Miracle-dz/UniFAR-FTUs huggingface dataset:
Improved three-way catalytic activity of bimetallic Ir–Rh catalysts supported on CeO2–ZrO2

The addition of a small amount of Ir caused a significant increase in not only CO and C3H6 oxidation activity but also NO reduction activity of Rh/CeO2–ZrO2 with a Ce/Zr molar ratio of 1/4 for NO–CO–C3H6–H2–O2 reaction under stoichiometric conditions. The optimum Ir/Rh atomic ratio was 1/9. The Ir additive was found to suppress the preferential oxidation of CO and C3H6 by O2, leading to an improvement in the efficiency of CO and C3H6 utilization for NO reduction. Catalyst characterization such as H2-TPR and X-ray absorption spectroscopy (XANES and EXAFS) revealed that the Rh species in the Ir–Rh/CeO2–ZrO2 samples is mainly present as Rh2O3 nanoparticles and that the addition of a small amount of Ir (Ir/Rh = 1/9) can improve the reducibility of Rh2O3 nanoparticles. On the basis of surface analysis by XPS and FT-IR spectroscopy following CO adsorption and STEM observation, the formation of Ir–Rh nanoparticles composed of finely-divided Ir species on the surface of Rh particles with a size of 1 nm was considered to be responsible for the high catalytic activity of Ir–Rh/CeO2–ZrO2 for NO–CO–C3H6–H2–O2 reaction. Ir–Rh/CeO2–ZrO2 with Ir–Rh loading of 0.3 wt% still showed high catalytic performance, which is comparable with that of 0.5 wt% Rh/CeO2–ZrO2, minimizing Rh usage.
Solubility of CaSO4 Phases in Aqueous HCl + CaCl2 Solutions from 283 K to 353 K

Solubilities of calcium sulfate dihydrate, hemihydrate, and anhydrite in concentrated HCl, CaCl2, and their mixed aqueous solutions were measured by using the classic isothermal dissolution method at the temperature range from (283 to 353) K. The concentration investigated for HCl is up to 12 mol·dm-3 and for CaCl2 is up to 3.5 mol·dm-3 at room temperature. The solubility of CaSO4 phases in all cases investigated was found to increase with the temperature increment with the exception of anhydrite in CaCl2 solutions. In pure HCl media, increasing the acid concentration in the range of (0.0 to 3) mol·dm-3 HCl causes the solubility of CaSO4·2H2O or CaSO4 to increase reaching a maximum value and then decrease gradually with further increasing HCl concentration. In the concentrated range of (8 to 12) mol·dm-3 HCl, the solubility of CaSO4·1/2H2O decreases with acid concentration. In HCl + CaCl2 mixed media, the addition of CaCl2 causes the solubility of all three phases to decrease due apparently to common ion .
Auger ionization in carbon nanotubes and graphene nanoribbons under laser irradiation

We calculated the Auger ionization rate between excitons in semiconducting carbon nanotubes and graphene nanoribbons. Because of the enhanced Coulomb interaction, the exciton is ionized into free electron-hole continuum by the exciton-exciton scattering. Our calculation shows that the Auger ionization time is small, picosecond order, compared with the other recombination processes, and it is useful to generate free electron-hole carriers (© 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)
BRepNet: A topological message passing system for solid models

Boundary representation (B-rep) models are the standard way 3D shapes are described in Computer-Aided Design (CAD) applications. They combine lightweight parametric curves and surfaces with topological information which connects the geometric entities to describe manifolds. In this paper we introduce BRepNet, a neural network architecture designed to operate directly on B-rep data structures, avoiding the need to approximate the model as meshes or point clouds. BRepNet defines convolutional kernels with respect to oriented coedges in the data structure. In the neighborhood of each coedge, a small collection of faces, edges and coedges can be identified and patterns in the feature vectors from these entities detected by specific learnable parameters. In addition, to encourage further deep learning research with B-reps, we publish the Fusion 360 Gallery segmentation dataset. A collection of over 35,000 B-rep models annotated with information about the modeling operations which created each face. We demonstrate that BRepNet can segment these models with higher accuracy than methods working on meshes, and point clouds.
Kinetic study on the homogeneous esterification of acetic acid with isoamyl alcohol

