Miracle-dz/UniFAR-FTUs huggingface dataset:
On the appearance of non-local MRI in Keplerian accretion discs

We revisit the modal analysis of small perturbations in Keplerian ideal gas flows leading to magneto-rotational instability (MRI) using the non-local approach. We consider the case of constant vertical background magnetic field, as well as the case of radially dependent background Alfv\'en velocity. In the case of constant Alfv\'en velocity, MRI modes are described by a Schr\"odinger-like differential equation with some effective potential including 'repulsive' ($1/r^2$) and 'attractive' ($-1/r^3$) terms. Taking into account the radial dependence of the background Alfv\'en speed leads to a qualitative change in the shape of the effective potential. It is shown that there are no stationary energy levels corresponding to unstable modes $\omega^2<0$ in ``shallow'' potentials. In thin accretion disks, the wavelength of the disturbance $\lambda=2\pi/k_z$ is smaller than the half-thickness $h$ of the disk only in ``deep'' potentials. The limiting value of the background Alfv\'en speed $(c_A)_\mathrm{cr}$, above which the magnetorotational instability does not occur, is found. In thin accretion disks with low background Alfv\'en speed $c_A\ll (c_A)_\mathrm{cr}$, the increment of the magnetorotational instability $\omega\approx -\sqrt{3}\mathrm{i}c_Ak_z$ is suppressed compared to the value obtained in the local perturbation analysis.
Learning Deep Robotic Skills on Riemannian Manifolds

In this paper, we propose RiemannianFlow, a deep generative model that allows robots to learn complex and stable skills evolving on Riemannian manifolds. Examples of Riemannian data in robotics include stiffness (symmetric and positive definite matrix (SPD)) and orientation (unit quaternion (UQ)) trajectories. For Riemannian data, unlike Euclidean ones, different dimensions are interconnected by geometric constraints which have to be properly considered during the learning process. Using distance preserving mappings, our approach transfers the data between their original manifold and the tangent space, realizing the removing and re-fulfilling of the geometric constraints. This allows to extend existing frameworks to learn stable skills from Riemannian data while guaranteeing the stability of the learning results. The ability of RiemannianFlow to learn various data patterns and the stability of the learned models are experimentally shown on a dataset of manifold motions. Further, we analyze from different perspectives the robustness of the model with different hyperparameter combinations. It turns out that the model’s stability is not affected by different hyperparameters, a proper combination of the hyperparameters leads to a significant improvement (up to 27.6%) of the model accuracy. Last, we show the effectiveness of RiemannianFlow in a real peg-in-hole (PiH) task where we need to generate stable and consistent position and orientation trajectories for the robot starting from different initial poses.
The Next Generation Virgo Cluster Survey (NGVS). XXXI. The Kinematics of Intracluster Globular Clusters in the Core of the Virgo Cluster

Intracluster (IC) populations are expected to be a natural result of the hierarchical assembly of clusters, yet their low space densities make them difficult to detect and study. We present the first definitive kinematic detection of an IC population of globular clusters (GCs) in the Virgo cluster, around the central galaxy M87. This study focuses on the Virgo core, for which the combination of Next Generation Virgo Cluster Survey photometry and follow-up spectroscopy allows us to reject foreground star contamination and explore GC kinematics over the full Virgo dynamical range. The GC kinematics changes gradually with galactocentric distance, decreasing in mean velocity and increasing in velocity dispersion, eventually becoming indistinguishable from the kinematics of Virgo dwarf galaxies at R > 320 kpc. By kinematically tagging M87 halo and intracluster GCs, we find that (1) the M87 halo has a smaller fraction (52 ± 3%) of blue clusters with respect to the IC counterpart (77 ± 10%), (2) the (g′−r′)0 versus (i′−z′)0 color–color diagrams reveal a galaxy population that is redder than the IC population, which may be due to a different composition in chemical abundance and progenitor mass, and (3) the ICGC distribution is shallower and more extended than the M87 GCs, yet still centrally concentrated. The ICGC specific frequency, SN,ICL = 10.2 ± 4.8, is consistent with what is observed for the population of quenched, low-mass galaxies within 1 Mpc from the cluster’s center. The IC population at Virgo’s center is thus consistent with being an accreted component from low-mass galaxies tidally stripped or disrupted through interactions, with a total mass of .
End-to-End Pareto Set Prediction with Graph Neural Networks for Multi-objective Facility Location

