Miracle-dz/UniFAR-FTUs huggingface dataset:
Bridging Modality Gap for Visual Grounding with Effecitve Cross-modal Distillation

Visual grounding aims to align visual information of specific regions of images with corresponding natural language expressions. Current visual grounding methods leverage pre-trained visual and language backbones separately to obtain visual features and linguistic features. Although these two types of features are then fused via delicately designed networks, the heterogeneity of the features makes them inapplicable for multi-modal reasoning. This problem arises from the domain gap between the single-modal pre-training backbone used in current visual grounding methods, which can hardly be overcome by the traditional end-to-end training method. To alleviate this, our work proposes an Empowering pre-trained model for Visual Grounding (EpmVG) framework, which distills a multimodal pre-trained model to guide the visual grounding task. EpmVG is based on a novel cross-modal distillation mechanism, which can effectively introduce the consistency information of images and texts in the pre-trained model, to reduce the domain gap existing in the backbone networks, thereby improving the performance of the model in the visual grounding task. Extensive experiments are carried out on five conventionally used datasets, and results demonstrate that our method achieves better performance than state-of-the-art methods.
Scale invariance in early embryonic development

The body plan of the fruit fly is determined by the expression of just a handful of genes. We show that the spatial patterns of expression for several of these genes scale precisely with the size of the embryo. Concretely, discrete positional markers such as the peaks in striped patterns have absolute positions along the anterior-posterior axis that are proportional to embryo length, with better than 1% accuracy. Further, the information (in bits) that graded patterns of expression provide about position can be decomposed into information about fractional or scaled position and information about absolute position or embryo length; all of the available information is about scaled position, again with ~1% accuracy. These observations suggest that the underlying genetic network exhibits scale invariance in a deeper mathematical sense. Taking this mathematical statement seriously requires that the network dynamics have a zero mode, which connects to many other observations on this system.
Field transformations and invariant quantities in scalar-teleparallel theories of gravity

We study transformations of the dynamical fields - a metric, a flat affine connection and a scalar field - in scalar-teleparallel gravity theories. The theories we study belong either to the general teleparallel setting, where no further condition besides vanishing curvature is imposed on the affine connection, or the symmetric or metric teleparallel gravity, where one also imposes vanishing torsion or nonmetricity, respectively. For each of these three settings, we find a general class of scalar-teleparallel action functionals which retain their form under the aforementioned field transformations. This is achieved by generalizing the constraint of vanishing torsion or nonmetricity to non-vanishing, but algebraically constrained torsion or nonmetricity. We find a number of invariant quantities which characterize these theories independently of the choice of field variables, and relate these invariants to analogues of the conformal frames known from scalar-curvature gravity. Using these invariants, we are able to identify a number of physically relevant subclasses of scalar-teleparallel theories. We also generalize our results to multiple scalar fields, and speculate on further extended theories with non-vanishing, but algebraically constrained curvature.
STanHop: Sparse Tandem Hopfield Model for Memory-Enhanced Time Series Prediction

We present STanHop-Net (Sparse Tandem Hopfield Network) for multivariate time series prediction with memory-enhanced capabilities. At the heart of our approach is STanHop, a novel Hopfield-based neural network block, which sparsely learns and stores both temporal and cross-series representations in a data-dependent fashion. In essence, STanHop sequentially learn temporal representation and cross-series representation using two tandem sparse Hopfield layers. In addition, StanHop incorporates two additional external memory modules: a Plug-and-Play module and a Tune-and-Play module for train-less and task-aware memory-enhancements, respectively. They allow StanHop-Net to swiftly respond to certain sudden events. Methodologically, we construct the StanHop-Net by stacking STanHop blocks in a hierarchical fashion, enabling multi-resolution feature extraction with resolution-specific sparsity. Theoretically, we introduce a sparse extension of the modern Hopfield model (Generalized Sparse Modern Hopfield Model) and show that it endows a tighter memory retrieval error compared to the dense counterpart without sacrificing memory capacity. Empirically, we validate the efficacy of our framework on both synthetic and real-world settings.
Extension of the law of the wall exploiting weak universality of velocity fluctuations in a channel

