Miracle-dz/UniFAR-FTUs huggingface dataset:
Theory of nematic and polar active fluid surfaces

We derive a fully covariant theory of the hydrodynamics of nematic and polar active surfaces, subjected to internal and external forces and torques. We study the symmetries of polar and nematic surfaces and ﬁnd that in addition to 5 diﬀerent types of in-plane isotropic surfaces, polar and nematic surfaces can be classiﬁed into 5 polar, 2 pseudopolar, 5 nematic and 2 pseudonematic types of surfaces. We give examples of physical realisations of the diﬀerent types of surfaces we have identiﬁed. We obtain expressions for the equilibrium tensions, moments, and external forces and torques acting on a passive polar or nematic surface. We calculate the entropy production rate using the framework of thermodynamics close to equilibrium and ﬁnd constitutive equations for polar and nematic active surfaces with diﬀerent symmetries. We study the instabilities of a conﬁned ﬂat planar-chiral polar active layer and of a conﬁned deformable polar active surface with broken up-down symmetry. with a tangent polar or nematic order. B . The distribution of stresses within a thin layer gives rise to tensions and torques acting within the surface, when integrated across the thick-ness of the layer. C . Internal and external forces and torques act on a surface element with surface area dS . D . The order parameter changes in time and is transported by the ﬂow. Lagrangian time derivatives are taken by comparing the order parameter at two times t and t + dt , following the ﬂow of the surface.
Beamforming Towards Seamless Sensing Coverage for Cellular Integrated Sensing and Communication

The sixth generation (6G) mobile communication networks are expected to offer a new paradigm of cellular integrated sensing and communication (ISAC). However, due to the intrinsic difference between sensing and communication in terms of coverage requirement, current cellular networks that are deliberately planned mainly for communication coverage are difficult to achieve seamless sensing coverage. To address this issue, this paper studies the beamforming optimization towards seamless sensing coverage for a basic bi-static ISAC system, while ensuring that the communication requirements of multiple users equipment (UEs) are satisfied. Towards this end, an optimization problem is formulated to maximize the worst-case sensing signal-to-noise ratio (SNR) in a prescribed coverage region, subject to the signal-to-interference-plus-noise ratio (SINR) requirement for each UE. To gain some insights, we first investigate the special case with one single UE and one single sensing point, for which a closed-from expression of the optimal beamforming is obtained. For the general case with multiple communication UEs and contiguous regional sensing coverage, an efficient algorithm based on successive convex approximation (SCA) is proposed to solve the non-convex beamforming optimization problem. Numerical results demonstrate that the proposed design is able to achieve seamless sensing coverage in the prescribed region, while guaranteeing the communication requirements of the UEs.
Tensor factorization based method for low rank matrix completion and its application on tensor completion

Low rank matrix and tensor completion problems are to recover the incomplete two and higher order data by using their low rank structures. The essential problem in the matrix and tensor completion problems is how to improve the efficiency. To this end, we first establish the relationship between matrix rank and tensor tubal rank, and then reformulate matrix completion problem as a tensor completion problem. For the reformulated tensor completion problem, we adopt a two-stage strategy based on tensor factorization algorithm. In this way, a matrix completion problem of big size can be solved via some matrix computations of smaller sizes. For a third order tensor completion problem, to fully exploit the low rank structures, we introduce the double tubal rank which combines the tubal rank and the rank of the mode-3 unfolding matrix. For the mode-3 unfolding matrix rank, we follow the idea of matrix completion. Based on this, we establish a novel model and modify the tensor factorization based algorithm for third order tensor completion. Extensive numerical experiments demonstrate that the proposed methods outperform state-of-the-art methods in terms of both accuracy and running time.
Partition-Based Active Learning for Graph Neural Networks

