Miracle-dz/UniFAR-FTUs huggingface dataset:
Bondi-Hoyle-Lyttleton accretion onto a rotating black hole with ultralight scalar hair

We present a numerical study of relativistic Bondi-Hoyle-Lyttleton (BHL) accretion onto an asymptotically flat black hole with synchronized hair. The hair is sourced by an ultralight, complex scalar field, minimally coupled to Einstein's gravity. Our simulations consider a supersonic flow parametrized by the asymptotic values of the fluid quantities and a sample of hairy black holes with different masses, angular momenta, and amount of scalar hair. For all models, steady-state BHL accretion solutions are attained that are characterized by the presence of a shock-cone and a stagnation point downstream. For the models of the sample with the largest component of scalar field, the shock-cone envelops fully the black hole, transitioning into a bow-shock, and the stagnation points move further away downstream. Analytical expressions for the mass accretion rates are obtained after fitting the numerical results, which can be used to analyze black-hole formation scenarios in the presence of ultralight scalar fields. The formation of a shock-cone leads to regions where sound waves can be trapped and resonant oscillations excited. We measure the frequencies of such quasi-periodic oscillations and point out a possible association with quasi-periodic oscillations in the X-ray light curve of Sgr A* and microquasars.
Convergence of First-Order Algorithms for Meta-Learning with Moreau Envelopes

In this work, we consider the problem of minimizing the sum of Moreau envelopes of given functions, which has previously appeared in the context of meta-learning and personalized federated learning. In contrast to the existing theory that requires running subsolvers until a certain precision is reached, we only assume that a finite number of gradient steps is taken at each iteration. As a special case, our theory allows us to show the convergence of First-Order Model-Agnostic Meta-Learning (FO-MAML) to the vicinity of a solution of Moreau objective. We also study a more general family of first-order algorithms that can be viewed as a generalization of FO-MAML. Our main theoretical achievement is a theoretical improvement upon the inexact SGD framework. In particular, our perturbed-iterate analysis allows for tighter guarantees that improve the dependency on the problem's conditioning. In contrast to the related work on meta-learning, ours does not require any assumptions on the Hessian smoothness, and can leverage smoothness and convexity of the reformulation based on Moreau envelopes. Furthermore, to fill the gaps in the comparison of FO-MAML to the Implicit MAML (iMAML), we show that the objective of iMAML is neither smooth nor convex, implying that it has no convergence guarantees based on the existing theory.
Invariant formulation of phenomenological failure criteria for orthotropic sheets and optimisation of their strength

The strength optimisation of orthotropic materials is an open problem. The majority of the studies conducted on strength optimisation of anisotropic materials have been performed with the help of a phenomenological failure criterion that describe the conditions to obtain a limit state identified with the ultimate strength of the material. This paper is a study concerning the bidimensional linear case. Our approach concerns the formulation of a failure criterion through tensors invariants, in order to reduce the optimisation variables to only one: the orientation of material properties of the continuum. Finally, the analytical approach is introduced in order to minimise the failure index and results obtained with the use of three different failure criteria that are compared in order to understand the role of the three formulations with respect to the strength's behaviour. Copyright © 2012 John Wiley & Sons, Ltd.
A Distributed Scheme for Voltage and Frequency Control and Power Sharing in Inverter-Based Microgrids

Grid-forming inverter-based autonomous microgrids present new operational challenges as the stabilizing rotational inertia of synchronous machines is absent. The design of efficient control policies for grid-forming inverters is, however, a nontrivial problem where multiple performance objectives need to be satisfied, including voltage/frequency regulation, current limiting capabilities, and active power sharing and a scalable operation. We propose, in this article, a novel control architecture for frequency and voltage control, which allows current limitation via an inner loop, active power sharing via a distributed secondary control policy, and scalability by satisfying a passivity property. In particular, the frequency controller employs the inverter output current and angle to provide an angle droop-like policy, which improves its stability properties. This also allows us to incorporate a secondary control policy for which we provide an analytical stability result, which takes line conductances into account (in contrast to the lossless line assumptions in the literature). The distinctive feature of the voltage control scheme is that it has a double-loop structure that uses the dc voltage in the feedback control policy to implement a power-balancing strategy to improve performance. The performance of the control policy is illustrated via simulations with detailed nonlinear models in a realistic setting.
Differentially closed fields and universality on a cone