This paper explores the universality of the streamwise velocity fluctuations in a channel. In the analysis, we employ a one-dimensional scalar variant of the proper orthogonal decomposition (POD). This approach naturally motivates the introduction of two different levels of universality which we will refer to as strong and weak universality. Strong universality requires that all POD modes are universal with respect to the Reynolds number, while weak universality only requires that the first few POD modes are universal. As POD concerns information at more than one location, these universalities are more general than various similarities and universality found in the literature concerning single-point flow statistics, e.g., similarity of the wake region or universality of the log law. We examine flows at Re τ = 180, 540, 1000, and 5200. Strong universality is observed in the wake region, and weak universality is found in both the viscous wall region and the outer part of the logarithmic layer. The presence of weak universality suggests the existence of an extension to the law of the wall (LoW). We propose such an extension based on the results from the one-dimensional POD analysis. The usefulness of the LoW extension is then assessed by comparing flow reconstructions according to the conventional equilibrium LoW and the extended LoW. We show that the extended LoW provides accurate flow reconstructions in the wall layer, capturing fine-scale motions that are entirely missed by the equilibrium LoW.
Extension of the law of the wall exploiting weak similarity of velocity fluctuations in turbulent channels

This paper explores the similarity of the streamwise velocity fluctuations in turbulent channels. In the analysis, we employ a one-dimensional scalar variant of the proper orthogonal decomposition (POD). This approach naturally motivates the introduction of two different levels of similarity which we will refer to as strong and weak similarity. Strong similarity requires that the two-point correlation and thus, all POD modes, show Reynolds number similarity, while weak similarity only requires that the first few POD modes show similarity. As POD concerns information at more than one location, these similarities are more general than various similarities found in the literature concerning single-point flow statistics. We examine flows at Reτ=180, 540, 1000, and 5200. Strong similarity is observed in the viscous layer and the wake region, and weak similarity is found in both the viscous wall region and the outer part of the logarithmic layer. The presence of weak similarity suggests the existence of an extension to the law of the wall (LoW). We propose such an extension based on the results from the one-dimensional POD analysis. The usefulness of the LoW extension is then assessed by comparing flow reconstructions according to the conventional equilibrium LoW and the extended LoW. We show that the extended LoW provides accurate flow reconstructions in the wall layer, capturing fine-scale motions that are entirely missed by the equilibrium LoW.
ImputeFormer: Low Rankness-Induced Transformers for Generalizable Spatiotemporal Imputation

Missing data is a pervasive issue in both scientific and engineering tasks, especially for the modeling of spatiotemporal data. This problem attracts many studies to contribute to machine learning solutions. Existing imputation solutions mainly include low-rank models and deep learning models. On the one hand, low-rank models assume general structural priors, but have limited model capacity. On the other hand, deep learning models possess salient features of expressivity, while lack prior knowledge of the spatiotemporal process. Leveraging the strengths of both two paradigms, we demonstrate a low rankness-induced Transformer model to achieve a balance between strong inductive bias and high model expressivity. The exploitation of the inherent structures of spatiotemporal data enables our model to learn balanced signal-noise representations, making it versatile for a variety of imputation problems. We demonstrate its superiority in terms of accuracy, efficiency, and generality in heterogeneous datasets, including traffic speed, traffic volume, solar energy, smart metering, and air quality. Comprehensive case studies are performed to further strengthen interpretability. Promising empirical results provide strong conviction that incorporating time series primitives, such as low-rank properties, can substantially facilitate the development of a generalizable model to approach a wide range of spatiotemporal imputation problems.
Large volume limit fibrations over fanifolds

