nils-herrmann/clean_scirepeval huggingface dataset:
Stability of scalarized black hole solutions in scalar-Gauss-Bonnet gravity

Scalar-tensor theories of gravity where a new scalar degree of freedom couples to the Gauss-Bonnet invariant can exhibit the phenomenon of spontaneous black hole scalarization. These theories admit both the classic black hole solutions predicted by general relativity as well as novel hairy black hole solutions. The stability of hairy black holes is strongly dependent on the precise form of the scalar-gravity coupling. A radial stability investigation revealed that all scalarized black hole solutions are unstable when the coupling between the scalar field and the Gauss-Bonnet invariant is quadratic in the scalar, whereas stable solutions exist for exponential couplings. Here, we elucidate this behavior. We demonstrate that, while the quadratic term controls the onset of the tachyonic instability that gives rise to the black hole hair, the higher-order coupling terms control the nonlinearities that quench that instability and, hence, also control the stability of the hairy black hole solutions.
Lorentz invariant mass and length scales

We show that the standard Lorentz transformations admit an invariant mass (length) scale, such as the Planck scale. In other words, the frame independence of such a scale is built-in within those transformations, and one does not need to invoke the principle of relativity for their invariance. This automatically ensures the frame-independence of the spectrum of geometrical operators in quantum gravity. Furthermore, we show that the above predicts a small but measurable difference between the inertial and gravitational mass of any object, regardless of its size or whether it is elementary or composite.
First-passage time distribution for random walks on complex networks using inverse Laplace transform and mean-field approximation

We obtain an exact formula for the first-passage time probability distribution for random walks on complex networks using inverse Laplace transform. We write the formula as the summation of finitely many terms with different frequencies corresponding to the poles of Laplace transformed function and separate the short-term and long-term behavior of the first-passage process. We give a formula of the decay rate $\beta$, which is inversely proportional to the characteristic relaxation time $\tau$ of the target node. This exact formula for the first-passage probability between two nodes at a given time can be approximately solved in the mean field approximation by estimation of the characteristic relaxation time $\tau$. Our theoretical results compare well with numerical simulation on artificial as well as real networks.
Neutrino decoupling beyond the Standard Model: CMB constraints on the Dark Matter mass with a fast and precise Neff evaluation

The number of effective relativistic neutrino species represents a fundamental probe of the thermal history of the early Universe, and as such of the Standard Model of Particle Physics. Traditional approaches to the neutrino decoupling are either very technical and computationally expensive, or assume that neutrinos decouple instantaneously. In this work, we aim to fill the gap between these two approaches by modeling the neutrino decoupling in terms of two simple coupled differential equations for the electromagnetic and neutrino sector temperatures, in which all the relevant interactions are taken into account and which allows for a straightforward implementation of BSM species. Upon including finite temperature QED corrections we reach an accuracy on Neff in the SM of 0.01. We illustrate the usefulness of this approach to the neutrino decoupling by considering, in a model independent manner, the impact of MeV thermal dark matter on Neff. We show that Planck rules out electrophilic and neutrinophilic thermal dark matter particles of m< 3.8 MeV at 95% CL regardless of their spin, and of their annihilation being s-wave or p-wave. We point out that thermal dark matter particles with non-negligible interactions with both electrons and neutrinos are more elusive to CMB observations than purely electrophilic or neutrinophilic ones. In addition, assisted by the accuracy of our approach, we show that CMB Stage-IV experiments will generically test thermal dark matter particles with m ≲ 20 MeV. We make publicly available the codes developed for this study at this https://github.com/MiguelEA/nudec_BSM.
Detections and constraints on white dwarf variability from time-series GALEX observations

We search for photometric variability in more than 23 000 known and candidate white dwarfs (WDs), the largest ultraviolet survey compiled for a single study of WDs. We use gphoton, a publicly available calibration/reduction pipeline, to generate time-series photometry of WDs observed by GALEX. By implementing a system of weighted metrics, we select sources with variability due to pulsations and eclipses. Although GALEX observations have short baselines (≤30 min), we identify intrinsic variability in sources as faint as Gaia G = 20 mag. With our ranking algorithm, we identify 48 new variable WDs in archival GALEX observations. We detect 40 new pulsators: 36 have hydrogen-dominated atmospheres (DAVs), including one possible massive DAV, and four are helium-dominated pulsators (DBVs). We also detect eight new eclipsing systems; five are new discoveries, and three were previously known spectroscopic binaries. We perform synthetic injections of the light curve of WD 1145+017, a system with known transiting debris, to test our ability to recover similar systems. We find that the 3σ maximum occurrence rate of WD 1145+017-like transiting objects is $\le 0.5{{\ \rm per\ cent}}$.
A Module for Boosted Dark Matter Event Generation in GENIE