The facility location problems (FLPs) are a typical class of NP-hard combinatorial optimization problems, which are widely seen in the supply chain and logistics. Many mathematical and heuristic algorithms have been developed for optimizing the FLP. In addition to the transportation cost, there are usually multiple conflicting objectives in realistic applications. It is therefore desirable to design algorithms that find a set of Pareto solutions efficiently without enormous search cost. In this paper, we consider the multi-objective facility location problem (MO-FLP) that simultaneously minimizes the overall cost and maximizes the system reliability. We develop a learning-based approach to predicting the distribution probability of the entire Pareto set for a given problem. To this end, the MO-FLP is modeled as a bipartite graph optimization problem and two graph neural networks are constructed to learn the implicit graph representation on nodes and edges. The network outputs are then converted into the probability distribution of the Pareto set, from which a set of non-dominated solutions can be sampled non-autoregressively. Experimental results on MO-FLP instances of different scales show that the proposed approach achieves a comparable performance to a widely used multi-objective evolutionary algorithm in terms of the solution quality while significantly reducing the computational cost for search.
Crystal base of the negative half of the quantum superalgebra $U_q(\mathfrak{gl}(m|n))$

We construct a crystal base of U q ( gl ( m | n )) − , the negative half of the quantum superalgebra U q ( gl ( m | n )). We give a combinatorial description of the associated crystal B m | n ( ∞ ), which is equal to the limit of the crystals of the ( q -deformed) Kac modules K ( λ ). We also construct a crystal base of a parabolic Verma module X ( λ ) associated with the subalgebra U q ( gl 0 | n ), and show that it is compatible with the crystal base of U q ( gl ( m | n )) − and the Kac module K ( λ ) under the canonical embedding and projection of X ( λ ) to U q ( gl ( m | n )) − and K ( λ ), respectively.
Multi-Valued State Component Reliability Systems.

Abstract : Consider a reliability system that is composed of n components each of which is operating at some performance level. And suppose that there exists a nondecreasing function phi, called the structure function, such that phi (x sub 1, ., x sub n) denotes the performance level of the system when the ith component's performance level is x sub i , i = 1, . , n. Whereas almost all previous work assumed that both x sub i and phi (x sub 1, ., x sub n) were binary variables we shall allow both to be arbitrary nonnegative numbers and we extend many of the important results of the usual binary model to this more general framework. In particular a fundamental inequality is obtained for E(phi(X sub 1, ., X sub n)) when phi is binary which can, among other things, be used to generate a host of inequalities concerning IFRA distributions including, as a special case, the IFRA convolution theorem. In Section 2 we define the concept of an IFRA stochastic process and prove the analog of the IFRA closure theorem; and in Section 3 we do the same for NBU stochastic processes.
Exact Gradient Computation for Spiking Neural Networks Through Forward Propagation

Spiking neural networks (SNN) have recently emerged as alternatives to traditional neural networks, owing to energy efficiency benefits and capacity to better capture biological neuronal mechanisms. However, the classic backpropagation algorithm for training traditional networks has been notoriously difficult to apply to SNN due to the hard-thresholding and discontinuities at spike times. Therefore, a large majority of prior work believes exact gradients for SNN w.r. t. their weights do not exist and has focused on approximation methods to produce surrogate gradients. In this paper, (1) by applying the implicit function theorem to SNN at the discrete spike times, we prove that, albeit being non-differentiable in time, SNNs have well-defined gradients w.r. t. their weights, and (2) we propose a novel training algorithm, called \emph{forward propagation} (FP), that computes exact gradients for SNN. FP exploits the causality structure between the spikes and allows us to parallelize computation forward in time. It can be used with other algorithms that simulate the forward pass, and it also provides insights on why other related algorithms such as Hebbian learning and also recently-proposed surrogate gradient methods may perform well.
Detecting fake accounts through Generative Adversarial Network in online social media

