Miracle-dz/UniFAR-FTUs huggingface dataset:
Gmunu: paralleled, grid-adaptive, general-relativistic magnetohydrodynamics in curvilinear geometries in dynamical space–times

We present an update on the General-relativistic multigrid numerical (Gmunu) code, a parallelized, multidimensional curvilinear, general relativistic magnetohydrodynamics code with an efficient non-linear cell-centred multigrid elliptic solver, which is fully coupled with an efficient block-based adaptive mesh refinement module. To date, as described in this paper, Gmunu is able to solve the elliptic metric equations in the conformally flat condition approximation with the multigrid approach and the equations of ideal general-relativistic magnetohydrodynamics by means of high-resolution shock-capturing finite-volume method with reference metric formularised multidimensionally in Cartesian, cylindrical, or spherical geometries. To guarantee the absence of magnetic monopoles during the evolution, we have developed an elliptical divergence cleaning method by using the multigrid solver. In this paper, we present the methodology, full evolution equations and implementation details of Gmunu and its properties and performance in some benchmarking and challenging relativistic magnetohydrodynamics problems.
Effects of Shell Thickness on Cross-Helicity Generation in Convection-Driven Spherical Dynamos

The relative importance of the helicity and cross-helicity electromotive dynamo effects for self-sustained magnetic field generation by chaotic thermal convection in rotating spherical shells is investigated as a function of shell thickness. Two distinct branches of dynamo solutions are found to coexist in direct numerical simulations for shell aspect ratios between 0.25 and 0.6—a mean-field dipolar regime and a fluctuating dipolar regime. The properties characterising the coexisting dynamo attractors are compared and contrasted, including differences in temporal behaviour and spatial structures of both magnetic fields and rotating thermal convection. The helicity α-effect and the cross-helicity γ-effect are found to be comparable in intensity within the fluctuating dipolar dynamo regime, where their ratio does not vary significantly with the shell thickness. In contrast, within the mean-field dipolar dynamo regime the helicity α-effect dominates by approximately two orders of magnitude and becomes stronger with decreasing shell thickness.
Crystal and molecular structure of V‐anhydrous amylose

The conformation and crystalline packing of V‐anhydrous amylose has been investigated by a combination of linked atom model building and X‐ray diffraction analysis. The unit cell, the P212121 space group, the left‐handed sixfold helical conformation with all O(6) in gt rotational positions, and the intrahelical O(2)‐‐‐O(3) and O(2)‐‐‐O(6) hydrogen bonds are substantially in agreement with previous studies. A new model for packing of the chains in the unit cell and the presence of crystallographic water is proposed. Packing appears to be stabilized by corner‐to‐center chain O(2)‐‐‐O(2) hydrogen bonds. The nature of the transition from the amylose–DMSO complex to Va‐amylose was considered and it is shown that the transition involves translation of the amylose chains parallel to the a and b unit cell axes with only slight changes in the orientation of the helix. No significant conformational changes result from the transition.
Localization Attack by Precoder Feedback Overhearing in 5G Networks and Countermeasures

In fifth-generation (5G) cellular networks, users feed back to the base station the index of the precoder (from a codebook) to be used for downlink transmission. The precoder is strongly related to the user channel and in turn to the user position within the cell. We propose a method by which an external attacker determines the user position by passively overhearing this unencrypted layer-2 feedback signal. The attacker first builds a map of fed back precoder indices in the cell. Then, by overhearing the precoder index fed back by the victim user, the attacker finds its position on the map. We focus on the type-I single-panel codebook, which today is the only mandatory solution in the 3GPP standard. We analyze the attack and assess the obtained localization accuracy against various parameters. We analyze the localization error of a simplified precoder feedback model and describe its asymptotic localization precision. We also propose a mitigation against our attack, wherein the user randomly selects the precoder among those providing the highest rate. Simulations confirm that the attack can achieve a high localization accuracy, which is significantly reduced when the mitigation solution is adopted, at the cost of a negligible rate degradation.
Bifurcations and Chaos in Ho\v{r}ava-Lifshitz Cosmology

The nature of generic spacelike singularities in general relativity is connected with first principles, notably Lorentzian causal structure, scale invariance and general covariance. To bring a new perspective on how these principles affect generic spacelike singularities, we consider the initial singularity in spatially homogeneous Bianchi type VIII and IX vacuum models in Ho\v{r}ava-Lifshitz gravity, where relativistic first principles are replaced with anisotropic scalings of Lifshitz type. Within this class of models, General Relativity is shown to be a bifurcation where chaos becomes generic. To describe the chaotic features of generic singularities in Ho\v{r}ava-Lifshitz cosmology, we introduce symbolic dynamics within Cantor sets and iterated function systems.
A Visual Mining Approach to Improved Multiple-Instance Learning

