Miracle-dz/UniFAR-FTUs huggingface dataset:
The Effect of Parties on Voters’ Satisfaction with Democracy

Electoral ‘winners’ (i.e., voters casting a ballot for a party included in the post-electoral government) are acknowledged to be more satisfied with democracy than supporters of opposition parties. However, little is known about the influence of parties and their specifics on the boost in satisfaction with democracy experienced by their voters. To address this question, the research utilizes 17 surveys from 12 countries included in the European Social Survey rounds 1–8, for which a government replacement took place during the survey period. This allows this research to employ discontinuity design and examine the effect of two attributes related to parties—differences in party vote shares, and voters’ feeling of closeness to a party. The findings suggest that these factors have a negligible influence on voters’ satisfaction with democracy and only scant evidence is found that closeness to a party tends to increase their satisfaction. When voters’ attitudes from before and after a government replacement are compared, changes in government do not seem to strike voters as a surprise and thus they do not cause any sudden and lasting changes in the general attitudes of electorates. Nevertheless, this indicates a novel contribution to the literature: the effect of losing needs some time to fully develop until it results in a decrease in satisfaction level. Based on these findings, the research concludes that when it comes to parties’ characteristics, it is primarily the government/opposition status which determines voters’ degree of satisfaction with democracy.
The Gaia-ESO Survey: asymmetric expansion of the Lagoon Nebula cluster NGC 6530 from GES and Gaia DR2

The combination of precise radial velocities from multi-object spectroscopy and highly accurate proper motions from Gaia DR2 opens up the possibility for detailed 3D kinematic studies of young star forming regions and clusters. Here, we perform such an analysis by combining Gaia-ESO Survey spectroscopy with Gaia astrometry for ~900 members of the Lagoon Nebula cluster, NGC 6530. We measure the 3D velocity dispersion of the region to be $5.35^{+0.39}_{-0.34}$~km~s$^{-1}$, which is large enough to suggest the region is gravitationally unbound. The velocity ellipsoid is anisotropic, implying that the region is not sufficiently dynamically evolved to achieve isotropy, though the central part of NGC 6530 does exhibit velocity isotropy that suggests sufficient mixing has occurred in this denser part. We find strong evidence that the stellar population is expanding, though this is preferentially occurring in the declination direction and there is very little evidence for expansion in the right ascension direction. This argues against a simple radial expansion pattern, as predicted by models of residual gas expulsion. We discuss these findings in the context of cluster formation, evolution and disruption theories.
Strong bound between trace distance and Hilbert-Schmidt distance for low-rank states

The trace distance between two quantum states, $\rho$ and $\sigma$, is an operationally meaningful quantity in quantum information theory. However, in general it is difficult to compute, involving the diagonalization of $\rho - \sigma$. In contrast, the Hilbert-Schmidt distance can be computed without diagonalization, although it is less operationally significant. Here, we relate the trace distance and the Hilbert-Schmidt distance with a bound that is particularly strong when either $\rho$ or $\sigma$ is low rank. Our bound is stronger than the bound one could obtain via the norm equivalence of the Frobenius and trace norms. We also consider bounds that are useful not only for low-rank states but also for low-entropy states.
The fourth data release of the Kilo-Degree Survey: ugri imaging and nine-band optical-IR photometry over 1000 square degrees

