nils-herrmann/clean_scirepeval huggingface dataset:
A polynomial distribution applied to income and wealth distribution

Income and wealth distribution affect stability of a society to a large extent and high inequality affects it negatively. Moreover, in the case of developed countries, recently has been proven that inequality is closely related to all negative phenomena affecting society. So far, Econophysics papers tried to analyse income and wealth distribution by employing distributions such as Fermi-Dirac, Bose-Einstein, Maxwell-Boltzmann, lognormal (Gibrat), and exponential. Generally, distributions describe mostly income and less wealth distribution for low and middle income segment of population, which accounts about 90% of the population. Our approach is based on a totally new distribution, not used so far in the literature regarding income and wealth distribution. Using cumulative distribution method, we find that polynomial functions, regardless of their degree (first, second, or higher), can describe with very high accuracy both income and wealth distribution. Moreover, we find that polynomial functions describe income and wealth distribution for entire population including upper income segment for which traditionally Pareto distribution is used.
Artificial ozone holes

This article considers an opportunity of disinfecting a part of the Earth surface, occupying a large area of ten thousand square kilometers. The sunlight will cause dissociation of molecular bromine into atoms; each bromine atom kills thirty thousand molecules of ozone. Each bromine plate has a mass of forty milligrams grams and destroys ozone in the area of hundred square meters. Thus, to form the ozone hole over the area of ten thousand square kilometers, it is required to have the total mass of bromine equal to the following four tons.
ROBUST FUNDAMENTAL THEOREM FOR CONTINUOUS PROCESSES

We study a continuous‐time financial market with continuous price processes under model uncertainty, modeled via a family P of possible physical measures. A robust notion NA 1(P) of no‐arbitrage of the first kind is introduced; it postulates that a nonnegative, nonvanishing claim cannot be superhedged for free by using simple trading strategies. Our first main result is a version of the fundamental theorem of asset pricing: NA 1(P) holds if and only if every P∈P admits a martingale measure that is equivalent up to a certain lifetime. The second main result provides the existence of optimal superhedging strategies for general contingent claims and a representation of the superhedging price in terms of martingale measures.
Dense 3D Face Correspondence

We present an algorithm that automatically establishes dense correspondences between a large number of 3D faces. Starting from automatically detected sparse correspondences on the outer boundary of 3D faces, the algorithm triangulates existing correspondences and expands them iteratively by matching points of distinctive surface curvature along the triangle edges. After exhausting keypoint matches, further correspondences are established by generating evenly distributed points within triangles by evolving level set geodesic curves from the centroids of large triangles. A deformable model (K3DM) is constructed from the dense corresponded faces and an algorithm is proposed for morphing the K3DM to fit unseen faces. This algorithm iterates between rigid alignment of an unseen face followed by regularized morphing of the deformable model. We have extensively evaluated the proposed algorithms on synthetic data and real 3D faces from the FRGCv2, Bosphorus, BU3DFE and UND Ear databases using quantitative and qualitative benchmarks. Our algorithm achieved dense correspondences with a mean localisation error of 1.28 mm on synthetic faces and detected 14 anthropometric landmarks on unseen real faces from the FRGCv2 database with 3 mm precision. Furthermore, our deformable model fitting algorithm achieved 98.5 percent face recognition accuracy on the FRGCv2 and 98.6 percent on Bosphorus database. Our dense model is also able to generalize to unseen datasets.
Learning Vague Concepts for the Semantic Web

Ontologies can be a powerful tool for structuring knowledge, and they are currently the subject of extensive research. Updating the contents of an ontology or improving its interoperability with other ontologies is an important but difficult process. In this paper, we focus on the presence of vague concepts, which are pervasive in natural language, within the framework of formal ontologies. We will adopt a framework in which vagueness is captured via numerical restrictions that can be automatically adjusted. Since updating vague concepts, either through ontology alignment or ontology evolution, can lead to inconsistent sets of axioms, we define and implement a method to detecting and repairing such inconsistencies in a local fashion.
On Iterative Hard Thresholding Methods for High-dimensional M-Estimation

The use of M-estimators in generalized linear regression models in high dimensional settings requires risk minimization with hard L0 constraints. Of the known methods, the class of projected gradient descent (also known as iterative hard thresholding (IHT)) methods is known to offer the fastest and most scalable solutions. However, the current state-of-the-art is only able to analyze these methods in extremely restrictive settings which do not hold in high dimensional statistical models. In this work we bridge this gap by providing the first analysis for IHT-style methods in the high dimensional statistical setting. Our bounds are tight and match known minimax lower bounds. Our results rely on a general analysis framework that enables us to analyze several popular hard thresholding style algorithms (such as HTP, CoSaMP, SP) in the high dimensional regression setting. Finally, we extend our analysis to the problem of low-rank matrix recovery.
Prediction of Synchrostate Transitions in EEG Signals Using Markov Chain Models