We lift the stratified torus fibration over a fanifold constructed by Gammage--Shende to the associated Weinstein manifold-with-boundary, which is homotopic to a filtered stratified integrable system with noncompact fibers. When the fanifold admits a dual stratified space in a suitable sense, we give a stratified fibration over it completing SYZ picture. For the fanifold associated with a very affine hypersurface, we realize the latter fibration as a restriction of SYZ fibrations over the tropical hypersurface proposed by Abouzaid--Auroux--Katzarkov.
Global coupled-channel analysis of $e^+e^-\to c\bar{c}$ processes in $\sqrt{s}=3.75-4.7$ GeV

Recent high-precision $e^+e^-\to c\bar{c}$ data from the BESIII and Belle are highly useful to understand the vector charmonium pole structure and puzzling lineshapes due to the exotic hadron candidates $Y$. We thus conduct a global coupled-channel analysis of most of the available data (9 two-body, 9 three-body, and 1 four-body final states) in $\sqrt{s}=3.75-4.7$ GeV. Not only cross sections but also invariant mass distributions of subsystems are fitted. Our model includes dozens of (quasi) two-body states that nonperturbatively couple with each other through bare charmonium excitations and particle-exchange mechanisms required by the three-body unitarity. The amplitudes obtained from the fits are analytically continued to vector charmonium and $Z_c$ poles. We do not find a $\psi(4160)$ pole that has been considered well-established. Instead, we find two poles of $\sim 4230$ MeV; $\psi(4230)$ with $\Gamma= 36$ MeV and a broader one with $\Gamma= 114$ MeV. Two $Z_c$ poles are found as virtual states $\sim 40$ MeV below the $D^*\bar{D}^{(*)}$ thresholds, being consistent with lattice QCD results. This work presents the first global analysis to determine the vector charmonium and $Z_c$ poles, thereby paving the way to extracting detailed properties of the prominent exotic hadron candidates from data.
The Euclidean-hyperboloidal foliation method. Application to f(R) modified gravity

This paper is a part of a series devoted to the Euclidean-hyperboloidal foliation method introduced by the authors for investigating the global existence problem associated with nonlinear systems of coupled wave-Klein-Gordon equations with small data. This method was developed especially for investigating the initial value problem for the Einstein-massive field system in wave gauge. Here, we study the (fourth-order) field equations of f(R) modified gravity and investigate the global dynamical behavior of the gravitational field in the near-Minkowski regime. We establish the existence of a globally hyperbolic Cauchy development approaching Minkowski spacetime (in spacelike, null, and timelike directions), when the initial data set is sufficiently close to an asymptotically Euclidean and spacelike hypersurface in Minkowski spacetime. We cast the (fourth-order) f(R) field equations in the form of a second-order wave-Klein-Gordon system, which has an analogous structure to the Einstein-massive field system but, in addition, involves a (possibly small) effective mass parameter. We establish the nonlinear stability of the Minkowski spacetime in the context of $f(R)$ gravity, when the integrand f(R) in the action functional can be taken to be arbitrarily close to the integrand R of the standard Hilbert-Einstein action.
The deep eclipses of RW Aur revisited by long‐term photometric and spectroscopic monitoring

RW Aurigae is a young stellar system containing a classical T Tauri star as the primary component. It shows deep, irregular dimmings, first detected in 2010. At the University Observatory Jena, we carried out optical follow‐up observations. We performed multiband (BVRI) photometry of the system with the Cassegrain‐Teleskop‐Kamera II and the Schmidt‐Teleskop‐Kamera between September 2016 and April 2019, as well as spectroscopy, using the Fibre Linked ÉCHelle Astronomical Spectrograph between September 2016 and April 2018. We present the apparent photometry of RW Aur, which is consistent with and complementary to photometric data from the American Association of Variable Star Observers. The V‐band magnitude of RW Aur changed by up to three magnitudes during the timespan of our monitoring campaign. For the observing epochs 2016/2017 and 2017/2018 we report a decreasing brightness, while in the epoch 2018/2019 the system remained in a relatively constant bright state. In the color‐magnitude diagram, we see that RW Aur lies close to a track for grey extinction. The spectra show a decreasing equivalent width (EW) of the emission line for decreasing brightness, whereas the EW of the line increases, indicating an increased outflow activity during the obscuration. Both give further evidence for the favored theory that the obscurations are caused by a hot dusty wind emerging from the inner disk.
Mean-field solution of the neural dynamics in a Greenberg-Hastings model with excitatory and inhibitory units

