Miracle-dz/UniFAR-FTUs huggingface dataset:
Byzantine-Resilient Non-Convex Stochastic Gradient Descent

We study adversary-resilient stochastic distributed optimization, in which $m$ machines can independently compute stochastic gradients, and cooperate to jointly optimize over their local objective functions. However, an $\alpha$-fraction of the machines are $\textit{Byzantine}$, in that they may behave in arbitrary, adversarial ways. We consider a variant of this procedure in the challenging $\textit{non-convex}$ case. Our main result is a new algorithm SafeguardSGD which can provably escape saddle points and find approximate local minima of the non-convex objective. The algorithm is based on a new concentration filtering technique, and its sample and time complexity bounds match the best known theoretical bounds in the stochastic, distributed setting when no Byzantine machines are present. Our algorithm is practical: it improves upon the performance of prior methods when training deep neural networks, it is relatively lightweight, and is the first method to withstand two recently-proposed Byzantine attacks.
A Cortical-Inspired Sub-Riemannian Model for Poggendorff-Type Visual Illusions

We consider Wilson-Cowan-type models for the mathematical description of orientation-dependent Poggendorff-like illusions. Our modelling improves two previously proposed cortical-inspired approaches, embedding the sub-Riemannian heat kernel into the neuronal interaction term, in agreement with the intrinsically anisotropic functional architecture of V1 based on both local and lateral connections. For the numerical realisation of both models, we consider standard gradient descent algorithms combined with Fourier-based approaches for the efficient computation of the sub-Laplacian evolution. Our numerical results show that the use of the sub-Riemannian kernel allows us to reproduce numerically visual misperceptions and inpainting-type biases in a stronger way in comparison with the previous approaches.
RR Lyrae Variables in Messier 53: Near-infrared Period–Luminosity Relations and the Calibration Using Gaia Early Data Release 3

We present new near-infrared, JHK s , period–luminosity relations (PLRs) for RR Lyrae variables in the Messier 53 (M53 or NGC 5024) globular cluster. Multi-epoch JHK s observations, obtained with the WIRCam instrument on the 3.6 m Canada–France–Hawaii Telescope, are used for the first time to estimate precise mean magnitudes for 63 RR Lyrae stars in M53 including 29 fundamental-mode (RRab) and 34 first-overtone mode (RRc) variables. The JHK s -band PLRs for RR Lyrae stars are best constrained for RRab types with a minimal scatter of 22, 23, and 19 mmag, respectively. The combined sample of RR Lyrae is used to derive the K s -band PLR, Ks=−2.303(0.063)logP+15.212(0.016) , exhibiting a 1σ dispersion of only 0.027 mag. Theoretical period–luminosity–metallicity (PLZ) relations are used to predict parallaxes for 400 Galactic RR Lyrae, resulting in a median parallax zero-point offset of −7 ± 3 μas in Gaia Early Data Release 3 (EDR3), which increases to 22 ± 2 μas if the parallax corrections are applied. We also estimate a robust distance modulus, μ M53 = 16.403 ± 0.024 (statistical) ± 0.033 (systematic) mag, to M53 based on theoretical calibrations. Homogeneous and precise mean magnitudes for RR Lyrae in M53 together with similar literature data for M3, M4, M5, and ω Cen are used to empirically calibrate a new RR Lyrae PLZKs relation, Ks=−0.848(0.007)−2.320(0.006)logP+0.166(0.011)[Fe/H] , anchored with Gaia EDR3 distances and theoretically predicted relations, and to simultaneously estimate precise RR Lyrae-based distances to these globular clusters.
Learning Generalized Spatial-Temporal Deep Feature Representation for No-Reference Video Quality Assessment