Multiple-instance learning (MIL) is a paradigm of machine learning that aims to classify a set (bag) of objects (instances), assigning labels only to the bags. This problem is often addressed by selecting an instance to represent each bag, transforming an MIL problem into standard supervised learning. Visualization can be a useful tool to assess learning scenarios by incorporating the users’ knowledge into the classification process. Considering that multiple-instance learning is a paradigm that cannot be handled by current visualization techniques, we propose a multiscale tree-based visualization called MILTree to support MIL problems. The first level of the tree represents the bags, and the second level represents the instances belonging to each bag, allowing users to understand the MIL datasets in an intuitive way. In addition, we propose two new instance selection methods for MIL, which help users improve the model even further. Our methods can handle both binary and multiclass scenarios. In our experiments, SVM was used to build the classifiers. With support of the MILTree layout, the initial classification model was updated by changing the training set, which is composed of the prototype instances. Experimental results validate the effectiveness of our approach, showing that visual mining by MILTree can support exploring and improving models in MIL scenarios and that our instance selection methods outperform the currently available alternatives in most cases.
RegulaTOR: A Powerful Website Fingerprinting Defense

Website Fingerprinting (WF) attacks are used by passive, local attackers to determine the destination of encrypted internet traffic by comparing the sequences of packets sent to and received by the user to a previously recorded data set. As a result, WF attacks are of particular concern to privacy-enhancing technologies such as Tor. In response, a variety of WF defenses have been developed, though they tend to incur a high bandwidth and latency overhead or require additional infrastructure, making them difficult to implement in practice. Some lighter-weight defenses have been presented as well; still, they attain only moderate effectiveness against recently published WF attacks. In this paper, we aim to present a realistic and novel defense, Regulator, that demonstrates improved overhead and high effectiveness against current WF attacks. In the closed-world setting, this defense reduces the accuracy of the state-of-the-art attack, Tik-Tok, against lightweight defenses from 66% to 22.9%. To achieve this performance, it requires minimal added latency and a bandwidth overhead 38.1% less than the leading lightweight defense. In the open-world setting, Regulator limits a precision-tuned Tik-Tok attack to an F-score of . 087, compared to . 625 for the best comparable lightweight defense.
D-LEMA: Deep Learning Ensembles from Multiple Annotations - Application to Skin Lesion Segmentation

Medical image segmentation annotations suffer from inter- and intra-observer variations even among experts due to intrinsic differences in human annotators and ambiguous boundaries. Leveraging a collection of annotators’ opinions for an image is an interesting way of estimating a gold standard. Although training deep models in a supervised setting with a single annotation per image has been extensively studied, generalizing their training to work with datasets containing multiple annotations per image remains a fairly unexplored problem. In this paper, we propose an approach to handle annotators’ disagreements when training a deep model. To this end, we propose an ensemble of Bayesian fully convolutional networks (FCNs) for the segmentation task by considering two major factors in the aggregation of multiple ground truth annotations: (1) handling contradictory annotations in the training data originating from inter-annotator disagreements and (2) improving confidence calibration through the fusion of base models’ predictions. We demonstrate the superior performance of our approach on the ISIC Archive and explore the generalization performance of our proposed method by cross-dataset evaluation on the PH2 and DermoFit datasets.
Quintessential inflation in Palatini 
f(R)
 gravity

We investigate in detail a family of quintessential inflation models in the context of $R+R^2$ Palatini modified gravity. We find that successful inflation and quintessence are obtained with an inflaton scalar potential that is approximately quadratic in inflation and inverse quartic in quintessence. We show that corrections for the kination period due to Palatini modified gravity are subdominant, while the setup does not challenge constraints on modified gravity from solar system observations and microscopic experiments, in contrast to the metric case. We obtain concrete predictions regarding primordial tensors, to be probed in the near future.
Deep Reinforcement Learning for Tropical Air Free-Cooled Data Center Control

Air free-cooled data centers (DCs) have not existed in the tropical zone due to the unique challenges of year-round high ambient temperature and relative humidity (RH). The increasing availability of servers that can tolerate higher temperatures and RH due to the regulatory bodies’ prompts to raise DC temperature setpoints sheds light upon the feasibility of air free-cooled DCs in the tropics. However, due to the complex psychrometric dynamics, operating the air free-cooled DC in the tropics generally requires adaptive control of supply air condition to maintain the computing performance and reliability of the servers. This article studies the problem of controlling the supply air temperature and RH in a free-cooled tropical DC below certain thresholds. To achieve the goal, we formulate the control problem as Markov decision processes and apply deep reinforcement learning (DRL) to learn the control policy that minimizes the cooling energy while satisfying the requirements on the supply air temperature and RH. We also develop a constrained DRL solution for performance improvements. Extensive evaluation based on real data traces collected from an air free-cooled testbed and comparisons among the unconstrained and constrained DRL approaches as well as two other baseline approaches show the superior performance of our proposed solutions.
Teacher-Student Asynchronous Learning with Multi-Source Consistency for Facial Landmark Detection