The class of all countable diﬀerentially closed diﬀerential ﬁelds K of characteristic 0 was shown by Marker and the author in [21] to be “one jump away” from universal for spectra of structures: for every nontrivial countable structure M , there is some K whose spectrum is the preimage under jump of the spectrum of M , and conversely, for every K , there is such an M . We show that the missing jump can be accounted for by adding to the signature of diﬀerential ﬁelds a predicate describing a certain algebraic transcendence property. The ensuing universality results for diﬀerentially closed ﬁelds in the new signature include not only spectra of structures, but also many properties related to computable categoricity. However, these latter universality results hold only on the cone above a speciﬁc Σ 0 1 oracle set, whose decidability status remains unknown. Moreover, diﬀerentially closed ﬁelds simply fail ﬂat-out to be universal for automorphism groups, even non-eﬀectively. We also include a small erratum (Remark 3.2) to [21].
CNN-Based Action Recognition and Pose Estimation for Classifying Animal Behavior from Videos: A Survey

Classifying the behavior of humans or animals from videos is important in biomedical ﬁelds for understanding brain function and response to stimuli. Action recognition, classifying activities performed byone or more subjects ina trimmed video,forms thebasisof many of thesetechniques. Deeplearn-ingmodels for human action recognition have progressed signiﬁcantly over the last decade. Recently, there is an increased interest in research that incorporates deep learning-based action recognition for animal behavior clas- siﬁcation. However,humanaction recognition methodsaremoredeveloped. This survey presents an overview of human action recognition and pose es- timation methods thatare basedon convolutional neuralnetwork (CNN) architectures and have beenadapted for animal behaviorclassiﬁcation in neu- roscience. Pose estimation, estimating joint positions from an image frame, is included because it is often applied before classifying animal behavior. First, we provide foundational information on algorithms that learn spatiotemporal features through 2D, two-stream, and 3D CNNs. We explore motivating factors that determine optimizers, loss functions and training procedures, and compare their performance on benchmark datasets. Next, we review animal behavior frameworks that use or build upon these methods, organized by the level of supervision they require. Our discussion is uniquely focused on the technical evolution of the underlying CNN models and their architectural adaptations (which we illustrate), rather than their usability in a neuroscience lab. We conclude by discussing open research problems, and possible research directions. Our survey is designed to be a resource for researchers developing fully unsupervised animal behavior classiﬁcation systems of which there are only a few examples in the litera-ture.
Large optical forces on a barium monofluoride molecule using laser pulses for absorption and stimulated emission: A full density-matrix simulation

A full density-matrix simulation is performed for optical deflection of a barium monofluoride (BaF) beam. Pairs of counter-propagating laser pulses are used for stimulated absorption followed by stimulated emission. The scheme produces a force which is nearly an order of magnitude larger than that obtainable using continuous-wave laser deflection, and yields a force-to-spontaneous-decay ratio which is more than an order of magnitude larger. The large reduction in spontaneous decay is key to optical deflection of molecules, where branching ratios to other vibrational states do not allow for cycling transitions. This work is part of an effort by the EDM$^3$ collaboration to measure the electric dipole moment of the electron using BaF molecules embedded in a cryogenic argon solid. Deflection of BaF molecules will separate them from the other ablation products coming from a buffer-gas-cooled ablation source, before embedding them into the argon solid. Our simulations show that sufficiently large deflections for this separation are feasible.
Searching for Molecular Jets from High-mass Protostars

We report Very Large Array observations in the Q band toward 10 ionized jet candidates to search for SiO emission, a well-known shocked gas tracer. We detected 7 mm continuum counterparts toward 90% of the jet candidates. In most cases, the jet candidate is located toward the center of the 7 mm core, and the high masses (≈100 M ⊙) and densities (≈107 cm−3) of the cores suggest that the central objects are very young high-mass protostars. We detected SiO J = 1–0 emission associated with six target sources. In all cases, the morphology and spectrum of the emission is consistent with what is expected for molecular jets along an outflow axis, thus confirming the jet nature of 60% of our sample. Our data suggest a positive correlation between the SiO luminosity L SiO, and both the bolometric luminosity L Bol and the radio luminosity S ν d 2 of the driving sources.
USER: Unified Semantic Enhancement With Momentum Contrast for Image-Text Retrieval

As a fundamental and challenging task in bridging language and vision domains, Image-Text Retrieval (ITR) aims at searching for the target instances that are semantically relevant to the given query from the other modality, and its key challenge is to measure the semantic similarity across different modalities. Although significant progress has been achieved, existing approaches typically suffer from two major limitations: (1) It hurts the accuracy of the representation by directly exploiting the bottom-up attention based region-level features where each region is equally treated. (2) It limits the scale of negative sample pairs by employing the mini-batch based end-to-end training mechanism. To address these limitations, we propose a Unified Semantic Enhancement Momentum Contrastive Learning (USER) method for ITR. Specifically, we delicately design two simple but effective Global representation based Semantic Enhancement (GSE) modules. One learns the global representation via the self-attention algorithm, noted as Self-Guided Enhancement (SGE) module. The other module benefits from the pre-trained CLIP module, which provides a novel scheme to exploit and transfer the knowledge from an off-the-shelf model, noted as CLIP-Guided Enhancement (CGE) module. Moreover, we incorporate the training mechanism of MoCo into ITR, in which two dynamic queues are employed to enrich and enlarge the scale of negative sample pairs. Meanwhile, a Unified Training Objective (UTO) is developed to learn from mini-batch based and dynamic queue based samples. Extensive experiments on the benchmark MSCOCO and Flickr30K datasets demonstrate the superiority of both retrieval accuracy and inference efficiency. For instance, compared with the existing best method NAAF, the metric R@1 of our USER on the MSCOCO 5K Testing set is improved by 5% and 2.4% on caption retrieval and image retrieval without any external knowledge or pre-trained model while enjoying over 60 times faster inference speed. Our source code will be released at https://github.com/zhangy0822/USER.
Bike Frames: Understanding the Implicit Portrayal of Cyclists in the News

