nils-herrmann/clean_scirepeval huggingface dataset:
A Unified Speaker Adaptation Method for Speech Synthesis using Transcribed and Untranscribed Speech with Backpropagation

By representing speaker characteristic as a single fixed-length vector extracted solely from speech, we can train a neural multi-speaker speech synthesis model by conditioning the model on those vectors. This model can also be adapted to unseen speakers regardless of whether the transcript of adaptation data is available or not. However, this setup restricts the speaker component to just a single bias vector, which in turn limits the performance of adaptation process. In this study, we propose a novel speech synthesis model, which can be adapted to unseen speakers by fine-tuning part of or all of the network using either transcribed or untranscribed speech. Our methodology essentially consists of two steps: first, we split the conventional acoustic model into a speaker-independent (SI) linguistic encoder and a speaker-adaptive (SA) acoustic decoder; second, we train an auxiliary acoustic encoder that can be used as a substitute for the linguistic encoder whenever linguistic features are unobtainable. The results of objective and subjective evaluations show that adaptation using either transcribed or untranscribed speech with our methodology achieved a reasonable level of performance with an extremely limited amount of data and greatly improved performance with more data. Surprisingly, adaptation with untranscribed speech surpassed the transcribed counterpart in the subjective test, which reveals the limitations of the conventional acoustic model and hints at potential directions for improvements.
AGN-driven quenching of satellite galaxies

We explore the effect of active galactic nucleus (AGN) feedback from central galaxies on their satellites by comparing two sets of cosmological zoom-in runs of 27 haloes with masses ranging from 1012 to 1013.4$\rm {M}_{\odot}$ at z = 0, with (wAGN) and without (noAGN) AGN feedback. Both simulations include stellar feedback from multiple processes, including powerful winds from supernovae, stellar winds from young massive stars, AGB stars, radiative heating within Strömgren spheres and photoelectric heating. Our wAGN model is identical to the noAGN model except that it also includes a model for black hole seeding and accretion, as well as AGN feedback via high-velocity broad absorption line winds and Compton/photoionization heating. We show that the inclusion of AGN feedback from the central galaxy significantly affects the star formation history and the gas content of the satellite galaxies. AGN feedback starts to affect the gas content and the star formation of the satellites as early as z = 2. The mean gas-rich fraction of satellites at z = 0 decreases from 15 per cent in the noAGN simulation to 5 per cent in the wAGN simulation. The difference between the two sets extends as far out as five times the virial radius of the central galaxy at z = 1. We investigate the quenching mechanism by studying the physical conditions in the surroundings of pairs of satellites matched across the wAGN and noAGN simulations and find an increase in the temperature and relative velocity of the intergalactic gas.
PREDICATIVE COLLAPSING PRINCIPLES

We show that arithmetical transfinite recursion is equivalent to a suitable formalization of the following: For every ordinal α there exists an ordinal β such that $1 + \beta \cdot \left( {\beta + \alpha } \right)$ (ordinal arithmetic) admits an almost order preserving collapse into β. Arithmetical comprehension is equivalent to a statement of the same form, with $\beta \cdot \alpha$ at the place of $\beta \cdot \left( {\beta + \alpha } \right)$. We will also characterize the principles that any set is contained in a countable coded ω-model of arithmetical transfinite recursion and arithmetical comprehension, respectively.
Screening nature of the van der Waals density functional method: a review and analysis of the many-body physics foundation