Online social media is integral to human life, facilitating messaging, information sharing, and confidential communication while preserving privacy. Platforms like Twitter, Instagram, and Facebook exemplify this phenomenon. However, users face challenges due to network anomalies, often stemming from malicious activities such as identity theft for financial gain or harm. This paper proposes a novel method using user similarity measures and the Generative Adversarial Network (GAN) algorithm to identify fake user accounts in the Twitter dataset. Despite the problem's complexity, the method achieves an AUC rate of 80\% in classifying and detecting fake accounts. Notably, the study builds on previous research, highlighting advancements and insights into the evolving landscape of anomaly detection in online social networks.
A Rapidly-Exploring Random Trees Motion Planning Algorithm for Hybrid Dynamical Systems*

This paper proposes a rapidly-exploring random trees (RRT) algorithm to solve the motion planning problem for hybrid systems. At each iteration, the proposed algorithm, called HyRRT, randomly picks a state sample and extends the search tree by flow or jump, which is also chosen randomly when both regimes are possible. Through a definition of concatenation of functions defined on hybrid time domains, we show that HyRRT is probabilistically complete, namely, the probability of failing to find a motion plan approaches zero as the number of iterations of the algorithm increases. This property is guaranteed under mild conditions on the data defining the motion plan, which include a relaxation of the usual positive clearance assumption imposed in the literature of classical systems. The motion plan is computed through the solution of two optimization problems, one associated with the flow and the other with the jumps of the system. The proposed algorithm is applied to a walking robot so as to highlight its generality and computational features.
Generalization of the Sullivan construction for Transitive Lie Algebroids

D.Sullivan (1977) (see also the book by H.Whitney "Geometric Integration Theory",1957) considered a new model for underlying cochain complex for classical cohomologies with rational coefficients for arbitrary simplicial spaces that gives the isomorphism with classical rational cohomologies. We apply the key ideas developed by K.MacKenzie (2005) and J.Kubarski (1991) to generalization of the D.Sullivan model for transitive Lie algebroids. The generalization is based on the existence of the inverse image of the transitive Lie algebroids and on the property of transitive Lie algebroids being trivial over contractible manifolds. Using these properties one can construct an underlying cochain complex of differential forms on simplicial space.
Simulator-Based Inference with WALDO: Confidence Regions by Leveraging Prediction Algorithms and Posterior Estimators for Inverse Problems

Prediction algorithms, such as deep neural networks (DNNs), are used in many domain sciences to directly estimate internal parameters of interest in simulator-based models, especially in settings where the observations include images or complex high-dimensional data. In parallel, modern neural density estimators, such as normalizing flows, are becoming increasingly popular for uncertainty quantification, especially when both parameters and observations are high-dimensional. However, parameter inference is an inverse problem and not a prediction task; thus, an open challenge is to construct conditionally valid and precise confidence regions, with a guaranteed probability of covering the true parameters of the data-generating process, no matter what the (unknown) parameter values are, and without relying on large-sample theory. Many simulator-based inference (SBI) methods are indeed known to produce biased or overly confident parameter regions, yielding misleading uncertainty estimates. This paper presents WALDO, a novel method to construct confidence regions with finite-sample conditional validity by leveraging prediction algorithms or posterior estimators that are currently widely adopted in SBI. WALDO reframes the well-known Wald test statistic, and uses a computationally efficient regression-based machinery for classical Neyman inversion of hypothesis tests. We apply our method to a recent high-energy physics problem, where prediction with DNNs has previously led to estimates with prediction bias. We also illustrate how our approach can correct overly confident posterior regions computed with normalizing flows.
A Mini-Block Fisher Method for Deep Neural Networks

