Miracle-dz/UniFAR-FTUs huggingface dataset:
Soft-lubrication interactions between a rigid sphere and an elastic wall

Abstract The motion of an object within a viscous fluid and in the vicinity of a soft surface induces a hydrodynamic stress field that deforms the latter, thus modifying the boundary conditions of the flow. This results in elastohydrodynamic interactions experienced by the particle. Here, we derive a soft-lubrication model, in order to compute all the forces and torque applied on a rigid sphere that is free to translate and rotate near an elastic wall. We focus on the limit of small deformations of the surface with respect to the fluid-gap thickness, and perform a perturbation analysis in dimensionless compliance. The response is computed in the framework of linear elasticity, for planar elastic substrates in the limiting cases of thick and thin layers. The EHD forces are also obtained analytically using the Lorentz reciprocal theorem.
Crystal, local atomic, and local electronic structures of 
YbFe2Zn20−xCdx(0≤x≤1.4)
: A multiband system with possible coexistence of light and heavy fermions

The partial (up to 7 %) substitution of Cd for Zn in the Yb-based heavy-fermion material YbFe2Zn20 is known to induce a slight (∼ 20 %) reduction of the Sommerfeld specific heat coefficient γ and a huge (up to two orders of magnitude) reduction of the T 2 resistivity coefficient A, corresponding to a drastic and unexpected reduction of the Kadowaki-Woods ratio A/γ. Here, Yb L3-edge X-ray absorption spectroscopy shows that the Yb valence state is close to 3+ for all x, whereas X-ray diffraction reveals that Cd replace the Zn ions only at the 16c site of the Fd3̄m cubic structure, leaving the 48f and 96g sites with full Zn occupation. Ab-initio electronic structure calculations in pure and Cd-doped materials, carried out without considering correlations, show multiple conduction bands with only minor modifications of the band dispersions near the Fermi level and therefore do not explain the resistivity drop introduced by Cd substitution. We propose that the site-selective Cd substitution introduces light conduction bands with substantial contribution of Cd(16c) 5p levels that have weak coupling to the Yb 4f moments. These light fermions coexist with heavy fermions originated from other conduction bands with larger participation of Zn(48f and 96g) 4p levels that remain strongly coupled with the Yb local moments.
Towards General Purpose Vision Systems: An End-to-End Task-Agnostic Vision-Language Architecture

Computer vision systems today are primarily N-purpose systems, designed and trained for a predefined set of tasks. Adapting such systems to new tasks is challenging and often requires nontrivial modifications to the network architecture (e.g. adding new output heads) or training process (e.g. adding new losses). To reduce the time and expertise required to develop new applications, we would like to create general purpose vision systems that can learn and perform a range of tasks without any modification to the architecture or learning process. In this paper, we propose GPV-1, a task-agnostic vision-language architecture that can learn and perform tasks that involve receiving an image and producing text and/or bounding boxes, including classification, localization, visual question answering, captioning, and more. We also propose evaluations of generality of architecture, skill-concept11For this work, we define concepts, skills and tasks as follows: Concepts - nouns (e.g. car, person, dog), Skills - operations that we wish to perform on the given inputs (e.g. classification, object detection, image captioning), Tasks - predefined combinations of a set of skills performed on a set of concepts (e.g. ImageNet classification task involves the skill of image classification across 1000 concepts). transfer, and learning efficiency that may informfuture work on general purpose vision. Our experiments indicate GPV-1 is effective at multiple tasks, reuses some concept knowledge across tasks, can perform the Referring Expressions task zero-shot, and further improves upon the zero-shot performance using a few training samples.
The Simulated Sky: Stellarium for Cultural Astronomy Research