We review the screening nature and many-body physics foundation of the van der Waals density functional (vdW-DF) method [Berland K et al 2015 Rep. ], a systematic approach to construct truly nonlocal exchange–correlation energy density functionals. To that end we define and focus on a class of consistent vdW-DF versions that adhere to the Lindhard screening logic of the full method formulation. The consistent-exchange vdW-DF-cx version [Berland K and Hyldgaard P 2014 ] and its spin extension [Thonhauser T et al 2015 ] represent the first examples of this class; in general, consistent vdW-DFs reflect a concerted expansion of a formal recast of the adiabatic-connection formula [Hyldgaard P et al 2014 ], an exponential summation of contributions to the local-field response, and the Dyson equation. We argue that the screening emphasis is essential because the exchange–correlation energy reflects an effective electrodynamics set by a long-range interaction. Two consequences are that (1) there are, in principle, no wiggle room in how one balances exchange and correlation, for example, in vdW-DF-cx, and that (2) consistent vdW-DFs have a formal structure that allows them to incorporate vertex-correction effects, at least in the case of levels that experience recoil-less interactions (for example, near the Fermi surface). We explore the extent to which the strictly nonempirical vdW-DF-cx formulation can serve as a systematic extension of the constraint-based semilocal functionals. For validation, we provide a complete survey of vdW-DF-cx performance for broad molecular processes, for the full set of 55 benchmarks in GMTKN55 [Goerigk L et al 2017 ] and comparing to the quantum-chemistry calculations that are summarized in that paper. We also provide new vdW-DF-cx results for metal surface energies and work functions that we compare to experiment. Finally, we use the screening insight to separate the vdW-DF nonlocal-correlation term into pure-vdW-interaction and local-field-susceptibility effects and present tools to compute and map the binding signatures.
The tight groupoid of the inverse semigroups of left cancellative small categories

<p>We fix a path model for the space of filters of the inverse semigroup <inline-formula content-type="math/mathml">
<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" alttext="script upper S Subscript normal upper Lamda">
  <mml:semantics>
    <mml:msub>
      <mml:mrow class="MJX-TeXAtom-ORD">
        <mml:mi class="MJX-tex-caligraphic" mathvariant="script">S</mml:mi>
      </mml:mrow>
      <mml:mi mathvariant="normal">Λ<!-- Λ --></mml:mi>
    </mml:msub>
    <mml:annotation encoding="application/x-tex">\mathcal {S}_\Lambda</mml:annotation>
  </mml:semantics>
</mml:math>
</inline-formula> associated to a left cancellative small category <inline-formula content-type="math/mathml">
<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" alttext="normal upper Lamda">
  <mml:semantics>
    <mml:mi mathvariant="normal">Λ<!-- Λ --></mml:mi>
    <mml:annotation encoding="application/x-tex">\Lambda</mml:annotation>
  </mml:semantics>
</mml:math>
</inline-formula>. Then, we compute its tight groupoid, thus giving a representation of its <inline-formula content-type="math/mathml">
<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"  C Superscript asterisk">
  <mml:semantics>
    <mml:msup>
      <mml:mi>C</mml:mi>
      <mml:mo>∗<!-- ∗ --></mml:mo>
    </mml:msup>
    <mml:annotation encoding="application/x-tex">C^*</mml:annotation>
  </mml:semantics>
</mml:math>
</inline-formula>-algebra as a (full) groupoid algebra. Using it, we characterize simplicty for these algebras. Also, we determine amenability of the tight groupoid under mild, reasonable hypotheses.</p>
The K2 Galactic Caps Project – going beyond the Kepler field and ageing the Galactic disc

Analyses of data from spectroscopic and astrometric surveys have led to conflicting results concerning the vertical characteristics of the Milky Way. Ages are often used to provide clarity, but typical uncertainties of >40 per cent from photometry restrict the validity of the inferences made. Using the Kepler APOKASC sample for context, we explore the global population trends of two K2 campaign fields (3 and 6), which extend further vertically out of the Galactic plane than APOKASC. We analyse the properties of red giant stars utilizing three asteroseismic data analysis methods to cross-check and validate detections. The Bayesian inference tool PARAM is used to determine the stellar masses, radii, and ages. Evidence of a pronounced red giant branch bump and an [α/Fe] dependence on the position of the red clump is observed from the K2 fields radius distribution. Two peaks in the age distribution centred at ∼5 and ∼12 Gyr are found using a sample with σage < 35 per cent. In comparison with Kepler, we find the older peak to be more prominent for K2. This age bimodality is also observed based on a chemical selection of low-[α/Fe] (≤0.1) and high-[α/Fe] (>0.1) stars. As a function of vertical distance from the Galactic mid-plane (|Z|), the age distribution shows a transition from a young to old stellar population with increasing |Z| for the K2 fields. Further coverage of campaign targets with high-resolution spectroscopy is required to increase the yield of precise ages achievable with asteroseismology.
Attention Guided Graph Convolutional Networks for Relation Extraction