We study the problem of semi-supervised learning with Graph Neural Networks (GNNs) in an active learning setup. We propose GraphPart, a novel partition-based active learning approach for GNNs. GraphPart first splits the graph into disjoint partitions and then selects representative nodes within each partition to query. The proposed method is motivated by a novel analysis of the classification error under realistic smoothness assumptions over the graph and the node features. Extensive experiments on multiple benchmark datasets demonstrate that the proposed method outperforms existing active learning methods for GNNs under a wide range of annotation budget constraints. In addition, the proposed method does not introduce additional hyperparameters, which is crucial for model training, especially in the active learning setting where a labeled validation set may not be available.
COVID-19 mortality prediction: A case study for Ístanbul (preprint)/ en

In this paper, we use SEIR equations to make predictions for the number of mortality due to COVID-19 in Ístanbul. Using excess mortality method, we find the number of mortality for the previous three waves in 2020 and 2021. We show that the predictions of our model is consistent with number of moralities for each wave. Furthermore, we predict the number of mortality for the second wave of 2021. We also extend our analysis for Germany, Italy and Turkey to compare the basic reproduction number $R_0$ for Istanbul. Finally, we calculate the number of infected people in Istanbul for herd immunity.
A Bayesian Permutation Training Deep Representation Learning Method for Speech Enhancement with Variational Autoencoder

Recently, variational autoencoder (VAE), a deep representation learning (DRL) model, has been used to perform speech enhancement (SE). However, to the best of our knowledge, current VAE-based SE methods only apply VAE to model speech signal, while noise is modeled using the traditional non-negative matrix factorization (NMF) model. One of the most important reasons for using NMF is that these VAE-based methods cannot disentangle the speech and noise latent variables from the observed signal. Based on Bayesian theory, this paper derives a novel variational lower bound for VAE, which ensures that VAE can be trained in supervision, and can disentangle speech and noise latent variables from the observed signal. This means that the proposed method can apply the VAE to model both speech and noise signals, which is totally different from the previous VAE-based SE works. More specifically, the proposed DRL method can learn to impose speech and noise signal priors to different sets of latent variables for SE. The experimental results show that the proposed method can not only disentangle speech and noise latent variables from the observed signal, but also obtain a higher scale-invariant signal-to-distortion ratio and speech quality score than the similar deep neural network-based (DNN) SE method.
MIP-plicits: Level of Detail Factorization of Neural Implicits Sphere Tracing

We introduce MIP-plicits, a novel approach for rendering 3D and 4D Neural Implicits that divide the problem into macro and meso components. We rely on the iterative nature of the sphere tracing algorithm, the spatial continuity of the Neural Implicit representation, and the association of the network architecture complexity with the details it can represent. This approach does not rely on spatial data structures, and can be used to mix Neural Implicits trained previously and separately as detail levels. We also introduce Neural Implicit Normal Mapping, which is a core component of the problem factorization. This concept is very close and analogous to the classic normal mapping on meshes, broadly used in Computer Graphics. Finally, we derive an analytic equation and an algorithm to simplify the normal calculation of Neural Implicits, adapted to be evaluated by the General Matrix Multiply algorithm (GEMM). Current approaches rely on finite differences, which impose additional inferences on auxiliary points and discretization error.
A global two-layer radiative transfer model for axisymmetric, shadowed protoplanetary disks

Understanding the thermal structure of protoplanetary disks is crucial for modeling planet formation and interpreting disk observations. We present a new two-layer radiative transfer model for computing the thermal structure of axisymmetric irradiated disks. Unlike the standard two-layer model, our model accounts for the radial as well as vertical transfer of the starlight reprocessed at the disk surface. The model thus allows us to compute the temperature below “shadowed” surfaces receiving no direct starlight. Thanks to the assumed axisymmetry, the reprocessed starlight flux is given in a one-dimensional integral form that can be computed at a low cost. Furthermore, our model evolves the midplane temperature using a time-dependent energy equation and can therefore treat thermal instabilities. We apply our global two-layer model to disks with a planetary induced gap and confirm that the model reproduces the disks’ temperature profiles obtained from more computationally expensive Monte Carlo radiative transfer calculations to an accuracy of less than 20%. We also apply the model to study the long-term behavior of the thermal wave instability in irradiated disks. Being simple and computationally efficient, the global two-layer model will be suitable for studying the interplay between disks’ thermal evolution and dust evolution.
Hot-Refresh Model Upgrades with Regression-Alleviating Compatible Training in Image Retrieval

