nils-herrmann/clean_scirepeval huggingface dataset:
First results from the WISPDMX radio frequency cavity searches for hidden photon dark matter

The origin of non-baryonic Dark Matter remains elusive despite ongoing sensitive searches for heavy, thermally produced dark matter particles. Recently, it has been shown, that non-thermally produced vector bosons (sometimes called hidden photons) related to a broken U(1) gauge symmetry are among the possible WISP (weakly interacting slim particles) dark matter candidates. The WISP Dark Matter eXperiment (WISPDMX) is the first direct hidden photon dark matter search experiment probing the particle masses within the 0.8–2.07 μeV range with four resonant modes of a tunable radio frequency cavity and down to 0.4 neV outside of resonance. In this paper, we present the results from the first science run of WISPDMX comprising 22 000 broadband spectra with a 500 MHz bandwidth and a 50 Hz spectral resolution, obtained during 10-second integrations made at each individual tuning step of the measurements. No plausible dark matter candidate signal is found, both in the individual spectra reaching minimum detectable power of 8× 10−19 W and in the averaged spectrum of all the measurements with the minimum detectable power of 5× 10−22 W) attained for a total of 61 h of data taking. Using these spectra, we derive upper limits on the coupling constant of the hidden photon at the levels of 10−13 for the resonant frequency ranges and 10−12 for broadband mass range 0.2–2.07 μeV, and steadily increasing at masses below 0.2 μeV.
The $Z_3$ symmetric I(2+1)HDM

We introduce a 3-Higgs Doublet Model (3HDM) with two Inert (or dark) (pseudo)scalar doublets and an active Higgs one, hence termed I(2+1)HDM, in the presence of a discrete Z3 acting upon the three doublet fields. Assuming a maximally symmetric configuration of the parameters related to the two dark doublets, known in the literature as ‘dark democracy’, we show that such a construct yields a two-component Dark Matter (DM) model and the two DM candidates have opposite CP parity. Herein, the most interesting solutions, those that saturate the relic density and where contributions to the latter from the two DM ∗E-mail: fefo@ucol.mx †E-mail: danielah@ifuap.buap.mx ‡E-mail: jaime.hernandez@correo.buap.mx §E-mail: S.Moretti@soton.ac.uk ¶E-mail: D.Rojas-Ciofalo@soton.ac.uk ‖E-mail: shindou@cc.kogakuin.ac.jp 1 ar X iv :1 90 7. 12 47 0v 3 [ he pph ] 1 A ug 2 01 9 candidates are of similar magnitude, are achieved when there is a high level of degeneracy in the (dark) charged sector. In order to probe this phenomenology, we have produced a set of benchmark scenarios in the I(2+1)HDM, with the invoked Z3 symmetry, which are further compliant with (in)direct searches for DM as well as other experimental data impinging on both the dark and Higgs sectors of the model, chiefly, in the form of Electro-Weak Precision Observables (EWPOs), Standard Model (SM)-like Higgs boson measurements at the Large Hadron Collider (LHC) and void searches for additional (pseudo)scalar states at the CERN machine and previous colliders.
Action Grammars: A Cognitive Model for Learning Temporal Abstractions

Hierarchical Reinforcement Learning algorithms have successfully been applied to temporal credit assignment problems with sparse reward signals. However, state-ofthe-art algorithms require manual specification of subtask structures, a sample inefficient exploration phase and lack semantic interpretability. Human infants, on the other hand, efficiently detect hierarchical sub-structures induced by their surroundings. In this work we propose a cognitive-inspired Reinforcement Learning architecture which uses grammar induction to identify sub-goal policies. More specifically, by treating an on-policy trajectory as a sentence sampled from the policy-conditioned language of the environment, we identify hierarchical constituents with the help of unsupervised grammatical inference. The resulting set of temporal abstractions is called action grammars (Pastra & Aloimonos, 2012) and can be used to enable efficient imitation, transfer and online learning.
Joey NMT: A Minimalist NMT Toolkit for Novices

