Miracle-dz/UniFAR-FTUs huggingface dataset:
Spin-driven stationary turbulence in spinor Bose-Einstein condensates

We report the observation of stationary turbulence in antiferromagnetic spin-1 Bose-Einstein condensates driven by a radio-frequency magnetic field. The magnetic driving injects energy into the system by spin rotation and the energy is dissipated via dynamic instability, resulting in the emergence of an irregular spin texture in the condensate. Under continuous driving, the spinor condensate evolves into a nonequilibrium steady state with characteristic spin turbulence, while the low energy scale of spin excitations ensures that the sample's lifetime is minimally affected. When the driving strength is on par with the system's spin interaction energy and the quadratic Zeeman energy, remarkably, the stationary turbulent state exhibits spin-isotropic features in spin composition and spatial spin texture. We numerically show that ambient field fluctuations play a crucial role in sustaining the turbulent state within the system. These results open up new avenues for exploring quantum turbulence in spinor superfluid systems.
Domain Generalization Emerges from Dreaming

Recent studies have proven that DNNs, unlike human vision, tend to exploit texture information rather than shape. Such texture bias is one of the factors for the poor generalization performance of DNNs. We observe that the texture bias negatively affects not only in-domain generalization but also out-of-distribution generalization, i.e., Domain Generalization. Motivated by the observation, we propose a new framework to reduce the texture bias of a model by a novel optimization-based data augmentation, dubbed Stylized Dream. Our framework utilizes adaptive instance normalization (AdaIN) to augment the style of an original image yet preserve the content. We then adopt a regularization loss to predict consistent outputs between Stylized Dream and original images, which encourages the model to learn shape-based representations. Extensive experiments show that the proposed method achieves state-of-the-art performance in out-of-distribution settings on public benchmark datasets: PACS, VLCS, OfficeHome, TerraIncognita, and DomainNet.
A tutorial on networks in social systems: A mathematical modeling perspective

This article serves as an introduction to the study of networks of social systems. First, we introduce the reader to key mathematical tools to study social networks, including mathematical representations of networks and essential terminology. We describe several network properties of interest and techniques for measuring these properties. We also discuss some popular generative models of networks and see how the study of these models provides insight into the mechanisms for the emergence of structural patterns. Throughout, we will highlight the patterns that commonly emerge in social networks. The goal is to provide an accessible, broad, and solid foundation for a reader who is new to the field so that they may confidently engage more deeply with the mathematical study of social networks.
Exploring Invariant Representation for Visible-Infrared Person Re-Identification

Cross-spectral person re-identification, which aims to associate identities to pedestrians across different spectra, faces a main challenge of the modality discrepancy. In this paper, we address the problem from both image-level and feature-level in an end-to-end hybrid learning framework named robust feature mining network (RFM). In particular, we observe that the reflective intensity of the same surface in photos shot in different wavelengths could be transformed using a linear model. Besides, we show the variable linear factor across the different surfaces is the main culprit which initiates the modality discrepancy. We integrate such a reflection observation into an image-level data augmentation by proposing the linear transformation generator (LTG). Moreover, at the feature level, we introduce a cross-center loss to explore a more compact intra-class distribution and modality-aware spatial attention to take advantage of textured regions more efficiently. Experiment results on two standard cross-spectral person re-identification datasets, i.e., RegDB and SYSU-MM01, have demonstrated state-of-the-art performance.
On the Conditional Existence of Foliations by CMC and Willmore Type Half-Spheres

We study half-spheres with small radii sitting on the boundary of a smooth bounded domain while meeting it orthogonally. Even though it is known that there exist families of CMC and Willmore type half-spheres near a nondegenerate critical point p of the domains boundaries mean curvature, it is unknown in both cases whether these provide a foliation of any deleted neighborhood of p. We prove that this is not guaranteed and establish a criterion in terms of the boundaries geometry that ensures or prevents the respective surfaces from providing such a foliation. This perhaps surprising phenomenon of conditional foliations is absent in the closely related Riemannian setting, where a foliation is guaranteed. We show how this unconditional foliation arises from symmetry considerations and how these fail to apply to the 'domain-setting'.
The origin of the anomalous intensity ratio between very bright UV FeII lines and their satellites in gaseous condensations close to the star eta Carinae