For centuries, the rich nocturnal environment of the starry sky could be modelled only by analogue tools such as paper planispheres, atlases, globes and numerical tables. The immersive sky simulator of the twentieth century, the optomechanical planetarium, provided new ways for representing and teaching about the sky, but the high construction and running costs meant that they have not become common. However, in recent decades, “desktop planetarium programs” running on personal computers have gained wide attention. Modern incarnations are immensely versatile tools, mostly targeted towards the community of amateur astronomers and for knowledge transfer in transdisciplinary research. Cultural astronomers also value the possibilities they give of simulating the skies of past times or other cultures. With this paper, we provide Georg Zotti et al. 222 © 2021 EQUINOX PUBLISHING LTD an extended presentation of the open-source project Stellarium, which in the last few years has been enriched with capabilities for cultural astronomy research not found in similar, commercial
Data-driven balancing of linear dynamical systems

∗ Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstr. 1, 39106 Magdeburg, Germany. Email: gosea@mpi-magdeburg.mpg. de, ORCID: 0000-0003-3580-4116 † Department of Mathematics and Computational Modeling and Data Analytics Division, Academy of Integrated Science, Virginia Tech, Blacksburg, VA 24061, USA. Email: gugercin@vt.edu, ORCID: 0000-0003-4564-5999 Email: beattie@vt.edu, ORCID: 0000-0003-3302-4845
GABO: Graph Augmentations with Bi-level Optimization

Data augmentation refers to a wide range of techniques for improving model generalization by augmenting training examples. Oftentimes such methods require domain knowledge about the dataset at hand, spawning a plethora of recent literature surrounding automated techniques for data augmentation. In this work we apply one such method, bilevel optimization, to tackle the problem of graph classification on the ogbg-molhiv dataset. Our best performing augmentation achieved a test ROCAUC score of 77.77 % with a GIN+virtual classifier, which makes it the most effective augmenter for this classifier on the leaderboard. This framework combines a GIN layer augmentation generator with a bias transformation and outperforms the same classifier augmented using the state-of-the-art FLAG augmentation.
A Lieb–Robinson bound for quantum spin chains with strong on-site impurities

We consider a quantum spin chain with nearest neighbor interactions and sparsely distributed on-site impurities. We prove commutator bounds for its Heisenberg dynamics which incorporate the coupling strengths of the impurities. The impurities are assumed to satisfy a minimum spacing, and each impurity has a non-degenerate spectrum. Our results are proven in a broadly applicable setting, both in finite volume and in thermodynamic limit. We apply our results to improve Lieb–Robinson bounds for the Heisenberg spin chain with a random, sparse transverse field drawn from a heavy-tailed distribution.
Mining Trends of COVID-19 Vaccine Beliefs on Twitter With Lexical Embeddings: Longitudinal Observational Study

Background Social media plays a pivotal role in disseminating news globally and acts as a platform for people to express their opinions on various topics. A wide variety of views accompany COVID-19 vaccination drives across the globe, often colored by emotions that change along with rising cases, approval of vaccines, and multiple factors discussed online. Objective This study aims to analyze the temporal evolution of different emotions and the related influencing factors in tweets belonging to 5 countries with vital vaccine rollout programs, namely India, the United States, Brazil, the United Kingdom, and Australia. Methods We extracted a corpus of nearly 1.8 million Twitter posts related to COVID-19 vaccination and created 2 classes of lexical categories—emotions and influencing factors. Using cosine distance from selected seed words’ embeddings, we expanded the vocabulary of each category and tracked the longitudinal change in their strength from June 2020 to April 2021 in each country. Community detection algorithms were used to find modules in positive correlation networks. Results Our findings indicated the varying relationship among emotions and influencing factors across countries. Tweets expressing hesitancy toward vaccines represented the highest mentions of health-related effects in all countries, which reduced from 41% to 39% in India. We also observed a significant change (P<.001) in the linear trends of categories like hesitation and contentment before and after approval of vaccines. After the vaccine approval, 42% of tweets coming from India and 45% of tweets from the United States represented the “vaccine_rollout” category. Negative emotions like rage and sorrow gained the highest importance in the alluvial diagram and formed a significant module with all the influencing factors in April 2021, when India observed the second wave of COVID-19 cases. Conclusions By extracting and visualizing these tweets, we propose that such a framework may help guide the design of effective vaccine campaigns and be used by policy makers to model vaccine uptake and targeted interventions.
Impact of climate change on West Nile virus distribution in South America.