We present Joey NMT, a minimalist neural machine translation toolkit based on PyTorch that is specifically designed for novices. Joey NMT provides many popular NMT features in a small and simple code base, so that novices can easily and quickly learn to use it and adapt it to their needs. Despite its focus on simplicity, Joey NMT supports classic architectures (RNNs, transformers), fast beam search, weight tying, and more, and achieves performance comparable to more complex toolkits on standard benchmarks. We evaluate the accessibility of our toolkit in a user study where novices with general knowledge about Pytorch and NMT and experts work through a self-contained Joey NMT tutorial, showing that novices perform almost as well as experts in a subsequent code quiz. Joey NMT is available at https://github.com/joeynmt/joeynmt.
Precomputing Datalog Evaluation Plans in Large-Scale Scenarios

With the more and more growing demand for semantic Web services over large databases, an efficient evaluation of Datalog queries is arousing a renewed interest among researchers and industry experts. In this scenario, to reduce memory consumption and possibly optimize execution times, the paper proposes novel techniques to determine an optimal indexing schema for the underlying database together with suitable body-orderings for the Datalog rules. The new approach is compared with the standard execution plans implemented in DLV over widely used ontological benchmarks. The results confirm that the memory usage can be significantly reduced without paying any cost in efficiency.
A Syntactic Operator for Forgetting that Satisfies Strong Persistence

Whereas the operation of forgetting has recently seen a considerable amount of attention in the context of Answer Set Programming (ASP), most of it has focused on theoretical aspects, leaving the practical issues largely untouched. Recent studies include results about what sets of properties operators should satisfy, as well as the abstract characterization of several operators and their theoretical limits. However, no concrete operators have been investigated. In this paper, we address this issue by presenting the first concrete operator that satisfies strong persistence – a property that seems to best capture the essence of forgetting in the context of ASP – whenever this is possible, and many other important properties. The operator is syntactic, limiting the computation of the forgetting result to manipulating the rules in which the atoms to be forgotten occur, naturally yielding a forgetting result that is close to the original program.
Asymptotic safety, the Higgs boson mass, and beyond the standard model physics

There are many hints that gravity is asymptotically safe. The inclusion of gravitational corrections can result in the ultraviolet fundamental Standard Model and constrain the Higgs mass to take the smallest value such that electroweak vacuum is stable. Taking into account the current top quark mass measurements this value is $m_H \approx 130$ GeV.\\ This article considers the predictions of the Higgs mass in two minimal Beyond Standard Model scenarios, where the stability is improved. One is the sterile quark axion model, while the other is the $U(1)_{B-L}$ gauge symmetry model introducing a new massive $Z'$ gauge boson. The inclusion of $Z'$ boson gives the correct prediction for this mass, while inclusion of sterile quark(s) gives only a slight effect.\\ Also a new, gravitational solution to the strong CP problem is discussed.
Towards Automatic Screening of Typical and Atypical Behaviors in Children With Autism

Autism spectrum disorders (ASD) impact the cognitive, social, communicative and behavioral abilities of an individual. The development of new clinical decision support systems is of importance in reducing the delay between presentation of symptoms and an accurate diagnosis. In this work, we contribute a new database consisting of video clips of typical (normal) and atypical (such as hand flapping, spinning or rocking) behaviors, displayed in natural settings, which have been collected from the YouTube video website. We propose a preliminary non-intrusive approach based on skeleton keypoint identification using pretrained deep neural networks on human body video clips to extract features and perform body movement analysis that differentiates typical and atypical behaviors of children. Experimental results on the newly contributed database show that our platform performs best with decision tree as the classifier when compared to other popular methodologies and offers a baseline against which alternate approaches may developed and tested.
Packed Ultra-wideband Mapping Array (PUMA): A Radio Telescope for Cosmology and Transients

