Miracle-dz/UniFAR-FTUs huggingface dataset:
Scheme theoretic tropicalization

In this paper, we introduce ordered blueprints and ordered blue schemes, which serve as a common language for the different approaches to tropicalizations and which enhances tropical varieties with a schematic structure. As an abstract concept, we consider a tropicalization as a moduli problem about extensions of a given valuation $v:k\to T$ between ordered blueprints $k$ and $T$. If $T$ is idempotent, then we show that a generalization of the Giansiracusa bend relation leads to a representing object for the tropicalization, and that it has yet another interpretation in terms of a base change along $v$. We call such a representing object a scheme theoretic tropicalization. This theory recovers and improves other approaches to tropicalizations as we explain with care in the second part of this text. The Berkovich analytification and the Kajiwara-Payne tropicalization appear as rational point sets of a scheme theoretic tropicalization. The same holds true for its generalization by Foster and Ranganathan to higher rank valuations. The scheme theoretic Giansiracusa tropicalization can be recovered from the scheme theoretic tropicalizations in our sense. We obtain an improvement due to the resulting blueprint structure, which is sufficient to remember the Maclagan-Rinc\'on weights. The Macpherson analytification has an interpretation in terms of a scheme theoretic tropicalization, and we give an alternative approach to Macpherson's construction of tropicalizations. The Thuillier analytification and the Ulirsch tropicalization are rational point sets of a scheme theoretic tropicalization. Our approach yields a generalization to any, possibly nontrivial, valuation $v:k\to T$ with idempotent $T$ and enhances the tropicalization with a schematic structure.
simba: Cosmological simulations with black hole growth and feedback

We introduce the Simba simulations, the next generation of the Mufasa cosmological galaxy formation simulations run with Gizmo's meshless finite mass hydrodynamics. Simba includes updates to Mufasa's sub-resolution star formation and feedback prescriptions, and introduces black hole growth via the torque-limited accretion model of Angles-Alcazar et al. (2017) from cold gas and Bondi accretion from hot gas, along with black hole feedback via kinetic bipolar outflows and X-ray energy. Ejection velocities are taken to be ~10^3 km/s at high Eddington ratios, increasing to ~8000 km/s at Eddington ratios below 2%, with a constant momentum input of 20L/c. Simba further includes an on-the-fly dust production, growth, and destruction model. Our Simba run with (100 Mpc/h)^3 and 1024^3 gas elements reproduces numerous observables, including galaxy stellar mass functions at z=0-6, the stellar mass--star formation rate main sequence, HI and H2 fractions, the mass-metallicity relation at z=0 and z=2, star-forming galaxy sizes, hot gas fractions in massive halos, and z=0 galaxy dust properties. However, Simba also yields an insufficiently sharp truncation of the z=0 mass function, and too-large sizes for low-mass quenched galaxies. We show that Simba's jet feedback is primarily responsible for quenching massive galaxies.
Dielectron Production in Proton–Proton Collisions at √s=7 TeV with ALICE

The ALICE Collaboration measured dielectron production as a function of the invariant mass ( m ee ), the pair transverse momentum ( p T , ee ) and the pair distance of closest approach ( DCA ee ) in pp collisions at s = 7 TeV. Prompt and non-prompt dielectron sources can be separated with the DCA ee , which will give the opportunity in heavy-ion collisions to identify thermal radiation from the medium in the intermediate-mass range dominated by contributions from open-charm and beauty hadron decays. The charm and beauty total cross sections are extracted from the data by fitting the spectra with two different MC generators, i.e., PYTHIA a leading order event generator and POWHEG a next-to-leading order event generator. Significant model dependences are observed, reflecting the sensitivity of this measurement to the heavy-flavour production mechanisms.
Correlated Parameters to Accurately Measure Uncertainty in Deep Neural Networks

