Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards Anytime Optical Flow Estimation with Event Cameras

Optical flow estimation is a fundamental task in the field of autonomous driving. Event cameras are capable of responding to log-brightness changes in microseconds. Its characteristic of producing responses only to the changing region is particularly suitable for optical flow estimation. In contrast to the super low-latency response speed of event cameras, existing datasets collected via event cameras, however, only provide limited frame rate optical flow ground truth, (e.g., at 10Hz), greatly restricting the potential of event-driven optical flow. To address this challenge, we put forward a high-frame-rate, low-latency event representation Unified Voxel Grid, sequentially fed into the network bin by bin. We then propose EVA-Flow, an EVent-based Anytime Flow estimation network to produce high-frame-rate event optical flow with only low-frame-rate optical flow ground truth for supervision. The key component of our EVA-Flow is the stacked Spatiotemporal Motion Refinement (SMR) module, which predicts temporally dense optical flow and enhances the accuracy via spatial-temporal motion refinement. The time-dense feature warping utilized in the SMR module provides implicit supervision for the intermediate optical flow. Additionally, we introduce the Rectified Flow Warp Loss (RFWL) for the unsupervised evaluation of intermediate optical flow in the absence of ground truth. This is, to the best of our knowledge, the first work focusing on anytime optical flow estimation via event cameras. A comprehensive variety of experiments on MVSEC, DESC, and our EVA-FlowSet demonstrates that EVA-Flow achieves competitive performance, super-low-latency (5ms), fastest inference (9.2ms), time-dense motion estimation (200Hz), and strong generalization. Our code will be available at https://github.com/Yaozhuwa/EVA-Flow.
Feature Activation Map: Visual Explanation of Deep Learning Models for Image Classification

Decisions made by convolutional neural networks(CNN) can be understood and explained by visualizing discriminative regions on images. To this end, Class Activation Map (CAM) based methods were proposed as powerful interpretation tools, making the prediction of deep learning models more explainable, transparent, and trustworthy. However, all the CAM-based methods (e.g., CAM, Grad-CAM, and Relevance-CAM) can only be used for interpreting CNN models with fully-connected (FC) layers as a classifier. It is worth noting that many deep learning models classify images without FC layers, e.g., few-shot learning image classification, contrastive learning image classification, and image retrieval tasks. In this work, a post-hoc interpretation tool named feature activation map (FAM) is proposed, which can interpret deep learning models without FC layers as a classifier. In the proposed FAM algorithm, the channel-wise contribution weights are derived from the similarity scores between two image embeddings. The activation maps are linearly combined with the corresponding normalized contribution weights, forming the explanation map for visualization. The quantitative and qualitative experiments conducted on ten deep learning models for few-shot image classification, contrastive learning image classification and image retrieval tasks demonstrate the effectiveness of the proposed FAM algorithm.
Nature of the Schmid transition in a resistively shunted Josephson junction

We study the phase diagram of a resistively shunted Josephson junction (RSJJ) in the framework of the boundary sine-Gordon model. Using the non-perturbative functional renormalization group (FRG) we find that the transition is not controlled by a single fixed point but by a line of fixed points, and compute the continuously varying critical exponent $\nu$. We argue that the conductance also varies continuously along the transition line. In contrast to the traditional phase diagram of the RSJJ -- an insulating ground state when the shunt resistance $R$ is larger than $R_q=h/(2e)^2$ and a superconducting one when $R<R_q$ -- the FRG predicts the transition line in the plane $(\alpha,E_J/E_C)$ to bend in the region $\alpha=R_q/R<1$ but we cannot discard the possibility of a vertical line at $\alpha=1$ ($E_J$ and $E_C$ denote the Josephson and charging energies of the junction, respectively). Our results regarding the phase diagram and the nature of the transition are compared with Monte Carlo simulations and numerical renormalization group results.
Parameters of Six Selected Galactic Potential Models