Increasing the number of cyclists, whether for general transport or recreation, can provide health improvements and reduce the environmental impact of vehicular transportation. However, the public's perception of cycling may be driven by the ideologies and reporting standards of news agencies. For instance, people may identify cyclists on the road as"dangerous"if news agencies overly report cycling accidents, limiting the number of people that cycle for transportation. Moreover, if fewer people cycle, there may be less funding from the government to invest in safe infrastructure. In this paper, we explore the perceived perception of cyclists within news headlines. To accomplish this, we introduce a new dataset,"Bike Frames", that can help provide insight into how headlines portray cyclists and help detect accident-related headlines. Next, we introduce a multi-task (MT) regularization approach that increases the detection accuracy of accident-related posts, demonstrating improvements over traditional MT frameworks. Finally, we compare and contrast the perceptions of cyclists with motorcyclist-related headlines to ground the findings with another related activity for both male- and female-related posts. Our findings show that general news websites are more likely to report accidents about cyclists than other events. Moreover, cyclist-specific websites are more likely to report about accidents than motorcycling-specific websites, even though there is more potential danger for motorcyclists. Finally, we show substantial differences in the reporting about male vs. female-related persons, e.g., more male-related cyclists headlines are related to accidents, but more female-related motorcycling headlines about accidents. WARNING: This paper contains descriptions of accidents and death.
Convex Fujita numbers and the fundamental group

— We study eﬀective global generation of adjoint line bundles on smooth projective varieties. To measure the eﬀectivity we introduce the concept of the convex Fujita number of a smooth projective variety and compute its value for a class of varieties with prescribed dimension d ě 2 and an arbitrary projective group as fundamental group.
Training Methods of Multi-label Prediction Classifiers for Hyperspectral Remote Sensing Images

Hyperspectral remote sensing images, with their amalgamation of spectral richness and geometric precision, encapsulate intricate, non-linear information that poses significant challenges to traditional machine learning methodologies. Deep learning techniques, recognised for their superior representation learning capabilities, exhibit enhanced proficiency in managing such intricate data. In this study, we introduce a novel approach in hyperspectral image analysis focusing on multi-label, patch-level classification, as opposed to applications in the literature concentrating predominantly on single-label, pixel-level classification for hyperspectral remote sensing images. The proposed model comprises a two-component deep learning network and employs patches of hyperspectral remote sensing scenes with reduced spatial dimensions yet with a complete spectral depth derived from the original scene. Additionally, this work explores three distinct training schemes for our network: Iterative, Joint, and Cascade. Empirical evidence suggests the Joint approach as the optimal strategy, but it requires an extensive search to ascertain the optimal weight combination of the loss constituents. The Iterative scheme facilitates feature sharing between the network components from the early phases of training and demonstrates superior performance with complex, multi-labelled data. Subsequent analysis reveals that models with varying architectures, when trained on patches derived and annotated per our proposed single-label sampling procedure, exhibit commendable performance.
Detecting Stance of Authorities towards Rumors in Arabic Tweets: A Preliminary Study

A myriad of studies addressed the problem of rumor verification in Twitter by either utilizing evidence from the propagation networks or external evidence from the Web. However, none of these studies exploited evidence from trusted authorities. In this paper, we define the task of detecting the stance of authorities towards rumors in tweets, i.e., whether a tweet from an authority agrees, disagrees, or is unrelated to the rumor. We believe the task is useful to augment the sources of evidence utilized by existing rumor verification systems. We construct and release the first Authority STance towards Rumors (AuSTR) dataset, where evidence is retrieved from authority timelines in Arabic Twitter. Due to the relatively limited size of our dataset, we study the usefulness of existing datasets for stance detection in our task. We show that existing datasets are somewhat useful for the task; however, they are clearly insufficient, which motivates the need to augment them with annotated data constituting stance of authorities from Twitter.
AutoDDL: Automatic Distributed Deep Learning with Asymptotically Optimal Communication