In this article, a novel approach for training deep neural networks using Bayesian techniques is presented. The Bayesian methodology allows for an easy evaluation of model uncertainty and, additionally, is robust to overfitting. These are commonly the two main problems classical, i.e., non-Bayesian architectures have to struggle with. The proposed approach applies variational inference in order to approximate the intractable posterior distribution. In particular, the variational distribution is defined as the product of multiple multivariate normal distributions with tridiagonal covariance matrices. Every single normal distribution belongs either to the weights or to the biases corresponding to one network layer. The layerwise a posteriori variances are defined based on the corresponding expectation values, and furthermore, the correlations are assumed to be identical. Therefore, only a few additional parameters need to be optimized compared with non-Bayesian settings. The performance of the new approach is evaluated and compared with other recently developed Bayesian methods. Basis of the performance evaluations are the popular benchmark data sets MNIST and CIFAR-10. Among the considered approaches, the proposed one shows the best predictive accuracy. Moreover, extensive evaluations of the provided prediction uncertainty information indicate that the new approach often yields more useful uncertainty estimates than the comparison methods.
Learning Matchable Image Transformations for Long-Term Metric Visual Localization

Long-term metric self-localization is an essential capability of autonomous mobile robots, but remains challenging for vision-based systems due to appearance changes caused by lighting, weather, or seasonal variations. While experience-based mapping has proven to be an effective technique for bridging the ‘appearance gap,’ the number of experiences required for reliable metric localization over days or months can be very large, and methods for reducing the necessary number of experiences are needed for this approach to scale. Taking inspiration from color constancy theory, we learn a nonlinear RGB-to-grayscale mapping that explicitly maximizes the number of inlier feature matches for images captured under different lighting and weather conditions, and use it as a pre-processing step in a conventional single-experience localization pipeline to improve its robustness to appearance change. We train this mapping by approximating the target non-differentiable localization pipeline with a deep neural network, and find that incorporating a learned low-dimensional context feature can further improve cross-appearance feature matching. Using synthetic and real-world datasets, we demonstrate substantial improvements in localization performance across day-night cycles, enabling continuous metric localization over a 30-hour period using a single mapping experience, and allowing experience-based localization to scale to long deployments with dramatically reduced data requirements.
Commonsense Reasoning for Natural Language Understanding: A Survey of Benchmarks, Resources, and Approaches

Commonsense knowledge and commonsense reasoning are some of the main bottlenecks in machine intelligence. In the NLP community, many benchmark datasets and tasks have been created to address commonsense reasoning for language understanding. These tasks are designed to assess machines' ability to acquire and learn commonsense knowledge in order to reason and understand natural language text. As these tasks become instrumental and a driving force for commonsense research, this paper aims to provide an overview of existing tasks and benchmarks, knowledge resources, and learning and inference approaches toward commonsense reasoning for natural language understanding. Through this, our goal is to support a better understanding of the state of the art, its limitations, and future challenges.
A Function Emulation Approach for Doubly Intractable Distributions

Abstract Doubly intractable distributions arise in many settings, for example, in Markov models for point processes and exponential random graph models for networks. Bayesian inference for these models is challenging because they involve intractable normalizing “constants” that are actually functions of the parameters of interest. Although several computational methods have been developed for these models, each can be computationally burdensome or even infeasible for many problems. We propose a novel algorithm that provides computational gains over existing methods by replacing Monte Carlo approximations to the normalizing function with a Gaussian process-based approximation. We provide theoretical justification for this method. We also develop a closely related algorithm that is applicable more broadly to any likelihood function that is expensive to evaluate. We illustrate the application of our methods to challenging simulated and real data examples, including an exponential random graph model, a Markov point process, and a model for infectious disease dynamics. The algorithm shows significant gains in computational efficiency over existing methods, and has the potential for greater gains for more challenging problems. For a random graph model example, we show how this gain in efficiency allows us to carry out accurate Bayesian inference when other algorithms are computationally impractical. Supplementary materials for this article are available online.
The genuine operadic nerve