We present a mean field solution of the dynamics of a Greenberg-Hastings neural network with both excitatory and inhibitory units. We analyse the dynamical phase transitions that appear in the stationary state as the model parameters are varied. Analytical solutions are compared with numerical simulations of the microscopic model defined on a fully connected network. We found that the stationary state of this system exhibits a first order dynamical phase transition (with the associated hysteresis) when the fraction of inhibitory units $ff_t$ the first order transition is replaced by a pseudo critical one, namely a continuous crossover between regions of low and high activity that resembles the finite size behaviour of a continuous phase transition order parameter. However, in the thermodynamic limit, we found that $f_t\to 1/2$ and the activity for $f\geq f_t$ becomes negligible for any value of $T>0$, while the first order transition for $f
Space-time hypervolume meshing part 1: Point insertion, geometric predicates, and bistellar flips

Part 1 of this paper provides a comprehensive guide to generating unconstrained, simplicial, four-dimensional (4D), hypervolume meshes. While a general procedure for constructing unconstrained n-dimensional Delaunay meshes is well-known, many of the explicit implementation details are missing from the relevant literature for cases in which n>= 4. This issue is especially critical for the case in which n = 4, as the resulting meshes have important space-time applications. As a result, the purpose of this paper is to provide explicit descriptions of the key components in a 4D mesh-generation algorithm: namely, the point-insertion process, geometric predicates, element quality metrics, and bistellar flips. This paper represents a natural continuation of the work which was pioneered by Anderson et al. in"Surface and hypersurface meshing techniques for space-time finite element methods", Computer-Aided Design, 2023. In this previous paper, hypersurface meshes were generated using a novel, trajectory-tracking procedure. In the current paper, we are interested in generating coarse, 4D hypervolume meshes (boundary meshes) which are formed by sequentially inserting points from an existing hypersurface mesh. In the latter portion of this paper, we present numerical experiments which demonstrate the viability of this approach for a simple, convex domain. Although, our main focus is on the generation of hypervolume boundary meshes, the techniques described in this paper are broadly applicable to a much wider range of 4D meshing methods. We note that the more complex topics of constrained hypervolume meshing, and boundary recovery for non-convex domains will be covered in Part 2 of the paper.
Long-term Evolution in Ionization of Ejecta Illuminated by Eta Carinae

Changes in the flux and spectrum of Eta Carinae (η Car) since 1900 have been attributed to the evolution of the central binary by some. Others suggest evolution in the occulting ejecta. The brightness jump in the 1940s, which coincided with the appearance of narrow forbidden emission lines, may have been caused by the clearing and ionization of intervening circumstellar ejecta. The brightening changed at a slower pace up through 40 yr later. Here we continue earlier studies focused on the long-term, showing that the forbidden line emission increased in the early 1990s with no noticeable increase in the brightness of the Homunculus. We interpret that the increase in narrow-line emission is due to decreased extinction in the line of sight (LOS) from the central binary to the Weigelt clumps. In 2000, the central stellar core increased in brightness at a faster rate without associated changes in the Homunculus. By 2018, hundreds of narrow-line absorptions from singly ionized metals in our LOS from (η Car) disappeared, thought to be caused by increased ionization of metals. These three events (1990, 2000, and 2018) are explained by the dissipation of circumstellar material within the Homunculus close to the binary. Combining these changes with the steadiness of the Homunculus and the primary winds over the past four decades indicates that circumstellar ejecta in our direction have been cleared.
An accurate machine learning interatomic potential for FCC and HCP nickel

