Miracle-dz/UniFAR-FTUs huggingface dataset:
A dual adaptive explicit time integration algorithm for efficiently solving the cardiac monodomain equation

The monodomain model is widely used in in‐silico cardiology to describe excitation propagation in the myocardium. Frequently, operator splitting is used to decouple the stiff reaction term and the diffusion term in the monodomain model so that they can be solved separately. Commonly, the diffusion term is solved implicitly with a large time step while the reaction term is solved by using an explicit method with adaptive time stepping. In this work, we propose a fully explicit method for the solution of the decoupled monodomain model. In contrast to semi‐implicit methods, fully explicit methods present lower memory footprint and higher scalability. However, such methods are only conditionally stable. We overcome the conditional stability limitation by proposing a dual adaptive explicit method in which adaptive time integration is applied for the solution of both the reaction and diffusion terms. We perform a set of numerical examples where cardiac propagation is simulated under physiological and pathophysiological conditions. Results show that the proposed method presents preserved accuracy and improved computational efficiency as compared to standard operator splitting‐based methods.
Neural Network Compression Via Sparse Optimization

The compression of deep neural networks (DNNs) to reduce inference cost becomes increasingly important to meet realistic deployment requirements of various applications. There have been a significant amount of work regarding network compression, while most of them are heuristic rule-based or typically not friendly to be incorporated into varying scenarios. On the other hand, sparse optimization yielding sparse solutions naturally fits the compression requirement, but due to the limited study of sparse optimization in stochastic learning, its extension and application onto model compression is rarely well explored. In this work, we propose a model compression framework based on the recent progress on sparse stochastic optimization. Compared to existing model compression techniques, our method is effective and requires fewer extra engineering efforts to incorporate with varying applications, and has been numerically demonstrated on benchmark compression tasks. Particularly, we achieve up to 7.2 and 2.9 times FLOPs reduction with the same level of evaluation accuracy on VGG16 for CIFAR10 and ResNet50 for ImageNet compared to the baseline heavy models, respectively.
Perception Improvement for Free: Exploring Imperceptible Black-box Adversarial Attacks on Image Classification

Deep neural networks are vulnerable to adversarial attacks. White-box adversarial attacks can fool neural networks with small adversarial perturbations, especially for large size images. However, keeping successful adversarial perturbations imperceptible is especially challenging for transfer-based black-box adversarial attacks. Often such adversarial examples can be easily spotted due to their unpleasantly poor visual qualities, which compromises the threat of adversarial attacks in practice. In this study, to improve the image quality of black-box adversarial examples perceptually, we propose structure-aware adversarial attacks by generating adversarial images based on psychological perceptual models. Specifically, we allow higher perturbations on perceptually insignificant regions, while assigning lower or no perturbation on visually sensitive regions. In addition to the proposed spatial-constrained adversarial perturbations, we also propose a novel structure-aware frequency adversarial attack method in the discrete cosine transform (DCT) domain. Since the proposed attacks are independent of the gradient estimation, they can be directly incorporated with existing gradient-based attacks. Experimental results show that, with the comparable attack success rate (ASR), the proposed methods can produce adversarial examples with considerably improved visual quality for free. With the comparable perceptual quality, the proposed approaches achieve higher attack success rates: particularly for the frequency structure-aware attacks, the average ASR improves more than 10% over the baseline attacks.
MP-ResNet: Multipath Residual Network for the Semantic Segmentation of High-Resolution PolSAR Images