Abstract This paper is devoted to the refinement of the parameters of the six three-component (bulge, disk, halo) axisymmetric Galactic gravitational potential models on the basis of modern data on circular velocities of Galactic objects located at distances up to 200 kpc from the Galactic center. In all models the bulge and disk are described by the Miyamoto-Nagai expressions. To describe the halo, the models of Allen-Santillán (I), Wilkinson-Evans (II), Navarro- Frenk-White (III), Binney (IV), Plummer (V), and Hernquist (VI) are used. The sought-for parameters of potential models are determined by fitting the model rotation curves to the measured velocities, taking into account restrictions on the local dynamical matter density p⊙ - 0.1M⊙ pc−3 and the vertical force |Kz=1.1|/2πG = 77M⊙ pc−2. A comparative analysis of the refined potential models is made and for each of the models the estimates of a number of the Galactic characteristics are presented.
Thermoelectric effect on diffusion in the two-dimensional Hubbard model

We study charge and heat transport in the square lattice Hubbard model at strong coupling using the finite-temperature Lanczos method. We construct the diffusion matrix and estimate the effect of thermoelectric terms on diffusive and hydrodynamic time evolution. The thermoelectric terms prevent the interpretation of the diffusion in terms of a single time scale. We discuss our results in relation to cold-atom experiments and measurements of heat conductivity based on the measurements of heat diffusion.
Rate of convergence of empirical measures for exchangeable sequences

Abstract Let S be a finite set, (Xn) an exchangeable sequence of S-valued random variables, and μn = (1/n) ∑i=1n $\sum\limits_{i=1}^{n} $ δXi the empirical measure. Then, μn(B) ⟶a.s. $\overset{a.s. }{\longrightarrow} $ μ(B) for all B ⊂ S and some (essentially unique) random probability measure μ. Denote by 𝓛(Z) the probability distribution of any random variable Z. Under some assumptions on 𝓛(μ), it is shown that an≤ρ[L(μn),L(μ)]≤bnandρ[L(μn),L(an)]≤cnu $$\begin{array}{} \displaystyle \frac{a}{n}\le\rho\bigl[\mathcal{L}(\mu_n), \mathcal{L}(\mu)\bigr]\le\frac{b}{n}\qquad\text{and}\qquad\rho\bigl[\mathcal{L}(\mu_n), \mathcal{L}(a_n)\bigr]\le\frac{c}{n^u} \end{array} $$ where ρ is the bounded Lipschitz metric and an(⋅) = P(Xn+1 ∈ ⋅ | X1, …, Xn) is the predictive measure. The constants a, b, c > 0 and u ∈ ( 12 $\frac{1}{2} $, 1] depend on 𝓛(μ) and card (S) only.
Inconsistency with De Sitter Spacetime of"Gravitational Pair Production and Black Hole Evaporation"

We study the recent Physical Review Letter [1] which presents a new mechanism for black hole evaporation through a spatially dependent temperature. This new temperature is comparable to the Hawking result near the black hole, but is very small far away, and therefore could be a small correction. Here we apply the proposed reasoning to the case of de Sitter space, finding that it over predicts the de Sitter temperature of a minimally coupled scalar by factor of $\approx 4.3$ and therefore cannot be ignored in any limit. This indicates an inconsistency in the proposed formalism.
Self-supervised adversarial masking for 3D point cloud representation learning

Self-supervised methods have been proven effective for learning deep representations of 3D point cloud data. Although recent methods in this domain often rely on random masking of inputs, the results of this approach can be improved. We introduce PointCAM, a novel adversarial method for learning a masking function for point clouds. Our model utilizes a self-distillation framework with an online tokenizer for 3D point clouds. Compared to previous techniques that optimize patch-level and object-level objectives, we postulate applying an auxiliary network that learns how to select masks instead of choosing them randomly. Our results show that the learned masking function achieves state-of-the-art or competitive performance on various downstream tasks. The source code is available at https://github.com/szacho/pointcam.
Development of Web-Based Learning Media Using Google Sites in Vocational High School Informatics Subjects