Deep neural networks (DNNs) are currently predominantly trained using first-order methods. Some of these methods (e.g., Adam, AdaGrad, and RMSprop, and their variants) incorporate a small amount of curvature information by using a diagonal matrix to precondition the stochastic gradient. Recently, effective second-order methods, such as KFAC, K-BFGS, Shampoo, and TNT, have been developed for training DNNs, by preconditioning the stochastic gradient by layer-wise block-diagonal matrices. Here we propose a"mini-block Fisher (MBF)"preconditioned gradient method, that lies in between these two classes of methods. Specifically, our method uses a block-diagonal approximation to the empirical Fisher matrix, where for each layer in the DNN, whether it is convolutional or feed-forward and fully connected, the associated diagonal block is itself block-diagonal and is composed of a large number of mini-blocks of modest size. Our novel approach utilizes the parallelism of GPUs to efficiently perform computations on the large number of matrices in each layer. Consequently, MBF's per-iteration computational cost is only slightly higher than it is for first-order methods. The performance of our proposed method is compared to that of several baseline methods, on both autoencoder and CNN problems, to validate its effectiveness both in terms of time efficiency and generalization power. Finally, it is proved that an idealized version of MBF converges linearly.
Integrating Symmetry into Differentiable Planning

We study how group symmetry helps improve data efficiency and generalization for end-to-end differentiable planning algorithms when symmetry appears in decision-making tasks. Motivated by equivariant convolution networks, we treat the path planning problem as \textit{signals} over grids. We show that value iteration in this case is a linear equivariant operator, which is a (steerable) convolution. This extends Value Iteration Networks (VINs) on using convolutional networks for path planning with additional rotation and reflection symmetry. Our implementation is based on VINs and uses steerable convolution networks to incorporate symmetry. The experiments are performed on four tasks: 2D navigation, visual navigation, and 2 degrees of freedom (2DOFs) configuration space and workspace manipulation. Our symmetric planning algorithms improve training efficiency and generalization by large margins compared to non-equivariant counterparts, VIN and GPPN.
IndicNLG Benchmark: Multilingual Datasets for Diverse NLG Tasks in Indic Languages

Natural Language Generation (NLG) for non-English languages is hampered by the scarcity of datasets in these languages. We present the IndicNLG Benchmark, a collection of datasets for benchmarking NLG for 11 Indic languages. We focus on five diverse tasks, namely, biography generation using Wikipedia infoboxes, news headline generation, sentence summarization, paraphrase generation and, question generation. We describe the created datasets and use them to benchmark the performance of several monolingual and multilingual baselines that leverage pre-trained sequence-to-sequence models. Our results exhibit the strong performance of multilingual language-specific pre-trained models, and the utility of models trained on our dataset for other related NLG tasks. Our dataset creation methods can be easily applied to modest-resource languages as they involve simple steps such as scraping news articles and Wikipedia infoboxes, light cleaning, and pivoting through machine translation data. To the best of our knowledge, the IndicNLG Benchmark is the first NLG benchmark for Indic languages and the most diverse multilingual NLG dataset, with approximately 8M examples across 5 tasks and 11 languages. The datasets and models will be publicly available.
One-body properties of nuclear matter with off-shell propagation

Symmetric nuclear matter is studied in the self-consistent, in-medium $T$-matrix approach. One-body spectral function, optical potential, and scattering width are calculated. Properties of quasi-particle excitations at the Fermi surface are discussed. Dispersive self-energies are dominated by contributions from the $^1S_0$, $3S_1-^3D_1$, and $^3P_1$ partial waves.
Density-relaxation part of the self energy