Nickel (Ni) is a commonly used metal and critical component in various structural alloys such as superalloys. It exhibits two allotropy phases, namely stable face-centered cubic (FCC) and metastable hexagonal close-packed (HCP) phases. Recent studies, both experimental and computational, have focused on the phase transformation and defect properties in allotropic Ni. However, accurate density functional theory (DFT) calculations are computationally inefficient and insufficient for studying the material behavior which often necessitates the system size exceeding one thousand atoms and simulation time surpassing one nanosecond. In this study, we implement a straightforward strategy to deal with atomic spin polarization in Ni for developing a machine-learning Deep Potential (DP) model for Ni FCC and HCP phases, using training datasets derived from spin-polarized DFT calculations. The resulting DP-Ni model demonstrates high transferability and representability across various properties among allotropies, such as (finite-temperature) lattice parameters, elastic constants, phonon spectra, and defect properties. The examination of the FCC-HCP allotropic transition under uniaxial tensile conditions unveiled a substantial critical strain requirement, in which the orientation relationship is {100}FCC // {0001}HCP and<010>FCC //<11-20>HCP. The DP-Ni model not only facilitates accurate large-scale atomistic simulations for complex mechanical behaviors, but also serves as a foundation for developing interatomic potentials for Ni-based superalloys and other multi-principal component alloys.
Momentum and angular correlations in \texorpdfstring{$Z/\gamma$}{Z/gamma}-hadron production in relativistic heavy-ion collisions

We carry out a detailed study of medium modifications on momentum and angular correlations between a large transverse momentum hadron and a $Z/\gamma$ trigger in relativistic heavy-ion collisions within a perturbative QCD parton model improved by the Sudakov resummation technique. The total energy loss of a hard parton propagating inside the medium is employed to modify the fragmentation function, while the medium-induced transverse momentum broadening is included in the resummation approach, and both of them are related to the jet transport parameter and obtained by the high-twist formalism. We obtain good agreements with the existing data on transverse momentum and azimuthal angular correlations for the $Z/\gamma$-hadron pairs in $pp$ and $AA$ collisions, and predict the correlations for the $\gamma$-hadron in central $PbPb$ collisions at 5.02 TeV. The numerical analyses for the $Z/\gamma$-hadron in central $PbPb$ collisions show that the normalized angular distribution is decorrelated due to the medium-induced transverse momentum broadening, however, the angular correlation is enhanced due to the parton energy loss, namely anti-broadening. The observed modification of the angular correlation is a result of the competition between the broadening and the anti-broadening. This work provides a reliable theoretical tool for a comprehensive and precise study of jet quenching in relativistic heavy-ion collisions.
Commonsense for Zero-Shot Natural Language Video Localization

Zero-shot Natural Language-Video Localization (NLVL) methods have exhibited promising results in training NLVL models exclusively with raw video data by dynamically generating video segments and pseudo-query annotations. However, existing pseudo-queries often lack grounding in the source video, resulting in unstructured and disjointed content. In this paper, we investigate the effectiveness of commonsense reasoning in zero-shot NLVL. Specifically, we present CORONET, a zero-shot NLVL framework that leverages commonsense to bridge the gap between videos and generated pseudo-queries via a commonsense enhancement module. CORONET employs Graph Convolution Networks (GCN) to encode commonsense information extracted from a knowledge graph, conditioned on the video, and cross-attention mechanisms to enhance the encoded video and pseudo-query representations prior to localization. Through empirical evaluations on two benchmark datasets, we demonstrate that CORONET surpasses both zero-shot and weakly supervised baselines, achieving improvements up to 32.13% across various recall thresholds and up to 6.33% in mIoU. These results underscore the significance of leveraging commonsense reasoning for zero-shot NLVL.