In this work, we propose a no-reference video quality assessment method, aiming to achieve high-generalization capability in cross-content, -resolution and -frame rate quality prediction. In particular, we evaluate the quality of a video by learning effective feature representations in spatial-temporal domain. In the spatial domain, to tackle the resolution and content variations, we impose the Gaussian distribution constraints on the quality features. The unified distribution can significantly reduce the domain gap between different video samples, resulting in more generalized quality feature representation. Along the temporal dimension, inspired by the mechanism of visual perception, we propose a pyramid temporal aggregation module by involving the short-term and long-term memory to aggregate the frame-level quality. Experiments show that our method outperforms the state-of-the-art methods on cross-dataset settings, and achieves comparable performance on intra-dataset configurations, demonstrating the high-generalization capability of the proposed method. The codes are released at https://github.com/Baoliang93/GSTVQA
Urban volumetrics: spatial complexity and wayfinding, extending space syntax to three dimensional space

Wayfinding behavior and pedestrian movement pattern research relies on objective spatial configuration representation and analysis, such as space syntax, to quantify and control for the difficulty of wayfinding in multi-level buildings and urban built environments. However, the space syntax's representation oversimplifies multi-level vertical connections. The more recent segment and angular approaches to space syntax remain un-operationalizable in three-dimensional space. The two-dimensional axial-map and segment map line representations are reviewed to determine their extension to three-dimensional space line representation. Using an extreme case study research strategy, four representations of a large scale complex multi-level outdoor and indoor built environment are tested against observed pedestrian movement patterns N = 17,307. Association with the movement patterns increases steadily as the representation increases toward high three-dimensional space level of definition and completeness. A novel hybrid angular-Euclidean analysis was used for the objective description of three-dimensional built environment complexity. The results suggest that pedestrian wayfinding and movement pattern research in a multi-level built environment should include interdependent outdoor and indoor, and use full threedimensional line representation.
A Novel Approach to Include Social Costs in Humanitarian Objective Functions

Social cost functions in humanitarian operations are defined as the sum of logistics and deprivation costs. They are widely regarded as appropriate objective functions, even though the way they were introduced requires cautiously formulated deprivation cost functions for the analyzed goods and do not allow decision makers to include their individual preferences. We develop this approach further and introduce a normalized weighted sum approach to increase decision makers' understanding of the tradeoffs between cost and suffering and, therefore, increase transparency significantly. Furthermore, we apply the approach to a case study of a hypothetical water system failure in the city of Berlin. We show that the normalized weighted sum approach significantly improves transparency and leads to a deeper understanding of the tradeoffs during the crisis. Consequently, it proved itself as a powerful tool for decision makers preparing for or navigating through a crisis.
Analyzing Advantages and Benefits of Information Technologies in Organizations

The aim of this research is analyzing advantages and benefits of information technologies in organizations. For this purpose, 357 customers of electronic banking services participated in the study. They replied to questionnaires of perceived ease of use, perceived usefulness, quality of system service quality, attitudes to technology, service experience, and intention to use technology services. Structural equation modeling with SMARTPLS software has been used to analyze data. The results indicate that the impact of technology excellence on service experience as well as attitude to technology on intention to use technology services is positive and significant.
A GCICA Grant-Free Random Access Scheme for M2M Communications in Crowded Massive MIMO Systems

A novel grant-free random access scheme with a high success rate is proposed to support massive access for machine-to-machine communications in massive multiple-input–multiple-output (MIMO) systems. This scheme allows active user equipments (UEs) to transmit their modulated uplink messages and super pilots consisting of multiple subpilots to a base station (BS). Then, the BS performs channel state information (CSI) estimation and uplink message decoding by utilizing a proposed graph combined clustering independent component analysis (GCICA) decoding algorithm and then employs the estimated CSIs to detect active UEs by using the characteristic of asymptotic favorable propagation of massive MIMO channel. We call this proposed scheme as the GCICA-based random access (GCICA-RA) scheme. We analyze the successful access probability, missed detection probability, and uplink throughput of the GCICA-RA scheme. Numerical results show that the GCICA-RA scheme significantly improves the successful access probability and uplink throughput, decreases missed detection probability, and provides low CSI estimation error at the same time.
Three-dimensional dynamics of a pair of deformable bubbles rising initially in line. Part 1. Moderately inertial regimes

