Miracle-dz/UniFAR-FTUs huggingface dataset:
Raman Spectra of Polycrystalline CeO2: A Density Functional Theory Study

Cerium oxide is an important material for catalytic and fuel cell applications. We present an ab initio density functional theory (DFT) study of the vibrational properties of ceria focusing on the interpretation of Raman spectra of polycrystalline powder samples, with vibrational bands in the frequency region between 250 and 1200 cm–1. The model systems include the oxidized CeO2 as well as the reduced CeO2–x and Ce2O3 bulk materials together with the CeO2(111) and oxygen defective CeO2–x(111) surfaces. The experimentally observed band at 250 cm–1 is assigned to a surface mode of the clean CeO2(111) surface, in agreement with our Raman spectra of ceria (CeO2) powders with varying crystal size (Filtschew, A.; Hofmann, K.; Hess, C., J. Phys. Chem. C 2016, 120, 6694). The reduced model systems display signature vibrational bands in the 480–600 cm–1 region associated with the presence of oxygen defects and reduced Ce3+ ions. In the high-frequency region between 800 and 900 cm–1, characteristic peroxide (O22–) .
G ENERATIVE MODELING WITH ONE RECURSIVE NETWORK

We propose to train a multilayer perceptron simultaneously as an encoder and a decoder in order to create a high quality generative model. In one call a network is optimized as either an encoder or decoder, and in a second recursive call the network uses its own outputs to learn the remaining corresponding function, allowing for the minimization of popular statistical divergence measures over a single feed-forward function. This new approach derives from a simple reformulation of variational bayes and extends naturally to the domain of Generative Adversarial Nets. Here we demonstrate a single network which learns a generative model via an adversarial minimax game played against itself. Experiments demonstrate comparable efficacy for the single-network approach versus corresponding multinetwork formulations.
Studies of cerebral function in learning. XIII. Apparent absence of transcortical association in maze learning

The older literature dealing with the functions of the cerebral cortex contains many speculations concerning the activities of transcortical associative fibers. Munk believed that direct interaction of the various sensory areas constitutes the basis of integrated, intelligent behavior. Hitzig, Flechsig, Rianchi, and many others postulated higher associative centers integrating the activity of sensoiy areas and playing upon a super-associative area in which the final integration of the personality is effected. The view that patterns integrated in association areas are finally transmitted through the cortex to the motor areas and thence as impulses to voluntary movement through the pyramidal tracts was and still is almost universally held. Such interpretations of cerebral function imply an elaborate system of interconnections between almost all parts of the cortex. Special syndromes have been ascribed to the interruption of associative fibers, especially in the case of apraxias and “conduction” aphasias. Recent detailed analyses of symptoms carried out by Head, Goldstein, Weisenberg and McBridge, and others have failed to confirm the distinct types of transcortical aphasia and the anatomic classification of symptoms is being replaced by a more dynamic interpretation. Nevertheless, such dynamic concepts as “action of the brain as a whole,” or “field organization” imply an even closer intra-eerebra1 integration than did earlier notions of distinct associative, areas, or of conditioned-reflex pathways, and such concepts have been developed with no regard to the mechanisms of interaction between diverse cerebral ’areas. Little direct evidence concerning the mode of interaction of different parts of the cerebral cortex is available. The studies of Dusser
Calibrated Adversarial Refinement for Stochastic Semantic Segmentation