The liquid-phase esterification of acetic acid and isoamyl alcohol has been studied to develop a kinetic model using a sequential experimental design based on the divergence criterion. Data come from batch reactor experiments, performed in the temperature range of 316–363 K. Discrimination among 36 possible kinetic models, written in terms of activity, mole fractions, and molar densities, is possible through the deviance information criterion, as estimated by a Markov chain Monte Carlo technique. The obtained results indicate a negligible heat of reaction and a clear autocatalytic effect of acetic acid on the esterification rate. © 2012 Wiley Periodicals, Inc. Int J Chem Kinet 45: 10–18, 2013
TubeR: Tube-Transformer for Action Detection

In this paper, we propose TubeR: the ﬁrst transformer based network for end-to-end action detection, with an encoder and decoder optimized for modeling action tubes with variable lengths and aspect ratios. TubeR does not rely on hand-designed tube structures, automatically links predicted action boxes over time and learns a set of tube queries re-lated to actions. By learning action tube embeddings, Tu-beR predicts more precise action tubes with ﬂexible spatial and temporal extents. Our experiments demonstrate TubeR achieves state-of-the-art among single-stream methods on UCF101-24 and J-HMDB. TubeR outperforms existing one-model methods on AVA and is even competitive with the two-model methods. Moreover, we observe TubeR has the potential on tracking actors with different actions, which will foster future research in long-range video understanding.
Misinformation Warning Labels: Twitter's Soft Moderation Effects on COVID-19 Vaccine Belief Echoes

Twitter, prompted by the rapid spread of alternative narratives, started actively warning users about the spread of COVID-19 misinformation. This form of soft moderation comes in two forms: as a warning cover before the Tweet is displayed to the user and as a warning tag below the Tweet. This study investigates how each of the soft moderation forms affects the perceived accuracy of COVID-19 vaccine misinformation on Twitter. The results suggest that the warning covers work, but not the tags, in reducing the perception of accuracy of COVID-19 vaccine misinformation on Twitter. "Belief echoes"do exist among Twitter users, unfettered by any warning labels, in relationship to the perceived safety and efficacy of the COVID-19 vaccine as well as the vaccination hesitancy for themselves and their children. The implications of these results are discussed in the context of usable security affordances for combating misinformation on social media.
AI Fairness via Domain Adaptation

While deep learning (DL) approaches are reaching human-level performance for many tasks, including for diagnostics AI, the focus is now on challenges possibly affecting DL deployment, including AI privacy, domain generalization, and fairness. This last challenge is addressed in this study. Here we look at a novel method for ensuring AI fairness with respect to protected or sensitive factors. This method uses domain adaptation via training set enhancement to tackle bias-causing training data imbalance. More specifically, it uses generative models that allow the generation of more synthetic training samples for underrepresented populations. This paper applies this method to the use case of detection of age related macular degeneration (AMD). Our experiments show that starting with an originally biased AMD diagnostics model the method has the ability to improve fairness.
An Evaluation of the Archive of Formal Proofs

The Archive of Formal Proofs (AFP) is an online repository of formal proofs for the Isabelle proof assistant. It serves as a central location for publishing, discovering, and viewing libraries of proofs. We conducted an online survey in November 2020 to assess the suitability of the website. In this report, we present and discuss the results, which showed that long-term users of the website are generally satisfied with the AFP but that there are a number of areas, such as navigation, search and script browsing, that need improvement.
Refined Mass and Geometric Measurements of the High-mass PSR J0740+6620