There are limited studies on the semantic segmentation of high-resolution polarimetric synthetic aperture radar (PolSAR) images due to the scarcity of training data and the complexity of managing speckle noise. The Gaofen contest has provided open access a high-quality PolSAR semantic segmentation dataset. Taking this opportunity, we propose a multipath residual network (MP-ResNet) architecture for the semantic segmentation of high-resolution PolSAR images. Compared to conventional U-shape encoder–decoder convolutional neural network (CNN) architectures, the MP-ResNet learns semantic context with its parallel multiscale branches, which greatly enlarges its valid receptive fields and improves the embedding of local discriminative features. In addition, MP-ResNet adopts a multilevel feature fusion design in its decoder to effectively exploit the features learned from its different branches. Comparisons with the baseline method of fully connected network (FCN with ResNet34) show that the MP-ResNet has achieved significant accuracy improvements. It also surpasses several state-of-the-art methods in terms of overall accuracy (OA), $\text{m}F_{1}$ and frequency weighted intersection over union (fwIoU), with only a limited increase of computational costs. This CNN architecture can be used as a baseline method for future studies on the semantic segmentation of PolSAR images. The code is available at: https://github.com/ggsDing/SARSeg.
MUSE: Illustrating Textual Attributes by Portrait Generation

We propose a novel approach, MUSE, to illustrate textual attributes visually via portrait generation. MUSE takes a set of attributes written in text, in addition to facial features extracted from a photo of the subject as input. We propose 11 attribute types to represent inspirations from a subject's profile, emotion, story, and environment. We propose a novel stacked neural network architecture by extending an image-to-image generative model to accept textual attributes. Experiments show that our approach significantly outperforms several state-of-the-art methods without using textual attributes, with Inception Score score increased by 6% and Fr\'echet Inception Distance (FID) score decreased by 11%, respectively. We also propose a new attribute reconstruction metric to evaluate whether the generated portraits preserve the subject's attributes. Experiments show that our approach can accurately illustrate 78% textual attributes, which also help MUSE capture the subject in a more creative and expressive way.
AMICO galaxy clusters in KiDS-DR3: Evolution of the luminosity function between z = 0.1 and z = 0.8

Aims. By means of the r-band luminosity function (LF) of galaxies in a sample of about 4000 clusters detected by the cluster finder AMICO in the KiDS-DR3 area of about 400 deg2, we studied the evolution with richness and redshift of the passive evolving (red), star-forming (blue), and total galaxy populations. This analysis was performed for clusters in the redshift range [0.1, 0.8] and in the mass range [1013 M⊙, 1015 M⊙]. Methods. To compute LFs, we binned the luminosity distribution in magnitude and statistically subtracted the background. Then, we divided the cluster sample in bins of both redshift and richness/mass. We stacked LF counts in each 2D bin for the total, red, and blue galaxy populations; finally, we fitted the stacked LF with a Schechter function and studied the trend of its parameters with redshift and richness/mass. Results. We found a passive evolution with z for the bright part of the LF for the red and total populations and no significant trends for the faint galaxies. The mass/richness dependence is clear for the density parameter Φ⋆, increasing with richness, and for the total population faint end, which is shallower in the rich clusters.
Interacting quantum fields in various charts of anti–de Sitter spacetime

The goal of the present note is to understand whether it is possible to define interacting quantum field theory in global anti de Sitter space-time with Lorentzian signature, in its covering space-time (whose time coordinate is not periodic) and in its Poincare patch. We show that in global anti de Sitter space-time there are certain problems to define quantum field theory properly. This is due to an additional UV singularity of the Feynman propagator which is sitting on the light-cone emanating from the antipodal point of the source. There is no such singularity in flat space-time. At the same time quantum field theory in Poincare region of the AdS space-time can be well defined and is related to the one in Euclidean AdS via the analytical continuation. In principle one can also define and analytically continue quantum field theory in the covering anti de Sitter space-time. However, to do that one has to use an unusual $i\epsilon$-prescription in the Feynman propagator, which cannot be used in loop calculations in non-stationary situations.
Coronal mass ejections observed by heliospheric imagers at 0.2 and 1 au