In semantic segmentation tasks, input images can often have more than one plausible interpretation, thus allowing for multiple valid labels. To capture such ambiguities, recent work has explored the use of probabilistic networks that can learn a distribution over predictions. However, these do not necessarily represent the empirical distribution accurately. In this work, we present a strategy for learning a calibrated predictive distribution over semantic maps, where the probability associated with each prediction reflects its ground truth correctness likelihood. To this end, we propose a novel two-stage, cascaded approach for calibrated adversarial refinement: (i) a standard segmentation network is trained with categorical cross entropy to predict a pixelwise probability distribution over semantic classes and (ii) an adversarially trained stochastic network is used to model the inter-pixel correlations to refine the output of the first network into coherent samples. Importantly, to calibrate the refinement network and prevent mode collapse, the expectation of the samples in the second stage is matched to the probabilities predicted in the first. We demonstrate the versatility and robustness of the approach by achieving state-of-the-art results on the multigrader LIDC dataset and on a modified Cityscapes dataset with injected ambiguities. In addition, we show that the core design can be adapted to other tasks requiring learning a calibrated predictive distribution by experimenting on a toy regression dataset. We provide an open source implementation of our method at https://github.com/EliasKassapis/CARSSS.
Low-Complexity Scaling Methodsfor DCT-II Approximations

This paper introduces a collection of scaling methods for generating <inline-formula><tex-math notation="LaTeX">$\text{2}\,N$</tex-math></inline-formula>-point DCT-II approximations based on <inline-formula><tex-math notation="LaTeX">$N$</tex-math></inline-formula>-point low-complexity transformations. Such scaling is based on the Hou recursive matrix factorization of the exact <inline-formula><tex-math notation="LaTeX">$\text{2}\,N$</tex-math></inline-formula>-point DCT-II matrix. Encompassing the widely employed Jridi-Alfalou-Meher scaling method, the proposed techniques are shown to produce DCT-II approximations that outperform the transforms resulting from the JAM scaling method according to total error energy and mean squared error. Orthogonality conditions are derived and an extensive error analysis based on statistical simulation demonstrates the good performance of the introduced scaling methods. A hardware implementation is also provided demonstrating the competitiveness of the proposed methods when compared to the JAM scaling method.
Varstrometry for Off-nucleus and Dual Subkiloparsec AGN (VODKA): Hubble Space Telescope Discovers Double Quasars

Dual supermassive black holes (SMBHs) at ∼kiloparsec scales are the progenitor population of SMBH mergers and play an important role in understanding the pairing and dynamical evolution of massive black holes in galaxy mergers. Because of the stringent resolution requirement and the apparent rareness of these small-separation pairs, there are scarce observational constraints on this population, with few confirmed dual SMBHs at <10 kpc separations at z > 1. Here we present results from a pilot search for kiloparsec-scale dual quasars selected with Gaia Data release 2 (DR2) astrometry and followed up with Hubble Space Telescope (HST) Wide Field Camera 3 dual-band (F475W and F814W) snapshot imaging. Our targets are quasars primarily selected with the varstrometry technique, i.e., light centroid jitter caused by asynchronous variability from both members in an unresolved quasar pair, supplemented by subarcsecond pairs already resolved by Gaia DR2. We find an overall high fraction of HST-resolved pairs among the varstrometry-selected quasars (unresolved in Gaia DR2), ∼30%–50%, increasing toward high redshift (∼60%–80% at z > 1.5). We discuss the nature of the 45 resolved subarcsecond pairs based on HST and supplementary data. A substantial fraction (∼40%) of these pairs are likely physical quasar pairs or gravitationally lensed quasars. We also discover a triple quasar candidate and a quadruply lensed quasar, which is among the smallest-separation quadruple lenses. These results provide important guidelines to improve varstrometry selection and follow-up confirmation of ~kiloparsec-scale dual SMBHs at high redshift.
A Comparative Study of Atmospheric Chemistry with VULCAN

