Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploiting Human Social Cognition for the Detection of Fake and Fraudulent Faces via Memory Networks

Advances in computer vision have brought us to the point where we have the ability to synthesise realistic fake content. Such approaches are seen as a source of disinformation and mistrust, and pose serious concerns to governments around the world. Convolutional Neural Networks (CNNs) demonstrate encouraging results when detecting fake images that arise from the specific type of manipulation they are trained on. However, this success has not transitioned to unseen manipulation types, resulting in a significant gap in the line-of-defense. We propose a Hierarchical Memory Network (HMN) architecture, which is able to successfully detect faked faces by utilising knowledge stored in neural memories as well as visual cues to reason about the perceived face and anticipate its future semantic embeddings. This renders a generalisable face tampering detection framework. Experimental results demonstrate the proposed approach achieves superior performance for fake and fraudulent face detection compared to the state-of-the-art.
The Formation and Evolution of Wide-orbit Stellar Multiples In Magnetized Clouds

Stars rarely form in isolation. Nearly half of the stars in the Milky Way have a companion, and this fraction increases in star-forming regions. However, why some dense cores and filaments form bound pairs while others form single stars remains unclear. We present a set of three-dimensional, gravo-magnetohydrodynamic simulations of turbulent star-forming clouds, aimed at understanding the formation and evolution of multiple-star systems formed through large-scale (≳103 au) turbulent fragmentation. We investigate three global magnetic field strengths, with global mass-to-flux ratios of μϕ = 2, 8, and 32. The initial separations of protostars in multiples depend on the global magnetic field strength, with stronger magnetic fields (e.g., μϕ = 2) suppressing fragmentation on smaller scales. The overall multiplicity fraction (MF) is between 0.4 and 0.6 for our strong and intermediate magnetic field strengths, which is in agreement with observations. The weak field case has a lower fraction. The MF is relatively constant throughout the simulations, even though stellar densities increase as collapse continues. While the MF rarely exceeds 60% in all three simulations, over 80% of all protostars are part of a binary system at some point. We additionally find that the distribution of binary spin misalignment angles is consistent with a randomized distribution. In all three simulations, several binaries originate with wide separations and dynamically evolve to ≲102 au separations. We show that a simple model of mass accretion and dynamical friction with the gas can explain this orbital evolution.
On the path partition number of 6‐regular graphs

A path partition (also referred to as a linear forest) of a graph G $G$ is a set of vertex‐disjoint paths which together contain all the vertices of G $G$ . An isolated vertex is considered to be a path in this case. The path partition conjecture states that every n $n$ ‐vertex d $d$ ‐regular graph has a path partition with at most n d + 1 $\frac{n}{d+1}$ paths. The conjecture has been proved for all d < 6 $d\lt 6$ . We prove the conjecture for d = 6 $d=6$ .
KISS: Keeping It Simple for Scene Text Recognition

Over the past few years, several new methods for scene text recognition have been proposed. Most of these methods propose novel building blocks for neural networks. These novel building blocks are specially tailored for the task of scene text recognition and can thus hardly be used in any other tasks. In this paper, we introduce a new model for scene text recognition that only consists of off-the-shelf building blocks for neural networks. Our model (KISS) consists of two ResNet based feature extractors, a spatial transformer, and a transformer. We train our model only on publicly available, synthetic training data and evaluate it on a range of scene text recognition benchmarks, where we reach state-of-the-art or competitive performance, although our model does not use methods like 2D-attention, or image rectification.
Vision-Language Navigation With Self-Supervised Auxiliary Reasoning Tasks

Vision-Language Navigation (VLN) is a task where an agent learns to navigate following a natural language instruction. The key to this task is to perceive both the visual scene and natural language sequentially. Conventional approaches fully exploit vision and language features in cross-modal grounding. However, the VLN task remains challenging, since previous works have implicitly neglected the rich semantic information contained in environments (such as navigation graphs or sub-trajectory semantics). In this paper, we introduce Auxiliary Reasoning Navigation (AuxRN), a framework with four self-supervised auxiliary reasoning tasks to exploit the additional training signals derived from these semantic information. The auxiliary tasks have four reasoning objectives: explaining the previous actions, evaluating the trajectory consistency, estimating the progress and predict the next direction. As a result, these additional training signals help the agent to acquire knowledge of semantic representations in order to reason about its activities and build a thorough perception of environments. Our experiments demonstrate that auxiliary reasoning tasks improve both the performance of the main task and the model generalizability by a large margin. We further demonstrate empirically that an agent trained with self-supervised auxiliary reasoning tasks substantially outperforms the previous state-of-the-art method, being the best existing approach on the standard benchmark.
CAGFuzz: Coverage-Guided Adversarial Generative Fuzzing Testing of Deep Learning Systems