The task of hot-refresh model upgrades of image retrieval systems plays an essential role in the industry but has never been investigated in academia before. Conventional cold-refresh model upgrades can only deploy new models after the gallery is overall backfilled, taking weeks or even months for massive data. In contrast, hot-refresh model upgrades deploy the new model immediately and then gradually improve the retrieval accuracy by backfilling the gallery on-the-fly. Compatible training has made it possible, however, the problem of model regression with negative flips poses a great challenge to the stable improvement of user experience. We argue that it is mainly due to the fact that new-to-old positive query-gallery pairs may show less similarity than new-to-new negative pairs. To solve the problem, we introduce a Regression-Alleviating Compatible Training (RACT) method to properly constrain the feature compatibility while reducing negative flips. The core is to encourage the new-to-old positive pairs to be more similar than both the new-to-old negative pairs and the new-to-new negative pairs. An efficient uncertainty-based backfilling strategy is further introduced to fasten accuracy improvements. Extensive experiments on large-scale retrieval benchmarks (e.g., Google Landmark) demonstrate that our RACT effectively alleviates the model regression for one more step towards seamless model upgrades. The code will be available at https://github.com/binjiezhang/RACT_ICLR2022.
Non-decimated 2D Wavelet Spectrum and Its Use in Breast Cancer Diagnostics

To improve diagnostic accuracy of breast cancer detection, several researchers have used the wavelet-based tools, which provide additional insight and information for aiding diagnostic decisions. The accuracy of such diagnoses, however, can be improved. This paper introduces a waveletbased technique, non-decimated wavelet transform (NDWT)-based scaling estimation, that improves scaling parameter estimation over the traditional methods. One distinctive feature of NDWT is that it does not decimate wavelet coefficients at multiscale levels resulting in redundant outputs which are used to lower the variance of scaling estimators. Another interesting feature of the proposed methodology is the freedom of dyadic constraints for inputs, typical for standard wavelet-based approaches. To compare the estimation performance of the NDWT method to a conventional orthogonal wavelet transform-based method, we use simulation to estimate the Hurst exponent in two-dimensional fractional Brownian fields. The results of the simulation show that the proposed method improves the ∗Corresponding Author: brani@stat.tamu. edu 1 ar X iv :2 20 1. 09 31 3v 1 [ st at . M E ] 2 3 Ja n 20 22 conventional estimators of scaling and yields estimators with smaller meansquared errors. We apply the NDWT method to classification of mammograms as cancer or control and, for publicly available mammogram images from the database at the University of South Florida, find the the diagnostic accuracy in excess of 80%.
Analyzing Multispectral Satellite Imagery of South American Wildfires Using CNNs and Unsupervised Learning

