nils-herrmann/clean_scirepeval huggingface dataset:
Effective networks: a model to predict network structure and critical transitions from datasets

Real-world complex systems such as ecological communities and neuron networks are essential parts of our everyday lives. These systems are composed of units which interact through intricate networks. The ability to predict sudden changes in network behaviour, known as critical transitions, from data is important to avert disastrous consequences of major disruptions. Predicting such changes is a major challenge as it requires forecasting the behaviour for parameter ranges for which no data on the system is available. In this paper, we address this issue for networks with weak individual interactions and chaotic local dynamics. We do this by building a model network, termed an effective network, consisting of the underlying local dynamics at each node and a statistical description of their interactions. We illustrate this approach by reconstructing the dynamics and structure of realistic neuronal interaction networks of the cat cerebral cortex. We reconstruct the community structure by analysing the stochastic fluctuations generated by the network and predict critical transitions for coupling parameters outside the observed range.
Morphological Word-Embeddings

Linguistic similarity is multi-faceted. For instance, two words may be similar with respect to semantics, syntax, or morphology inter alia. Continuous word-embeddings have been shown to capture most of these shades of similarity to some degree. This work considers guiding word-embeddings with morphologically annotated data, a form of semi-supervised learning, encouraging the vectors to encode a word's morphology, i.e., words close in the embedded space share morphological features. We extend the log-bilinear model to this end and show that indeed our learned embeddings achieve this, using German as a case study.
Multimodal Uncertainty Reduction for Intention Recognition in Human-Robot Interaction

Assistive robots can potentially improve the quality of life and personal independence of elderly people by supporting everyday life activities. To guarantee a safe and intuitive interaction between human and robot, human intentions need to be recognized automatically. As humans communicate their intentions multimodally, the use of multiple modalities for intention recognition may not just increase the robustness against failure of individual modalities but especially reduce the uncertainty about the intention to be recognized. This is desirable as particularly in direct interaction between robots and potentially vulnerable humans a minimal uncertainty about the situation as well as knowledge about this actual uncertainty is necessary. Thus, in contrast to existing methods, in this work a new approach for multimodal intention recognition is introduced that focuses on uncertainty reduction through classifier fusion. For the four considered modalities speech, gestures, gaze directions and scene objects individual intention classifiers are trained, all of which output a probability distribution over all possible intentions. By combining these output distributions using the Bayesian method Independent Opinion Pool  the uncertainty about the intention to be recognized can be decreased. The approach is evaluated in a collaborative human-robot interaction task with a 7-DoF robot arm. The results show that fused classifiers, which combine multiple modalities, outperform the respective individual base classifiers with respect to increased accuracy, robustness, and reduced uncertainty.
Multi-Task Learning for Coherence Modeling

We address the task of assessing discourse coherence, an aspect of text quality that is essential for many NLP tasks, such as summarization and language assessment. We propose a hierarchical neural network trained in a multi-task fashion that learns to predict a document-level coherence score (at the network’s top layers) along with word-level grammatical roles (at the bottom layers), taking advantage of inductive transfer between the two tasks. We assess the extent to which our framework generalizes to different domains and prediction tasks, and demonstrate its effectiveness not only on standard binary evaluation coherence tasks, but also on real-world tasks involving the prediction of varying degrees of coherence, achieving a new state of the art.
Unique determination of several coefficients in a fractional diffusion(-wave) equation by a single measurement

We consider the inverse problem of determining different type of information about a diffusion process, described by ordinary or fractional diffusion equations stated on a bounded domain, like the density of the medium or the velocity field associated with the moving quantities from a single boundary measurement. This properties will be associated with some general class of time independent coefficients that we recover from a single Neumann boundary measurement, on some parts of the boundary, of the solution of our diffusion equation with a suitable boundary input, located on some parts of the boundary.
From voxels to pixels and back: Self-supervision in natural-image reconstruction from fMRI