Abstract The buoyancy-driven motion of two identical gas bubbles released in line in a liquid at rest is examined with the help of highly resolved simulations, focusing on moderately inertial regimes in which the path of an isolated bubble is vertical. Assuming first an axisymmetric evolution, equilibrium configurations of the bubble pair are determined as a function of the buoyancy-to-viscous and buoyancy-to-capillary force ratios which define the Galilei ($Ga$) and Bond ($Bo$) numbers of the system, respectively. The three-dimensional solutions reveal that this axisymmetric equilibrium is actually never reached. Instead, provided $Bo$ stands below a critical $Ga$-dependent threshold determining the onset of coalescence, two markedly different evolutions are observed. At the lower end of the explored $(Ga, Bo)$-range, the tandem follows a drafting–kissing–tumbling scenario, which eventually yields a planar side-by-side motion. For larger $Ga$, the trailing bubble drifts laterally and gets out of the wake of the leading bubble, barely altering the path of the latter. In this second scenario, the late configuration is characterized by a significant inclination of the tandem ranging from $30^\circ$ to $40^\circ$ with respect to the vertical. Bubble deformation has a major influence on the evolution of the system. It controls the magnitude of vortical effects in the wake of the leading bubble, hence the strength of the attractive force acting on the trailing bubble. It also governs the strength and even the sign of the lateral force acting on this bubble, a mechanism of particular importance when the tandem is released with a small angular deviation.
Population Quasi-Monte Carlo

Abstract Monte Carlo methods are widely used for approximating complicated, multidimensional integrals for Bayesian inference. Population Monte Carlo (PMC) is an important class of Monte Carlo methods, which adapts a population of proposals to generate weighted samples that approximate the target distribution. When the target distribution is expensive to evaluate, PMC may encounter computational limitations since it requires many evaluations of the target distribution. To address this, we propose a new method, Population Quasi-Monte Carlo (PQMC), which integrates Quasi-Monte Carlo ideas within the sampling and adaptation steps of PMC. A key novelty in PQMC is the idea of importance support points resampling, a deterministic method for finding an “optimal” subsample from the weighted proposal samples. Moreover, within the PQMC framework, we develop an efficient covariance adaptation strategy for multivariate normal proposals. Finally, a new set of correction weights is introduced for the weighted PMC estimator to improve the efficiency from the standard PMC estimator. We demonstrate the improved empirical performance of PQMC over PMC in extensive numerical simulations and a friction drilling application. Supplementary materials for this article are available online.
Infrared Spectrum and Force Field of Crystalline Hydrogen Peroxide

The infrared spectrum of crystalline and vitreous hydrogen peroxide was studied from 300 cm—1 to 4000 cm—1. A normal coordinate analysis of the tetragonal crystal with four molecules/unit cell is given. The hydrogen bonds are only slightly weaker than in H2O but the O–H linewidths are only 80 cm—1. From the coupled motion of the torsion and the high‐frequency libration, the torsional frequency in the gas is estimated to be about 230 cm—1.
Model Optimization for Deep Space Exploration via Simulators and Deep Learning

Machine learning, and eventually true artificial intelligence techniques, are extremely important advancements in astrophysics and astronomy. We explore the application of deep learning using neural networks in order to automate the detection of astronomical bodies for future exploration missions, such as missions to search for signatures or suitability of life. The ability to acquire images, analyze them, and send back those that are important, as determined by the deep learning algorithm, is critical in bandwidth-limited applications. Our previous foundational work solidified the concept of using simulator images and deep learning in order to detect planets. Optimization of this process is of vital importance, as even a small loss in accuracy might be the difference between capturing and completely missing a possibly-habitable nearby planet. Through computer vision, deep learning, and simulators, we introduce methods that optimize the detection of exoplanets. We show that maximum achieved accuracy can hit above 98% for multiple model architectures, even with a relatively small training set.
Natural inflation with a nonminimal coupling to gravity