Context. The Kilo-Degree Survey (KiDS) is an ongoing optical wide-field imaging survey with the OmegaCAM camera at the VLT Survey Telescope, specifically designed for measuring weak gravitational lensing by galaxies and large-scale structure. When completed it will consist of 1350 square degrees imaged in four filters (ugri). Aims. Here we present the fourth public data release which more than doubles the area of sky covered by data release 3. We also include aperture-matched ZYJHKs photometry from our partner VIKING survey on the VISTA telescope in the photometry catalogue. We illustrate the data quality and describe the catalogue content. Methods. Two dedicated pipelines are used for the production of the optical data. The ASTRO-WISE information system is used for the production of co-added images in the four survey bands, while a separate reduction of the r-band images using the THELI pipeline is used to provide a source catalogue suitable for the core weak lensing science case. All data have been re-reduced for this data release using the latest versions of the pipelines. The VIKING photometry is obtained as forced photometry on the THELI sources, using a re-reduction of the VIKING data that starts from the VISTA pawprints. Modifications to the pipelines with respect to earlier releases are described in detail. The photometry is calibrated to the Gaia DR2 G band using stellar locus regression. Results. In this data release a total of 1006 square-degree survey tiles with stacked ugri images are made available, accompanied by weight maps, masks, and single-band source lists. We also provide a multi-band catalogue based on r-band detections, including homogenized photometry and photometric redshifts, for the whole dataset. Mean limiting magnitudes (5σ in a 2″ aperture) and the tile-to-tile rms scatter are 24.23 ± 0.12, 25.12 ± 0.14, 25.02 ± 0.13, 23.68 ± 0.27 in ugri, respectively, and the mean r-band seeing is 0. ​​″70.
Upper bounds on secure key against non-signaling adversary via non-signaling squashed secrecy monotones

We provide upper bounds on device independent key secure against a non-signaling adversary (NSDI) achieved by a class of operations, currently used in both quantum and non-signaling device independent protocols. As the main tool, we introduce a family of measures of non-locality by "squashing" secrecy monotones known to upper bound secret key in the secret key agreement scenario. In particular a squashed secret key rate can be considered itself as an upper bound on the key in NSDI, however, we construct a much more computable example: - the non-signaling squashed intrinsic information of a conditional distribution (called squashed non-locality). We prove that the squashed non-locality exhibits several useful properties such as convexity, monotonicity, additivity on the tensor product of conditional distributions, and asymptotic continuity. Hence, as a measure of non-locality, is interesting on its own. We demonstrate this approach by numerical upper bounds on this measure, {that} suggests, in particular, that the non-locality and secrecy in NSDI are not equivalent. We construct explicit examples of a conditional distribution violating CHSH inequality, from which no key in NSDI can be obtained with direct measurements and public communication followed by classical post-processing. We define the secret key rate in terms of the complete extension, a counterpart of quantum purification in non-signaling scenario, recently introduced in Winczewski et al. [arXiv:1810.02222]. We show that the presented approach is equivalent to the already existing ones, including the one by H{a}nggi Renner and Wolf [EUROCRYPT, 2010]. Finally, we simplify the class of operations attaining maximal distinguishability of two devices with several unary inputs, and one input of arbitrary cardinality, which is of independent interest on its own in the context of Generalized Probability Theories.
Hubble Space Telescope Nondetection of PSR J2144–3933: The Coldest Known Neutron Star

We report nondetections of the ∼3 × 108 yr old, slow, isolated, rotation-powered pulsar PSR J2144–3933 in observations with the Hubble Space Telescope in one optical band (F475X) and two far-ultraviolet bands (F125LP and F140LP), yielding upper bounds , , and , at the pivot wavelengths 4940 Å, 1438 Å and 1528 Å, respectively. Assuming a blackbody spectrum, we deduce a conservative upper bound on the surface (unredshifted) temperature of the pulsar of 42,000 K. This makes PSR J2144–3933 the coldest known neutron star, allowing us to study thermal evolution models of old neutron stars. This temperature is consistent with models with either direct or modified Urca reactions including rotochemical heating, and, considering frictional heating from the motion of neutron vortex lines, it puts an upper bound on the excess angular momentum in the neutron superfluid, J < 1044 erg s.
THE BOREL CHARACTER

