Miracle-dz/UniFAR-FTUs huggingface dataset:
Dissecting Nearby Galaxies with piXedfit. II. Spatially Resolved Scaling Relations among Stars, Dust, and Gas

We study spatially resolved scaling relations among stars, dust, and gas in ten nearby spiral galaxies. In a preceding paper, we have derived spatially resolved properties of the stellar population and dust by a panchromatic spectral energy distribution fitting using piXedfit. Now, we investigate resolved star formation ( ΣH2 –ΣSFR–Σ*) and dust scaling relations. While the relations with all subgalactic regions of the galaxies are reasonably tight (σ ≲ 0.3 dex), we find that most of the scaling relations exhibit galaxy-to-galaxy variations in normalization and shape. Only two relations of Σdust–Σgas and Σdust– ΣH2 do not show noticeable galaxy-to-galaxy variations among our sample galaxies. We further investigate the correlations among the scaling relations. We find significant correlations among the normalization of the ΣH2 –ΣSFR–Σ* relations, which suggest that the galaxies with higher levels of resolved H2 fraction ( fH2 ) tend to have higher levels of resolved star formation efficiency (SFE) and specific star formation rate (sSFR). We also observe that the galaxies with higher levels of resolved dust-to-stellar mass ratios tend to have higher levels of resolved sSFR, SFE, and fH2 . Moreover, we find that the galaxies with higher global sSFR and less compact morphology tend to have higher levels of the resolved sSFR, SFE, and fH2 , which can explain the variations in the normalization of the ΣH2 –ΣSFR–Σ* relationships. Overall, we observe indications of the contributions of both global and local factors in governing the star formation process in galaxies.
Towards the Human Global Context: Does the Vision-Language Model Really Judge Like a Human Being?

As computer vision and NLP make progress, Vision-Language (VL) is becoming an impor-tant area of research. Despite the importance, evaluation metrics of the research domain is still at a preliminary stage of development. In this paper, we propose a quantitative metric "Equivariance Score" and evaluation dataset "Human Puzzle" to assess whether a VL model is understanding an image like a human. We observed that the VL model does not interpret the overall context of an input image but instead shows biases toward a speciﬁc object or shape that forms the local context. We aim to quantitatively measure a model’s performance in understanding context. To verify the current existing VL model’s capability, we sliced the original input image into pieces and randomly placed them, distorting the global context of the image. Our paper discusses each VL model’s level of interpretation on global context and addresses how the structural characteristics inﬂuenced the results.
Partial Disentanglement via Mechanism Sparsity

Disentanglement via mechanism sparsity was introduced recently as a principled approach to ex-tract latent factors without supervision when the causal graph relating them in time is sparse, and/or when actions are observed and affect them sparsely. However, this theory applies only to ground-truth graphs satisfying a specific criterion. In this work, we introduce a generalization of this theory which applies to any ground-truth graph and specifies qualitatively how disentangled the learned representation is expected to be, via a new equivalence relation over models we call consistency . This equivalence captures which factors are expected to remain entangled and which are not based on the specific form of the ground-truth graph. We call this weaker form of identifiability partial disentanglement . The graphical criterion that allows complete disentanglement, proposed in an earlier work, can be derived as a special case of our theory. Finally, we enforce graph sparsity with constrained optimization and illustrate our theory and algorithm in simulations.
Supporting Unified Shader Specialization by Co-opting C++ Features

Modern unified programming models (such as CUDA and SYCL) that combine host (CPU) code and GPU code into the same programming language, same file, and same lexical scope lack adequate support for GPU code specialization, which is a key optimization in real-time graphics. Furthermore, current methods used to implement specialization do not translate to a unified environment. In this paper, we create a unified shader programming environment in C++ that provides first-class support for specialization by co-opting C++'s attribute and virtual function features and reimplementing them with alternate semantics to express the services required. By co-opting existing features, we enable programmers to use familiar C++ programming techniques to write host and GPU code together, while still achieving efficient generated C++ and HLSL code via our source-to-source translator.
On the Value of Multistage Risk-Averse Stochastic Facility Location With or Without Prioritization