Reconstructing observed images from fMRI brain recordings is challenging. Unfortunately, acquiring sufficient "labeled" pairs of {Image, fMRI} (i.e., images with their corresponding fMRI responses) to span the huge space of natural images is prohibitive for many reasons. We present a novel approach which, in addition to the scarce labeled data (training pairs), allows to train fMRI-to-image reconstruction networks also on "unlabeled" data (i.e., images without fMRI recording, and fMRI recording without images). The proposed model utilizes both an Encoder network (image-to-fMRI) and a Decoder network (fMRI-to-image). Concatenating these two networks back-to-back (Encoder-Decoder & Decoder-Encoder) allows augmenting the training with both types of unlabeled data. Importantly, it allows training on the unlabeled test-fMRI data. This self-supervision adapts the reconstruction network to the new input test-data, despite its deviation from the statistics of the scarce training data.
Cryptocurrency Egalitarianism: A Quantitative Approach

Since the invention of Bitcoin one decade ago, numerous cryptocurrencies have sprung into existence. Among these, proof-of-work is the most common mechanism for achieving consensus, whilst a number of coins have adopted "ASIC-resistance" as a desirable property, claiming to be more "egalitarian,"S where egalitarianism refers to the power of each coin to participate in the creation of new coins. While proof-of-work consensus dominates the space, several new cryptocurrencies employ alternative consensus, such as proof-of-stake in which block minting opportunities are based on monetary ownership. A core criticism of proof-of-stake revolves around it being less egalitarian by making the rich richer, as opposed to proof-of-work in which everyone can contribute equally according to their computational power. In this paper, we give the first quantitative definition of a cryptocurrency's \emph{egalitarianism}. Based on our definition, we measure the egalitarianism of popular cryptocurrencies that (may or may not) employ ASIC-resistance, among them Bitcoin, Ethereum, Litecoin, and Monero. Our simulations show, as expected, that ASIC-resistance increases a cryptocurrency's egalitarianism. We also measure the egalitarianism of a stake-based protocol, Ouroboros, and a hybrid proof-of-stake/proof-of-work cryptocurrency, Decred. We show that stake-based cryptocurrencies, under correctly selected parameters, can be perfectly egalitarian, perhaps contradicting folklore belief.
High-dimensional Gaussian graphical model for network-linked data

Graphical models are commonly used to represent conditional dependence relationships between variables. There are multiple methods available for exploring them from high-dimensional data, but almost all of them rely on the assumption that the observations are independent and identically distributed. At the same time, observations connected by a network are becoming increasingly common, and tend to violate these assumptions. Here we develop a Gaussian graphical model for observations connected by a network with potentially different mean vectors, varying smoothly over the network. We propose an efficient estimation algorithm and demonstrate its effectiveness on both simulated and real data, obtaining meaningful and interpretable results on a statistics coauthorship network. We also prove that our method estimates both the inverse covariance matrix and the corresponding graph structure correctly under the assumption of network âcohesionâ, which refers to the empirically observed phenomenon of network neighbors sharing similar traits.
The Debiased Spatial Whittle likelihood

We provide a computationally and statistically efficient method for estimating the parameters of a stochastic Gaussian model observed on a regular spatial grid in any number of dimensions. Our proposed method, which we call the debiased spatial Whittle likelihood, makes important corrections to the well-known Whittle likelihood to account for large sources of bias caused by boundary effects and aliasing. We generalise the approach to flexibly allow for significant volumes of missing data, for the usage of irregular sampling schemes including those with lower-dimensional substructure, and for irregular sampling boundaries. We build a theoretical framework under relatively weak assumptions which ensures consistency and asymptotic normality in numerous practical settings. We provide detailed implementation guidelines which ensure the estimation procedure can still be conducted in $\mathcal{O}(n\log n)$ operations, where $n$ is the number of points of the encapsulating rectangular grid, thus keeping the computational scalability of Fourier and Whittle-based methods for large data sets. We validate our procedure over a range of simulated and real world settings, and compare with state-of-the-art alternatives, demonstrating the enduring significant practical appeal of Fourier-based methods, provided they are corrected by the constructive procedures developed in this paper.
Learning Threshold-Type Investment Strategies with Stochastic Gradient Method