Dependency trees convey rich structural information that is proven useful for extracting relations among entities in text. However, how to effectively make use of relevant information while ignoring irrelevant information from the dependency trees remains a challenging research question. Existing approaches employing rule based hard-pruning strategies for selecting relevant partial dependency structures may not always yield optimal results. In this work, we propose Attention Guided Graph Convolutional Networks (AGGCNs), a novel model which directly takes full dependency trees as inputs. Our model can be understood as a soft-pruning approach that automatically learns how to selectively attend to the relevant sub-structures useful for the relation extraction task. Extensive results on various tasks including cross-sentence n-ary relation extraction and large-scale sentence-level relation extraction show that our model is able to better leverage the structural information of the full dependency trees, giving significantly better results than previous approaches.
Robust Reinforcement Learning for Continuous Control with Model Misspecification

We provide a framework for incorporating robustness -- to perturbations in the transition dynamics which we refer to as model misspecification -- into continuous control Reinforcement Learning (RL) algorithms. We specifically focus on incorporating robustness into a state-of-the-art continuous control RL algorithm called Maximum a-posteriori Policy Optimization (MPO). We achieve this by learning a policy that optimizes for a worst case expected return objective and derive a corresponding robust entropy-regularized Bellman contraction operator. In addition, we introduce a less conservative, soft-robust, entropy-regularized objective with a corresponding Bellman operator. We show that both, robust and soft-robust policies, outperform their non-robust counterparts in nine Mujoco domains with environment perturbations. In addition, we show improved robust performance on a high-dimensional, simulated, dexterous robotic hand. Finally, we present multiple investigative experiments that provide a deeper insight into the robustness framework. This includes an adaptation to another continuous control RL algorithm as well as learning the uncertainty set from offline data. Performance videos can be found online at this https URL.
Mimicking Human Process: Text Representation via Latent Semantic Clustering for Classification

Considering that words with different characteristic in the text have different importance for classification, grouping them together separately can strengthen the semantic expression of each part. Thus we propose a new text representation scheme by clustering words according to their latent semantics and composing them together to get a set of cluster vectors, which are then concatenated as the final text representation. Evaluation on five classification benchmarks proves the effectiveness of our method. We further conduct visualization analysis showing statistical clustering results and verifying the validity of our motivation.
The Impact of Ambiguity on the Optimal Exercise Timing of Integral Option Contracts

We consider the impact of ambiguity on the optimal timing of a class of two-dimensional integral option contracts when the exercise payoff is a positively homogeneous measurable function. Hence, the considered class of exercise payoffs includes discontinuous functions as well. We identify a parameterized family of excessive functions generating an appropriate class of supermartingales for the considered problems and then express the value of the optimal policy as well as the worst case measure in terms of these processes. The advantage of our approach is that it reduces the analysis of the multidimensional problem to the analysis of an ordinary one-dimensional static optimization problem. In that way it simplifies earlier treatments of the problem without ambiguity considerably. We also illustrate our findings in explicitly parameterized examples.
Locate, Size, and Count: Accurately Resolving People in Dense Crowds via Detection