Deep Learning systems (DL) based on Deep Neural Networks (DNNs) are more and more used in various aspects of our life, including unmanned vehicles, speech processing, and robotics. However, due to the limited dataset and the dependence on manual labeling data, DNNs often fail to detect their erroneous behaviors, which may lead to serious problems. Several approaches have been proposed to enhance the input examples for testing DL systems. However, they have the following limitations. First, they design and generate adversarial examples from the perspective of model, which may cause low generalization ability when they are applied to other models. Second, they only use surface feature constraints to judge the difference between the adversarial example generated and the original example. The deep feature constraints, which contain high-level semantic information, such as image object category and scene semantics are completely neglected. To address these two problems, in this paper, we propose CAGFuzz, a Coverage-guided Adversarial Generative Fuzzing testing approach, which generates adversarial examples for a targeted DNN to discover its potential defects. First, we train an adversarial case generator (AEG) from the perspective of general data set. Second, we extract the depth features of the original and adversarial examples, and constrain the adversarial examples by cosine similarity to ensure that the semantic information of adversarial examples remains unchanged. Finally, we retrain effective adversarial examples to improve neuron testing coverage rate. Based on several popular data sets, we design a set of dedicated experiments to evaluate CAGFuzz. The experimental results show that CAGFuzz can improve the neuron coverage rate, detect hidden errors, and also improve the accuracy of the target DNN.
Effects of a k⃗-dependent Hybridisation on the Fermi surface of an extended d–p Hubbard model

ABSTRACT The topology of the Fermi surface of an extended d–p Hubbard model is investigated using Green's function technique in a n-pole approximation. The effects of the d–p hybridisation on the Fermi surface are the main focus in the present work. Nevertheless, the effects of doping, Coulomb interaction and hopping to second-nearest-neighbours on the Fermi surface, are also studied. Particularly, it is shown that the crossover from hole-like to electron-like Fermi surface (Lifshitz transition) is deeply affected by the d–p hybridisation. Moreover, the pseudogap present in the low doping regime is also affected by the hybridisation. The results show that both the doping and the hybridisation act in the sense of suppresses the pseudogap. Therefore, the systematic investigation of the Fermi surface topology, shows that not only the doping but also the hybridisation can be considered as a control parameter for both the pseudogap and the Lifshitz transition. Assuming that the hybridisation is sensitive to external pressure, the present results agree qualitatively with recent experimental data for the cuprate Nd-LSCO.
ISP4ML: Understanding the Role of Image Signal Processing in Efficient Deep Learning Vision Systems

Convolutional neural networks (CNNs) are now predominant components in a variety of computer vision (CV) systems. These systems typically include an image signal processor (ISP), even though the ISP is traditionally designed to produce images that look appealing to humans. In CV systems, it is not clear what the role of the ISP is, or if it is even required at all for accurate prediction. In this work, we investigate the efficacy of the ISP in CNN classification tasks, and outline the system-level trade-offs between prediction accuracy and computational cost. To do so, we build software models of a configurable ISP and an imaging sensor in order to train CNNs on ImageNet with a range of different ISP settings and functionality. Results on ImageNet show that an ISP improves accuracy by 4.6%-12.2% on MobileNet architectures of different widths. Results using ResNets demonstrate that these trends also generalize to deeper networks. An ablation study of the various processing stages in a typical ISP reveals that the tone mapper is the most significant stage when operating on high dynamic range (HDR) images, by providing 5.8% average accuracy improvement alone. Overall, the ISP benefits system efficiency because the memory and computational costs of the ISP is minimal compared to the cost of using a larger CNN to achieve the same accuracy.
Non-existence theorems and solutions of the Wu-Yang monopole equation

We consider the dynamics of the classical $SU(2)$ Wu-Yang monopole problem and show a set of new directions for its analysis starting from a variational setting. This allows us to give a new interpretation of the monopole charge as a string in a centrifugal potential. The field equations are solved using both standard power series, as well as a new Pade approach. Furthermore, we discuss non-existence, and give a proof that rule out even singular non-trivial smooth finite energy solutions. Due to its non-vanishing chromomagnetic fields, the monopole can be interpreted as a collection of magnetic dipoles. Along the same lines, a non-smooth glue-ball solution is discussed that may serve as a toy model of the proton or a radiation-dominated cosmological expansion. Finally, we discuss generalizations of the Wu-Yang monopole.
Neural Forest Learning