We present an update of the open-source photochemical kinetics code VULCAN to include C–H–N–O–S networks and photochemistry. The additional new features are advection transport, condensation, various boundary conditions, and temperature-dependent UV cross sections. First, we validate our photochemical model for hot Jupiter atmospheres by performing an intercomparison of HD 189733b models between Moses et al., Venot et al., and VULCAN, to diagnose possible sources of discrepancy. Second, we set up a model of Jupiter extending from the deep troposphere to upper stratosphere to verify the kinetics for low temperature. Our model reproduces hydrocarbons consistent with observations, and the condensation scheme successfully predicts the locations of water and ammonia ice clouds. We show that vertical advection can regulate the local ammonia distribution in the deep atmosphere. Third, we validate the model for oxidizing atmospheres by simulating Earth and find agreement with observations. Last, VULCAN is applied to four representative cases of extrasolar giant planets: WASP-33b, HD 189733b, GJ 436b, and 51 Eridani b. We look into the effects of the C/O ratio and chemistry of titanium/vanadium species for WASP-33b, we revisit HD 189733b for the effects of sulfur and carbon condensation, the effects of internal heating and vertical mixing (K zz) are explored for GJ 436b, and we test updated planetary properties for 51 Eridani b with S8 condensates. We find that sulfur can couple to carbon or nitrogen and impact other species, such as hydrogen, methane, and ammonia. The observable features of the synthetic spectra and trends in the photochemical haze precursors are discussed for each case.
Degree bounds for invariant skew polynomials

When we consider the action of a finite group on a polynomial ring, a polynomial unchanged by the action is called an invariant polynomial. A famous result of Noether states that in characteristic zero the maximal degree of a minimal invariant polynomial is bounded above by the order of the group. Our work establishes that the same bound holds for invariant skew polynomials in the exterior algebra. Our approach to the problem relies on a theorem of Derksen that connects invariant theory to the study of ideals of subspace arrangements. We adapt his proof over the polynomial ring to the exterior algebra, reducing the question to establishing a bound on the Castelnuovo-Mumford regularity of intersections of linear ideals in the exterior algebra. We prove the required regularity bound using tools from representation theory. In particular, the proof relies on the existence of a functor on the category of polynomial functors that translates resolutions of ideals of subspace arrangements over the polynomial ring to resolutions of ideals of subspace arrangements over the exterior algebra.
Nonconvex Factorization and Manifold Formulations are Almost Equivalent in Low-rank Matrix Optimization

In this paper, we consider the geometric landscape connection of the widely studied manifold and factorization formulations in low-rank positive semidefinite (PSD) and general matrix optimization. We establish a sandwich relation on the spectrum of Riemannian and Euclidean Hessians at first-order stationary points (FOSPs). As a result of that, we obtain an equivalence on the set of FOSPs, second-order stationary points (SOSPs) and strict saddles between the manifold and the factorization formulations. In addition, we show the sandwich relation can be used to transfer more quantitative geometric properties from one formulation to another. Similarities and differences in the landscape connection under the PSD case and the general case are discussed. To the best of our knowledge, this is the first geometric landscape connection between the manifold and the factorization formulations for handling rank constraints, and it provides a geometric explanation for the similar empirical performance of factorization and manifold approaches in low-rank matrix optimization observed in the literature. In the general low-rank matrix optimization, the landscape connection of two factorization formulations (unregularized and regularized ones) is also provided. By applying these geometric landscape connections, in particular, the sandwich relation, we are able to solve unanswered questions in literature and establish stronger results in the applications on geometric analysis of phase retrieval, well-conditioned low-rank matrix optimization, and the role of regularization in factorization arising from machine learning and signal processing.
CoSHA: Code for Stellar Properties Heuristic Assignment—for the MaStar Stellar Library