Abstract The main purpose of this article is to define a quadratic analogue of the Chern character, the so-called Borel character, that identifies rational higher Grothendieck-Witt groups with a sum of rational Milnor-Witt (MW)-motivic cohomologies and rational motivic cohomologies. We also discuss the notion of ternary laws due to Walter, a quadratic analogue of formal group laws, and compute what we call the additive ternary laws, associated with MW-motivic cohomology. Finally, we provide an application of the Borel character by showing that the Milnor-Witt K-theory of a field F embeds into suitable higher Grothendieck-Witt groups of F modulo explicit torsion.
The impact of corruption on firms’ access to bank loans: evidence from China

Abstract Current theories cannot explain the coexistence of China’s 40 years of rapid economic growth and its imperfect financial system, insufficient investor protection, and government intervention. This study empirically tests hypotheses regarding the effects of corruption on firms’ access to bank loans using econometric models based on survey data of 2,848 enterprises in China collected by the World Bank. The results show an inverted U-shaped relationship between corruption and firms’ access to bank loans: a low level of corruption increases firms’ access to bank loans, whereas a high level of corruption hinders firms from obtaining bank loans. Mild corruption may be a suboptimal choice for firms seeking bank loans, and bank funds allocation based on corruption can achieve Pareto optimality among firms. Moreover, government guarantees are conducive to firms’ access to financing and the link between corruption and firms’ access to bank loans can be explained by the role of government guarantees. The improvement of institutional quality is positively associated with firms’ access to bank loans and weakens the effects of corruption on firms’ external financing. This study thus sheds light on the real effects of corruption and the determinants of firms’ access to bank loans in developing countries.
El Niño Index Prediction Using Deep Learning with Ensemble Empirical Mode Decomposition

El Niño is an important quasi-cyclical climate phenomenon that can have a significant impact on ecosystems and societies. Due to the chaotic nature of the atmosphere and ocean systems, traditional methods (such as statistical methods) are difficult to provide accurate El Niño index predictions. The latest research shows that Ensemble Empirical Mode Decomposition (EEMD) is suitable for analyzing non-linear and non-stationary signal sequences, Convolutional Neural Network (CNN) is good at local feature extraction, and Recurrent Neural Network (RNN) can capture the overall information of the sequence. As a special RNN, Long Short-Term Memory (LSTM) has significant advantages in processing and predicting long, complex time series. In this paper, to predict the El Niño index more accurately, we propose a new hybrid neural network model, EEMD-CNN-LSTM, which combines EEMD, CNN, and LSTM. In this hybrid model, the original El Niño index sequence is first decomposed into several Intrinsic Mode Functions (IMFs) using the EEMD method. Next, we filter the IMFs by setting a threshold, and we use the filtered IMFs to reconstruct the new El Niño data. The reconstructed time series then serves as input data for CNN and LSTM. The above data preprocessing method, which first decomposes the time series and then reconstructs the time series, uses the idea of symmetry. With this symmetric operation, we extract valid information about the time series and then make predictions based on the reconstructed time series. To evaluate the performance of the EEMD-CNN-LSTM model, the proposed model is compared with four methods including the traditional statistical model, machine learning model, and other deep neural network models. The experimental results show that the prediction results of EEMD-CNN-LSTM are not only more accurate but also more stable and reliable than the general neural network model.
Early deafness leads to re-shaping of global functional connectivity beyond the auditory cortex.

Early sensory deprivation such as blindness or deafness shapes brain development in multiple ways. While it is established that deprived brain areas start to be engaged in the processing of stimuli from the remaining modalities and in high-level cognitive tasks, some reports have also suggested the possibility of structural and functional changes in non-deprived brain areas. We compared resting-state functional network organization of the brain in early-deaf adults and hearing controls by examining global network segregation and integration. Relative to hearing controls, deaf adults exhibited an altered modular organization, with regions of the salience network coupled with the fronto-parietal network. They showed weaker connections between auditory and somatomotor regions, stronger coupling between the fronto-parietal network and several other large-scale networks (visual, memory, cingulo-opercular and somatomotor), and an enlargement of the default mode network. Their overall functional segregation of brain networks was also lower. Our findings suggest that brain plasticity in deaf adults is not limited to changes in auditory cortex but additionally alters the coupling between other large-scale networks. These widespread functional connectivity changes may provide a mechanism for the superior behavioral performance of the deaf in visual and attentional tasks.
Meta-Learning surrogate models for sequential decision making