We propose Neural Forest Learning (NFL), a novel deep learning based random-forest-like method. In contrast to previous forest methods, NFL enjoys the benefits of end-to-end, data-driven representation learning, as well as pervasive support from deep learning software and hardware platforms, hence achieving faster inference speed and higher accuracy than previous forest methods. Furthermore, NFL learns non-linear feature representations in CNNs more efficiently than previous higher-order pooling methods, producing good results with negligible increase in parameters, floating point operations (FLOPs) and real running time. We achieve superior performance on 7 machine learning datasets when compared to random forests and GBDTs. On the fine-grained benchmarks CUB-200-2011, FGVC-aircraft and Stanford Cars, we achieve over 5.7%, 6.9% and 7.8% gains for VGG-16, respectively. Moreover, NFL can converge in much fewer epochs, further accelerating network training. On the large-scale ImageNet ILSVRC-12 validation set, integration of NFL into ResNet-18 achieves top-1/top-5 errors of 28.32%/9.77%, which outperforms ResNet-18 by 1.92%/1.15% with negligible extra cost and the improvement is consistent under various architectures.
2D Eigenvalue Problem I: Existence and Number of Solutions

A two dimensional eigenvalue problem (2DEVP) of a Hermitian matrix pair $(A, C)$ is introduced in this paper. The 2DEVP can be viewed as a linear algebraic formulation of the well-known eigenvalue optimization problem of the parameter matrix $H(\mu) = A - \mu C$. We present fundamental properties of the 2DEVP such as the existence, the necessary and sufficient condition for the finite number of 2D-eigenvalues and variational characterizations. We use eigenvalue optimization problems from the minmax of two Rayleigh quotients and the computation of distance to instability to show their connections with the 2DEVP and new insights of these problems derived from the properties of the 2DEVP.
Imprints of the early Universe on axion dark matter substructure

Despite considerable experimental progress large parts of the axion-like particle (ALP) parameter space remain difficult to probe in terrestrial experiments. In some cases, however, small-scale structure of the ALP dark matter (DM) distribution is strongly enhanced, offering opportunities for astrophysical tests. Such an enhancement can be produced by a period of pre-nucleosynthesis early matter domination (EMD). This cosmology arises in many ultraviolet completions and generates the correct relic abundance for weak coupling $f_a\sim 10^{16}$ GeV, ALP masses in the range $10^{-13}$ eV $<m_a < 1$ eV, and without fine-tuning of the initial misalignment angle. This range includes the QCD axion around $10^{-9}-10^{-8}$ eV. EMD enhances the growth of ALP small-scale structure, leading to the formation of dense ALP miniclusters. We study the interplay between the initial ALP oscillation, reheating temperature, and effective pressure to provide analytic estimates of the minicluster abundance and properties. ALP miniclusters in the EMD cosmology are denser and more abundant than in $\Lambda$CDM. While enhanced substructure generically reduces the prospects of direct detection experiments, we show that pulsar timing and lensing observations can discover these minihalos over a large range of ALP masses and reheating temperatures.
Simulation and Optimization of Mean First Passage Time Problems in 2-D using Numerical Embedded Methods and Perturbation Theory

We develop novel numerical methods and perturbation approaches to determine the mean first passage time (MFPT) for a Brownian particle to be captured by either small stationary or mobile traps inside a bounded 2-D confining domain. Of particular interest is to identify optimal arrangements of small absorbing traps that minimize the average MFPT. Although the MFPT, and the associated optimal trap arrangement problem, has been well-studied for disk-shaped domains, there are very few analytical or numerical results available for general star-shaped domains or for thin domains with large aspect ratio. Analytical progress is challenging owing to the need to determine the Neumann Green's function for the Laplacian, while the numerical challenge results from a lack of easy-to-use and fast numerical tools for first computing the MFPT and then optimizing over a class of trap configurations. In this direction, and for the stationary trap problem, we develop a simple embedded numerical method, based on the Closest Point Method (CPM), to perform MFPT simulations on elliptical and star-shaped domains. For periodic mobile trap problems, we develop a robust CPM method to compute the average MFPT. Optimal trap arrangements are identified numerically through either a refined discrete sampling approach or from a particle-swarm optimization procedure. To confirm some of the numerical findings, novel perturbation approaches are developed to approximate the average MFPT and identify optimal trap configurations for a class of near-disk confining domains or for an arbitrary thin domain of large aspect ratio.
Simultaneous Region Localization and Hash Coding for Fine-grained Image Retrieval

Fine-grained image hashing is a challenging problem due to the difficulties of discriminative region localization and hash code generation. Most existing deep hashing approaches solve the two tasks independently. While these two tasks are correlated and can reinforce each other. In this paper, we propose a deep fine-grained hashing to simultaneously localize the discriminative regions and generate the efficient binary codes. The proposed approach consists of a region localization module and a hash coding module. The region localization module aims to provide informative regions to the hash coding module. The hash coding module aims to generate effective binary codes and give feedback for learning better localizer. Moreover, to better capture subtle differences, multi-scale regions at different layers are learned without the need of bounding-box/part annotations. Extensive experiments are conducted on two public benchmark fine-grained datasets. The results demonstrate significant improvements in the performance of our method relative to other fine-grained hashing algorithms.
Cellular automaton model for substitutional binary diffusion in solids