Context. We study two coronal mass ejections (CMEs) observed between April 1-2, 2019 by both the inner Wide-Field Imager for Parker Solar Probe (WISPR-I) and the inner heliospheric imager (HI-1) on board STEREO-A. This is the first study of CME observations from two viewpoints in similar directions but at considerably different solar distances. Aims. Our objective is to understand how the PSP observations affect the CME kinematics, especially due to its proximity to the Sun. Methods. We estimate the CME positions, speeds, accelerations, propagation directions and longitudinal deflections using imaging observations from two spacecraft, and a set of analytical expressions that consider the CME as a point structure and take into account the rapid change in spacecraft position. Results. We find that both CMEs are slow ($< 400\ km\ s^{-1}$), propagating eastward of the Sun-Earth line. The second CME seems to accelerate between $\sim 0.1$ to $\sim 0.2\ au$ and deflect westward with an angular speed consistent with the solar rotation speed. We find some discrepancies in the CME solar distance (up to $0.05\ au$, particularly for CME \#1), latitude (up to $\sim10^{\circ}$) and longitude (up to $24^{\circ}$) when comparing results from different fit cases (different observations or set of free parameters). Conclusions. Discrepancies in longitude are likely due to the feature tracked visually rather than instrumental biases or fit assumptions. For similar reasons, the CME \#1 solar distance, as derived from WISPR-I observations, is larger than the HI-1 result, regardless of the fit parameters considered. Error estimates for CME kinematics do not show any clear trend associated to the observing instrument. The source region location and the lack of any clear in situ counterparts (both at near-Earth and at PSP) support our estimate of the propagation direction for both events.
Longitudinal modeling of MS patient trajectories improves predictions of disability progression

Research in Multiple Sclerosis (MS) has recently focused on extracting knowledge from real-world clinical data sources. This type of data is more abundant than data produced during clinical trials and potentially more informative about real-world clinical practice. However, this comes at the cost of less curated and controlled data sets. In this work, we address the task of optimally extracting information from longitudinal patient data in the real-world setting with a special focus on the sporadic sampling problem. Using the MSBase registry, we show that with machine learning methods suited for patient trajectories modeling, such as recurrent neural networks and tensor factorization, we can predict disability progression of patients in a two-year horizon with an ROC-AUC of 0.86, which represents a 33% decrease in the ranking pair error (1-AUC) compared to reference methods using static clinical features. Compared to the models available in the literature, this work uses the most complete patient history for MS disease progression prediction.
Stage-wise Channel Pruning for Model Compression

Auto-ML pruning methods aim at searching a pruning strategy automatically to reduce the computational complexity of deep Convolutional Neural Networks(deep CNNs). However, some previous works found that the results of many Auto-ML pruning methods even cannot surpass the results of the uniformly pruning method. In this paper, we first analyze the reason for the ineffectiveness of Auto-ML pruning. Subsequently, a stage-wise pruning(SP) method is proposed to solve the above problem. As with most of the previous Auto-ML pruning methods, SP also trains a super-net that can provide proxy performance for sub-nets and search the best sub-net who has the best proxy performance. Different from previous works, we split a deep CNN into several stages and use a full-net where all layers are not pruned to supervise the training and the searching of sub-nets. Remarkably, the proxy performance of sub-nets trained with SP is closer to the actual performance than most of the previous Auto-ML pruning works. Therefore, SP achieves the state-of-the-art on both CIFAR-10 and ImageNet under the mobile setting.
Distributed Distance Approximation

Diameter, radius and eccentricities are fundamental graph parameters, which are extensively studied in various computational settings. Typically, computing approximate answers can be much more efficient compared with computing exact solutions. In this paper, we give a near complete characterization of the trade-offs between approximation ratios and round complexity of distributed algorithms for approximating these parameters, with a focus on the weighted and directed variants. Furthermore, we study \emph{bi-chromatic} variants of these parameters defined on a graph whose vertices are colored either red or blue, and one focuses only on distances for pairs of vertices that are colored differently. Motivated by applications in computational geometry, bi-chromatic diameter, radius and eccentricities have been recently studied in the sequential setting [Backurs et al. STOC'18, Dalirrooyfard et al. ICALP'19]. We provide the first distributed upper and lower bounds for such problems. Our technical contributions include introducing the notion of \emph{approximate pseudo-center}, which extends the \emph{pseudo-centers} of [Choudhary and Gold SODA'20], and presenting an efficient distributed algorithm for computing approximate pseudo-centers. On the lower bound side, our constructions introduce the usage of new functions into the framework of reductions from 2-party communication complexity to distributed algorithms.
Dynamic Embeddings for Interaction Prediction