Since severe droughts are occurring more frequently and lengthening the dry season in the Amazon Rainforest, it is important to respond to active wildﬁres promptly and to forecast them before they become inextinguishable. Though computer vision researchers have applied algorithms on large databases to automatically detect wildﬁres, current models are computationally expensive and are not versatile enough for the low technology conditions of regions in South America. This comprehensive deep learning study ﬁrst trains a Fully Convolutional Neural Network with skip connections on multispectral Landsat 8 images of Ecuador and the Galapagos. The model uses Green and Short-wave Infrared bands as inputs to predict each image’s corresponding pixel-level binary ﬁre mask. This model achieves a 0.962 validation F2 score and a 0.932 F2 score on test data from Guyana and Suriname. Afterward, image segmentation is conducted on the Cirrus Cloud band using K-Means Clustering to simplify continuous pixel values into three discrete classes representing the degree of cirrus cloud contamination. Two additional Convolutional Neural Networks are trained to classify the presence of a wildﬁre in a patch of land using these segmented cirrus images. The “experimental” model trained on the segmented inputs achieves 96.5% binary accuracy and has smoother learning curves than the “control model” that is not given the segmented inputs. This proof of concept reveals that feature simpliﬁcation can improve the performance of wildﬁre detection models. Overall, the software built in this study is useful for early and accurate detection of wildﬁres in South America.
$\alpha$-Fairness User Pairing for Downlink NOMA Systems with Imperfect Successive Interference Cancellation

Non-orthogonal multiple access (NOMA) is considered as one of the predominant multiple access technique for the next-generation cellular networks. We consider a 2user pair downlink NOMA system with imperfect successive interference cancellation (SIC). We consider bounds on the power allocation factors and then formulate the power allocation as an optimization problem to achieve α-Fairness among the paired users. We show that α-Fairness based power allocation factor coincides with lower bound on power allocation factor in case of perfect SIC and α > 2. Further, as long as the proposed criterion is satisfied, it converges to the upper bound with increasing imperfection in SIC. Similarly, we show that, for 0 < α < 1, the optimal power allocation factor coincides with the derived lower bound on power allocation. Based on these observations, we then propose a low complexity suboptimal algorithm. Through extensive simulations, we analyse the performance of the proposed algorithm and compare the performance against the state-of-the-art algorithms. We show that even though Near-Far based pairing achieves better fairness than the proposed algorithms, it fails to achieve rates equivalent to its orthogonal multiple access counterparts with increasing imperfections in SIC. Further, we show that the proposed optimal and sub-optimal algorithms achieve significant improvements in terms of fairness as compared to the state-of-the-art algorithms.
Mars Entry Trajectory Planning with Range Discretization and Successive Convexification

Mars entry refers to the flight phase from entering the atmosphere to the deployment of deceleration devices, where the constraints of heat rate, pressure, and normal load should be met [1]. The reference trajectory tracking guidance has been widely adopted since constraints can be addressed in advance by trajectory planning [2, 3]. To ensure the safety of a Mars mission, it is essential to plan the entry trajectory that satisfies all constraints with high accuracy. Recently, convex optimization has attracted increasing attention of researchers to solve aerospace trajectory optimization problems due to its characteristics of polynomial time convergence and theoretical global optimality [4]. The pioneering lossless convexification technique was proposed by Acikmese’s team [5], and this approach was improved to take into account more complex physical factors, including the minimum-landing-error objective, planetary self-rotation effect and state-triggered constraint in more realistic problems [6-9]. For the atmospheric entry problem, Liu et al. utilized sine and cosine of the bank angle as new control inputs and introduced additional second-order cone inequality constraint to replace the nonconvex equality constraint on the new controls [10, 11], which can convert the constrained entry problem into a convex one and effectively suppress the high-frequency jitters of the bank angle. Then the convex problem was solved
Asymptotic theory in a class of directed random graph models with a differentially private bi-degree sequence

Although the asymptotic properties of the parameter estimator have been derived in the $p_{0}$ model for directed graphs with the differentially private bi-degree sequence, asymptotic theory in general models is still lacking. In this paper, we release the bi-degree sequence of directed graphs via the discrete Laplace mechanism, which satisfies differential privacy. We use the moment method to estimate the unknown model parameter. We establish a unified asymptotic result, in which consistency and asymptotic normality of the differentially private estimator holds. We apply the unified theoretical result to the Probit model. Simulations and a real data demonstrate our theoretical findings.
Table Pre-training: A Survey on Model Architectures, Pretraining Objectives, and Downstream Tasks