We present a model that explains the anomalous intensity ratios betweeneach of two very bright UV lines of Fe II at 2507 A and 2509A and its corresponding satellite line, observed in HST spectra ofa compact gaseous condensation (blob B) in the vicinity of the starprotect eta Car.The model is based on the assumption that the FeIItransitions have a substantial optical thickness, which varies as aresult of the photodepletion of the long-lived lower states,c4F7/2,9/2, by the intense Lyalpha radiation. Inconditions where the photodepletion rates of these two states differ,the resonance scattering lengths of the spectral lines become differentas well. In the presence of a weak, nonresonant (continuous) absorption,this gives rise to a difference in attenuation between the spectrallines (the Hummer effect (Hummer 1968)). It is the combination of thesetwo effects that explains the anomalous (as compared with laboratorydata) intensity ratios observed in the two pairs of UV FeII lines.Ionization channels of stimulated photodepletion by the Lyalpharadiation, including the possible role of autoionization levels, areconsidered. (Less)
Intermixing‐Driven Surface and Bulk Ferromagnetism in the Quantum Anomalous Hall Candidate MnBi6Te10

The recent realizations of the quantum anomalous Hall effect (QAHE) in MnBi2Te4 and MnBi4Te7 benchmark the (MnBi2Te4)(Bi2Te3)n family as a promising hotbed for further QAHE improvements. The family owes its potential to its ferromagnetically (FM) ordered MnBi2Te4 septuple layers (SLs). However, the QAHE realization is complicated in MnBi2Te4 and MnBi4Te7 due to the substantial antiferromagnetic (AFM) coupling between the SLs. An FM state, advantageous for the QAHE, can be stabilized by interlacing the SLs with an increasing number n of Bi2Te3 quintuple layers (QLs). However, the mechanisms driving the FM state and the number of necessary QLs are not understood, and the surface magnetism remains obscure. Here, robust FM properties in MnBi6Te10 (n = 2) with Tc ≈ 12 K are demonstrated and their origin is established in the Mn/Bi intermixing phenomenon by a combined experimental and theoretical study. The measurements reveal a magnetically intact surface with a large magnetic moment, and with FM properties similar to the bulk. This investigation thus consolidates the MnBi6Te10 system as perspective for the QAHE at elevated temperatures.
MTP-GO: Graph-Based Probabilistic Multi-Agent Trajectory Prediction With Neural ODEs

Enabling resilient autonomous motion planning requires robust predictions of surrounding road users' future behavior. In response to this need and the associated challenges, we introduce our model titled MTP-GO. The model encodes the scene using temporal graph neural networks to produce the inputs to an underlying motion model. The motion model is implemented using neural ordinary differential equations where the state-transition functions are learned with the rest of the model. Multimodal probabilistic predictions are obtained by combining the concept of mixture density networks and Kalman filtering. The results illustrate the predictive capabilities of the proposed model across various data sets, outperforming several state-of-the-art methods on a number of metrics.
Confidence and Dispersity Speak: Characterising Prediction Matrix for Unsupervised Accuracy Estimation