Models that produce a flux of semi-relativistic or relativistic boosted dark matter at large neutrino detectors are well-motivated extensions beyond the minimal weakly interacting massive particle (WIMP) paradigm. Current and upcoming liquid argon time projection chamber (LArTPC) based detectors will have improved sensitivity to such models, but also require improved theoretical modeling to better understand their signals and optimize their analyses. I present the first full Monte Carlo tool for boosted dark matter interacting with nuclei in the energy regime accessible to LArTPC detectors, including the Deep Underground Neutrino Experiment (DUNE). The code uses the nuclear and strong physics modeling of the GENIE neutrino Monte Carlo event generator with particle physics modeling for dark matter. The code will be available in GENIE v3. In addition, I present a code for generating a GENIE-compatible flux of boosted dark matter coming from the Sun that is released independently.
Nuclear structure factors for general spin-independent WIMP-nucleus scattering

We present nuclear structure factors that describe the generalized spin-independent coupling of weakly interacting massive particles (WIMPs) to nuclei. Our results are based on state-of-the-art nuclear structure calculations using the large-scale nuclear shell model. Starting from quark- and gluon-level operators, we consider all possible coherently enhanced couplings of spin-1/2 and spin-0 WIMPs to one and two nucleons up to third order in chiral effective field theory. This includes a comprehensive discussion of the structure factors corresponding to the leading two-nucleon currents covering, for the first time, the contribution of spin-2 operators. We provide results for the most relevant nuclear targets considered in present and planned dark matter direct detection experiments: fluorine, silicon, argon, and germanium, complementing our previous work on xenon. All results are also publicly available in a Python notebook.
SU(5)
 grand unified theory with 
A4
 modular symmetry

We present the first example of a grand unified theory (GUT) with a modular symmetry interpreted as a family symmetry. The theory is based on supersymmetric $SU(5)$ in 6d, where the two extra dimensions are compactified on a ${T}_{2}/{\mathbb{Z}}_{2}$ orbifold. We have shown that, if there is a finite modular symmetry, then it can only be ${A}_{4}$ with an (infinite) discrete choice of moduli, where we focus on $\ensuremath{\tau}=\ensuremath{\omega}={e}^{i2\ensuremath{\pi}/3}$, the unique solution with $|\ensuremath{\tau}|=1$. The fields on the branes respect a generalized $CP$ and flavor symmetry ${A}_{4}\ensuremath{\ltimes}{\mathbb{Z}}_{2}$ which is isomorphic to ${S}_{4}$ which leads to an effective $\ensuremath{\mu}\ensuremath{-}\ensuremath{\tau}$ reflection symmetry at low energies, implying maximal atmospheric mixing and maximal leptonic $CP$ violation. We construct an explicit model along these lines with two triplet flavons in the bulk, whose vacuum alignments are determined by orbifold boundary conditions, analogous to those used for $SU(5)$ breaking with doublet-triplet splitting. There are two right-handed neutrinos on the branes whose Yukawa couplings are determined by modular weights. The charged lepton and down-type quarks have diagonal and hierarchical Yukawa matrices, with quark mixing due to a hierarchical up-quark Yukawa matrix.
SIGNet: Semantic Instance Aided Unsupervised 3D Geometry Perception

Unsupervised learning for geometric perception (depth, optical flow, etc.) is of great interest to autonomous systems. Recent works on unsupervised learning have made considerable progress on perceiving geometry; however, they usually ignore the coherence of objects and perform poorly under scenarios with dark and noisy environments. In contrast, supervised learning algorithms, which are robust, require large labeled geometric dataset. This paper introduces SIGNet, a novel framework that provides robust geometry perception without requiring geometrically informative labels. Specifically, SIGNet integrates semantic information to make depth and flow predictions consistent with objects and robust to low lighting conditions. SIGNet is shown to improve upon the state-of-the-art unsupervised learning for depth prediction by 30% (in squared relative error). In particular, SIGNet improves the dynamic object class performance by 39% in depth prediction and 29% in flow prediction. Our code will be made available at
Fourier RNNs for Sequence Analysis and Prediction

Fourier methods have a long and proven track record in as an excellent tool in data processing. We propose to integrate Fourier methods into complex recurrent neural network architectures and show accuracy improvements on analysis and prediction tasks as well as computational load reductions. We predict synthetic data drawn from the synthetic-Lorenz equations as well as real world human motion prediction. We demonstrate the setup's analysis capabilities on the task of music recognition.
Euclidean Distance Degree of the Multiview Variety

The Euclidean distance degree of an algebraic variety is a well-studied topic in applied algebra and geometry. It has direct applications in geometric modeling, computer vision, and statistics. We use non-proper Morse theory to give a topological interpretation of the Euclidean distance degree of an affine variety in terms of Euler characteristics. As a concrete application, we solve the open problem in computer vision of determining the Euclidean distance degree of the affine multiview variety.
Fitness dependence of the fixation-time distribution for evolutionary dynamics on graphs.