We introduce a detection framework for dense crowd counting and eliminate the need for the prevalent density regression paradigm. Typical counting models predict crowd density for an image as opposed to detecting every person. These regression methods, in general, fail to localize persons accurate enough for most applications other than counting. Hence, we adopt an architecture that locates every person in the crowd, sizes the spotted heads with bounding box and then counts them. Compared to normal object or face detectors, there exist certain unique challenges in designing such a detection system. Some of them are direct consequences of the huge diversity in dense crowds along with the need to predict boxes contiguously. We solve these issues and develop our LSC-CNN model, which can reliably detect heads of people across sparse to dense crowds. LSC-CNN employs a multi-column architecture with top-down feature modulation to better resolve persons and produce refined predictions at multiple resolutions. Interestingly, the proposed training regime requires only point head annotation, but can estimate approximate size information of heads. We show that LSC-CNN not only has superior localization than existing density regressors, but outperforms in counting as well. The code for our approach is available at https://github.com/val-iisc/lsc-cnn.
Dark Matter in the Earth and the Sun -- Simulating Underground Scatterings for the Direct Detection of Low-Mass Dark Matter

If DM-matter scatterings are assumed to occur in a detector's target material, collisions will naturally take place inside the bulk of planets and stars as well. For large cross sections, these scatterings might occur in the Earth or Sun even prior to the detection. In this thesis, we study the impact of these pre-detection scatterings on direct searches of light DM with the use of Monte Carlo (MC) simulations. By simulating the trajectories and scatterings of many individual DM particles in the Earth or Sun, we determine the local distortions of the statistical properties of DM at any detector caused by elastic DM-nucleus collisions. Scatterings inside the Earth distort the underground DM density and velocity distribution. Any detector moves periodically through these inhomogeneities due to the Earth's rotation, and the expected event rate will vary throughout a sidereal day. Using MC simulations, we can determine the exact amplitude and phase of this diurnal modulation for any experiment. For even higher scattering probabilities, collisions in the overburden above the typically underground detectors start to attenuate the incoming DM flux. The critical cross section above which an experiment loses sensitivity to DM itself is determined for a variety of DM-nucleus and DM-electron scattering experiments and different interaction types. Furthermore, we develop the idea that sub-GeV DM particles can enter the Sun, gain kinetic energy by colliding on hot nuclei and get reflected with great speeds. By deriving an analytic expressions for the particle flux from solar reflection via a single scattering, we demonstrate the prospects of future experiments to probe reflected DM and extend their sensitivity to lower masses than accessible by halo DM alone. Finally, we present first results for MC simulations of solar reflections taking into account the effect of multiple scatterings.
Automatic learner summary assessment for reading comprehension

Automating the assessment of learner summary provides a useful tool for assessing learner reading comprehension. We present a summarization task for evaluating non-native reading comprehension and propose three novel approaches to automatically assess the learner summaries. We evaluate our models on two datasets we created and show that our models outperform traditional approaches that rely on exact word match on this task. Our best model produces quality assessments close to professional examiners.
Measuring Time with Minimal Clocks

Being able to measure time, whether directly or indirectly, is a significant advantage for an organism. It allows for timely reaction to regular or predicted events, reducing the pressure for fast processing of sensory input. Thus, clocks are ubiquitous in biology. In the present article, we consider minimal abstract pure clocks in different configurations and investigate their characteristic dynamics. We are especially interested in optimally time-resolving clocks. Among these, we find fundamentally diametral clock characteristics, such as oscillatory behavior for purely local time measurement or decay-based clocks measuring time periods on a scale global to the problem. We include also sets of independent clocks (clock bags), sequential cascades of clocks, and composite clocks with controlled dependence. Clock cascades show a condensation effect, and the composite clock shows various regimes of markedly different dynamics.
Differentiable Probabilistic Models of Scientific Imaging with the Fourier Slice Theorem