PUMA is a proposal for an ultra-wideband, low-resolution and transit interferometric radio telescope operating at $200-1100\,\mathrm{MHz}$. Its design is driven by six science goals which span three science themes: the physics of dark energy (measuring the expansion history and growth of the universe up to $z=6$), the physics of inflation (constraining primordial non-Gaussianity and primordial features) and the transient radio sky (detecting one million fast radio bursts and following up SKA-discovered pulsars). We propose two array configurations composed of hexagonally close-packed 6m dish arrangements with 50% fill factor. The initial 5,000 element 'petite array' is scientifically compelling, and can act as a demonstrator and a stepping stone to the full 32,000 element 'full array'. Viewed as a 21cm intensity mapping telescope, the program has the noise equivalent of a traditional spectroscopic galaxy survey comprised of 0.6 and 2.5 billion galaxies at a comoving wavenumber of $k=0.5\,h\mathrm{Mpc}^{-1}$ spanning the redshift range $z = 0.3 - 6$ for the petite and full configurations, respectively. At redshifts beyond $z=2$, the 21cm technique is a uniquely powerful way of mapping the universe, while the low-redshift range will allow for numerous cross-correlations with existing and upcoming surveys. This program is enabled by the development of ultra-wideband radio feeds, cost-effective dish construction methods, commodity radio-frequency electronics driven by the telecommunication industry and the emergence of sufficient computing power to facilitate real-time signal processing that exploits the full potential of massive radio arrays. The project has an estimated construction cost of 55 and 330 million FY19 USD for the petite and full array configurations. Including R&D, design, operations and science analysis, the cost rises to 125 and 600 million FY19 USD, respectively.
The High-mass Protostellar Population of a Massive Infrared Dark Cloud

We conduct a census of the high-mass protostellar population of the ∼70,000 M⊙ infrared dark cloud (IRDC) G028.37+00.07, identifying 35 sources based on their 70 μm emission, as reported in the Herschel Hi-GAL catalog of Molinari et al. We perform aperture photometry to construct spectral energy distributions, which are then fit with the massive protostar models of Zhang & Tan. We find that the sources span a range of isotropic luminosities from ∼20 to 4500 L⊙. The most luminous sources are predicted to have current protostellar masses of m* ∼ 10 M⊙ forming from cores of mass Mc ∼ 40 to 400 M⊙. The least luminous sources in our sample are predicted to be protostars with masses as low as ∼0.5 M⊙ forming from cores with Mc ∼ 10 M⊙, which are the minimum values explored in the protostellar model grid. The detected protostellar population has a total estimated protostellar mass of M* ∼ 100 M⊙. Allowing for completeness corrections, which are constrained by comparison with an ALMA study in part of the cloud, we estimate a star formation efficiency per freefall time of ∼3% in the IRDC. Finally, analyzing the spatial distribution of the sources, we find relatively low degrees of central concentration of the protostars. The protostars, including the most massive ones, do not appear to be especially centrally concentrated in the protocluster as defined by the IRDC boundary.
Merger Rate of Stellar Black Hole Binaries above the Pair-instability Mass Gap

In current stellar evolutionary models, the occurrence of pair-instability supernovae implies a lack of stellar black holes (BHs) with masses between about [60, 120] M⊙, resulting in the presence of an upper-mass gap in the BH mass distribution. In this Letter, we propose a simple approach to describe BHs beyond the pair-instability gap by convolving the initial mass function and star formation rate with the metallicity evolution across cosmic time. Under the ansatz that the underlying physics of binary formation does not change beyond the gap, we then construct the cosmic population of merging BH binaries. The detection rate of BH binaries with both mass components above the gap is found to range between ≃[0.4, 7] yr−1 for LIGO/Virgo at design sensitivity and [10, 460] yr−1 for third-generation ground-based detectors, considering the most pessimistic and optimistic scenarios. The Laser Interferometer Space Antenna (LISA) can individually detect these binaries up to thousands of years from coalescence. The number of events merging in less than four years, which enable multiband observation in sequence, is expected to be in the range [1, 20]. While ET will detect all these events, LIGO/Virgo is expected to detect ≲50% of them. Finally, we estimate that the gravitational-wave background from unresolved sources in the LISA band may in principle be detected with a signal-to-noise ratio between ≃2.5 and ≃80.
Limits to Perception by Quantum Monitoring with Finite Efficiency