Although natural inflation is a theoretically well-motivated model for cosmic inflation, it is in tension with recent Planck cosmic microwave background anisotropy measurements. We present a way to alleviate this tension by considering a very weak nonminimal coupling of the inflaton field to gravity in both contexts of metric and Palatini formulations of general relativity. We start our discussions with a generic form of the inflaton coupling to the Ricci scalar, then focus on a simple form to do phenomenological study. Our results show that such an extension can bring natural inflation's predictions to a good agreement with the Planck data. Depending on values of the coupling constant ξ and the symmetry breaking scale f, we find that with |ξ|∼ 10-3 and f≳ 2.0 Mpl predictions of the model stay inside 68% CL allowed region until f increases up to 7.7 Mpl, then only inside 95% CL region after f exceeds the latter value. The predictions from the metric and the Palatini theories are very similar due to the simple form of the coupling function we use and the small magnitude of the coupling ξ. Successful reheating can also be realized in this model.
Synthesis and properties of sulfonated polyimides derived from bis(sulfophenoxy) benzidines

A series of aromatic sulfonated polyimides (SPIs) bearing sulfophenoxy side groups have been successfully synthesized and evaluated as polymer electrolyte membranes for fuel cell applications. The SPIs had high viscosity and gave tough and flexible membranes. The SPI membranes showed anisotropic membrane swelling in water with much larger dimensional change in thickness direction than in plane one. They showed the better proton-conducting performance even in the lower relative humidity (RH) range than the other SPI membranes, for example, a high proton conductivity of 0.05 S/cm at 50 % RH and 120 °C. They maintained high mechanical strength and conductivity after aging in water at 130 °C for 500 h, showing much better water stability compared with the main-chain-type SPI and side-chain-type SPI membranes reported so far. In polymer electrolyte fuel cells (PEFCs) operated at 90 °C and 84–30%RH, they showed fairly high cell performances and have high potential for PEFC applications. © 2009 Wiley Periodicals, Inc. J Polym Sci Part A: Polym Chem 47: 1463–1477, 2009
Raman study of zircon-structured RPO4 (R = Y, Tb, Er, Tm) phosphates at high pressures

The Raman spectra of tetragonal RPO4 (R = Y, Tb, Er, Tm) phosphates belonging to the zircon-structure class (space group D4h19) have been measured at high pressures up to 7.9 GPa (YPO4, ErPO4) and 15.5 GPa (TbPO4, TmPO4). Most of the 12 Raman active modes of this crystal class have been observed at high pressures using a Diamond Anvil Cell. A characteristic feature in the spectra of all crystals studied is the anomalous pressure dependence of the B2g symmetry internal bending mode which softens substantially with increasing pressure to the extent that a level crossing occurs between this mode and one normal Eg rotational mode. These observations imply that an incipient structural phase transition of the crystals may occur at a higher pressure. Such a phase transition has been observed for TbPO4 at a pressure of ~9.5 GPa. The abrupt spectral variations observed at this pressure, indicate that TbPO4 undergoes a first-order phase transition from tetragonal zircon to a lower symmetry structure which is quenchable upon pressure release.
Recipes for oscillon longevity

Oscillons are localized states of scalar fields sustained by self interactions. They decay by emitting classical radiation, but their lifetimes are surprisingly large. We revisit the reasons behind their longevity, aiming at how the shape of the scalar potential V(ϕ) determines the lifetime. The corpuscular picture, where the oscillon is identified with a bound state of a large number of field quanta, allows to understand lifetimes of order of 103 cycles in generic potentials. At the non-perturbative level, two properties of the scalar potential can substantially boost the lifetime: the flattening of V(ϕ) and the positivity of V”(ϕ). These properties are realized in the axion monodromy family of potentials. Moreover, this class of models connects continuously with an exceptional potential that admits eternal oscillon solutions. We check these results with a new fast-forward numerical method that allows to evolve in time to stages that cannot be otherwise simulated on a computer. The method exploits the attractor properties of the oscillons and fully accounts for nonlinearities. We find lifetimes up to 1014 cycles, but larger values are possible. Our work shows that oscillons formed in the early Universe can be stable on cosmological time scales and thus contribute to the abundance of (ultra)light scalar dark matter.
Tropical Fock-Goncharov coordinates for $\mathrm{SL}_3$-webs on surfaces II: naturality