This work aims to assess how well a model performs under distribution shifts without using labels. While recent methods study prediction confidence, this work reports prediction dispersity is another informative cue. Confidence reflects whether the individual prediction is certain; dispersity indicates how the overall predictions are distributed across all categories. Our key insight is that a well-performing model should give predictions with high confidence and high dispersity. That is, we need to consider both properties so as to make more accurate estimates. To this end, we use the nuclear norm that has been shown to be effective in characterizing both properties. Extensive experiments validate the effectiveness of nuclear norm for various models (e.g., ViT and ConvNeXt), different datasets (e.g., ImageNet and CUB-200), and diverse types of distribution shifts (e.g., style shift and reproduction shift). We show that the nuclear norm is more accurate and robust in accuracy estimation than existing methods. Furthermore, we validate the feasibility of other measurements (e.g., mutual information maximization) for characterizing dispersity and confidence. Lastly, we investigate the limitation of the nuclear norm, study its improved variant under severe class imbalance, and discuss potential directions.
A CLASSIFICATION OF THE WADGE HIERARCHIES ON ZERO-DIMENSIONAL POLISH SPACES

We provide a complete classification, up to order-isomorphism, of all possible Wadge hierarchies on zero-dimensional Polish spaces using (essentially) countable ordinals as complete invariants. We also observe that although our assignment of invariants is very simple and there are only $\aleph_1$-many equivalence classes, the above classification problem is quite complex from the descriptive set-theoretic point of view: in particular, there is no Borel procedure to determine whether two zero-dimensional Polish spaces have isomorphic Wadge hierarchies. All results are based on a complete and explicit description of the Wadge hierarchy on an arbitrary zero-dimensional Polish space, depending on its topological properties.
Dynamical Quantum Phase Transitions of the Schwinger Model: Real-Time Dynamics on IBM Quantum

Simulating the real-time dynamics of gauge theories represents a paradigmatic use case to test the hardware capabilities of a quantum computer, since it can involve non-trivial input states’ preparation, discretized time evolution, long-distance entanglement, and measurement in a noisy environment. We implemented an algorithm to simulate the real-time dynamics of a few-qubit system that approximates the Schwinger model in the framework of lattice gauge theories, with specific attention to the occurrence of a dynamical quantum phase transition. Limitations in the simulation capabilities on IBM Quantum were imposed by noise affecting the application of single-qubit and two-qubit gates, which combine in the decomposition of Trotter evolution. The experimental results collected in quantum algorithm runs on IBM Quantum were compared with noise models to characterize the performance in the absence of error mitigation.
Zero-Leverage Puzzle

In this paper, I examine why some firms have zero leverage. I fail to find evidence that firms are unlevered because of managerial entrenchment since these firms do not have weaker corporate governance. I reject the hypothesis that firms become zero-leverage after prolonged periods of high market valuation, since before levering these firms do not suffer from declining valuations and continue to issue large amounts of equity. I find strong evidence in favor of the financial constraints explanation of the zero-leverage puzzle. Zero-leverage firms appear to be financially constrained using three different measures of financial constraints. I obtain mixed evidence on the financial flexibility hypothesis since all-equity firms increase investments and acquisitions after levering, but the probability of their levering decreased during the financial crisis. My results suggest that financial constraints are the first-order the driver of zero-leverage behavior and are more important than less obvious explanations such as managerial entrenchment.
Privacy Risk for anisotropic Langevin dynamics using relative entropy bounds

The privacy preserving properties of Langevin dynamics with additive isotropic noise have been extensively studied. However, the isotropic noise assumption is very restrictive: (a) when adding noise to existing learning algorithms to preserve privacy and maintain the best possible accuracy one should take into account the relative magnitude of the outputs and their correlations; (b) popular algorithms such as stochastic gradient descent (and their continuous time limits) appear to possess anisotropic covariance properties. To study the privacy risks for the anisotropic noise case, one requires general results on the relative entropy between the laws of two Stochastic Differential Equations with different drifts and diffusion coefficients. Our main contribution is to establish such a bound using stability estimates for solutions to the Fokker-Planck equations via functional inequalities. With additional assumptions, the relative entropy bound implies an $(\epsilon,\delta)$-differential privacy bound or translates to bounds on the membership inference attack success and we show how anisotropic noise can lead to better privacy-accuracy trade-offs. Finally, the benefits of anisotropic noise are illustrated using numerical results in quadratic loss and neural network setups.
Explicit two-sided unique-neighbor expanders