This study aims to create web-based instructional media with Google Sites and then assess the media's validity, practicability, and efficacy. This research used the R & D (Research and Development) technique with the 4-D development model (Four-D Model). Data collection used 3 types of questionnaires consisting of media, material, and language validation questionnaires. To test the effectiveness of the media, pretest and posttest questions were used, which had gone through a process of validation, reliability, discriminatory power and level of difficulty. The test results of all validations show that an average validity index of 0,919 in the Very Eligible criteria. Media practicality is in the Very Practical category, with an average practicality percentage of 84.91% across instructors and students. The media's effectiveness level is Effective Enough, with an average N-Gain score of 60.77% and a substantial T-Test value of 0.000, indicating that it may be utilized effectively in SMK Informatics learning. This study conducted trials on students who had just implemented the Merdeka curriculum.
Efficient 3D Articulated Human Generation with Layered Surface Volumes

Access to high-quality and diverse 3D articulated digital human assets is crucial in various applications, ranging from virtual reality to social platforms. Generative approaches, such as 3D generative adversarial networks (GANs), are rapidly replacing laborious manual content creation tools. However, existing 3D GAN frameworks typically rely on scene representations that leverage either template meshes, which are fast but offer limited quality, or volumes, which offer high capacity but are slow to render, thereby limiting the 3D fidelity in GAN settings. In this work, we introduce layered surface volumes (LSVs) as a new 3D object representation for articulated digital humans. LSVs represent a human body using multiple textured mesh layers around a conventional template. These layers are rendered using alpha compositing with fast differentiable rasterization, and they can be interpreted as a volumetric representation that allocates its capacity to a manifold of finite thickness around the template. Unlike conventional single-layer templates that struggle with representing fine off-surface details like hair or accessories, our surface volumes naturally capture such details. LSVs can be articulated, and they exhibit exceptional efficiency in GAN settings, where a 2D generator learns to synthesize the RGBA textures for the individual layers. Trained on unstructured, single-view 2D image datasets, our LSV-GAN generates high-quality and view-consistent 3D articulated digital humans without the need for view-inconsistent 2D upsampling networks.
Stability and genericity of bang-bang controls in affine problems

We analyse the role of the bang-bang property in affine optimal control problems. We show that many essential stability properties of affine problems are only satisfied when minimizers are bang-bang. Moreover, we prove that almost any perturbation in an affine optimal control problem leads to a bang-bang strict global minimizer. We work in an abstract framework that allows to cover many problems in the literature of optimal control, this includes problems constrained by partial and ordinary differential equations. We give examples that show the applicability of our results to specific optimal control problems.
Generalized Hall current on a finite lattice

Gapped fermion theories with gapless boundary fermions can exist in any number of dimensions. When the boundary has even space-time dimensions and hosts chiral fermions, a quantum Hall current flows from the bulk to the boundary in a background electric field. This current compensate for the boundary chiral anomaly. Such a current inflow picture is absent when the boundary theory is odd dimensional. However, in recent work, the idea of quantum Hall current has been generalized to describe odd dimensional boundary theories in continuous Euclidean space-time dimension of infinite volume. In this paper we extend this idea to a lattice regulated finite volume theory of 1+1 dimensional Wilson-Dirac fermions. This fermion theory with a domain wall in fermion mass can host gapless modes on the wall. The number of gapless fermions is equal to the integral of the divergence of the lattice generalized Hall current.
Temporal Graphs Anomaly Emergence Detection: Benchmarking For Social Media Interactions