In recommender systems (RSs), predicting the next item that a user interacts with is critical for user retention. While the last decade has seen an explosion of RSs aimed at identifying relevant items that match user preferences, there is still a range of aspects that could be considered to further improve their performance. For example, often RSs are centered around the user, who is modeled using her recent sequence of activities. Recent studies, however, have shown the effectiveness of modeling the mutual interactions between users and items using separate user and item embeddings. Building on the success of these studies, we propose a novel method called DeePRed that addresses some of their limitations. In particular, we avoid recursive and costly interactions between consecutive short-term embeddings by using long-term (stationary) embeddings as a proxy. This enable us to train DeePRed using simple mini-batches without the overhead of specialized mini-batches proposed in previous studies. Moreover, DeePRed’s effectiveness comes from the aforementioned design and a multi-way attention mechanism that inspects user-item compatibility. Experiments show that DeePRed outperforms the best state-of-the-art approach by at least 14% of Mean Reciprocal Rank (MRR) on next item prediction task, while gaining more than an order of magnitude speedup over the best performing baselines. Although this study is mainly concerned with temporal interaction networks, we also show the power and flexibility of DeePRed by adapting it to the case of static interaction networks, substituting the short- and long-term aspects with local and global ones.
Nonparametric Adaptive Bayesian Stochastic Control Under Model Uncertainty

In this paper we propose a new methodology for solving a discrete time stochastic Markovian control problem under model uncertainty. By utilizing the Dirichlet process, we model the unknown distribution of the underlying stochastic process as a random probability measure and achieve online learning in a Bayesian manner. Our approach integrates optimizing and dynamic learning. When dealing with model uncertainty, the nonparametric framework allows us to avoid model misspecification that usually occurs in other classical control methods. Then, we develop a numerical algorithm to handle the infinitely dimensional state space in this setup and utilizes Gaussian process surrogates to obtain a functional representation of the value function in the Bellman recursion. We also build separate surrogates for optimal control to eliminate repeated optimizations on out-of-sample paths and bring computational speed-ups. Finally, we demonstrate the financial advantages of the nonparametric Bayesian framework compared to parametric approaches such as strong robust and time consistent adaptive.
Nonlocal Conformal Theories Have State-dependent Central Charges

Using the recently developed fractional Virasoro algebra [1], we construct the equivalent operator product expansions for nonlocal quantum field theories in which the nonlocality is provided by the fractional Laplacian as has been shown to be relevant in the long-range Ising model. We find that the OPE's of a general nonlocal CFT are of the form $T_k(z)\Phi(w) \sim \frac{ h_\gamma \Phi}{(z-w)^{1+\gamma}}+\frac{\partial_w^\gamma \Phi}{z-w},$ and $T_k(z)T_k(w) \sim \frac{ c_kZ_\gamma}{(z-w)^{3\gamma+1}}+\frac{(1+\gamma ) T_k(w)}{(z-w)^{1+\gamma}}+\frac{\partial^\gamma_w T_k}{z-w}$ which naturally results in a central charge, $c_k$, that is state-dependent and hence not a constant. In fact, our work indicates that only those theories which are truly nonlocal have state-dependent central charges. All others can be mapped onto an equivalent Gaussian one using a suitable field redefinition.
Higher-Order Spectral Clustering of Directed Graphs