In online portfolio optimization the investor makes decisions based on new, continuously incoming information on financial assets (typically their prices). In our study we consider a learning algorithm, namely the Kiefer--Wolfowitz version of the Stochastic Gradient method, that converges to the log-optimal solution in the threshold-type, buy-and-sell strategy class. The systematic study of this method is novel in the field of portfolio optimization; we aim to establish the theory and practice of Stochastic Gradient algorithm used on parametrized trading strategies. We demonstrate on a wide variety of stock price dynamics (e.g. with stochastic volatility and long-memory) that there is an optimal threshold type strategy which can be learned. Subsequently, we numerically show the convergence of the algorithm. Furthermore, we deal with the typically problematic question of how to choose the hyperparameters (the parameters of the algorithm and not the dynamics of the prices) without knowing anything about the price other than a small sample.
Strongly semistable reduction of syzygy bundles on plane curves

We investigate degenerations of syzygy bundles on plane curves over p$p$ ‐adic fields. We use Mustafin varieties which are degenerations of projective spaces to find a large family of models of plane curves over the ring of integers such that the special fibre consists of multiple projective lines meeting in one point. On such models we investigate vector bundles whose generic fibre is a syzygy bundle and which become trivial when restricted to each projective line in the special fibre. Hence these syzygy bundles have strongly semistable reduction. This investigation is motivated by the fundamental open problem in p$p$ ‐adic Simpson theory to determine the category of Higgs bundles corresponding to continuous representations of the étale fundamental group of a curve. Faltings' p$p$ ‐adic Simpson correspondence and work of Deninger and the second author shows that bundles with Higgs field zero and potentially strongly semistable reduction fall into this category. Hence the results in the present paper determine a class of syzygy bundles on plane curves giving rise to a p$p$ ‐adic local system. We apply our methods to a concrete example on the Fermat curve suggested by Brenner and prove that this bundle has potentially strongly semistable reduction.
The Geometry of Community Detection via the MMSE Matrix

The information-theoretic limits of community detection have been studied extensively for network models with high levels of symmetry or homogeneity. The contribution of this paper is to study a broader class of network models that allow for variability in the sizes and behaviors of the different communities, and thus better reflect the behaviors observed in real-world networks. Our results show that the ability to detect communities can be described succinctly in terms of a matrix of effective signal-to-noise ratios that provides a geometrical representation of the relationships between the different communities. This characterization follows from a matrix version of the I-MMSE relationship and generalizes the concept of an effective scalar signal-to-noise ratio introduced in previous work. We provide explicit formulas for the asymptotic per-node mutual information and upper bounds on the minimum mean-squared error. The theoretical results are supported by numerical simulations.
Pedagogical Materials and Suggestions to Cure Misconceptions Connecting Special and General Relativity

Many professional physicists do not fully understand the implications of the Einstein equivalence principle of general relativity. Consequently, many are unaware of the fact that special relativity is fully capable of handling accelerated reference frames. We present results from our nationwide survey that confirm this is the case. We discuss possible origins of this misconception, then suggest new materials for educators to use while discussing the classic twin paradox example. Afterwards, we review typical introductions to general relativity, clarify the equivalence principle, then suggest additional material to be used when the Einstein equivalence principle is covered in an introductory course. All of our suggestions are straightforward enough to be administered to a sophomore-level modern physics class.
Understanding phonon properties in isoreticular metal-organic frameworks from first principles