Temporal graphs have become an essential tool for analyzing complex dynamic systems with multiple agents. Detecting anomalies in temporal graphs is crucial for various applications, including identifying emerging trends, monitoring network security, understanding social dynamics, tracking disease outbreaks, and understanding financial dynamics. In this paper, we present a comprehensive benchmarking study that compares 12 data-driven methods for anomaly detection in temporal graphs. We conduct experiments on two temporal graphs extracted from Twitter and Facebook, aiming to identify anomalies in group interactions. Surprisingly, our study reveals an unclear pattern regarding the best method for such tasks, highlighting the complexity and challenges involved in anomaly emergence detection in large and dynamic systems. The results underscore the need for further research and innovative approaches to effectively detect emerging anomalies in dynamic systems represented as temporal graphs.
Plasmonic polarons induced by alkali-atom deposition in hafnium disulfide (1$T$-HfS$_2$)

We combine ab-initio calculations based on many-body perturbation theory and the cumulant expansion with angle-resolved photoemission spectroscopy (ARPES) to quantify the electron-plasmon interaction in the highly-doped semiconducting transition metal dichalcogenide 1$T$-HfS$_2$. ARPES reveals the emergence of satellite spectral features in the vicinity of quasiparticle excitations at the bottom of the conduction band, suggesting coupling to bosonic excitations with a characteristic energy of 200 meV. Our first-principles calculations of the photoemission spectral function reveal that these features can be ascribed to electronic coupling to carrier plasmons (doping-induced collective charge-density fluctuations). We further show that reduced screening at the surface enhances the electron-plasmon interaction and is primarily responsible for the emergence of plasmonic polarons.
Transaction Fraud Detection via Spatial-Temporal-Aware Graph Transformer

How to obtain informative representations of transactions and then perform the identification of fraudulent transactions is a crucial part of ensuring financial security. Recent studies apply Graph Neural Networks (GNNs) to the transaction fraud detection problem. Nevertheless, they encounter challenges in effectively learning spatial-temporal information due to structural limitations. Moreover, few prior GNN-based detectors have recognized the significance of incorporating global information, which encompasses similar behavioral patterns and offers valuable insights for discriminative representation learning. Therefore, we propose a novel heterogeneous graph neural network called Spatial-Temporal-Aware Graph Transformer (STA-GT) for transaction fraud detection problems. Specifically, we design a temporal encoding strategy to capture temporal dependencies and incorporate it into the graph neural network framework, enhancing spatial-temporal information modeling and improving expressive ability. Furthermore, we introduce a transformer module to learn local and global information. Pairwise node-node interactions overcome the limitation of the GNN structure and build up the interactions with the target node and long-distance ones. Experimental results on two financial datasets compared to general GNN models and GNN-based fraud detectors demonstrate that our proposed method STA-GT is effective on the transaction fraud detection task.
Precise image generation on current noisy quantum computing devices

The quantum angle generator (QAG) is a new full quantum machine learning model designed to generate accurate images on current noise intermediate scale quantum devices. Variational quantum circuits form the core of the QAG model, and various circuit architectures are evaluated. In combination with the so-called MERA-upsampling architecture, the QAG model achieves excellent results, which are analyzed and evaluated in detail. To our knowledge, this is the first time that a quantum model has achieved such accurate results. To explore the robustness of the model to noise, an extensive quantum noise study is performed. In this paper, it is demonstrated that the model trained on a physical quantum device learns the noise characteristics of the hardware and generates outstanding results. It is verified that even a quantum hardware machine calibration change during training of up to 8% can be well tolerated. For demonstration, the model is employed in indispensable simulations in high energy physics required to measure particle energies and, ultimately, to discover unknown particles at the large Hadron Collider at CERN.
ATWM: Defense against adversarial malware based on adversarial training