We study the problem of constructing explicit sparse graphs that exhibit strong vertex expansion. Our main result is the first two-sided construction of imbalanced unique-neighbor expanders, meaning bipartite graphs where small sets contained in both the left and right bipartitions exhibit unique-neighbor expansion, along with algebraic properties relevant to constructing quantum codes. Our constructions are obtained from instantiations of the tripartite line product of a large tripartite spectral expander and a sufficiently good constant-sized unique-neighbor expander, a new graph product we defined that generalizes the line product in the work of Alon and Capalbo and the routed product in the work of Asherov and Dinur. To analyze the vertex expansion of graphs arising from the tripartite line product, we develop a sharp characterization of subgraphs that can arise in bipartite spectral expanders, generalizing results of Kahale, which may be of independent interest. By picking appropriate graphs to apply our product to, we give a strongly explicit construction of an infinite family of $(d_1,d_2)$-biregular graphs $(G_n)_{n\ge 1}$ (for large enough $d_1$ and $d_2$) where all sets $S$ with fewer than a small constant fraction of vertices have $\Omega(d_1\cdot |S|)$ unique-neighbors (assuming $d_1 \leq d_2$). Additionally, we can also guarantee that subsets of vertices of size up to $\exp(\Omega(\sqrt{\log |V(G_n)|}))$ expand losslessly.
A novel failure indexing approach with run-time values of program variables

Failures with different root causes can disturb multi-fault localization significantly, therefore, dividing failures into distinct groups according to the responsible faults is highly important. In such a failure indexing task, the crux lies in the failure proximity, which involves two points, i.e., how to effectively represent failures (e.g., extract the signature of failures) and how to properly measure the distance between the proxies for those failures. Existing studies have proposed a variety of failure proximities. The prevalent of them extract signatures of failures from execution coverage or suspiciousness ranking lists, and accordingly employ the Euclid or the Kendall tau distances. However, such strategies may not properly reflect the essential characteristics of failures, thus resulting in unsatisfactory effectiveness. In this paper, we propose a new failure proximity, namely, program variable-based failure proximity, and based on which present a novel failure indexing approach. Specifically, the proposed approach utilizes the run-time values of program variables to represent failures, and designs a set of rules to measure the similarity between them. Experimental results demonstrate the competitiveness of the proposed approach: it can achieve 44.12% and 27.59% improvements in faults number estimation, as well as 47.30% and 26.93% improvements in clustering effectiveness, compared with the state-of-the-art technique in this field, in simulated and real-world environments, respectively.
ReLOAD: Reinforcement Learning with Optimistic Ascent-Descent for Last-Iterate Convergence in Constrained MDPs

In recent years, Reinforcement Learning (RL) has been applied to real-world problems with increasing success. Such applications often require to put constraints on the agent's behavior. Existing algorithms for constrained RL (CRL) rely on gradient descent-ascent, but this approach comes with a caveat. While these algorithms are guaranteed to converge on average, they do not guarantee last-iterate convergence, i.e., the current policy of the agent may never converge to the optimal solution. In practice, it is often observed that the policy alternates between satisfying the constraints and maximizing the reward, rarely accomplishing both objectives simultaneously. Here, we address this problem by introducing Reinforcement Learning with Optimistic Ascent-Descent (ReLOAD), a principled CRL method with guaranteed last-iterate convergence. We demonstrate its empirical effectiveness on a wide variety of CRL problems including discrete MDPs and continuous control. In the process we establish a benchmark of challenging CRL problems.
Operator algebras of free wreath products