We consider a multiperiod stochastic capacitated facility location problem under uncertain demand and budget in each period. Using a scenario tree representation of the uncertainties, we formulate a multistage stochastic integer program to dynamically locate facilities in each period and compare it with a two-stage approach that determines the facility locations up front. In the multistage model, in each stage, a decision maker optimizes facility locations and recourse flows from open facilities to demand sites, to minimize certain risk measures of the cost associated with current facility location and shipment decisions. When the budget is also uncertain, a popular modeling framework is to prioritize the candidate sites (Koç and Morton 2015). In the two-stage model, the priority list is decided in advance and fixed through all periods, while in the multistage model, the priority list can change adaptively. In each period, the decision maker follows the priority list to open facilities according to the realized budget, and optimizes recourse flows given the realized demand. Using expected conditional risk measures (ECRMs), we derive tight lower bounds for the gaps between the optimal objective values of risk-averse multistage models and their two-stage counterparts in both settings with and without prioritization. Moreover, we propose two approximation algorithms to efficiently solve risk-averse two-stage and multistage models without prioritization, which are asymptotically optimal under an expanding market assumption. We also design a set of super-valid inequalities for riskaverse two-stage and multistage stochastic programs with prioritization to reduce the computational time. We conduct numerical studies using both randomly generated and real-world instances with diverse sizes, to demonstrate the tightness of the analytical bounds and efficacy of the approximation algorithms and prioritization cuts. We find that the gaps between risk-averse multistage and two-stage models increase as the variations of the uncertain parameters increase, and stagewise dependent scenario trees attain much higher gaps than the stagewise independent ones.
E-NeRV: Expedite Neural Video Representation with Disentangled Spatial-Temporal Context

Recently, the image-wise implicit neural representation of videos, NeRV, has gained popularity for its promising results and swift speed compared to regular pixel-wise implicit representations. However, the redundant parameters within the network structure can cause a large model size when scaling up for desirable performance. The key reason of this phenomenon is the coupled formulation of NeRV, which outputs the spatial and temporal information of video frames directly from the frame index input. In this paper, we propose E-NeRV, which dramatically expedites NeRV by decomposing the image-wise implicit neural representation into separate spatial and temporal context. Under the guidance of this new formulation, our model greatly reduces the redundant model parameters, while retaining the representation ability. We experimentally find that our method can improve the performance to a large extent with fewer parameters, resulting in a more than $8\times$ faster speed on convergence. Code is available at https://github.com/kyleleey/E-NeRV.
Towards Lightweight Super-Resolution with Dual Regression Learning

Deep neural networks have exhibited remarkable performance in image super-resolution (SR) tasks by learning a mapping from low-resolution (LR) images to high-resolution (HR) images. However, the SR problem is typically an ill-posed problem and existing methods would come with several limitations. First, the possible mapping space of SR can be extremely large since there may exist many different HR images that can be super-resolved from the same LR image. As a result, it is hard to directly learn a promising SR mapping from such a large space. Second, it is often inevitable to develop very large models with extremely high computational cost to yield promising SR performance. In practice, one can use model compression techniques to obtain compact models by reducing model redundancy. Nevertheless, it is hard for existing model compression methods to accurately identify the redundant components due to the extremely large SR mapping space. To alleviate the first challenge, we propose a dual regression learning scheme to reduce the space of possible SR mappings. Specifically, in addition to the mapping from LR to HR images, we learn an additional dual regression mapping to estimate the downsampling kernel and reconstruct LR images. In this way, the dual mapping acts as a constraint to reduce the space of possible mappings. To address the second challenge, we propose a dual regression compression (DRC) method to reduce model redundancy in both layer-level and channel-level based on channel pruning. Specifically, we first develop a channel number search method that minimizes the dual regression loss to determine the redundancy of each layer. Given the searched channel numbers, we further exploit the dual regression manner to evaluate the importance of channels and prune the redundant ones. Extensive experiments show the effectiveness of our method in obtaining accurate and efficient SR models.
Compton scattering from a unitary and causal eective eld theory

Proton Compton scattering is analyzed with the chiral Lagrangian. Partial-wave amplitudes are obtained by an analytic extrapolation of subthreshold reaction amplitudes computed in chiral perturbation theory, where the constraints set by electromagnetic-gauge invariance, causality and unitarity are used to stabilize the extrapolation. We present and discuss predictions for various spin observables and polarizabilties of the proton. While for the transition polarizabilities E1M2; M1E2 we recover the results of strict chiral perturbation theory, for the diagonal E1E1; M1M1 elements we nd signicant eects from rescattering.
Echo-enhanced molecular orientation at high temperatures