Evolutionary graph theory models the effects of natural selection and random drift on structured populations of competing mutant and nonmutant individuals. Recent studies have found that fixation times in such systems often have right-skewed distributions. Little is known, however, about how these distributions and their skew depend on mutant fitness. Here we calculate the fitness dependence of the fixation-time distribution for the Moran Birth-death process in populations modeled by two extreme networks: the complete graph and the one-dimensional ring lattice, obtaining exact solutions in the limit of large network size. We find that with non-neutral fitness, the Moran process on the ring has normally distributed fixation times, independent of the relative fitness of mutants and nonmutants. In contrast, on the complete graph, the fixation-time distribution is a fitness-weighted convolution of two Gumbel distributions. When fitness is neutral, the fixation-time distribution jumps discontinuously and becomes highly skewed on both the complete graph and the ring. Even on these simple networks, the fixation-time distribution exhibits a rich fitness dependence, with discontinuities and regions of universality. Extensions of our results to two-fitness Moran models, times to partial fixation, and evolution on random networks are discussed.
Selection mechanism design affects volatility in a market of evolving zero-intelligence agents

Financial asset markets are sociotechnical systems whose constituent agents are subject to evolutionary pressure as unprofitable agents exit the marketplace and more profitable agents continue to trade assets. Using a population of evolving zero-intelligence agents and a frequent batch auction price-discovery mechanism as substrate, we analyze the role played by evolutionary selection mechanisms in determining macro-observable market statistics. In particular, we show that selection mechanisms incorporating a local fitness-proportionate component are associated with high correlation between a micro, risk-aversion parameter and a commonly-used macro-volatility statistic, while a purely quantile-based selection mechanism shows significantly less correlation.
Artificial Intelligence Assisted Infrastructure Assessment using Mixed Reality Systems

Conventional methods for visual assessment of civil infrastructures have certain limitations, such as subjectivity of the collected data, long inspection time, and high cost of labor. Although some new technologies (i.e., robotic techniques) that are currently in practice can collect objective, quantified data, the inspector’s own expertise is still critical in many instances because these technologies are not designed to work interactively with a human inspector. This study aims to create a smart, human-centered method that offers significant contributions to infrastructure inspection, maintenance, management practice, and safety for the bridge owners. By developing a smart mixed reality (MR) framework, which can be integrated into a wearable holographic headset device, a bridge inspector, for example, can automatically analyze a certain defect such as a crack that he or she sees on an element, and display its dimension information in real-time along with the condition state. Such systems can potentially decrease the time and cost of infrastructure inspections by accelerating essential tasks of the inspector such as defect measurement, condition assessment, and data processing to management systems. The human-centered artificial intelligence (AI) will help the inspector collect more quantified and objective data while incorporating the inspector’s professional judgment. This study explains in detail the described system and related methodologies of implementing attention guided semisupervised deep learning into MR technology, which interacts with the human inspector during assessment. Thereby, the inspector and the AI will collaborate/communicate for improved visual inspection.
Forgetting in order to Remember Better

In human memory, forgetting occur rapidly after the remembering and the rate of forgetting slowed down as time went. This is so-called the Ebbinghaus forgetting curve. There are many explanations of how this curve occur based on the properties of the brains. In this article, we use a simple mathematical model to explain the mechanism of forgetting based on rearrangement inequality and get a general formalism for short-term and long-term memory and use it to fit the Ebbinghaus forgetting curve. We also find out that forgetting is not a flaw, instead it is help to improve the efficiency of remembering when human confront different situations by reducing the interference of information and reducing the number of retrievals. Furthurmove, we find that the interference of informations limits the capacity of human memory, which is the "magic number seven".
CIPHER: Construction of dIfferentially Private microdata from low-dimensional Histograms via solving linear Equations with Tikhonov Regularization

When government agencies, research institutes, industries release individual-level data for research and public use, the data is often perturbed in certain ways to provide some level of privacy protection. The recently developed differentially private data synthesis (DIPS) methods are built upon the concept of differential privacy and provides a strong mathematical privacy guarantee while aiming to maintain the statistical utility of the released sanitized data. We introduce a new DIPS algorithm, CIPHER, which generates differential private individual-level data from a set of low-dimensional histograms via solving a set of linear equations with the Tikhonov ($l_2$) regularization. CIPHER is conceptually very simple and requires nothing than decomposing joint probabilities via basic provability rules to construct the equation set and subsequently solving linear equations. CIPHER also has the ability to automatically "correct" for the inconsistency arising from the differential private sanitization among the histograms that share at least one common attributes. We compare CIPHER with the MWEM (multiplicative weighting via exponential mechanism) and the full-dimensional histogram (FDH) sanitization through simulation and case studies. The results demonstrate that CIPHER made significance improvements over MWEM in statistical inferences, both of which aim to generate differentially private synthetic individual-level data from a set of low-dimensional histograms. CIPHER also delivered similar performance as the FDH sanitation for most of the examined privacy budget range.
A Probe Towards Understanding GAN and VAE Models