Due to the high annotation cost of large-scale facial landmark detection tasks in videos, a semi-supervised paradigm that uses self-training for mining high-quality pseudo-labels to participate in training has been proposed by researchers. However, self-training based methods often train with a gradually increasing number of samples, whose performances vary a lot depending on the number of pseudo-labeled samples added. In this paper, we propose a teacher-student asynchronous learning~(TSAL) framework based on the multi-source supervision signal consistency criterion, which implicitly mines pseudo-labels through consistency constraints. Specifically, the TSAL framework contains two models with exactly the same structure. The radical student uses multi-source supervision signals from the same task to update parameters, while the calm teacher uses a single-source supervision signal to update parameters. In order to reasonably absorb student's suggestions, teacher's parameters are updated again through recursive average filtering. The experimental results prove that asynchronous-learning framework can effectively filter noise in multi-source supervision signals, thereby mining the pseudo-labels which are more significant for network parameter updating. And extensive experiments on 300W, AFLW, and 300VW benchmarks show that the TSAL framework achieves state-of-the-art performance.
From #jobsearch to #mask: improving COVID-19 cascade prediction with spillover effects

An information outbreak occurs on social media along with the COVID-19 pandemic and leads to infodemic. Predicting the popularity of online content, known as cascade prediction, allows for not only catching in advance hot information that deserves attention, but also identifying false information that will widely spread and require quick response to mitigate its impact. Among the various information diffusion patterns leveraged in previous works, the spillover effect of the information exposed to users on their decision to participate in diffusing certain information is still not studied. In this paper, we focus on the diffusion of information related to COVID-19 preventive measures. Through our collected Twitter dataset, we validated the existence of this spillover effect. Building on the finding, we proposed extensions to three cascade prediction methods based on Graph Neural Networks (GNNs). Experiments conducted on our dataset demonstrated that the use of the identified spillover effect significantly improves the state-of-the-art GNNs methods in predicting the popularity of not only preventive measure messages, but also other COVID-19 related messages.
Spatially resolved properties of galaxies with a kinematically distinct core

Aims. Interacting galaxies show unique irregularities in their kinematic structures. By investigating the spatially resolved kinematics and stellar population properties of galaxies that demonstrate irregularities, we can paint a detailed picture of the formation and evolutionary processes that took place during their lifetimes. Methods. In this work, we focused on galaxies with a specific kinematic irregularity: a kinematically distinct stellar core (KDC). In particular, we considered counter-rotating galaxies in which the core and main body of are rotating in opposite directions. We visually identified 11 MaNGA galaxies with a KDC from their stellar kinematics, and we investigated their spatially resolved stellar and gaseous kinematic properties, namely the two-dimensional stellar and gaseous velocity and velocity-dispersion (σ) maps. Additionally, we examined the stellar population properties, as well as spatially resolved recent star formation histories, using the Dn4000 and Hδ gradients. Results. The galaxies display multiple off-centred symmetrical peaks in the stellar σ maps. The gaseous velocity and σ maps display regular properties. The stellar population properties and their respective gradients show differing properties depending on the results of the spatially resolved emission line diagnostics of the galaxies, with some but not all galaxies showing inside-out quenching. The star formation histories also largely differ based on the spatially resolved emission line diagnostics, but most galaxies show indications of recent star formation either in their outskirts or core. Conclusions. We find a distinct difference in kinematic and stellar population properties in galaxies with a counter-rotating stellar core, which depends on its classification using spatially resolved emission line diagnostics.
Collisions of False-Vacuum Bubble Walls in a Quantum Spin Chain