We formulate limits to perception under continuous quantum measurements by comparing the quantum states assigned by agents that have partial access to measurement outcomes. To this end, we provide bounds on the trace distance and the relative entropy between the assigned state and the actual state of the system. These bounds are expressed solely in terms of the purity and von Neumann entropy of the state assigned by the agent, and are shown to characterize how an agent’s perception of the system is altered by access to additional information. We apply our results to Gaussian states and to the dynamics of a system embedded in an environment illustrated on a quantum Ising chain.
Improved mutual information measure for classification and community detection

The information theoretic measure known as mutual information is widely used as a way to quantify the similarity of two different labelings or divisions of the same set of objects, such as arises, for instance, in clustering and classification problems in machine learning or community detection problems in network science. Here we argue that the standard mutual information, as commonly defined, omits a crucial term which can become large under real-world conditions, producing results that can be substantially in error. We derive an expression for this missing term and hence write a corrected mutual information that gives accurate results even in cases where the standard measure fails. We discuss practical implementation of the new measure and give example applications.
Updated results on neutrino mass and mass hierarchy from cosmology with Planck 2018 likelihoods

In this work we update the bounds on ∑ mν from latest publicly available cosmological data and likelihoods using Bayesian analysis, while explicitly considering particular neutrino mass hierarchies. In the minimal ΛCDM + ∑ mν model with most recent CMB data from Planck 2018 TT,TE,EE, lowE, and lensing; and BAO data from BOSS DR12, MGS, and 6dFGS, we find that at 95% C.L. the bounds are: ∑ mν<0.12 eV (degenerate), ∑ mν<0.15 eV (normal), ∑ mν<0.17 eV (inverted). The bounds vary across the different mass orderings due to different priors on ∑ mν. Also, we find that the normal hierarchy is very mildly preferred relative to the inverted, using both minimum χ2 values and Bayesian Evidence ratios. In this paper we also provide bounds on ∑ mν considering different hierarchies in various extended cosmological models: ΛCDM + ∑ mν+r, wCDM+∑ mν, w0 wa CDM+∑ mν, w0 wa CDM+∑ mν with w(z)⩾ −1, Λ CDM + ∑ mν + Ωk, and Λ CDM + ∑ mν + ALens. We do not find any strong evidence of normal hierarchy over inverted hierarchy in the extended models either.
Multiple Subordinated Modeling of Asset Returns

Subordination is an often used stochastic process in modeling asset prices. Subordinated Levy price processes and local volatility price processes are now the main tools in modern dynamic asset pricing theory. In this paper, we introduce the theory of multiple internally embedded financial time-clocks motivated by behavioral finance. To be consistent with dynamic asset pricing theory and option pricing, as suggested by behavioral finance, the investors' view is considered by introducing an intrinsic time process which we refer to as a behavioral subordinator. The process is subordinated to the Brownian motion process in the well-known log-normal model, resulting in a new log-price process. The number of embedded subordinations results in a new parameter that must be estimated and this parameter is as important as the mean and variance of asset returns. We describe new distributions, demonstrating how they can be applied to modeling the tail behavior of stock market returns. We apply the proposed models to modeling S&P 500 returns, treating the CBOE Volatility Index as intrinsic time change and the CBOE Volatility-of-Volatility Index as the volatility subordinator. We find that these volatility indexes are not proper time-change subordinators in modeling the returns of the S&P 500.
Uniform intensity in multifocal microscopy using a spatial light modulator