In a companion paper (arXiv 2011.01768), we constructed nonnegative integer coordinates $\Phi_\mathscr{T}(\mathscr{W}_{3, \hat{S}}) \subset \mathbb{Z}_{\geq 0}^N$ for the collection $\mathscr{W}_{3, \hat{S}}$ of reduced $\mathrm{SL}_3$-webs on a finite-type punctured surface $\hat{S}$, depending on an ideal triangulation $\mathscr{T}$ of $\hat{S}$. We show that these coordinates are natural with respect to the choice of triangulation, in the sense that if a different triangulation $\mathscr{T}^\prime$ is chosen, then the coordinate change map relating $\Phi_\mathscr{T}(\mathscr{W}_{3, \hat{S}})$ to $\Phi_{\mathscr{T}^\prime}(\mathscr{W}_{3, \hat{S}})$ is a tropical $\mathcal{A}$-coordinate cluster transformation. We can therefore view the webs $\mathscr{W}_{3, \hat{S}}$ as a concrete topological model for the Fock-Goncharov-Shen positive integer tropical points $\mathcal{A}_{\mathrm{PGL}_3, \hat{S}}^+(\mathbb{Z}^t)$.
Towards A Category-extended Object Detector without Relabeling or Conflicts

Object detectors are typically learned based on fully-annotated training data with ﬁxed pre-deﬁned categories. However, not all possible categories of interest can be known beforehand, as classes are often required to be increased progressively in many realistic applications. In such scenario, only the original training set annotated with the old classes and some new training data labeled with the new classes are available. In this paper, we aim at leaning a strong uniﬁed detector that can handle all categories based on the limited datasets without extra manual labor. Vanilla joint training without considering label ambiguity leads to heavy biases and poor performance due to the incomplete annotations. To avoid such situation, we propose a practical framework which focuses on three aspects: better base model, better unlabeled ground-truth mining strategy and better retraining method with pseudo annotations. First, a conﬂict-free loss is proposed to obtain a usable base detector. Second, we employ Monte Carlo Dropout to calculate the localization conﬁdence, combined with the classiﬁ-cation conﬁdence, to mine more accurate bounding boxes. Third, we explore several strategies for making better use of pseudo annotations during retraining to achieve more powerful detectors. Extensive experiments conducted on multiple datasets demonstrate the effectiveness of our framework for category-extended object detectors.
Action Recognition with Kernel-based Graph Convolutional Networks

Learning graph convolutional networks (GCNs) is an emerging field which aims at generalizing deep learning to arbitrary non-regular domains. Most of the existing GCNs follow a neighborhood aggregation scheme, where the representation of a node is recursively obtained by aggregating its neighboring node representations using averaging or sorting operations. However, these operations are either ill-posed or weak to be discriminant or increase the number of training parameters and thereby the computational complexity and the risk of overfitting. In this paper, we introduce a novel GCN framework that achieves spatial graph convolution in a reproducing kernel Hilbert space (RKHS). The latter makes it possible to design, via implicit kernel representations, convolutional graph filters in a high dimensional and more discriminating space without increasing the number of training parameters. The particularity of our GCN model also resides in its ability to achieve convolutions without explicitly realigning nodes in the receptive fields of the learned graph filters with those of the input graphs, thereby making convolutions permutation agnostic and well defined. Experiments conducted on the challenging task of skeleton-based action recognition show the superiority of the proposed method against different baselines as well as the related work.