BACKGROUND West Nile virus (WNV) is a vector-borne pathogen of global relevance and is currently the most widely distributed flavivirus causing encephalitis worldwide. Climate conditions have direct and indirect impacts on vector abundance and virus dynamics within the mosquito. The significance of environmental variables as drivers in WNV epidemiology is increasing under the current climate change scenario. In this study we used a machine learning algorithm to model WNV distributions in South America. METHODS Our model evaluated eight environmental variables for their contribution to the occurrence of WNV since its introduction in South America in 2004. RESULTS Our results showed that environmental variables can directly alter the occurrence of WNV, with lower precipitation and higher temperatures associated with increased virus incidence. High-risk areas may be modified in the coming years, becoming more evident with high greenhouse gas emission levels. Countries such as Bolivia, Paraguay and several Brazilian areas, mainly in the northeast and midwest regions and the Pantanal biome, will be greatly affected, drastically changing the current WNV distribution. CONCLUSIONS Understanding the linkages between climatological and ecological change as determinants of disease emergence and redistribution will help optimize preventive strategies. Increased virus surveillance, integrated modelling and the use of geographically based data systems will provide more anticipatory measures by the scientific community.
AAformer: Auto-Aligned Transformer for Person Re-Identification

In person re-identification (re-ID), extracting part-level features from person images has been verified to be crucial to offer fine-grained information. Most of the existing CNN-based methods only locate the human parts coarsely, or rely on pretrained human parsing models and fail in locating the identifiable nonhuman parts (e.g., knapsack). In this article, we introduce an alignment scheme in transformer architecture for the first time and propose the auto-aligned transformer (AAformer) to automatically locate both the human parts and nonhuman ones at patch level. We introduce the "Part tokens (PARTs)", which are learnable vectors, to extract part features in the transformer. A PART only interacts with a local subset of patches in self-attention and learns to be the part representation. To adaptively group the image patches into different subsets, we design the auto-alignment. Auto-alignment employs a fast variant of optimal transport (OT) algorithm to online cluster the patch embeddings into several groups with the PARTs as their prototypes. AAformer integrates the part alignment into the self-attention and the output PARTs can be directly used as part features for retrieval. Extensive experiments validate the effectiveness of PARTs and the superiority of AAformer over various state-of-the-art methods.
SuperMeshing: A Novel Method for Boosting the Mesh Density in Numerical Computation within 2D Domain

Due to the limit of mesh density, the improvement of the spatial precision of numerical computation always leads to a decrease in computing efficiency. Aiming at this inability of numerical computation, we propose a novel method for boosting the mesh density in numerical computation within 2D domain. Based on the low mesh-density stress field in 2D plane strain problem computed by the finite element method, this method utilizes a deep neural network named SuperMeshingNet to learn the non-linear mapping from low mesh-density to high mesh-density stress field, and realizes the improvement of numerical computation accuracy and efficiency simultaneously. We adopt residual dense blocks to our mesh-density boost model – SuperMeshingNet for extracting abundant local features and enhancing the prediction capacity of the model. Experimental results show that the SuperMeshingNet proposed in this work can effectively boost the spatial resolution of the stress field under the multiple scaling factors: 2×, 4×, 8×. Compared to the results of finite element method, the predicted stress field error of SuperMeshingNet is only 0.54%, which is within the acceptable range of stress field estimation, and the SuperMeshingNet predicts the maximum stress value also without significant accuracy loss. We publicly share our work with full detail of implementation at https://github.com/zhenguonie/2021_SuperMeshing_2D_Plane_Strain.
Phase polynomials synthesis algorithms for NISQ architectures and beyond