Following the success of pre-training techniques in the natural language domain, a flurry of table pre-training frameworks have been proposed and have achieved new state-of-the-arts on various downstream tasks such as table question answering, table type recognition, column relation classification, table search, and formula prediction. Various model architectures have been explored to best capture the characteristics of (semi-)structured tables, especially specially-designed attention mechanisms. Moreover, to fully leverage the supervision signals in unlabeled tables, diverse pre-training objectives have been designed and evaluated, for example, denoising cell values, predicting numerical relationships, and learning a neural SQL executor. This survey aims to provide a comprehensive review of model designs, pre-training objectives, and downstream tasks for table pre-training, and we further share our thoughts on existing challenges and future opportunities.
On the Evolution of the Hubble Constant with the SNe Ia Pantheon Sample and Baryon Acoustic Oscillations: A Feasibility Study for GRB-Cosmology in 2030

The difference from 4 to 6 σ in the Hubble constant (H0) between the values observed with the local (Cepheids and Supernovae Ia, SNe Ia) and the high-z probes (Cosmic Microwave Background obtained by the Planck data) still challenges the astrophysics and cosmology community. Previous analysis has shown that there is an evolution in the Hubble constant that scales as f(z)=H0/(1+z)η, where H0 is H0(z=0) and η is the evolutionary parameter. Here, we investigate if this evolution still holds by using the SNe Ia gathered in the Pantheon sample and the Baryon Acoustic Oscillations. We assume H0=70kms−1Mpc−1 as the local value and divide the Pantheon into three bins ordered in increasing values of redshift. Similar to our previous analysis but varying two cosmological parameters contemporaneously (H0, Ω0m in the ΛCDM model and H0, wa in the w0waCDM model), for each bin we implement a Markov-Chain Monte Carlo analysis (MCMC) obtaining the value of H0 assuming Gaussian priors to restrict the parameters spaces to values we expect from our prior knowledge of the current cosmological models and to avoid phantom Dark Energy models with w<−1. Subsequently, the values of H0 are fitted with the model f(z). Our results show that a decreasing trend with η∼10−2 is still visible in this sample. The η coefficient reaches zero in 2.0 σ for the ΛCDM model up to 5.8 σ for w0waCDM model. This trend, if not due to statistical fluctuations, could be explained through a hidden astrophysical bias, such as the effect of stretch evolution, or it requires new theoretical models, a possible proposition is the modified gravity theories, f(R). This analysis is meant to further cast light on the evolution of H0 and it does not specifically focus on constraining the other parameters. This work is also a preparatory to understand how the combined probes still show an evolution of the H0 by redshift and what is the current status of simulations on GRB cosmology to obtain the uncertainties on the Ω0m comparable with the ones achieved through SNe Ia.
Are we always in strife? A longitudinal study of the echo chamber effect in the Australian Twittersphere

Contrary to expectations that the increased connectivity offered by the internet and particularly Online Social Networks (OSNs) would result in broad consensus on contentious issues, we instead frequently observe the formation of polarised echo chambers, in which only one side of an argument is entertained. These can progress to filter bubbles, actively filtering contrasting opinions, resulting in vulnerability to misinformation and increased polarisation on social and political issues. These have real-world effects when they spread offline, such as vaccine hesitation and violence. This work seeks to develop a better understanding of how echo chambers manifest in different discussions dealing with different issues over an extended period of time. We explore the activities of two groups of polarised accounts across three Twitter discussions in the Australian context. We found Australian Twitter accounts arguing against marriage equality in 2017 were more likely to support the notion that arsonists were the primary cause of the 2019/2020 Australian bushfires, and those supporting marriage equality argued against that arson narrative. We also found strong evidence that the stance people took on marriage equality in 2017 did not predict their political stance in discussions around the Australian federal election two years later. Although mostly isolated from each other, we observe that in certain situations the polarised groups may interact with the broader community, which offers hope that the echo chambers may be reduced with concerted outreach to members.