Scientific imaging techniques such as optical and electron microscopy and computed tomography (CT) scanning are used to study the 3D structure of an object through 2D observations. These observations are related to the original 3D object through orthogonal integral projections. For common 3D reconstruction algorithms, computational efficiency requires the modeling of the 3D structures to take place in Fourier space by applying the Fourier slice theorem. At present, it is unclear how to differentiate through the projection operator, and hence current learning algorithms can not rely on gradient based methods to optimize 3D structure models. In this paper we show how back-propagation through the projection operator in Fourier space can be achieved. We demonstrate the validity of the approach with experiments on 3D reconstruction of proteins. We further extend our approach to learning probabilistic models of 3D objects. This allows us to predict regions of low sampling rates or estimate noise. A higher sample efficiency can be reached by utilizing the learned uncertainties of the 3D structure as an unsupervised estimate of the model fit. Finally, we demonstrate how the reconstruction algorithm can be extended with an amortized inference scheme on unknown attributes such as object pose. Through empirical studies we show that joint inference of the 3D structure and the object pose becomes more difficult when the ground truth object contains more symmetries. Due to the presence of for instance (approximate) rotational symmetries, the pose estimation can easily get stuck in local optima, inhibiting a fine-grained high-quality estimate of the 3D structure.
A radio ridge connecting two galaxy clusters in a filament of the cosmic web

A radio ridge between two galaxy clusters Galaxy clusters contain dozens or hundreds of galaxies, vast quantities of hot gas, and large amounts of dark matter. The gas can emit at radio wavelengths if it contains electrons at relativistic speeds, which can be injected by active galaxies or accelerated during a merger between two clusters. Govoni et al. used the Low-Frequency Array (LOFAR) radio telescope to observe a ridge of radio-emitting plasma extending between two galaxy clusters that are approaching a merger. The results imply that intergalactic magnetic fields connect the two clusters and challenge theories of particle acceleration in the intergalactic medium. Science, this issue p. 981 A ridge of low-frequency radio emission connects two galaxy clusters that are approaching a merger. Galaxy clusters are the most massive gravitationally bound structures in the Universe. They grow by accreting smaller structures in a merging process that produces shocks and turbulence in the intracluster gas. We observed a ridge of radio emission connecting the merging galaxy clusters Abell 0399 and Abell 0401 with the Low-Frequency Array (LOFAR) telescope network at 140 megahertz. This emission requires a population of relativistic electrons and a magnetic field located in a filament between the two galaxy clusters. We performed simulations to show that a volume-filling distribution of weak shocks may reaccelerate a preexisting population of relativistic particles, producing emission at radio wavelengths that illuminates the magnetic ridge.
Origins versus fingerprints of the Jahn-Teller effect in 
d
-electron 
ABX3
 perovskites

The Jahn-Teller (JT) distortion that can remove electronic degeneracies in partially occupied states and results in systematic atomic displacements is a common underlying feature to many of the intriguing phenomena observed in 3d perovskites, encompassing magnetism, superconductivity, orbital ordering and colossal magnetoresistance. Although the seminal Jahn and Teller theorem has been postulated almost a century ago, the origins of this effect in perovskite materials are still debated, including propositions such as super exchange, spin-phonon coupling, sterically induced lattice distortions, and strong dynamical correlation effects. Here we analyze the driving forces behind the Jahn-Teller motions and associated electronic fingerprints in a full range of ABX3 compounds. We identify (i) compounds that are prone to an electronically-driven instabilities (i.e. a pure JT effect) such as KCrF3, KCuF3 or LaVO3 and proceed to relax the structures, finding quantitatively the JTD in excellent agreement with experiment; (ii) compounds such as LaMnO3 or LaTiO3 that do not show electronically driven JTD despite orbital degeneracies, because their strongly hybridized B, d-X, p states supply but too weak JT forces to overcome the needed atomic distortions; (iii) although LaVO3 exhibits similar B, d-X, p hybridizations as LaTiO3, the former compound exhibits a robust electronic instability while LaTiO3 has zero stabilization energy, the reason being that LaVO3 has two electrons t2g2 relative to LaTiO3 with just one t2g1. (iv) We explain the trends in "orbital ordering" whereby electrons occupy orbitals that point to orthogonal directions between all nearest-neighbor 3d atoms. We thereby provide a unified vision to explain octahedra deformations in perovskites that, at odds with common wisdom, does not require the celebrated Mott-Hubbard mechanism.