We introduce CoSHA: a Code for Stellar properties Heuristic Assignment. In order to estimate the stellar properties, CoSHA implements a Gradient Tree Boosting algorithm to label each star across the parameter space (T eff, logg , [Fe/H], and [α/Fe]). We use CoSHA to estimate the stellar atmospheric parameters of 22,000 unique stars in the MaNGA Stellar Library (MaStar). To quantify the reliability of our approach, we run internal tests, using both the Göttingen Stellar Library (a theoretical library) and the first data release of MaStar, and external tests, by comparing the resulting distributions in the parameter space with the APOGEE estimates of the same properties. In summary, our parameter estimates span the ranges T eff = [2900, 12,000] K, logg=[−0.5,5.6] , [Fe/H] = [−3.74, 0.81], and α M = [−0.22, 1.17]. We report internal (external) uncertainties of the properties of σTeff∼43(240) K, σlogg∼0.2(0.4) , σ [Fe/H] ∼ 0.16(0.24), and σ [α/Fe] ∼ 0.09(0.08). These uncertainties are comparable to those of other methods with similar objectives. Despite the fact that CoSHA is not aware of the spatial distributions of these physical properties in the Milky Way, we are able to recover the main trends known in the literature. The catalog of physical properties for MaStar can be accessed online.
Gap theorems for ends of smooth metric measure spaces

In this paper, we establish two gap theorems for ends of smooth metric measure space ( M n ,g,e − f dv ) with the Bakry-´Emery Ricci tensor Ric f ≥ − ( n − 1) in a geodesic ball B o ( R ) with radius R and center o ∈ M n . When Ric f ≥ 0 and f has some degeneration outside B o ( R ), we show that there exists an ǫ = ǫ ( n, sup B o (1) | f | ) such that such a space has at most two ends if R ≤ ǫ . When Ric f ≥ 1 2 and f ( x ) ≤ 1 4 d 2 ( x,B o ( R ))+ c for some constant c > 0 outside B o ( R ), we can also get the same gap conclusion.
FPB: Feature Pyramid Branch for Person Re-Identification

High performance person Re-Identification (Re-ID) requires the model to focus on both global silhouette and local details of pedestrian. To extract such more representative features, an effective way is to exploit deep models with multiple branches. However, most multi-branch based methods implemented by duplication of part backbone structure normally lead to severe increase of computational cost. In this paper, we propose a lightweight Feature Pyramid Branch (FPB) to extract features from different layers of networks and aggregate them in a bidirectional pyramid structure. Cooperated by attention modules and our proposed cross orthogonality regularization, FPB significantly prompts the performance of backbone network by only introducing less than 1.5M extra parameters. Extensive experimental results on standard benchmark datasets demonstrate that our proposed FPB based model outperforms state-of-the-art methods with obvious margin as well as much less model complexity. FPB borrows the idea of the Feature Pyramid Network (FPN) from prevailing object detection methods. To our best knowledge, it is the first successful application of similar structure in person Re-ID tasks, which empirically proves that pyramid network as affiliated branch could be a potential structure in related feature embedding models. The source code is publicly available at https://github.com/anocodetest1/FPB.git.
Higher Order Information Identifies Tie Strength

A key question in the analysis of sociological processes on networks is to identify pairs of individuals who have strong or weak social ties. Existing approaches mathematically model the social network as a graph, and tie strength is often inferred by examining the number of shared neighbors between individuals, or equivalently, the number of triangles in the graph which contain a specific pair of individuals. However, this approach misses out on critical information because it does not distinguish the case where interactions occur among groups involving more than two individuals. In this work, we measure tie strength by explicitly accounting for these higher order interactions in the network, through the use of a new measure called Edge PageRank. We show how Edge PageRank can be interpreted as the steady-state outcome of a dynamic, message-passing social process that characterizes the strength of weak ties by appropriately discounting the effect of higher-order interactions involving three individuals. Empirically, we find that Edge PageRank outperforms standard measures in identifying tie strength in several large-scale social networks. These results provide a new perspective on tie strength and demonstrate the importance of incorporating higher-order interactions in social network analysis.
Evidence of the multi-thermal nature of spicular downflows. Impact on solar atmospheric heating