We introduce a unified probabilistic framework for solving sequential decision making problems ranging from Bayesian optimisation to contextual bandits and reinforcement learning. This is accomplished by a probabilistic model-based approach that explains observed data while capturing predictive uncertainty during the decision making process. Crucially, this probabilistic model is chosen to be a Meta-Learning system that allows learning from a distribution of related problems, allowing data efficient adaptation to a target task. As a suitable instantiation of this framework, we explore the use of Neural processes due to statistical and computational desiderata. We apply our framework to a broad range of problem domains, such as control problems, recommender systems and adversarial attacks on RL agents, demonstrating an efficient and general black-box learning approach.
Observation of Edge Waves in a Two-Dimensional Su-Schrieffer-Heeger Acoustic Network

In this work, we experimentally report the acoustic realization the two-dimensional (2D) Su-Schrieffer-Heeger (SSH) model in a simple network of air channels. We analytically study the steady state dynamics of the system using a set of discrete equations for the acoustic pressure, leading to the 2D SSH Hamiltonian matrix without using tight binding approximation. By building an acoustic network operating in audible regime, we experimentally demonstrate the existence of topological band gap. More supremely, within this band gap we observe the associated edge waves even though the system is open to free space. Our results not only experimentally demonstrate topological edge waves in a zero Berry curvature system but also provide a flexible platform for the study of topological properties of sound waves.
Interpretation of machine learning predictions for patient outcomes in electronic health records

Electronic health records are an increasingly important resource for understanding the interactions between patient health, environment, and clinical decisions. In this paper we report an empirical study of predictive modeling of seven patient outcomes using three state-of-the-art machine learning methods. Our primary goal is to validate the models by interpreting the importance of predictors in the final models. Central to interpretation is the use of feature importance scores, which vary depending on the underlying methodology. In order to assess feature importance, we compared univariate statistical tests, information-theoretic measures, permutation testing, and normalized coefficients from multivariate logistic regression models. In general we found poor correlation between methods in their assessment of feature importance, even when their performance is comparable and relatively good. However, permutation tests applied to random forest and gradient boosting models showed the most agreement, and the importance scores matched the clinical interpretation most frequently.
Numerical study of filament suspensions at finite inertia

We present a numerical study on the rheology of semi-dilute and concentrated filament suspensions of different bending stiffness and Reynolds number, with the immersed boundary method used to couple the fluid and solid. The filaments are considered as one-dimensional inextensible slender bodies with fixed aspect ratio, obeying the Euler–Bernoulli beam equation. To understand the global suspension behaviour we relate it to the filament microstructure, deformation and elastic energy and examine the stress budget to quantify the effect of the elastic contribution. At fixed volume fraction, the viscosity of the suspension reduces when decreasing the bending rigidity and grows when increasing the Reynolds number. The change in the relative viscosity is stronger at finite inertia, although still in the laminar flow regime, as considered here. Moreover, we find the first normal stress difference to be positive as in polymeric fluids, and to increase with the Reynolds number; its value has a peak for an intermediate value of the filament bending stiffness. The peak value is found to be proportional to the Reynolds number, moving towards more rigid suspensions at larger inertia. Moreover, the viscosity increases when increasing the filament volume fraction, and the rate of increase of the filament stress with the bending rigidity is stronger at higher Reynolds numbers and reduces with the volume fraction. We show that this behaviour is associated with the formation of a more ordered structure in the flow, where filaments tend to be more aligned and move as a compact aggregate, thus reducing the filament–filament interactions despite their volume fraction increases.
Evaluating the Efficacy and Optimal Deployment of Thermal Infrared and True-Colour Imaging When Using Drones for Monitoring Kangaroos

