form. Convex then automatically chooses an appropriate backend solver to solve the conic form problem.
Generalized Conditional Gradient for Sparse Estimation

Structured sparsity is an important modeling tool that expands the applicability of convex formulations for data analysis, however it also creates significant challenges for efficient algorithm design. In this paper we investigate the generalized conditional gradient (GCG) algorithm for solving structured sparse optimization problems---demonstrating that, with some enhancements, it can provide a more efficient alternative to current state of the art approaches. After providing a comprehensive overview of the convergence properties of GCG, we develop efficient methods for evaluating polar operators, a subroutine that is required in each GCG iteration. In particular, we show how the polar operator can be efficiently evaluated in two important scenarios: dictionary learning and structured sparse estimation. A further improvement is achieved by interleaving GCG with fixed-rank local subspace optimization. A series of experiments on matrix completion, multi-class classification, multi-view dictionary learning and overlapping group lasso shows that the proposed method can significantly reduce the training cost of current alternatives.
The LIGO Open Science Center

The LIGO Open Science Center (LOSC) fulfills LIGO's commitment to release, archive, and serve LIGO data in a broadly accessible way to the scientific community and to the public, and to provide the information and tools necessary to understand and use the data. In August 2014, the LOSC published the full dataset from Initial LIGO's “S5” run at design sensitivity, the first such large-scale release and a valuable testbed to explore the use of LIGO data by non-LIGO researchers and by the public, and to help teach gravitational-wave data analysis to students across the world. In addition to serving the S5 data, the LOSC web portal (losc.ligo.org) now offers documentation, data-location and data-quality queries, tutorials and example code, and more. We review the mission and plans of the LOSC, focusing on the S5 data release.
Impact of shadow banks on financial contagion

An asset network systemic risk (ANWSER) model is presented to investigate the impact of how shadow banks are intermingled in a financial system on the severity of financial contagion. Particularly, the focus of this study is the impact of the following three representative topologies of an interbank loan network between shadow banks and regulated banks. (1) Random mixing network: shadow banks and regulated banks are intermingled randomly. (2) Asset-correlated mixing network: banks having bigger assets are a regulated bank and other banks are shadow banks. (3) Layered mixing network: banks in a shadow bank layer are connected to banks in a regulated bank layer with some interbank loans.