We report results from continued timing observations of PSR J0740+6620, a high-mass, 2.8 ms radio pulsar in orbit with a likely ultracool white dwarf companion. Our data set consists of combined pulse arrival-time measurements made with the 100 m Green Bank Telescope and the Canadian Hydrogen Intensity Mapping Experiment telescope. We explore the significance of timing-based phenomena arising from general relativistic dynamics and variations in pulse dispersion. When using various statistical methods, we find that combining ∼1.5 yr of additional, high-cadence timing data with previous measurements confirms and improves on previous estimates of relativistic effects within the PSR J0740+6620 system, with the pulsar mass mp=2.08−0.07+0.07M⊙ (68.3% credibility) determined by the relativistic Shapiro time delay. For the first time, we measure secular variation in the orbital period and argue that this effect arises from apparent acceleration due to significant transverse motion. After incorporating contributions from Galactic differential rotation and off-plane acceleration in the Galactic potential, we obtain a model-dependent distance of d=1.14−0.15+0.17 kpc (68.3% credibility). This improved distance confirms the ultracool nature of the white dwarf companion determined from recent optical observations. We discuss the prospects for future observations with next-generation facilities, which will likely improve the precision on m p for J0740+6620 by an order of magnitude within the next few years.
Deep variational quantum eigensolver for excited states and its application to quantum chemistry calculation of periodic materials

Kaoru Mizuta, 2, ∗ Mikiya Fujii, Shigeki Fujii, Kazuhide Ichikawa, Yutaka Imamura, Yukihiro Okuno, and Yuya O. Nakagawa QunaSys Inc., Aqua Hakusan Building 9F, 1-13-7 Hakusan, Bunkyo, Tokyo 113-0001, Japan Department of Physics, Kyoto University, Kyoto 606-8502, Japan Technology division, Innovation Promotion Sector, Panasonic Corporation, 1006 Kadoma, Kadoma City, Osaka 571-8508, Japan Innovative Technology Laboratories, AGC Inc., Yokohama 230-0045, Japan Analysis Technology Center, FUJIFILM Corporation, Minamiashigara City, Kanagawa 250-0193, Japan (Dated: August 30, 2021)
High-pressure study of the low-
Z
 rich superconductor 
Be22Re

J. Lim, A. C. Hire, 3 Y. Quan, 2, 3 J. Kim, L. Fanfarillo, 4 S. R. Xie, 3 R. S. Kumar, C. Park, R. J. Hemley, 7 Y. K. Vohra, R. G. Hennig, 3 P. J. Hirschfeld, G. R. Stewart, and J. J. Hamlin Department of Physics, University of Florida, Gainesville, Florida 32611, USA Department of Materials Science and Engineering, University of Florida, Gainesville, Florida 32611, USA Quantum Theory Project, University of Florida, Gainesville, Florida 32611, USA Scuola Internazionale Superiore di Studi Avanzati (SISSA), Via Bonomea 265, 34136 Trieste, Italy Department of Physics, University of Illinois at Chicago, Chicago, Illinois 60607, USA HPCAT, X-ray Science Division, Argonne National Laboratory, Argonne, Illinois 60439, USA Department of Chemistry, University of Illinois at Chicago, Chicago, Illinois 60607, USA Department of Physics, University of Alabama at Birmingham, Birmingham, Alabama 35294, USA (Dated: April 6, 2021)
Modeling the Galactic Neutron Star Population for Use in Continuous Gravitational-wave Searches

Searches for continuous gravitational waves (GW) from unknown Galactic neutron stars provide limits on the shapes of neutron stars. A rotating neutron star will produce GW if asymmetric deformations exist in its structure that are characterized by the star’s ellipticity. In this study, we use a simple model of the spatial and spin distribution of Galactic neutron stars to estimate the total number of neutron stars probed, using GW, to a given upper limit on the ellipticity. This may help optimize future searches with improved sensitivity. The improved sensitivity of third-generation GW detectors may increase the number of neutron stars probed, to a given ellipticity, by factors of 100 to 1000.
PhyAug: Physics-Directed Data Augmentation for Deep Sensing Model Transfer in Cyber-Physical Systems

Run-time domain shifts from training-phase domains are common in sensing systems designed with deep learning. The shifts can be caused by sensor characteristic variations and/or discrepancies between the design-phase model and the actual model of the sensed physical process. To address these issues, existing transfer learning techniques require substantial target-domain data and thus incur high post-deployment overhead. This paper proposes to exploit the first principle governing the domain shift to reduce the demand on target-domain data. Specifically, our proposed approach called PhyAug uses the first principle fitted with few labeled or unlabeled source/target-domain data pairs to transform the existing source-domain training data into augmented data for updating the deep neural networks. In two case studies of keyword spotting and DeepSpeech2-based automatic speech recognition, with 5-second unlabeled data collected from the target microphones, PhyAug recovers the recognition accuracy losses due to microphone characteristic variations by 37% to 72%. In a case study of seismic source localization with TDoA fingerprints, by exploiting the first principle of signal propagation in uneven media, PhyAug only requires 3% to 8% of labeled TDoA measurements required by the vanilla fingerprinting approach in achieving the same localization accuracy.
Hybrid and Generalized Bayesian Cramér-Rao Inequalities via Information Geometry