We construct a generalization of the operadic nerve, providing a translation between the equivariant simplicially enriched operadic world to the parametrized $\infty$-categorical perspective. This naturally factors through genuine equivariant operads, a model for "equivariant operads with norms up to homotopy". We introduce the notion of an op-fibration of genuine equivariant operads, extending Grothendieck op-fibrations, and characterize fibrant operads as the image of genuine equivariant symmetric monoidal categories. Moreover, we show that under the operadic nerve, this image is sent to $G$-symmetric monoidal $G$-$\infty$-categories. Finally, we produce a functor comparing the notion of algebra over an operad in each of these two contexts.
Lower bounds on the non-Clifford resources for quantum computations

Treating stabilizer operations as free, we establish lower bounds on the number of resource states, also known as magic states, needed to perform various quantum computing tasks. Our bounds apply to adaptive computations using measurements with an arbitrary number of stabilizer ancillas. We consider (1) resource state conversion, (2) single-qubit unitary synthesis, and (3) computational subroutines including the quantum adder and the multiply-controlled Z gate. To prove our resource conversion bounds we introduce two new monotones, the stabilizer nullity and the dyadic monotone, and make use of the already-known stabilizer extent. We consider conversions that borrow resource states, known as catalyst states, and return them at the end of the algorithm. We show that catalysis is necessary for many conversions and introduce new catalytic conversions, some of which are optimal. By finding a canonical form for post-selected stabilizer computations, we show that approximating a single-qubit unitary to within diamond-norm precision ɛ requires at least 1/7 ⋅ log2(1/ɛ) − 4/3T-states on average. This is the first lower bound that applies to synthesis protocols using fall-back, mixing techniques, and where the number of ancillas used can depend on ɛ. Up to multiplicative factors, we optimally lower bound the number of T or CCZ states needed to implement the ubiquitous modular adder and multiply-controlled-Z operations. When the probability of Pauli measurement outcomes is 1/2, some of our bounds become tight to within a small additive constant.
PCB and DDE methyl sulfones in mammals from Canada and Sweden

Levels of PCB methyl sulfones (MeSO[sub 2]-CBs) and DDE methyl sulfones (MeSO[sub 2]-DDEs) have been determined in tissues from polar bear (Ursus martimus), beluga whale (Delphinapterus leucas), and false killer whale (Pseudorca crassidens) from the Canadian environment, and grey seal (Halichoerus grypus), otter (Lutra lutra), and wild mink (Mustela vison) from the Swedish environment. Up to 30 MeSO[sub 2]-CB congeners and three MeSO[sub 2]-DdE isomers were shown to be present in the analyzed tissues. The concentration of total MeSO[sub 2]-CBs ranged from 0.1 to 21 [mu]g/g extracted lipids. 3-MeSO[sub 2]-2,5,2[prime],4[prime],5[prime]-penta-CB is the dominating MeSO[sub 2]-CB congener in all the analyzed samples, but the corresponding 4-MeSO[sub 2]-CB also is present in high concentrations. A smaller number of MeSO[sub 2]-CBs, always dominated by the meta-substituted MeSO[sub 2]-CBs, were present in livers of grey seal, otter, and mink than in adipose tissue or muscle. In all studied mammals the concentration of MeSO[sub 2]-CBs were higher in liver than in blubber or muscle. Seven PCB congeners were identified as precursors of the PCB methyl sulfones: 2,4,2[prime],5[prime]-tetra-CB (CB-49),2,5,3[prime],4[prime]-tetra-CB (CB-70), 2,4,5,2[prime],5[prime]-penta-CB (CB-101),2,3,4,5,2[prime],5[prime]-penta-CB (CB-87),2,3,6,2[prime],4[prime],5[prime]-hexa-CB (CB-149),2,3,4,2[prime],3[prime],6[prime]-hexa-CB (CB-132), and 2,3,4,2[prime],5[prime]-hexa-CB (CB-141). All species except beluga whale contained 3-MeSO[sub 2]-4,4[prime]-DDE, but at a much lower concentration in mink and ottermore » than in the other mammals. Polar bear and grey seal liver also contained 2-MeSO[sub 2]-4,4[prime]-DDE. The concentration of 2- and 3-MeSo[sub 2]-DDE ranged from 0.01 to 1.3 [mu]g/g extracted lipids. « less
Approximation of one-dimensional relativistic point interactions by regular potentials revised