Deep learning technology has made great achievements in the field of image. In order to defend against malware attacks, researchers have proposed many Windows malware detection models based on deep learning. However, deep learning models are vulnerable to adversarial example attacks. Malware can generate adversarial malware with the same malicious function to attack the malware detection model and evade detection of the model. Currently, many adversarial defense studies have been proposed, but existing adversarial defense studies are based on image sample and cannot be directly applied to malware sample. Therefore, this paper proposes an adversarial malware defense method based on adversarial training. This method uses preprocessing to defend simple adversarial examples to reduce the difficulty of adversarial training. Moreover, this method improves the adversarial defense capability of the model through adversarial training. We experimented with three attack methods in two sets of datasets, and the results show that the method in this paper can improve the adversarial defense capability of the model without reducing the accuracy of the model.
DRMC: A Generalist Model with Dynamic Routing for Multi-Center PET Image Synthesis

Multi-center positron emission tomography (PET) image synthesis aims at recovering low-dose PET images from multiple different centers. The generalizability of existing methods can still be suboptimal for a multi-center study due to domain shifts, which result from non-identical data distribution among centers with different imaging systems/protocols. While some approaches address domain shifts by training specialized models for each center, they are parameter inefficient and do not well exploit the shared knowledge across centers. To address this, we develop a generalist model that shares architecture and parameters across centers to utilize the shared knowledge. However, the generalist model can suffer from the center interference issue, \textit{i.e.} the gradient directions of different centers can be inconsistent or even opposite owing to the non-identical data distribution. To mitigate such interference, we introduce a novel dynamic routing strategy with cross-layer connections that routes data from different centers to different experts. Experiments show that our generalist model with dynamic routing (DRMC) exhibits excellent generalizability across centers. Code and data are available at: https://github.com/Yaziwel/Multi-Center-PET-Image-Synthesis.
Planar Curve Registration using Bayesian Inversion

We study parameterisation-independent closed planar curve matching as a Bayesian inverse problem. The motion of the curve is modelled via a curve on the diffeomorphism group acting on the ambient space, leading to a large deformation diffeomorphic metric mapping (LDDMM) functional penalising the kinetic energy of the deformation. We solve Hamilton's equations for the curve matching problem using the Wu-Xu element [S. Wu, J. Xu, Nonconforming finite element spaces for $2m^\text{th}$ order partial differential equations on $\mathbb{R}^n$ simplicial grids when $m=n+1$, Mathematics of Computation 88 (316) (2019) 531-551] which provides mesh-independent Lipschitz constants for the forward motion of the curve, and solve the inverse problem for the momentum using Bayesian inversion. Since this element is not affine-equivalent we provide a pullback theory which expedites the implementation and efficiency of the forward map. We adopt ensemble Kalman inversion using a negative Sobolev norm mismatch penalty to measure the discrepancy between the target and the ensemble mean shape. We provide several numerical examples to validate the approach.
Disentangled Contrastive Image Translation for Nighttime Surveillance

Nighttime surveillance suffers from degradation due to poor illumination and arduous human annotations. It is challengable and remains a security risk at night. Existing methods rely on multi-spectral images to perceive objects in the dark, which are troubled by low resolution and color absence. We argue that the ultimate solution for nighttime surveillance is night-to-day translation, or Night2Day, which aims to translate a surveillance scene from nighttime to the daytime while maintaining semantic consistency. To achieve this, this paper presents a Disentangled Contrastive (DiCo) learning method. Specifically, to address the poor and complex illumination in the nighttime scenes, we propose a learnable physical prior, i.e., the color invariant, which provides a stable perception of a highly dynamic night environment and can be incorporated into the learning pipeline of neural networks. Targeting the surveillance scenes, we develop a disentangled representation, which is an auxiliary pretext task that separates surveillance scenes into the foreground and background with contrastive learning. Such a strategy can extract the semantics without supervision and boost our model to achieve instance-aware translation. Finally, we incorporate all the modules above into generative adversarial networks and achieve high-fidelity translation. This paper also contributes a new surveillance dataset called NightSuR. It includes six scenes to support the study on nighttime surveillance. This dataset collects nighttime images with different properties of nighttime environments, such as flare and extreme darkness. Extensive experiments demonstrate that our method outperforms existing works significantly. The dataset and source code will be released on GitHub soon.