Advances in drone technology have given rise to much interest in the use of drone-mounted thermal imagery in wildlife monitoring. This research tested the feasibility of monitoring large mammals in an urban environment and investigated the influence of drone flight parameters and environmental conditions on their successful detection using thermal infrared (TIR) and true-colour (RGB) imagery. We conducted 18 drone flights at different altitudes on the Sunshine Coast, Queensland, Australia. Eastern grey kangaroos (Macropus giganteus) were detected from TIR (n=39) and RGB orthomosaics (n=33) using manual image interpretation. Factors that predicted the detection of kangaroos from drone images were identified using unbiased recursive partitioning. Drone-mounted imagery achieved an overall 73.2% detection success rate using TIR imagery and 67.2% using RGB imagery when compared to on-ground counts of kangaroos. We showed that the successful detection of kangaroos using TIR images was influenced by vegetation type, whereas detection using RGB images was influenced by vegetation type, time of day that the drone was deployed, and weather conditions. Kangaroo detection was highest in grasslands, and kangaroos were not successfully detected in shrublands. Drone-mounted TIR and RGB imagery are effective at detecting large mammals in urban and peri-urban environments.
The conditionality principle in high-dimensional regression

Consider a high-dimensional linear regression problem, where the number of covariates is larger than the number of observations and the interest is in estimating the conditional variance of the response variable given the covariates. A conditional and an unconditional framework are considered, where conditioning is with respect to the covariates, which are ancillary to the parameter of interest. In recent papers, a consistent estimator was developed in the unconditional framework when the marginal distribution of the covariates is normal with known mean and variance. In the present work, a certain Bayesian hypothesis test is formulated under the conditional framework, and it is shown that the Bayes risk is a constant. This implies that no consistent estimator exists in the conditional framework. However, when the marginal distribution of the covariates is normal, the conditional error of the above consistent estimator converges to zero, with probability converging to one. It follows that even in the conditional setting, information about the marginal distribution of an ancillary statistic may have a significant impact on statistical inference. The practical implication in the context of high-dimensional regression models is that additional observations where only the covariates are given are potentially very useful and should not be ignored. This finding is most relevant to semi-supervised learning problems where covariate information is easy to obtain.
Effects of Hadron-Quark Phase Transitions in Hybrid Stars within the NJL Model

We study local and non-local Polyakov Nambu-Jona-Lasinio models and analyze their respective phase transition diagram. We construct hybrid stars using the zero temperature limit of the local and non-local versions of Nambu-Jona-Lasinio model for quark matter and the modern GM1(L) parametrization of the non-linear relativistic mean field model for hadronic matter. We compare our models with data from PSR J1614-2230 and PSR J0343+0432 and also from GW170817 and its electromagnetic counterpart GRB170817A and AT2017gfo. We study observational signatures of the appearance of a mixed phase as a result of modeling a phase transition that mimics the Gibbs formalism and compare the results with the sharp first-order phase transition obtained using the Maxwell construction. We also study in detail the g-mode associated with discontinuities in the equation of state, and calculate non-radial oscillation modes using relativistic Cowling approximation.
Parallaxes for Star-forming Regions in the Inner Perseus Spiral Arm

We report trigonometric parallax and proper motion measurements of 6.7 GHz CH3OH and 22 GHz H2O masers in eight high-mass star-forming regions (HMSFRs) based on Very Long Baseline Array (VLBA) observations as part of the Bar and Spiral Structure Legacy (BeSSeL) Survey. The distances of these HMSFRs combined with their Galactic coordinates, radial velocities, and proper motions, allow us to assign them to a segment of the Perseus arm with ℓ ≲ 70°. These HMSFRs are clustered in Galactic longitude from ≈30° to ≈50° neighboring a dearth of such sources between longitudes ≈50° to ≈90°.