We show that the one-dimensional Dirac operator with quite general point interaction may be approximated in the norm resolvent sense by the Dirac operator with a scaled regular potential of the form 1/ε h(x/ε)⊗B, where B is a suitable 2 × 2 matrix. Moreover, we prove that the limit does not depend on the particular choice of h as long as it integrates to a constant value.
Adversarial Attacks against Deep Saliency Models

Currently, a plethora of saliency models based on deep neural networks have led great breakthroughs in many complex high-level vision tasks (e.g. scene description, object detection). The robustness of these models, however, has not yet been studied. In this paper, we propose a sparse feature-space adversarial attack method against deep saliency models for the first time. The proposed attack only requires a part of the model information, and is able to generate a sparser and more insidious adversarial perturbation, compared to traditional image-space attacks. These adversarial perturbations are so subtle that a human observer cannot notice their presences, but the model outputs will be revolutionized. This phenomenon raises security threats to deep saliency models in practical applications. We also explore some intriguing properties of the feature-space attack, e.g. 1) the hidden layers with bigger receptive fields generate sparser perturbations, 2) the deeper hidden layers achieve higher attack success rates, and 3) different loss functions and different attacked layers will result in diverse perturbations. Experiments indicate that the proposed method is able to successfully attack different model architectures across various image scenes.
Multiphase Gas Flows in the Nearby Seyfert Galaxy ESO428–G014. Paper I

We present ALMA 230 GHz continuum and CO(2–1) observations of the nearby Compton-thick Seyfert galaxy ESO428–G14, with angular resolution 0. ″7 (78 pc). CO(2–1) is distributed in clumpy spiral arms, a lopsided circumnuclear ring (CNR) with ∼200 pc radius, and a transverse gas lane with size <100 pc, which crosses the nucleus and connects the two portions of the CNR. The main CO velocity gradient is consistent with a rotating disk with dynamical mass Mdyn = 5 × 109 M⊙ within ∼1 kpc. We detect off-plane gas motions with respect to the main disk plane which likely trace a molecular outflow with rate , along a biconical structure with radius 700 pc. The CO outflow smoothly joins the warm molecular outflow detected in SINFONI/Very Large Telescope data in the central 170 pc, suggesting that the outflow may cool with increasing distance. Our dynamical modeling of the inner 100 pc region suggests a warped disk or bar, and of fast gas streams which may trace an inflow toward the AGN. The inner warped disk overlaps with the most obscured, CT region seen in X-rays. There, we derive a column density , suggesting that molecular gas may contribute significantly to the AGN obscuration. Most of the hard X-ray emitting nuclear region is deprived of cold molecular gas and shows a CO-cavity. The CO-cavity is filled with warm molecular gas traced by H2, confirming that the 3–6 keV continuum and Fe Kα emission are due to scattering from dense ISM clouds.
The Effects of Stress on Second Harmonics in Plate-Like Structures

Cumulative second harmonic of ultrasonic guided waves is considered to have great application potential in evaluating internal stress of structures. One difficulty with the application is the diversity and complexity of modal response to the stress change in waveguide. At present, there is a lack of relevant theoretical studies and experimental results to guide the applications. In this article, a method is proposed to characterize the amplitude change of cumulative second harmonic mode in a plate under stress through calculating the amplitude coefficient, which can be acquired based on mode shape analysis. The steel plate is taken as an example to demonstrate the analysis method. Experimental studies are presented with results consistent with the theoretical predictions. The results of this study indicate that the amplitudes of different cumulative second harmonic modes may increase or decrease monotonically with the change of stress. Therefore, when the phenomenon of modes mixing occurs in the waveguide, it is necessary to analyze and predict the amplitude of selected cumulative second harmonic mode with the change of stress in advance; otherwise, wrong results may be obtained. The method and conclusions proposed in this paper can also be applicable to waveguide of arbitrary cross-section and have universality.
Do better search engines really equate to better clinical decisions? If not, why not?