This project report compares some known GAN and VAE models proposed prior to 2017. There has been significant progress after we finished this report. We upload this report as an introduction to generative models and provide some personal interpretations supported by empirical evidence. Both generative adversarial network models and variational autoencoders have been widely used to approximate probability distributions of data sets. Although they both use parametrized distributions to approximate the underlying data distribution, whose exact inference is intractable, their behaviors are very different. We summarize our experiment results that compare these two categories of models in terms of fidelity and mode collapse. We provide a hypothesis to explain their different behaviors and propose a new model based on this hypothesis. We further tested our proposed model on MNIST dataset and CelebA dataset.
Ablation of a Robot's Brain: Neural Networks Under a Knife

It is still not fully understood exactly how neural networks are able to solve the complex tasks that have recently pushed AI research forward. We present a novel method for determining how information is structured inside a neural network. Using ablation (a neuroscience technique for cutting away parts of a brain to determine their function), we approach several neural network architectures from a biological perspective. Through an analysis of this method's results, we examine important similarities between biological and artificial neural networks to search for the implicit knowledge locked away in the network's weights.
Bayesian Sparsification of Gated Recurrent Neural Networks

Bayesian methods have been successfully applied to sparsify weights of neural networks and to remove structure units from the networks, e. g. neurons. We apply and further develop this approach for gated recurrent architectures. Specifically, in addition to sparsification of individual weights and neurons, we propose to sparsify preactivations of gates and information flow in LSTM. It makes some gates and information flow components constant, speeds up forward pass and improves compression. Moreover, the resulting structure of gate sparsity is interpretable and depends on the task. Code is available on github: this https URL
KeV Scale Frozen-in Self-Interacting Fermionic Dark Matter

We present a model in which the dark matter particle is frozen-in at MeV scale. In this model, the mediator between the standard model sector and the dark sector can automatically provide a self-interaction for dark matter. The interaction strength is naturally to be the in the region in favor of the cluster mass deficit anomaly. Due to the self-scattering, the Lyman-$\alpha$ constraint can be relaxed to $m_D \gtrsim 2 $ keV. In this region the self-interaction and the Fermi pressure both play roles on forming a dark matter core at the center of the dwarf galaxies.
Network Coding with Random Packet-Index Assignment for Data Collection Networks

This paper considers data collection using a network of uncoordinated, heterogeneous, and possibly mobile devices. Using medium and short-range radio technologies, multi-hop communication is required to deliver data to some sink. While numerous techniques from managed networks can be adapted, one of the most efficient (from the energy and spectrum use perspective) is network coding (NC). NC is well suited to networks with mobility and unreliability, however, practical NC requires a precise identification of individual packets that have been mixed together. In a purely decentralized system, this requires either conveying identifiers in headers along with coded information as in COPE, or integrating a more complex protocol in order to efficiently identify the sources (participants) and their payloads. 
 
A novel solution, Network Coding with Random Packet Index Assignment (NeCoRPIA), is presented where packet indices in NC headers are selected in a decentralized way, by choosing them randomly. Traditional network decoding can be applied when all original packets have different indices. When this is not the case, \emph{i.e.}, in case of collisions of indices, a specific decoding algorithm is proposed. A theoretical analysis of its performance in terms of complexity and decoding error probability is described. Simulation results match well the theoretical results. Comparisons of NeCoRPIA header lengths with those of a COPE-based NC protocol are also provided.
Stochastic Gradient Descent for Spectral Embedding with Implicit Orthogonality Constraint

In this paper, we propose a scalable algorithm for spectral embedding. The latter is a standard tool for graph clustering. However, its computational bottleneck is the eigendecomposition of the graph Laplacian matrix, which prevents its application to large-scale graphs. Our contribution consists of reformulating spectral embedding so that it can be solved via stochastic optimization. The idea is to replace the orthogonality constraint with an orthogonalization matrix injected directly into the criterion. As the gradient can be computed through a Cholesky factorization, our reformulation allows us to develop an efficient algorithm based on mini-batch gradient descent. Experimental results, both on synthetic and real data, confirm the efficiency of the proposed method in term of execution speed with respect to similar existing techniques.