We use the cellular automaton (CA) approach to model binary diffusion in solids. We define an asynchronous CA model and formally take its continuum limit and show it approaches a differential equation model derived in previous work (Ribera, Wetton, and Myers, 2019, arXiv:1911.07359 [cond-mat.stat-mech]) that exhibits the Kirkendall effect. The framework allows the exploration of other state change rules based on additional physical mechanisms.
Online Learned Continual Compression with Stacked Quantization Module

We introduce and study the problem of Online Continual Compression, where one attempts to learn to compress and store a representative dataset from a non i.i. d data stream, while only observing each sample once. This problem is highly relevant for downstream online continual learning tasks, as well as standard learning methods under resource constrained data collection. To address this we propose a new architecture which Stacks Quantization Modules (SQM), consisting of a series of discrete autoencoders, each equipped with their own memory. Every added module is trained to reconstruct the latent space of the previous module using fewer bits, allowing the learned representation to become more compact as training progresses. This modularity has several advantages: 1) moderate compressions are quickly available early in training, which is crucial for remembering the early tasks, 2) as more data needs to be stored, earlier data becomes more compressed, freeing memory, 3) unlike previous methods, our approach does not require pretraining, even on challenging datasets. We show several potential applications of this method. We first replace the episodic memory used in Experience Replay with SQM, leading to significant gains on standard continual learning benchmarks using a fixed memory budget. We then apply our method to online compression of larger images like those from Imagenet, and show that it is also effective with other modalities, such as LiDAR data.
End-to-end ASR: from Supervised to Semi-Supervised Learning with Modern Architectures

We study pseudo-labeling for the semi-supervised training of ResNet, Time-Depth Separable ConvNets, and Transformers for speech recognition, with either CTC or Seq2Seq loss functions. We perform experiments on the standard LibriSpeech dataset, and leverage additional unlabeled data from LibriVox through pseudo-labeling. We show that while Transformer-based acoustic models have superior performance with the supervised dataset alone, semi-supervision improves all models across architectures and loss functions and bridges much of the performance gaps between them. In doing so, we reach a new state-of-the-art for end-to-end acoustic models decoded with an external language model in the standard supervised learning setting, and a new absolute state-of-the-art with semi-supervised training. Finally, we study the effect of leveraging different amounts of unlabeled audio, propose several ways of evaluating the characteristics of unlabeled audio which improve acoustic modeling, and show that acoustic models trained with more audio rely less on external language models.
Mixed-curvature Variational Autoencoders

Euclidean geometry has historically been the typical "workhorse" for machine learning applications due to its power and simplicity. However, it has recently been shown that geometric spaces with constant non-zero curvature improve representations and performance on a variety of data types and downstream tasks. Consequently, generative models like Variational Autoencoders (VAEs) have been successfully generalized to elliptical and hyperbolic latent spaces. While these approaches work well on data with particular kinds of biases e.g. tree-like data for a hyperbolic VAE, there exists no generic approach unifying and leveraging all three models. We develop a Mixed-curvature Variational Autoencoder, an efficient way to train a VAE whose latent space is a product of constant curvature Riemannian manifolds, where the per-component curvature is fixed or learnable. This generalizes the Euclidean VAE to curved latent spaces and recovers it when curvatures of all latent space components go to 0.
Tell Me What They're Holding: Weakly-supervised Object Detection with Transferable Knowledge from Human-object Interaction

In this work, we introduce a novel weakly supervised object detection (WSOD) paradigm to detect objects belonging to rare classes that have not many examples using transferable knowledge from human-object interactions (HOI). While WSOD shows lower performance than full supervision, we mainly focus on HOI as the main context which can strongly supervise complex semantics in images. Therefore, we propose a novel module called RRPN (relational region proposal network) which outputs an object-localizing attention map only with human poses and action verbs. In the source domain, we fully train an object detector and the RRPN with full supervision of HOI. With transferred knowledge about localization map from the trained RRPN, a new object detector can learn unseen objects with weak verbal supervision of HOI without bounding box annotations in the target domain. Because the RRPN is designed as an add-on type, we can apply it not only to the object detection but also to other domains such as semantic segmentation. The experimental results on HICO-DET dataset show the possibility that the proposed method can be a cheap alternative for the current supervised object detection paradigm. Moreover, qualitative results demonstrate that our model can properly localize unseen objects on HICO-DET and V-COCO datasets.