Information geometry is the study of statistical models from a Riemannian geometric point of view. The Fisher information matrix plays the role of a Riemannian metric in this framework. This tool helps us obtain Cramér-Rao lower bound (CRLB). This chapter summarizes the recent results which extend this framework to more general Cramér-Rao inequalities. We apply Eguchi’s theory to a generalized form of Czsiszár 𝑓 -divergence to obtain a Riemannian metric that, at once, is used to obtain deterministic CRLB, Bayesian CRLB, and their generalizations.
Finite section method for aperiodic Schrödinger operators

We consider 1D discrete Schr\"odinger operators with aperiodic potentials given by a Sturmian word, which is a natural generalisation of the Fibonacci Hamiltonian. Via a standard approximation by periodic potentials, we establish Hausdorff convergence of the corresponding spectra for the Schr\"odinger operators on the axis as well as for their compressions to the half-axis. Based on the half-axis results, we study the finite section method, which is another operator approximation, now by compressions to finite but growing intervals, that is often used to solve operator equations approximately. We find that, also for this purpose, the aperiodic case can be studied via its periodic approximants. Our results on the finite section method of the aperiodic operator are illustrated by confirming a result on the finite sections of the special case of the Fibonacci Hamiltonian.
Permutation-Invariant Subgraph Discovery

We introduce Permutation and Structured Perturbation Inference (PSPI), a new problem formulation that abstracts many graph matching tasks that arise in systems biology. PSPI can be viewed as a robust formulation of the permutation inference or graph matching, where the objective is to find a permutation between two graphs under the assumption that a set of edges may have undergone a perturbation due to an underlying cause. For example, suppose there are two gene regulatory networks X and Y from a diseased and normal tissue respectively. Then, the PSPI problem can be used to detect if there has been a structural change between the two networks which can serve as a signature of the disease. Besides the new problem formulation, we propose an ADMM algorithm (STEPD) to solve a relaxed version of the PSPI problem. An extensive case study on comparative gene regulatory networks (GRNs) is used to demonstrate that STEPD is able to accurately infer structured perturbations and thus provides a tool for computational biologists to identify novel prognostic signatures. A spectral analysis confirms that STEPD can recover small clique-like perturbations making it a useful tool for detecting permutation-invariant changes in graphs.
Bayesian Structural Learning for an Improved Diagnosis of Cyber-Physical Systems

The diagnosis of cyber-physical systems aims to detect faulty behaviour, its root cause and a mitigation or even prevention policy. Therefore, diagnosis relies on a representation of the system's functional and faulty behaviour combined with observations of the system taken at runtime. The main challenges are the time-intensive building of a model, possible state-explosion while searching for the root cause and interpretability of the results. In this paper we propose a scalable algorithm tackling these challenges. We use a Bayesian network to learn a structured model automatically and optimise the model by a genetic algorithm. Our approach differs from existing work in two aspects: instead of selecting features prior to the analysis we learn a global representation using all available information which is then transformed to a smaller, label-specific one and we focus on interpretability to facilitate repairs. The evaluation shows that our approach is able to learn a model with equal performance to state-of-the-art algorithms while giving better interpretability and having a reduced size.
The geodesics and isoparametric functions on a Randers sphere ∗

In this paper, we give the global expression of geodesics, isoparametric functions and isoparametric families on a Randers sphere via navigation. We construct some examples of closed and non-closed geodesics, which show directly that the lower bound of the shortest closed geodesic on (S2, F ) in [1] is sharp. We express isoparametric functions and the focal submanifolds of isoparametric hypersurfaces on (Sn, F, dμBH) by Cartan-Münzner polynomials. Furthermore, we obtain some examples of isoparametric functions, isoparametric families and focal submanifolds.
The Thousand-Pulsar-Array programme on MeerKAT – V. Scattering analysis of single-component pulsars