Metal-organic frameworks (MOFs) are crystalline materials consisting of metal centers and organic linkers forming open and porous structures. They have been extensively studied due to various possible applications exploiting their large amount of internal surface area. Phonon properties of MOFs are, however, still largely unexplored, despite their relevance for thermal and electrical conductivities, thermal expansion, and mechanical properties. Here, we use quantum-mechanical simulations to provide an in-depth analysis of the phonon properties of isoreticular MOFs. We consider phonon band structures, spatial confinements of modes, projected densities of states, and group velocity distributions. We find that more complex linkers shift the spectral weight of the phonon density of states towards higher frequencies, while increasing the mass of the metal atoms in the nodes has the opposite effect. Due to the high porosity of MOFs, we observe a particularly pronounced polarization dependence of the dispersion of acoustic phonons with rather high group velocities for longitudinal acoustic modes. Interestingly, also for several optical phonon modes group velocities amounting to several thousand m/s are obtained. For heterogeneous systems like MOFs, correlating group velocities and the displacement of modes is particularly relevant. Here we find that high group velocities are generally associated with delocalized vibrations, while the inverse correlation does not necessarily hold. To quantify anharmonicities, we calculate mode Gruneisen parameters, which we find to be significant only for phonons with frequencies below ~3 THz. The presented results provide the foundations for an in-depth understanding of the vibrational properties of MOF, and, thus, pave the way for a future rational design of systems with well-defined phonon properties.
On Validating, Repairing and Refining Heuristic ML Explanations

Recent years have witnessed a fast-growing interest in computing explanations for Machine Learning (ML) models predictions. For non-interpretable ML models, the most commonly used approaches for computing explanations are heuristic in nature. In contrast, recent work proposed rigorous approaches for computing explanations, which hold for a given ML model and prediction over the entire instance space. This paper extends earlier work to the case of boosted trees and assesses the quality of explanations obtained with state-of-the-art heuristic approaches. On most of the datasets considered, and for the vast majority of instances, the explanations obtained with heuristic approaches are shown to be inadequate when the entire instance space is (implicitly) considered.
Convolutional Neural Network-based Speech Enhancement for Cochlear Implant Recipients

Attempts to develop speech enhancement algorithms with improved speech intelligibility for cochlear implant (CI) users have met with limited success. To improve speech enhancement methods for CI users, we propose to perform speech enhancement in a cochlear filter-bank feature space, a feature-set specifically designed for CI users based on CI auditory stimuli. We leverage a convolutional neural network (CNN) to extract both stationary and non-stationary components of environmental acoustics and speech. We propose three CNN architectures: (1) vanilla CNN that directly generates the enhanced signal; (2) spectral-subtraction-style CNN (SS-CNN) that first predicts noise and then generates the enhanced signal by subtracting noise from the noisy signal; (3) Wiener-style CNN (Wiener-CNN) that generates an optimal mask for suppressing noise. An important problem of the proposed networks is that they introduce considerable delays, which limits their real-time application for CI users. To address this, this study also considers causal variations of these networks. Our experiments show that the proposed networks (both causal and non-causal forms) achieve significant improvement over existing baseline systems. We also found that causal Wiener-CNN outperforms other networks, and leads to the best overall envelope coefficient measure (ECM). The proposed algorithms represent a viable option for implementation on the CCi-MOBILE research platform as a pre-processor for CI users in naturalistic environments.
Identifying stellar streams in Gaia DR2 with data mining techniques

Streams of stars from captured dwarf galaxies and dissolved globular clusters are identifiable through the similarity of their orbital parameters, a fact that remains true long after the streams have dispersed spatially. We calculate the integrals of motion for 31 234 stars, to a distance of 4 kpc from the Sun, which have full and accurate 6D phase space positions in the Gaia DR2 catalogue. We then apply a novel combination of data mining, numerical, and statistical techniques to search for stellar streams. This process returns five high confidence streams (including one which was previously undiscovered), all of which display tight clustering in the integral of motion space. Colour–magnitude diagrams indicate that these streams are relatively simple, old, metal-poor populations. One of these resolved streams shares very similar kinematics and metallicity characteristics with the Gaia-Enceladus dwarf galaxy remnant, but with a slightly younger age. The success of this project demonstrates the usefulness of data mining techniques in exploring large data sets.
AtomNeb: IDL Library for Atomic Data of Ionized Nebulae