Clustering is an important topic in algorithms, and has a number of applications in machine learning, computer vision, statistics, and several other research disciplines. Traditional objectives of graph clustering are to find clusters with low conductance. Not only are these objectives just applicable for undirected graphs, they are also incapable to take the relationships between clusters into account, which could be crucial for many applications. To overcome these downsides, we study directed graphs (digraphs) whose clusters exhibit further "structural" information amongst each other. Based on the Hermitian matrix representation of digraphs, we present a nearly-linear time algorithm for digraph clustering, and further show that our proposed algorithm can be implemented in sublinear time under reasonable assumptions. The significance of our theoretical work is demonstrated by extensive experimental results on the UN Comtrade Dataset: the output clustering of our algorithm exhibits not only how the clusters (sets of countries) relate to each other with respect to their import and export records, but also how these clusters evolve over time, in accordance with known facts in international trade.
Drift kinetic theory of alpha transport by tokamak perturbations

Upcoming tokamak experiments fuelled with deuterium and tritium are expected to have large alpha particle populations. Such experiments motivate new attention to the theory of alpha particle confinement and transport. A key topic is the interaction of alpha particles with perturbations to the tokamak fields, including those from ripple and magnetohydrodynamic modes like Alfvén eigenmodes. These perturbations can transport alphas, leading to changed localization of alpha heating, loss of alpha power and damage to device walls. Alpha interaction with these perturbations is often studied with single-particle theory. In contrast, we derive a drift kinetic theory to calculate the alpha heat flux resulting from arbitrary perturbation frequency and periodicity (provided these can be studied drift kinetically). Novel features of the theory include the retention of a large effective collision frequency resulting from the resonant alpha collisional boundary layer, correlated interactions over many poloidal transits and finite orbit effects. Heat fluxes are considered for the example cases of ripple and the toroidal Alfvén eigenmode (TAE). The ripple heat flux is small. The TAE heat flux is significant and scales with the square of the perturbation amplitude, allowing the derivation of constraints on mode amplitude for avoidance of significant alpha depletion. A simple saturation condition suggests that TAEs in one upcoming experiment will not cause significant alpha transport via the mechanisms in this theory. However, saturation above the level suggested by the simple condition, but within numerical and experimental experience, which could be accompanied by the onset of stochasticity, could cause significant transport.
Hiding Signatures of Gravitational Instability in Protoplanetary Disks with Planets

We carry out three-dimensional smoothed particle hydrodynamics simulations to show that a migrating giant planet strongly suppresses the spiral structure in self-gravitating disks. We present mock Atacama Large Millimeter/submillimeter Array (ALMA) continuum observations that show that in the absence of a planet, spiral arms due to gravitational instability are easily observed. Whereas in the presence of a giant planet, the spiral structures are suppressed by the migrating planet resulting in a largely axisymmetric disk with a ring and gap structure. Our modeling of the gas kinematics shows that the planet’s presence could be inferred, for example, using optically thin 13C16O. Our results show that it is not necessary to limit the gas mass of disks by assuming high dust-to-gas mass ratios in order to explain a lack of spiral features that would otherwise be expected in high-mass disks.
Restless Legs Syndrome, and symptoms of Restless Syndrome in patients with Graves’ disease: a cross-sectional survey

OBJECTIVES: Restless legs syndrome (RLS) is a frequent comorbid condition associated with distinct unrelated diseases. While the incidence of RLS has not been definitively confirmed, RLS-like symptoms have been reported in a section of Asian population who also had hyperthyroidism. The prevalence of RLS is generally low in Asian populations. Under these circumstances, we hypothesized that in a population where RLS is common, such as in Brazil, RLS could manifest as a comorbid ailment alongside Graves’ disease, a common hyperthyroid condition. METHODS: In a cross-sectional survey, 108 patients who presented with Graves' disease were analyzed for restless legs or associated symptoms. RESULTS: Twelve patients (11.1%) displayed symptoms of RLS prior to the incidence of Graves’ disease. These patients experienced worsening of the symptoms during their hyperthyroid state. Six patients (5.6%) developed RLS, consequent upon the incidence of Graves’ disease as per the consensus of the panel of the experts. Fifteen patients (13.9%) also presented with RLS-like symptoms without any discernible circadian feature of the syndrome. CONCLUSION: Our findings confirm that Graves’ disease might trigger restless legs-like symptoms, while the condition of hyperthyroidism could also be complicated by definite RLS.