Recent advances in deep learning base on growing model sizes and the necessary scaling of compute power. Training such large-scale models requires an intricate combination of data-, operator-, and pipeline parallelism in complex distributed systems. We show how to use OneFlow's Split, Broadcast, and Partial Sum (SBP) tensor formulations to enable new distributed training methods with asymptotically optimal communication overheads. Using these insights, we develop AutoDDL, a distributed training framework that combines an exhaustive performance model and automated configuration search to find distributions with near-optimal communication overheads. We conduct evaluations on Multi-Node-Single-GPU and Multi-Node-Multi-GPU machines using different models, including VGG and Transformer. Compared to expert-optimized implementations, AutoDDL reduces the end-to-end training time by up to 31.1\% and 10\% for Transformer and up to 17.7\% and 71.5\% for VGG on the two different systems, respectively.
Model-based Transfer Learning for Automatic Optical Inspection based on domain discrepancy

Transfer learning is a promising method for AOI applications since it can significantly shorten sample collection time and improve efficiency in today’s smart manufacturing. However, related research enhanced the network models by applying TL without considering the domain similarity among datasets, the data long-tailedness of a source dataset, and mainly used linear transformations to mitigate the lack of samples. This research applies model-based TL via domain similarity to improve the overall performance and data augmentation in both target and source domains to enrich the data quality and reduce the imbalance. Given a group of source datasets from similar industrial processes, we define which group is the most related to the target through the domain discrepancy score and the number of samples each has. Then, we transfer the chosen pre-trained backbone weights to train and fine-tune the target network. Our research suggests increases in the F1 score and the PR curve up to 20% compared with TL using benchmark datasets
DRL-VO: Learning to Navigate Through Crowded Dynamic Scenes Using Velocity Obstacles

This article proposes a novel learning-based control policy with strong generalizability to new environments that enables a mobile robot to navigate autonomously through spaces filled with both static obstacles and dense crowds of pedestrians. The policy uses a unique combination of input data to generate the desired steering angle and forward velocity: a short history of lidar data, kinematic data about nearby pedestrians, and a subgoal point. The policy is trained in a reinforcement learning setting using a reward function that contains a novel term based on velocity obstacles to guide the robot to actively avoid pedestrians and move toward the goal. Through a series of 3-D simulated experiments with up to 55 pedestrians, this control policy is able to achieve a better balance between collision avoidance and speed (i.e., higher success rate and faster average speed) than state-of-the-art model-based and learning-based policies, and it also generalizes better to different crowd sizes and unseen environments. An extensive series of hardware experiments demonstrate the ability of this policy to directly work in different real-world environments with different crowd sizes with zero retraining. Furthermore, a series of simulated and hardware experiments show that the control policy also works in highly constrained static environments on a different robot platform without any additional training. Lastly, several important lessons that can be applied to other robot learning systems are summarized.
Polaronic and Mott insulating phase of layered magnetic vanadium trihalide 
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Two-dimensional (2D) van der Waals (vdW) magnetic $3d$-transition metal trihalides are a new class of functional materials showing exotic physical properties useful for spintronic and memory storage applications. In this article, we report the synthesis and electromagnetic characterization of single-crystalline vanadium trichloride, VCl$_3$, a novel 2D layered vdW Mott insulator, which has a rhombohedral structure (R$\overline{3}$, No. 148) at room temperature. VCl$_3$ undergoes a structural phase transition at 103 K and a subsequent antiferromagnetic transition at 21.8 K. Combining core levels and valence bands x-ray photoemission spectroscopy (XPS) with first-principles density functional theory (DFT) calculations, we demonstrate the Mott Hubbard insulating nature of VCl$_3$ and the existence of electron small 2D magnetic polarons localized on V atom sites by V-Cl bond relaxation. The polarons strongly affect the electromagnetic properties of VCl$_3$ promoting the occupation of dispersion-less spin-polarized V-3d $a_{1g}$ states and band inversion with $e^{'}_{g}$ states. Within the polaronic scenario, it is possible to reconcile different experimental evidences on vanadium trihalides, suggesting that also VI$_3$ hosts polarons. Our results highlight the complex physical behavior of this class of crystals determined by charge trapping, lattice distortions, correlation effects, mixed valence states, and magnetic states.
Weighted and Branching Bisimilarities from Generalized Open Maps

In the open map approach to bisimilarity, the paths and their runs in a given state-based system are the first-class citizens, and bisimilarity becomes a derived notion. While open maps were successfully used to model bisimilarity in non-deterministic systems, the approach fails to describe quantitative system equivalences such as probabilistic bisimilarity. In the present work, we see that this is indeed impossible and we thus generalize the notion of open maps to also accommodate weighted and probabilistic bisimilarity. Also, extending the notions of strong path and path bisimulations into this new framework, we show that branching bisimilarity can be captured by this extended theory and that it can be viewed as the history preserving restriction of weak bisimilarity.