Spectra emitted from ionized nebulae typically contain collisionally excited and recombination lines, which can be used to trace physical conditions and chemical abundances of the interstellar medium in our Galaxy and other galaxies. "AtomNeb" is a database containing atomic data stored in the Flexible Image Transport System (FITS) file format, including the data for collisionally excited and recombination lines generally observed in nebular astrophysics. The AtomNeb interface library is equipped with several application programming interface (API) functions developed in the Interactive Data Language (IDL), which can be also used in the GNU Data Language (GDL). This IDL library relies on the FITS file related IDL procedures from the IDL Astronomy User's library. The AtomNeb IDL library, together with the "proEQUIB" IDL library, can be used to perform plasma diagnostics and abundance analysis of emission lines from ionized gaseous nebulae.
A SHARP view of H0LiCOW: H0 from three time-delay gravitational lens systems with adaptive optics imaging

We present the measurement of the Hubble constant, H0, with three strong gravitational lens systems. We describe a blind analysis of both PG 1115+080 and HE 0435−1223 as well as an extension of our previous analysis of RXJ 1131−1231. For each lens, we combine new adaptive optics (AO) imaging from the Keck Telescope, obtained as part of the SHARP (Strong-lensing High Angular Resolution Programme) AO effort, with Hubble Space Telescope (HST) imaging, velocity dispersion measurements, and a description of the line-of-sight mass distribution to build an accurate and precise lens mass model. This mass model is then combined with the COSMOGRAIL-measured time delays in these systems to determine H0. We do both an AO-only and an AO + HST analysis of the systems and find that AO and HST results are consistent. After unblinding, the AO-only analysis gives $H_{0}=82.8^{+9.4}_{-8.3}~\rm km\, s^{-1}\, Mpc^{-1}$ for PG 1115+080, $H_{0}=70.1^{+5.3}_{-4.5}~\rm km\, s^{-1}\, Mpc^{-1}$ for HE 0435−1223, and $H_{0}=77.0^{+4.0}_{-4.6}~\rm km\, s^{-1}\, Mpc^{-1}$ for RXJ 1131−1231. The joint AO-only result for the three lenses is $H_{0}=75.6^{+3.2}_{-3.3}~\rm km\, s^{-1}\, Mpc^{-1}$. The joint result of the AO + HST analysis for the three lenses is $H_{0}=76.8^{+2.6}_{-2.6}~\rm km\, s^{-1}\, Mpc^{-1}$. All of these results assume a flat Λ cold dark matter cosmology with a uniform prior on Ωm in [0.05, 0.5] and H0 in [0, 150] $\rm km\, s^{-1}\, Mpc^{-1}$. This work is a collaboration of the SHARP and H0LiCOW teams, and shows that AO data can be used as the high-resolution imaging component in lens-based measurements of H0. The full time-delay cosmography results from a total of six strongly lensed systems are presented in a companion paper.
String Regge trajectories in de Sitter space and implications for inflation

We study the spectrum of semiclassical rotating strings on de Sitter space and its consistency, generalizing the Gubser-Klebanov-Polyakov (GKP) string on anti-de Sitter space. Even though a naive extrapolation of the linear Regge trajectory on flat space implies a violation of the Higuchi bound (a unitarity bound on the mass of higher-spin particles in de Sitter space), the curved space effects turn out to modify the trajectory to respect the bound. Interestingly, we find that there exists a maximum spin for each Regge trajectory as a consequence of accelerated expansion, which is helpful to make the spectrum consistent with the Higuchi bound, but at the same time it could be an obstruction to stringy UV completion based on an infinite higher-spin tower. By pushing further this observation, we demonstrate that the vacuum energy $V$ inflating the universe has to be bounded by the string scale $M_s$ as $V\lesssim M_s^4$, if UV completion is achieved by the leading Regge trajectory. Its application to inflation at the early universe implies an upper bound on the tensor-to-scalar ratio, $r\lesssim 0.01\times(M_s/10^{16} \text{GeV})^{4}$, which is within the scope of the near future CMB experiments.