A comment is made on the large-cluster limit of the self-energy correction for the quasiparticle energy gap in silicon clusters presented by Serdar Ogut, James R. Chelikowsky and Steven G. Louie in Phys. Rev. Lett. 79, 1770 (1997).
Superconductivity induced by interband nesting in the three-dimensional honeycomb lattice

In order to study whether the interband nesting can favor superconductivity arising from electron-electron repulsion in a three-dimensional system, we have looked at the repulsive Hubbard model on a stack of honeycomb (i.e., non-Bravais) lattices with the fluctuation exchange approximation, partly motivated by the superconductivity observed in ${\mathrm{MgB}}_{2}. $ By systematically changing the shape of Fermi surface with varied band filling n and the third-direction hopping, we have found that the pair scattering across the two bands is indeed found to give rise to gap functions that change sign across the bands and behave as an s or d wave within each band. This implies (a) the electron repulsion can assist gapful pairing when a phonon-mechanism pairing exists and (b) the electron repulsion alone, when strong enough, can give rise to a d-wave-like pairing, which should be, for a group-theoretic reason, a time-reversal broken $d+id$ with point nodes in the gap.
Skull and partial skeleton of a new pachycetine genus (Cetacea, Basilosauridae) from the Aridal Formation, Bartonian middle Eocene, of southwestern Morocco

Pachycetus paulsonii, Pachycetus wardii, and Antaecetus aithai are middle Eocene archaeocete whales found in Europe, North America, and Africa, respectively. The three are placed in the new basilosaurid subfamily Pachycetinae. Antaecetus is a new genus known from Egypt and Morocco, and the only pachycetine known from a substantial postcranial skeleton. The skull of A. aithai described here resembles that of Saghacetus osiris in size, but lacks the narrowly constricted rostrum of Saghacetus. Antaecetus is smaller than Pachycetus and its teeth are more gracile. Upper premolars differ in having two rather than three accessory cusps flanking the principal cusp. Pachycetines differ from dorudontines in having elongated posterior thoracic and lumbar vertebrae like those of Basilosaurus, but differ from basilosaurines and from dorudontines in having conspicuously pachyosteosclerotic vertebrae with dense and thickly laminated cortical bone surrounding a cancellous core. Pachycetinae are also distinctive in having transverse processes on lumbar vertebrae nearly as long anteroposteriorly as the corresponding centrum. We infer from their pachyosteosclerotic vertebrae that pachycetines were probably sirenian-like slow swimmers living in shallow coastal seas and feeding on passing fish and mobile invertebrates.
Computational Modeling of Virally-encoded Ion Channel Structure

Viroporins are ion channels encoded within a virus’s genome, that facilitate a range of devastating infectious diseases such as COVID-19, HIV, and rotavirus. The non-structural protein 4 (NSP4) from rotavirus includes a viroporin domain that disrupts cellular Ca2+ homeostasis, initiating viral replication, and leading to life-threatening vomiting and diarrhea. Though the structure of soluble segments of NSP4 has been determined, membrane-associated regions, including the viroporin domain, remain elusive when utilizing well-established available experimental methods such as x-ray crystallography. However, two recently published protein folding algorithms, AlphaFold2 and trRosetta, demonstrated a high degree of accuracy, when determining the structure of membrane proteins from their primary amino acid sequences, though their training datasets are known to exclude proteins from viral systems. We tested the ability of these non-viral algorithms to predict functional molecular structures of the full-length NSP4 from SA11 rotavirus. We also compared the accuracy of these structures to predictions of other experimental structures of eukaryotic proteins from the Protein Data Banks (PDB), and show that the algorithms predict models more similar to corresponding experimental data than what we saw for the viroporin structure. Our data suggest that while AlphaFold2 and trRosetta each produced distinct NSP4 models, constructs based on either model showed viroporin activity when expressed in E. coli, consistent with that seen from other historical NSP4 sequences.