Ultrashort laser pulses are widely used for transient ﬁeld-free molecular orientation – a phenomenon important in chemical reaction dynamics, ultrafast molecular imaging, high harmonics generation, and attosecond science. However, signiﬁcant molecular orientation usually requires rotationally cold molecules, like in rariﬁed molecular beams, because chaotic thermal motion is detrimental to the orientation process. Here we propose to use the mechanism of the echo phenomenon previously observed in hadron accelerators, free-electron lasers, and laser-excited molecules to overcome the destructive thermal eﬀects and achieve eﬃcient ﬁeld-free molecular orientation at high temperatures. In our scheme, a linearly polarized short laser pulse transforms a broad thermal distribution in the molecular rotational phase space into many separated narrow ﬁlaments due to the nonlinear phase mixing during the post-pulse free evolution. Molecular subgroups belonging to individual ﬁlaments have much-reduced dispersion of angular velocities. They are rotationally cold, and a subsequent moderate terahertz (THz) pulse can easily orient them. The overall enhanced orientation of the molecular gas is achieved with some delay, in the course of the echo process combining the contributions of diﬀerent ﬁlaments. Our results demonstrate that the echo-enhanced orientation is an order of magnitude higher than that of the THz pulse alone. The mechanism is robust – it applies to diﬀerent types of molecules, and the degree of orientation is relatively insensitive to the temperature. The laser and THz pulses used in the scheme are readily available, allowing quick experimental demonstration and testing in various applications. Breaking the phase space to individual ﬁlaments to overcome hindering thermal conditions may ﬁnd a wide range of applications beyond molecular orientation.
DeFlowSLAM: Self-Supervised Scene Motion Decomposition for Dynamic Dense SLAM

We present a novel dual-flow representation of scene motion that decomposes the optical flow into a static flow field caused by the camera motion and another dynamic flow field caused by the objects' movements in the scene. Based on this representation, we present a dynamic SLAM, dubbed DeFlowSLAM, that exploits both static and dynamic pixels in the images to solve the camera poses, rather than simply using static background pixels as other dynamic SLAM systems do. We propose a dynamic update module to train our DeFlowSLAM in a self-supervised manner, where a dense bundle adjustment layer takes in estimated static flow fields and the weights controlled by the dynamic mask and outputs the residual of the optimized static flow fields, camera poses, and inverse depths. The static and dynamic flow fields are estimated by warping the current image to the neighboring images, and the optical flow can be obtained by summing the two fields. Extensive experiments demonstrate that DeFlowSLAM generalizes well to both static and dynamic scenes as it exhibits comparable performance to the state-of-the-art DROID-SLAM in static and less dynamic scenes while significantly outperforming DROID-SLAM in highly dynamic environments. The code and pre-trained model will be available on the project webpage: \urlstyle{tt} \textcolor{url_color}{\url{https://zju3dv.github. io/deflowslam/}}.
Cross-Domain Cross-Set Few-Shot Learning via Learning Compact and Aligned Representations

Few-shot learning (FSL) aims to recognize novel queries with only a few support samples through leveraging prior knowledge from a base dataset. In this paper, we consider the domain shift problem in FSL and aim to address the domain gap between the support set and the query set. Different from previous cross-domain FSL work (CD-FSL) that considers the domain shift between base and novel classes, the new problem, termed cross-domain cross-set FSL (CDSC-FSL), requires few-shot learners not only to adapt to the new domain, but also to be consistent between different domains within each novel class. To this end, we propose a novel approach, namely stabPA, to learn prototypical compact and cross-domain aligned representations, so that the domain shift and few-shot learning can be addressed simultaneously. We evaluate our approach on two new CDCS-FSL benchmarks built from the DomainNet and Office-Home datasets respectively. Remarkably, our approach outperforms multiple elaborated baselines by a large margin, e.g., improving 5-shot accuracy by 6.0 points on average on DomainNet. Code is available at https://github.com/WentaoChen0813/CDCS-FSL
Threat Model-Agnostic Adversarial Defense using Diffusion Models

Deep Neural Networks (DNNs) are highly sensitive to imperceptible malicious perturbations, known as adversarial attacks. Following the discovery of this vulnerability in real-world imaging and vision applications, the associated safety concerns have attracted vast research attention, and many defense techniques have been developed. Most of these defense methods rely on adversarial training (AT) -- training the classification network on images perturbed according to a specific threat model, which defines the magnitude of the allowed modification. Although AT leads to promising results, training on a specific threat model fails to generalize to other types of perturbations. A different approach utilizes a preprocessing step to remove the adversarial perturbation from the attacked image. In this work, we follow the latter path and aim to develop a technique that leads to robust classifiers across various realizations of threat models. To this end, we harness the recent advances in stochastic generative modeling, and means to leverage these for sampling from conditional distributions. Our defense relies on an addition of Gaussian i.i. d noise to the attacked image, followed by a pretrained diffusion process -- an architecture that performs a stochastic iterative process over a denoising network, yielding a high perceptual quality denoised outcome. The obtained robustness with this stochastic preprocessing step is validated through extensive experiments on the CIFAR-10 dataset, showing that our method outperforms the leading defense methods under various threat models.
The First Detection of a Protostellar CO Outflow in the Small Magellanic Cloud with ALMA