We study the real-time dynamics of a small bubble of "false vacuum" in a 1+1-dimensional quantum spin chain. Such a bubble can also be thought of as a well-separated, but confined, kink-antikink pair. We construct initial bubbles as infinite Matrix Product States via a quasiparticle wavepacket ansatz and approximately simulate their (unitary) time evolution, analyzing the outcomes of the resulting kink-antikink collisions. In the transverse-field Ising chain with a longitudinal field, we do not observe particle production, despite the nonintegrability of the model, supporting recent observations of nonthermalizing mesonic states with the same model. After adding extra terms to the Hamiltonian, however, we are able to observe production of both confined and unconfined particle pairs. We find the methods used are adequate for simulating low-energy events, in which few particles are produced. However, our observation of entanglement growth with energy and with the number of collisions suggests that accurate simulations at higher energies will ultimately exhaust our methods. Quantum devices, in contrast, are not limited by entanglement production, and promise to allow us to go far beyond classical methods. We consider simulations of false-vacuum dynamics to be exciting applications for both analog and digital quantum simulation.
Network-Cognizant Time-Coupled Aggregate Flexibility of Distribution Systems Under Uncertainties

Increasing integration of distributed energy resources (DERs) within distribution feeders provides unprecedented flexibility at the distribution-transmission interconnection. To exploit this flexibility and to use the capacity potential of aggregate DERs, feasible substation power injection trajectories need to be efficiently characterized. This letter provides an ellipsoidal inner approximation of the set of feasible power injection trajectories at the substation such that for any point in the set, there exists a feasible disaggregation strategy of DERs for any load uncertainty realization. The problem is formulated as one of finding the robust maximum volume ellipsoid inside the flexibility region under uncertainty. Though the problem is NP-hard even in the deterministic case, this letter derives novel approximations of the resulting adaptive robust optimization problem based on optimal second-stage policies. The proposed approach yields less conservative flexibility characterization than existing flexibility region approximation formulations. The efficacy of the proposed method is demonstrated on a realistic distribution feeder.
Traffic rate network tomography with higher‐order cumulants

Network tomography aims at estimating source–destination traffic rates from link traffic measurements. This inverse problem was formulated by Vardi in 1996 for Poisson traffic over networks operating under deterministic as well as random routing regimes. In this article, we expand Vardi's second‐order moment matching rate estimation approach to higher‐order cumulant matching with the goal of increasing the column rank of the mapping and consequently improving the rate estimation accuracy. We develop a systematic set of linear cumulant matching equations and express them compactly in terms of the Khatri–Rao product. Both least squares estimation and iterative minimum I‐divergence estimation are considered. We develop an upper bound on the mean squared error (MSE) in least squares rate estimation from empirical cumulants. We demonstrate that supplementing Vardi's approach with the third‐order empirical cumulant reduces its minimum averaged normalized MSE in rate estimation by almost 20% when iterative minimum I‐divergence estimation was used.
Periocular in the Wild Embedding Learning with Cross-Modal Consistent Knowledge Distillation

Periocular biometric, or peripheral area of ocular, is a collaborative alternative to face, especially if a face is occluded or masked. In practice, sole periocular biometric captures least salient facial features, thereby suffering from intra-class compactness and inter-class dispersion issues particularly in the wild environment. To address these problems, we transfer useful information from face to support periocular modality by means of knowledge distillation (KD) for embedding learning. However, applying typical KD techniques to heterogeneous modalities directly is suboptimal. We put forward in this paper a deep face-to-periocular distillation networks, coined as cross-modal consistent knowledge distillation (CM-CKD) henceforward. The three key ingredients of CM-CKD are (1) shared-weight networks, (2) consistent batch normalization, and (3) a bidirectional consistency distillation for face and periocular through an effectual CKD loss. To be more specific, we leverage face modality for periocular embedding learning, but only periocular images are targeted for identification or verification tasks. Extensive experiments on six constrained and unconstrained periocular datasets disclose that the CM-CKD-learned periocular embeddings extend identification and verification performance by 50% in terms of relative performance gain computed based upon face and periocular baselines. The experiments also reveal that the CM-CKD-learned periocular features enjoy better subject-wise cluster separation, thereby refining the overall accuracy performance.
Molecular graph generation with Graph Neural Networks

The generation of graph-structured data is an emerging problem in the field of deep learning. Various solutions have been proposed in the last few years, yet the exploration of this branch is still in an early phase. In sequential approaches, the construction of a graph is the result of a sequence of decisions, in which, at each step, a node or a group of nodes is added to the graph, along with its connections. A very relevant application of graph generation methods is the discovery of new drug molecules, which are naturally represented as graphs. In this paper, we introduce a sequential molecular graph generator based on a set of graph neural network modules, which we call MG^2N^2. Its modular architecture simplifies the training procedure, also allowing an independent retraining of a single module. The use of graph neural networks maximizes the information in input at each generative step, which consists of the subgraph produced during the previous steps. Experiments of unconditional generation on the QM9 dataset show that our model is capable of generalizing molecular patterns seen during the training phase, without overfitting. The results indicate that our method outperforms very competitive baselines, and can be placed among the state of the art approaches for unconditional generation on QM9.