Context. Spectroscopic observations of the emission lines formed in the solar transition region commonly show persistent downflows on the order of 10−15 km s−1. The cause of such downflows, however, is still not fully clear and has remained a matter of debate. Aims. We aim to understand the cause of such downflows by studying the coronal and transition region responses to the recently reported chromospheric downflowing rapid redshifted excursions (RREs) and their impact on the heating of the solar atmosphere. Methods. We have used two sets of coordinated data from the Swedish 1 m Solar Telescope, the Interface Region Imaging Spectrograph, and the Solar Dynamics Observatory for analyzing the response of the downflowing RREs in the transition region and corona. To provide theoretical support, we use an already existing 2.5D magnetohydrodynamic simulation of spicules performed with the Bifrost code. Results. We find ample occurrences of downflowing RREs and show several examples of their spatio-temporal evolution, sampling multiple wavelength channels ranging from the cooler chromospheric to the hotter coronal channels. These downflowing features are thought to be likely associated with the returning components of the previously heated spicular plasma. Furthermore, the transition region Doppler shifts associated with them are close to the average redshifts observed in this region, which further implies that these flows could (partly) be responsible for the persistent downflows observed in the transition region. We also propose two mechanisms – (i) a typical upflow followed by a downflow and (ii) downflows along a loop –from the perspective of a numerical simulation that could explain the ubiquitous occurrence of such downflows. A detailed comparison between the synthetic and observed spectral characteristics reveals a distinctive match and further suggests an impact on the heating of the solar atmosphere. Conclusions. We present evidence that suggests that at least some of the downflowing RREs are the chromospheric counterparts of the transition region and lower coronal downflows.
On the Exploitability of Audio Machine Learning Pipelines to Surreptitious Adversarial Examples

Machine learning (ML) models are known to be vulnerable to adversarial examples. Applications of ML to voice biometrics authentication are no exception. Yet, the implications of audio adversarial examples on these real-world systems remain poorly understood given that most research targets limited defenders who can only listen to the audio samples. Conflating detectability of an attack with human perceptibility, research has focused on methods that aim to produce imperceptible adversarial examples which humans cannot distinguish from the corresponding benign samples. We argue that this perspective is coarse for two reasons: 1. Imperceptibility is impossible to verify; it would require an experimental process that encompasses variations in listener training, equipment, volume, ear sensitivity, types of background noise etc, and 2. It disregards pipeline-based detection clues that realistic defenders leverage. This results in adversarial examples that are ineffective in the presence of knowledgeable defenders. Thus, an adversary only needs an audio sample to be plausible to a human. We thus introduce surreptitious adversarial examples, a new class of attacks that evades both human and pipeline controls. In the white-box setting, we instantiate this class with a joint, multi-stage optimization attack. Using an Amazon Mechanical Turk user study, we show that this attack produces audio samples that are more surreptitious than previous attacks that aim solely for imperceptibility. Lastly we show that surreptitious adversarial examples are challenging to develop in the black-box setting.
Variational quantum eigensolver for the Heisenberg antiferromagnet on the kagome lattice

Establishing the nature of the ground state of the Heisenberg antiferromagnet (HAFM) on the kagome lattice is well known to be a prohibitively difficult problem for classical computers. Here, we give a detailed proposal for a Variational Quantum Eigensolver (VQE) intending to solve this physical problem on a quantum computer. At the same time, this VQE constitutes an explicit experimental proposal for showing a useful quantum advantage on Noisy Intermediate-Scale Quantum (NISQ) devices because of its natural hardware compatibility. We classically emulate noiseless and noisy quantum computers with either 2D-grid or all-to-all connectivity and simulate patches of the kagome HAFM of up to 20 sites. In the noiseless case, the ground-state energy, as found by the VQE, approaches the true ground-state energy exponentially as a function of the circuit depth. Furthermore, VQEs for the HAFM on any graph can inherently perform their quantum computations in a decoherence-free subspace that protects against collective longitudinal and collective transversal noise, adding to the noise-resilience of these algorithms. Nevertheless, the extent of the effects of other noise types suggests the need for error mitigation and performance targets alternative to high-fidelity ground-state preparation, even for essentially hardware-native VQEs.