Multifocal microscopy (MFM) offers high-speed three-dimensional imaging through the simultaneous image capture from multiple focal planes. Conventional MFM systems use a fabricated grating in the emission path for a single emission wavelength band and one set of focal plane separations. While a Spatial Light Modulator (SLM) can add more flexibility as a replacement to the fabricated grating, the relatively small number of pixels in the SLM chip, cross-talk between the pixels, and aberrations in the imaging system can produce non-uniform intensity in the different axially separated image planes. We present an in situ iterative SLM calibration algorithm that overcomes these optical- and hardware-related limitations to deliver near-uniform intensity across all focal planes. Using immobilized gold nanoparticles under darkfield illumination, we demonstrate superior intensity evenness compared to current methods. We also demonstrate applicability across emission wavelengths, axial plane separations, imaging modalities, SLM settings, and different SLM manufacturers. Therefore, our microscope design and algorithms provide an alternative to the use of fabricated gratings in MFM, as they are relatively simple and could find broad applications in the wider research community.
Cassini Composite Infrared Spectrometer (CIRS) Observations of Titan 2004–2017

From 2004 to 2017, the Cassini spacecraft orbited Saturn, completing 127 close flybys of its largest moon, Titan. Cassini’s Composite Infrared Spectrometer (CIRS), one of 12 instruments carried on board, profiled Titan in the thermal infrared (7–1000 μm) throughout the entire 13 yr mission. CIRS observed on both targeted encounters (flybys) and more distant opportunities, collecting 8.4 million spectra from 837 individual Titan observations over 3633 hr. Observations of multiple types were made throughout the mission, building up a vast mosaic picture of Titan’s atmospheric state across spatial and temporal domains. This paper provides a guide to these observations, describing each type and chronicling its occurrences and global-seasonal coverage. The purpose is to provide a resource for future users of the CIRS data set, as well as those seeking to put existing CIRS publications into the overall context of the mission, and to facilitate future intercomparison of CIRS results with those of other Cassini instruments and ground-based observations.
A procedure for loss-optimising default definitions across simulated credit risk scenarios

A new procedure is presented for the objective comparison and evaluation of default definitions. This allows the lender to find a default threshold at which the financial loss of a loan portfolio is minimised, in accordance with Basel II. Alternative delinquency measures, other than simply measuring payments in arrears, can also be evaluated using this optimisation procedure. Furthermore, a simulation study is performed in testing the procedure from `first principles' across a wide range of credit risk scenarios. Specifically, three probabilistic techniques are used to generate cash flows, while the parameters of each are varied, as part of the simulation study. The results show that loss minima can exist for a select range of credit risk profiles, which suggests that the loss optimisation of default thresholds can become a viable practice. The default decision is therefore framed anew as an optimisation problem in choosing a default threshold that is neither too early nor too late in loan life. These results also challenges current practices wherein default is pragmatically defined as `90 days past due', with little objective evidence for its overall suitability or financial impact, at least beyond flawed roll rate analyses or a regulator's decree.
Artistic Domain Generalisation Methods are Limited by their Deep Representations

The cross-depiction problem refers to the task of recognising visual objects regardless of their depictions; whether photographed, painted, sketched, . In the past, some researchers considered cross-depiction to be domain adaptation (DA). More recent work considers cross-depiction as domain generalisation (DG), in which algorithms extend recognition from one set of domains (such as photographs and coloured artwork) to another (such as sketches). We show that fixing the last layer of AlexNet to random values provides a performance comparable to state of the art DA and DG algorithms, when tested over the PACS benchmark. With support from background literature, our results lead us to conclude that texture alone is insufficient to support generalisation; rather, higher-order representations such as structure and shape are necessary.
Task classification model for visual fixation, exploration, and search

Yarbus' claim to decode the observer's task from eye movements has received mixed reactions. In this paper, we have supported the hypothesis that it is possible to decode the task. We conducted an exploratory analysis on the dataset by projecting features and data points into a scatter plot to visualize the nuance properties for each task. Following this analysis, we eliminated highly correlated features before training an SVM and Ada Boosting classifier to predict the tasks from this filtered eye movements data. We achieve an accuracy of 95.4% on this task classification problem and hence, support the hypothesis that task classification is possible from a user's eye movement data.