We present a framework for the synthesis of phase polynomials that addresses both cases of full connectivity and partial connectivity for NISQ architectures. In most cases, our algorithms generate circuits with lower CNOT count and CNOT depth than the state of the art or have a significantly smaller running time for similar performances. We also provide methods that can be applied to our algorithms in order to trade an increase in the CNOT count for a decrease in execution time, thereby filling the gap between our algorithms and faster ones.
Blind Exploration and Exploitation of Stochastic Experts

We present blind exploration and exploitation (BEE) algorithms for identifying the most reliable stochastic expert based on formulations that employ posterior sampling, upper-confidence bounds, empirical Kullback-Leibler divergence, and minmax methods for the stochastic multi-armed bandit problem. Joint sampling and consultation of experts whose opinions depend on the hidden and random state of the world becomes challenging in the unsupervised, or blind, framework as feedback from the true state is not available. We propose an empirically realizable measure of expert competence that can be inferred instantaneously using only the opinions of other experts. This measure preserves the ordering of true competences and thus enables joint sampling and consultation of stochastic experts based on their opinions on dynamically changing tasks. Statistics derived from the proposed measure is instantaneously available allowing both blind exploration-exploitation and unsupervised opinion aggregation. We discuss how the lack of supervision affects the asymptotic regret of BEE architectures that rely on UCB1, KL-UCB, MOSS, IMED, and Thompson sampling. We demonstrate the performance of different BEE algorithms empirically and compare them to their standard, or supervised, counterparts.
Defending Against Image Corruptions Through Adversarial Augmentations

Modern neural networks excel at image classification, yet they remain vulnerable to common image corruptions such as blur, speckle noise or fog. Recent methods that focus on this problem, such as AugMix and DeepAugment, introduce defenses that operate in expectation over a distribution of image corruptions. In contrast, the literature on $\ell_p$-norm bounded perturbations focuses on defenses against worst-case corruptions. In this work, we reconcile both approaches by proposing AdversarialAugment, a technique which optimizes the parameters of image-to-image models to generate adversarially corrupted augmented images. We theoretically motivate our method and give sufficient conditions for the consistency of its idealized version as well as that of DeepAugment. Our classifiers improve upon the state-of-the-art on common image corruption benchmarks conducted in expectation on CIFAR-10-C and improve worst-case performance against $\ell_p$-norm bounded perturbations on both CIFAR-10 and ImageNet.
Four Xanthones from Swertia calycina Franch

Four xanthones were isolated from the ethyl acetate fraction of the whole herb, Swertia calycina Franch. The structures were confirmed by spectroscopic methods as swerchirin, bellidifodin, norbellidifodin and l,3,7-trihydroxy-8-methoxyxanthone. Swerchirin and bellidifodin were tested for pharmacological activity. Swerchirin was found to have a significant protective effect against the haemato-immunological deficiency induced by 60Co-irradiation in mice. Bellidifodin markedly lowered the blood sugar concentration in 4-opyincidine-induced diabetic rats.
A comparison of similarity based instance selection methods for cross project defect prediction

Context: Previous studies have shown that training data instance selection based on nearest neighborhood (NN) information can lead to better performance in cross project defect prediction (CPDP) by reducing heterogeneity in training datasets. However, neighborhood calculation is computationally expensive and approximate methods such as Locality Sensitive Hashing (LSH) can be as effective as exact methods. Aim: We aim at comparing instance selection methods for CPDP, namely LSH, NN-filter, and Genetic Instance Selection (GIS). Method: We conduct experiments with five base learners, optimizing their hyper parameters, on 13 datasets from PROMISE repository in order to compare the performance of LSH with benchmark instance selection methods NN-Filter and GIS. Results: The statistical tests show six distinct groups for F-measure performance. The top two group contains only LSH and GIS benchmarks whereas the bottom two groups contain only NN-Filter variants. LSH and GIS favor recall more than precision. In fact, for precision performance only three significantly distinct groups are detected by the tests where the top group is comprised of NN-Filter variants only. Recall wise, 16 different groups are identified where the top three groups contain only LSH methods, four of the next six are GIS only and the bottom five contain only NN-Filter. Finally, NN-Filter benchmarks never outperform the LSH counterparts with the same base learner, tuned or non-tuned. Further, they never even belong to the same rank group, meaning that LSH is always significantly better than NN-Filter with the same learner and settings. Conclusions: The increase in performance and the decrease in computational overhead and runtime make LSH a promising approach. However, the performance of LSH is based on high recall and in environments where precision is considered more important NN-Filter should be considered.
Remote Sensing Image Classification with the SEN12MS Dataset