Previous research has found that improved search engine effectiveness—evaluated using a batch‐style approach—does not always translate to significant improvements in user task performance; however, these prior studies focused on simple recall and precision‐based search tasks. We investigated the same relationship, but for realistic, complex search tasks required in clinical decision making. One hundred and nine clinicians and final year medical students answered 16 clinical questions. Although the search engine did improve answer accuracy by 20 percentage points, there was no significant difference when participants used a more effective, state‐of‐the‐art search engine. We also found that the search engine effectiveness difference, identified in the lab, was diminished by around 70% when the search engines were used with real users. Despite the aid of the search engine, half of the clinical questions were answered incorrectly. We further identified the relative contribution of search engine effectiveness to the overall end task success. We found that the ability to interpret documents correctly was a much more important factor impacting task success. If these findings are representative, information retrieval research may need to reorient its emphasis towards helping users to better understand information, rather than just finding it for them.
Comparison and Combination of GNSS and Strong-Motion Observations: A Case Study of the 2016 Mw 7.0 Kumamoto Earthquake

The 2016 Mw 7.0 Kumamoto earthquake resulted in exceptional datasets of Global Navigation Satellite Systems (GNSS) and seismic data. We explore the spatial similarity of the signals and investigate procedures for combining collocated sensor data. GNSS enables the direct observation of the long-period ground displacements, limited by noise levels in regimes of millimeters to several centimeters. Strong-motion accelerometers are inertial sensors and therefore optimally resolve middle- to high-frequency strong ground motion. The double integration from acceleration to displacement amplifies long-period errors introduced by tilt, rotation, noise, and nonlinear instrument responses and can lead to large nonphysical drifts. For the case study of the Kumamoto earthquake, 39 GNSS stations (1 samples/s) with nearby located strong-motion accelerometers (100 samples/s) are investigated. The GNSS waveforms obtained by precise point positioning under real-time conditions prove to be very similar to the postprocessed result. Real-time GNSS and nearby located accelerometers show consistent observations for periods between ∼3–5 and ∼50–100 s. The matching frequency range is defined by the long-period noise of the accelerometer and the low signal-to-noise ratio (SNR) of GNSS, when it comes to small displacements close to its noise level. Current procedures in fusing the data with a Kalman filter are verified for the dataset of this event. Combined data result in a very broadband waveform that covers the optimal frequency range of each sensor. We explore how to integrate fused processing in a real-time network, including event detection and magnitude estimation. Carrying out a statistical test on the GNSS records allows us to identify seismic events and sort out stations with a low SNR, which would otherwise impair the quality of downstream products. The results of this study reinforce the emerging consensus that there is real benefit to collocation GNSS and strong-motion sensors for the monitoring of moderate-to-large earthquakes.
Anomalous relaxation and the high-temperature structure factor of XXZ spin chains

Significance The XXZ model is a canonical model of quantum magnetism. In one dimension, this model is integrable and has ballistically moving quasiparticles; thus, energy spreads ballistically, but, surprisingly, spin transport can be diffusive or superdiffusive. Here, we show that spin transport is even richer in the presence of a magnetic field: Some fraction of the spin moves out ballistically, but the dynamical spin-structure factor decays with an anomalous, continuously varying exponent. A hitherto-unnoticed phase transition takes place at finite fields, separating ballistic from subballistic decay of local correlations. We analytically derive these results using generalized hydrodynamics and support our findings numerically. These anomalous exponents can readily be measured via the frequency and wavevector dependence of the spin conductivity. We compute the spin-structure factor of XXZ spin chains in the Heisenberg and gapped (Ising) regimes in the high-temperature limit for nonzero magnetization, within the framework of generalized hydrodynamics, including diffusive corrections. The structure factor shows a hierarchy of timescales in the gapped phase, owing to s-spin magnon bound states (“strings”) of various sizes. Although short strings move ballistically, long strings move primarily diffusively as a result of their collisions with short strings. The interplay between these effects gives rise to anomalous power-law decay of the spin-structure factor, with continuously varying exponents, at any fixed separation in the late-time limit. We elucidate the cross-over to diffusion (in the gapped phase) and to superdiffusion (at the isotropic point) in the half-filling limit. We verify our results via extensive matrix product operator calculations.