This letter proposes a stochastic model using the concept of Markov chains for the inter-state transitions of the millisecond order quasi-stable phase synchronized patterns or synchrostates, found in multi-channel Electroencephalogram (EEG) signals. First and second order transition probability matrices are estimated for Markov chain modelling from 100 trials of 128-channel EEG signals during two different face perception tasks. Prediction accuracies with such finite Markov chain models for synchrostate transition are also compared, under a data-partitioning based cross-validation scheme.
A Three-Scale Network Model for the Early Growth Dynamics of 2014 West Africa Ebola Epidemic

In mid-October 2014, the number of cases of the West Africa Ebola virus epidemic in Guinea, Sierra Leone and Liberia exceeded 9,000 cases. The early growth dynamics of the epidemic has been qualitatively different for each of the three countries. However, it is important to understand these disparate dynamics as trends of a single epidemic spread over regions with similar geographic and cultural aspects, with likely common parameters for transmission rates and reproduction number R0.  We combine a discrete, stochastic SEIR model with a three-scale community network model to demonstrate that the different regional trends may be explained by different community mixing rates. Heuristically, the effect of different community mixing rates may be understood as the observation that two individuals infected by the same chain of transmission are more likely to share the same contacts in a less-mixed community. Local saturation effects occur as the contacts of an infected individual are more likely to already be exposed by the same chain of transmission.  The effects of community mixing, together with stochastic effects, can explain the qualitative difference in the growth of Ebola virus cases in each country, and why the probability of large outbreaks may have recently increased. An increase in the rate of Ebola cases in Guinea in late August, and a local fitting of the transient dynamics of the Ebola cases in Liberia, suggests that the epidemic in Liberia has been more severe, and the epidemic in Guinea is worsening, due to discrete seeding events as the epidemic spreads into new communities.  A relatively simple network model provides insight on the role of local effects such as saturation that would be difficult to otherwise quantify. Our results predict that exponential growth of an epidemic is driven by the exposure of new communities, underscoring the importance of limiting this spread.
Improved Asymmetric Locality Sensitive Hashing (ALSH) for Maximum Inner Product Search (MIPS)

Recently it was shown that the problem of Maximum Inner Product Search (MIPS) is efficient and it admits provably sub-linear hashing algorithms. Asymmetric transformations before hashing were the key in solving MIPS which was otherwise hard. In the prior work, the authors use asymmetric transformations which convert the problem of approximate MIPS into the problem of approximate near neighbor search which can be efficiently solved using hashing. In this work, we provide a different transformation which converts the problem of approximate MIPS into the problem of approximate cosine similarity search which can be efficiently solved using signed random projections. Theoretical analysis show that the new scheme is significantly better than the original scheme for MIPS. Experimental evaluations strongly support the theoretical findings.
ON THE ACCRETION-FED GROWTH OF NEUTRON STARS DURING COMMON ENVELOPE

This paper models the orbital inspiral of a neutron star (NS) through the envelope of its giant-branch companion during a common envelope (CE) episode. These CE episodes are necessary to produce close pairs of NSs that can inspiral and merge due to gravitational wave losses in less than a Hubble time. Because cooling by neutrinos can be very efficient, NSs have been predicted to accumulate significant mass during CE events, perhaps enough to lead them to collapse to black holes. We revisit this conclusion with the additional consideration of CE structure, particularly density gradients across the embedded NS's accretion radius. This work is informed by our recent numerical simulations that find that the presence of a density gradient strongly limits accretion by imposing a net angular momentum to the flow around the NS. Our calculations suggest that NSs should survive CE encounters. They accrete only modest amounts of envelope material, ≲ 0.1 M☉, which is broadly consistent with mass determinations of double NS binaries. With less mass gain, NSs must spiral deeper to eject their CE, leading to a potential increase in mergers. The survival of NSs in CE events has implications for the formation mechanism of observed double NS binaries, as well as for predicted rates of NS binary gravitational wave inspirals and their electromagnetic counterparts.
Glow in the dark matter: observing galactic halos with scattered light.