Abstract. Image classification is one of the main drivers of the rapid developments in deep learning with convolutional neural networks for computer vision. So is the analogous task of scene classification in remote sensing. However, in contrast to the computer vision community that has long been using well-established, large-scale standard datasets to train and benchmark high-capacity models, the remote sensing community still largely relies on relatively small and often application-dependend datasets, thus lacking comparability. With this paper, we present a classification-oriented conversion of the SEN12MS dataset. Using that, we provide results for several baseline models based on two standard CNN architectures and different input data configurations. Our results support the benchmarking of remote sensing image classification and provide insights to the benefit of multi-spectral data and multi-sensor data fusion over conventional RGB imagery.
Analyzing and Quantifying Generalization in Convolutional Neural Networks

Generalization is the key capability of convolutional neural networks (CNNs). However, it is still quite elusive for differentiating the CNNs with good or poor generalization. It results in the barrier for providing reliable quantitative measure of generalization ability. To this end, this paper aims to clarify the generalization status of individual units in typical CNNs and quantify the generalization ability of networks using image classiﬁcation task with multiple classes data. Firstly, we propose a feature quantity, role share, consisting of four discriminate statuses for a certain unit based on its contribution to generalization. The distribution of role shares across all units provides a straight-forward visualization for the generalization of a network. Secondly, using only training sets, we propose a novel metric for quantifying the intrinsic generalization ability of networks. Lastly, a predictor of testing accuracy via only training accuracy of typical CNN is given. Empirical experiments using practical network model (VGG) and dataset (ImageNet) illustrate the rationality and effectiveness of our feature quantity, metric and predictor.
The Diurnal Path to Persistent Convective Self‐Aggregation

Clustering of tropical thunderstorms constitutes an important climate feedback because it influences the radiative balance. Convective self‐aggregation (CSA) is a profound modeling paradigm for explaining the clustering of tropical oceanic thunderstorms. However, CSA is hampered in the realistic limit of fine model resolution when cold pools—dense air masses beneath thunderstorm clouds—are well‐resolved. Studies on CSA usually assume the surface temperature to be constant, despite realistic surface temperatures varying significantly between night and day. Here we mimic the diurnal cycle in cloud‐resolving numerical experiments by prescribing a surface temperature oscillation. Our simulations show that the diurnal cycle enables CSA at fine resolutions, and that the process is even accelerated by finer resolutions. We attribute these findings to vigorous combined cold pools emerging in symbiosis with mesoscale convective systems. Such cold pools suppress buoyancy in extended regions (∼100 km) and enable the formation of persistent dry patches. Our findings help clarify how the tropical cloud field forms sustained clusters under the diurnal forcing and may have implications for the origin of extreme thunderstorm rainfall and tropical cyclones.
Studies on Novel Yb-based Candidate Triangular Quantum Antiferromagnets: Ba3YbB3O9 and Ba3YbB9O18

Yb-based triangular magnets have recently attracted attention as promising candidates to explore frustrated quantum magnetism. However, some candidates have turned out to have significant amounts of site disorder, which significantly affects the low-temperature magnetic behavior. To overcome this issue, Yb-based frustrated systems without structural disorder are required. In this paper, we report physical properties of two Yb-based triangular magnets Ba3YbB3O9 and Ba3YbB9O18, without structural disorder. Via magnetic susceptibility, magnetization, and muon spin rotation measurements, we verified that both Ba3YbB3O9 and, particularly studied for the first time, Ba3YbB9O18 do not show long range magnetic order or glass-like spin freezing down to 0.28 K, but show typical paramagnetic behavior.