We have measured the scattering time-scale, τ, and the scattering spectral index, α, for 84 single-component pulsars. Observations were carried out with the MeerKAT telescope as part of the Thousand-Pulsar-Array programme in the MeerTime project at frequencies between 0.895 and 1.670 GHz. Our results give a distribution of values for α (defined in terms of τ and frequency ν as τ ∝ ν−α) for which, upon fitting a Gaussian, we obtain a mean and standard deviation of 〈α〉 = 4.0 ± 0.6. This is due to our identification of possible causes of inaccurate measurement of τ, which, if not filtered out of modelling results, tend to lead to underestimation of α. The pulsars in our sample have large dispersion measures and are therefore likely to be distant. We find that a model using an isotropic scatter broadening function is consistent with the data, likely due to the averaging effect of multiple scattering screens along the line of sight. Our sample of scattering parameters provides a strong data set upon which we can build to test more complex and time-dependent scattering phenomena, such as extreme scattering events.
Fairness in Network-Friendly Recommendations

As mobile traffic is dominated by content services (e.g., video), which typically use recommendation systems, the paradigm of network-friendly recommendations (NFR) has been proposed recently to boost the network performance by promoting content that can be efficiently delivered (e.g., cached at the edge). NFR increase the network performance, however, at the cost of being unfair towards certain contents when compared to the standard recommendations. This unfairness is a side effect of NFR that has not been studied in literature. Nevertheless, retaining fairness among contents is a key operational requirement for content providers. This paper is the first to study the fairness in NFR, and design fair-NFR. Specifically, we use a set of metrics that capture different notions of fairness, and study the unfairness created by existing NFR schemes. Our analysis reveals that NFR can be significantly unfair. We identify an inherent trade-off between the network gains achieved by NFR and the resulting unfairness, and derive bounds for this trade-off. We show that existing NFR schemes frequently operate far from the bounds, i.e., there is room for improvement. To this end, we formulate the design of Fair-NFR (i.e., NFR with fairness guarantees compared to the baseline recommendations) as a linear optimization problem. Our results show that the Fair-NFR can achieve high network gains (similar to non-fair-NFR) with little unfairness.
Fully Understanding Generic Objects: Modeling, Segmentation, and Reconstruction

Inferring 3D structure of a generic object from a 2D image is a long-standing objective of computer vision. Conventional approaches either learn completely from CADgenerated synthetic data, which have difficulty in inference from real images, or generate 2.5D depth image via intrinsic decomposition, which is limited compared to the full 3D reconstruction. One fundamental challenge lies in how to leverage numerous real 2D images without any 3D ground truth. To address this issue, we take an alternative approach with semi-supervised learning. That is, for a 2D image of a generic object, we decompose it into latent representations of category, shape, albedo, lighting and camera projection matrix, decode the representations to segmented 3D shape and albedo respectively, and fuse these components to render an image well approximating the input image. Using a category-adaptive 3D joint occupancy field (JOF), we show that the complete shape and albedo modeling enables us to leverage real 2D images in both modeling and model fitting. The effectiveness of our approach is demonstrated through superior 3D reconstruction from a single image, being either synthetic or real, and shape segmentation. Code is available at http://cvlab.cse. msu.edu/project-fully3dobject.html.
The modularity of random graphs on the hyperbolic plane

Modularity is a quantity which has been introduced in the context of complex networks in order to quantify how close a network is to an ideal modular network in which the nodes form small interconnected communities that are joined together with relatively few edges. In this paper, we consider this quantity on a recent probabilistic model of complex networks introduced by Krioukov et al. (Phys. Rev. E 2010). This model views a complex network as an expression of hidden hierarchies, encapsulated by an underlying hyperbolic space. For certain parameters, this model was proved to have typical features that are observed in complex networks such as power law degree distribution, bounded average degree, clustering coefficient that is asymptotically bounded away from zero, and ultra-small typical distances. In the present work, we investigate its modularity and we show that, in this regime, it converges to 1 in probability.