Protostellar outflows are one of the most outstanding features of star formation. Observational studies over the last several decades have successfully demonstrated that outflows are ubiquitously associated with low- and high-mass protostars in solar-metallicity Galactic conditions. However, the environmental dependence of protostellar outflow properties is still poorly understood, particularly in the low-metallicity regime. Here we report the first detection of a molecular outflow in the Small Magellanic Cloud with 0.2 Z ⊙, using Atacama Large Millimeter/submillimeter Array observations at a spatial resolution of 0.1 pc toward the massive protostar Y246. The bipolar outflow is nicely illustrated by high-velocity wings of CO(3–2) emission at ≳15 km s−1. The evaluated properties of the outflow (momentum, mechanical force, etc.) are consistent with those of the Galactic counterparts. Our results suggest that the molecular outflows, i.e., the guidepost of the disk accretion at the small scale, might be universally associated with protostars across the metallicity range of ∼0.2–1 Z ⊙.
wPINNs: Weak Physics informed neural networks for approximating entropy solutions of hyperbolic conservation laws

Physics informed neural networks (PINNs) require regularity of solutions of the underlying PDE to guarantee accurate approximation. Consequently, they may fail at approximating discontinuous solutions of PDEs such as nonlinear hyperbolic equations. To ameliorate this, we propose a novel variant of PINNs, termed as weak PINNs (wPINNs) for accurate approximation of entropy solutions of scalar conservation laws. wPINNs are based on approximating the solution of a min-max optimization problem for a residual, defined in terms of Kruzkhov entropies, to determine parameters for the neural networks approximating the entropy solution as well as test functions. We prove rigorous bounds on the error incurred by wPINNs and illustrate their performance through numerical experiments to demonstrate that wPINNs can approximate entropy solutions accurately.
Flows into de Sitter space from anisotropic initial conditions: An effective field theory approach

For decades, physicists have analyzed various versions of a ``cosmic no-hair"conjecture, to understand under what conditions a spacetime that is initially spatially anisotropic and/or inhomogeneous will flow into an isotropic and homogeneous state. Wald's theorem, in particular, established that homogeneous but anisotropic spacetimes, if filled with a positive cosmological constant plus additional matter sources that satisfy specific energy conditions, will necessarily flow toward an (isotropic) de Sitter state at late times. In this paper we study the flow of homogeneous but anisotropic spacetimes toward isotropic states under conditions more general than those to which Wald's theorem applies. We construct an effective field theory (EFT) treatment for generic ``single-clock"systems in anisotropic spacetimes -- which are not limited to realizations compatible with scalar-field constructions -- and identify fixed points in the resulting phase space. We identify regions of this phase space that flow to isotropic fixed points -- including a de Sitter fixed point -- even in the absence of a bare cosmological constant, and for matter sources that do not obey the energy conditions required for Wald's theorem. Such flows into de Sitter reveal the emergence of an effective cosmological constant.
A Meta-Reinforcement Learning Algorithm for Causal Discovery

Causal discovery is a major task with the utmost importance for machine learning since causal structures can enable models to go beyond pure correlation-based inference and significantly boost their performance. However, finding causal structures from data poses a significant challenge both in computational effort and accuracy, let alone its impossibility without interventions in general. In this paper, we develop a meta-reinforcement learning algorithm that performs causal discovery by learning to perform interventions such that it can construct an explicit causal graph. Apart from being useful for possible downstream applications, the estimated causal graph also provides an explanation for the data-generating process. In this article, we show that our algorithm estimates a good graph compared to the SOTA approaches, even in environments whose underlying causal structure is previously unseen. Further, we make an ablation study that shows how learning interventions contribute to the overall performance of our approach. We conclude that interventions indeed help boost the performance, efficiently yielding an accurate estimate of the causal structure of a possibly unseen environment.
New Limit on Axionlike Dark Matter Using Cold Neutrons.

We report on a search for dark matter axionlike particles (ALPs) using a Ramsey-type apparatus for cold neutrons. A hypothetical ALP-gluon coupling would manifest in a neutron electric dipole moment signal oscillating in time. Twenty-four hours of data have been analyzed in a frequency range from 23 μHz to 1 kHz, and no significant oscillating signal has been found. The usage of present dark-matter models allows one to constrain the coupling of ALPs to gluons in the mass range from 10^{-19} to 4×10^{-12} eV. The best limit of C_{G}/f_{a}m_{a}=2.7×10^{13} GeV^{-2} (95% C.L.) is reached in the mass range from 2×10^{-17} to 2×10^{-14} eV.