We give a description of operator algebras of free wreath products in terms of fundamental algebras of graphs of operator algebras as well as an explicit formula for the Haar state. This allows us to deduce stability properties for certain approximation properties such as exactness, Haagerup property, hyperlinearity and K-amenability. We study qualitative properties of the associated von Neumann algebra: factoriality, fullness, primeness and absence of Cartan subalgebra and we give a formula for Connes' $T$-invariant and $\tau$-invariant. We also study maximal amenable von Neumann subalgebras. Finally, we give some explicit computations of K-theory groups for C*-algebras of free wreath products. As an application we show that the reduced C*-algebras of quantum reflection groups are pairwise non-isomorphic.
Learning PDE Solution Operator for Continuous Modeling of Time-Series

Learning underlying dynamics from data is important and challenging in many real-world scenarios. Incorporating differential equations (DEs) to design continuous networks has drawn much attention recently, however, most prior works make specific assumptions on the type of DEs, making the model specialized for particular problems. This work presents a partial differential equation (PDE) based framework which improves the dynamics modeling capability. Building upon the recent Fourier neural operator, we propose a neural operator that can handle time continuously without requiring iterative operations or specific grids of temporal discretization. A theoretical result demonstrating its universality is provided. We also uncover an intrinsic property of neural operators that improves data efficiency and model generalization by ensuring stability. Our model achieves superior accuracy in dealing with time-dependent PDEs compared to existing models. Furthermore, several numerical pieces of evidence validate that our method better represents a wide range of dynamics and outperforms state-of-the-art DE-based models in real-time-series applications. Our framework opens up a new way for a continuous representation of neural networks that can be readily adopted for real-world applications.
Local Implicit Neural Representations for Multi-Sequence MRI Translation

In radiological practice, multi-sequence MRI is routinely acquired to characterize anatomy and tissue. However, due to the heterogeneity of imaging protocols and contraindications to contrast agents, some MRI sequences, e.g. contrast-enhanced T1-weighted image (T1ce), may not be acquired. This creates difficulties for large-scale clinical studies for which heterogeneous datasets are aggregated. Modern deep learning techniques have demonstrated the capability of synthesizing missing sequences from existing sequences, through learning from an extensive multi-sequence MRI dataset. In this paper, we propose a novel MR image translation solution based on local implicit neural representations. We split the available MRI sequences into local patches and assign to each patch a local multi-layer perceptron (MLP) that represents a patch in the T1ce. The parameters of these local MLPs are generated by a hypernetwork based on image features. Experimental results and ablation studies on the BraTS challenge dataset showed that the local MLPs are critical for recovering fine image and tumor details, as they allow for local specialization that is highly important for accurate image translation. Compared to a classical pix2pix model, the proposed method demonstrated visual improvement and significantly improved quantitative scores (MSE 0.86 × 10−3 vs. 1.02 × 10−3 and SSIM 94.9 vs 94.3).
FedLAP-DP: Federated Learning by Sharing Differentially Private Loss Approximations

This work proposes FedLAP-DP, a novel privacy-preserving approach for federated learning. Unlike previous linear point-wise gradient-sharing schemes, such as FedAvg, our formulation enables a type of global optimization by leveraging synthetic samples received from clients. These synthetic samples, serving as loss surrogates, approximate local loss landscapes by simulating the utility of real images within a local region. We additionally introduce an approach to measure effective approximation regions reflecting the quality of the approximation. Therefore, the server can recover an approximation of the global loss landscape and optimize the model globally. Moreover, motivated by the emerging privacy concerns, we demonstrate that our approach seamlessly works with record-level differential privacy (DP), granting theoretical privacy guarantees for every data record on the clients. Extensive results validate the efficacy of our formulation on various datasets with highly skewed distributions. Our method consistently improves over the baselines, especially considering highly skewed distributions and noisy gradients due to DP. The source code and setup will be released upon publication.
Reduced basis approximation of parametric eigenvalue problems in presence of clusters and intersections

In this paper we discuss reduced order models for the approximation of parametric eigenvalue problems. In particular, we are interested in the presence of intersections or clusters of eigenvalues. The singularities originating by these phenomena make it hard a straightforward generalization of well known strategies normally used for standards PDEs. We investigate how the known results extend (or not) to higher order frequencies.