We consider the observation of diffuse halos of light around the discs of spiral galaxies, as a probe of the interaction cross section between dark matter (DM) and photons. Using the galaxy M101 as an example, we show that for a scattering cross section at the level of 10(-23)(m/GeV)  cm(2) or greater dark matter in the halo will scatter light out from the more luminous center of the disc to larger radii, contributing to an effective increased surface brightness at the edges of the observed area on the sky. This allows us to set an upper limit on the DM-photon cross section using data from the Dragonfly instrument. We then show how to improve this constraint, and the potential for discovery, by combining the radial profile of DM-photon scattering with measurements at multiple wavelengths. Observation of diffuse light presents a new and potentially powerful way to probe the interactions of dark matter with photons, a way that is complementary to existing searches.
Energy flows in graphene: hot carrier dynamics and cooling

Long lifetimes of hot carriers can lead to qualitatively new types of responses in materials. The magnitude and time scales for these responses reflect the mechanisms governing energy flows. We examine the microscopics of two processes which are key for energy transport, focusing on the unusual behavior arising due to graphene's unique combination of material properties. One is hot carrier generation in its photoexcitation dynamics, where hot carriers multiply through an Auger type carrier–carrier scattering cascade. The hot-carrier generation manifests itself through elevated electronic temperatures which can be accessed in a variety of ways, in particular optical conductivity measurements. Another process of high interest is electron-lattice cooling. We survey different cooling pathways and discuss the cooling bottleneck arising for the momentum-conserving electron–phonon scattering pathway. We show how this bottleneck can be relieved by higher-order collisions—supercollisions—and examine the variety of supercollision processes that can occur in graphene.
Toward a test of angular-momentum coherence in a twin-atom interferometer

We present a scheme well suited to investigate quantitatively the angular-momentum coherence of molecular fragments. Assuming that the dissociated molecule has a null total angular-momentum, we investigate the propagation of the corresponding atomic fragments in the apparatus. We show that the envisioned interferometer enables one to distinguish unambiguously a spin-coherent from a spin-incoherent dissociation, as well as to estimate the purity of the angular-momentum density matrix associated with the fragments. This setup, which may be seen as an atomic analogue of a twin-photon interferometer, can be used to investigate the suitability of molecule dissociation processes —such as the metastable hydrogen atoms H(22S)-H(22S) dissociation— for coherent twin-atom optics.
The Quantum Wavefunction

When most people think of physics, they think of what they learned in high school physics: that the world is fundamentally predictable. Given the position and velocity of a particle in space, it should be possible to predict its position at any moment in the future, right? Though this was thought to be true for thousands of years, recent developments in the field of physics have shown that this isn't actually true. Instead of being fundamentally predictable, the universe is fundamentally unpredictable. However, this doesn't seem to make sense. What happened to the centuries of physics developed by Newton, Bernoulli, and Lagrange? Well, as it turns out, they weren't actually wrong. Their equations were actually an approximation of a formula called the wavefunction, which is the "Newton's Laws" equivalent for modern physics. In this paper, we'll take a look at this peculiar wavefunction and why our intuition about the world is completely wrong.
Conditional Preference Orders and Their Numerical Representations

We provide an axiomatic system modeling conditional preference orders which is based on conditional set theory. Conditional numerical representations are introduced, and a conditional version of the theorems of Debreu on the existence of numerical representations is proved. The conditionally continuous representations follow from a conditional version of Debreu’s Gap Lemma the proof of which relies on a conditional version of the axiom of choice, free of any measurable selection argument. We give a conditional version of the von Neumann and Morgenstern representation as well as automatic conditional continuity results, and illustrate them by examples.
Using Mechanical Turk to Build Machine Translation Evaluation Sets

Building machine translation (MT) test sets is a relatively expensive task. As MT becomes increasingly desired for more and more language pairs and more and more domains, it becomes necessary to build test sets for each case. In this paper, we investigate using Amazon's Mechanical Turk (MTurk) to make MT test sets cheaply. We find that MTurk can be used to make test sets much cheaper than professionally-produced test sets. More importantly, in experiments with multiple MT systems, we find that the MTurk-produced test sets yield essentially the same conclusions regarding system performance as the professionally-produced test sets yield.
Quantum information of cosmological correlations

It has been shown that the primordial perturbations sourced by inflation are driven to classicality by unitary evolution alone. However, their coupling with the environment such as photons and subsequent decoherence renders the cosmological correlations quantum, losing primordial information in the process. We argue that the quantumness of the resulting cosmological correlations is given by quantum discord, which captures nonclassical behavior beyond quantum entanglement. By considering the environment as a quantum channel in which primordial information contained in the perturbations is transmitted to us, we can then ask how much of this information is inaccessible. We show that this amount of information is given by the discord of the joint primordial perturbations-environment system. To illustrate these points, we model the joint system as a mixed bimodal Gaussian state, and show that quantum discord is dependent on the basis which decoherence occurs.
4-Factor Model for Overnight Returns

We propose a 4-factor model for overnight returns and give explicit definitions of our 4 factors. Long horizon fundamental factors such as value and growth lack predictive power for overnight (or similar short horizon) returns and are not included. All 4 factors are constructed based on intraday price and volume data and are analogous to size (price), volatility, momentum and liquidity (volume). Historical regressions a la Fama and MacBeth (1973) suggest that our 4 factors have sizable serial t-statistic and appear to be relevant predictors for overnight returns. We check this by using our 4-factor model in an explicit intraday mean-reversion alpha.
Variational Reformulation of Bayesian Inverse Problems

The classical approach to inverse problems is based on the optimization of a misfit function. Despite its computational appeal, such an approach suffers from many shortcomings, e.g., non-uniqueness of solutions, modeling prior knowledge, etc. The Bayesian formalism to inverse problems avoids most of the difficulties encountered by the optimization approach, albeit at an increased computational cost. In this work, we use information theoretic arguments to cast the Bayesian inference problem in terms of an optimization problem. The resulting scheme combines the theoretical soundness of fully Bayesian inference with the computational efficiency of a simple optimization.
Where do goals come from? A Generic Approach to Autonomous Goal-System Development

Goals express agents' intentions and allow them to organize their behavior based on low-dimensional abstractions of high-dimensional world states. How can agents develop such goals autonomously? This paper proposes a detailed conceptual and computational account to this longstanding problem. We argue to consider goals as high-level abstractions of lower-level intention mechanisms such as rewards and values, and point out that goals need to be considered alongside with a detection of the own actions' effects. We propose Latent Goal Analysis as a computational learning formulation thereof, and show constructively that any reward or value function can by explained by goals and such self-detection as latent mechanisms. We first show that learned goals provide a highly effective dimensionality reduction in a practical reinforcement learning problem. Then, we investigate a developmental scenario in which entirely task-unspecific rewards induced by visual saliency lead to self and goal representations that constitute goal-directed reaching.
Production of high-PT cumulative particles in proton-nucleus interactions at 50 GeV

The data on production of cumulative particles in the high transverse momenta domain (up to ~ 3.5 GeV/c) in proton-nucleus interactions are presented for the first time. An indication on the local character of particle production in the cumulative domain is obtained. The observed strong dependence of the particle production cross section on the atomic mass of the target does not fit the A - dependence obtained in the pre-cumulative and cumulative domains for low transverse momenta at constant part of the exponent. The experiment was performed at U70 (IHEP) with the extracted 50 GeV/c proton beam.
Regularizing Recurrent Networks - On Injected Noise and Norm-based Methods

Advancements in parallel processing have lead to a surge in multilayer perceptrons' (MLP) applications and deep learning in the past decades. Recurrent Neural Networks (RNNs) give additional representational power to feedforward MLPs by providing a way to treat sequential data. However, RNNs are hard to train using conventional error backpropagation methods because of the difficulty in relating inputs over many time-steps. Regularization approaches from MLP sphere, like dropout and noisy weight training, have been insufficiently applied and tested on simple RNNs. Moreover, solutions have been proposed to improve convergence in RNNs but not enough to improve the long term dependency remembering capabilities thereof. 
In this study, we aim to empirically evaluate the remembering and generalization ability of RNNs on polyphonic musical datasets. The models are trained with injected noise, random dropout, norm-based regularizers and their respective performances compared to well-initialized plain RNNs and advanced regularization methods like fast-dropout. We conclude with evidence that training with noise does not improve performance as conjectured by a few works in RNN optimization before ours.
DNA methylation variation in Arabidopsis has a genetic basis and shows evidence of local adaptation

Epigenome modulation in response to the environment potentially provides a mechanism for organisms to adapt, both within and between generations. However, neither the extent to which this occurs, nor the molecular mechanisms involved are known. Here we investigate DNA methylation variation in Swedish Arabidopsis thaliana accessions grown at two different temperatures. Environmental effects on DNA methylation were limited to transposons, where CHH methylation was found to increase with temperature. Genome-wide association mapping revealed that the extensive CHH methylation variation was strongly associated with genetic variants in both cis and trans, including a major trans-association close to the DNA methyltransferase CMT2. Unlike CHH methylation, CpG gene body methylation (GBM) on the coding region of genes was not affected by growth temperature, but was instead strongly correlated with the latitude of origin. Accessions from colder regions had higher levels of GBM for a significant fraction of the genome, and this was correlated with elevated transcription levels for the genes affected. Genome-wide association mapping revealed that this effect was largely due to trans-acting loci, a significant fraction of which showed evidence of local adaptation. These findings constitute the first direct link between DNA methylation and adaptation to the environment, and provide a basis for further dissecting how environmentally driven and genetically determined epigenetic variation interact and influence organismal fitness.