Miracle-dz/UniFAR-FTUs huggingface dataset:
ASK: Adversarial Soft k-Nearest Neighbor Attack and Defense

K-Nearest Neighbor (kNN)-based deep learning methods have been applied to many applications due to their simplicity and geometric interpretability. However, the robustness of kNN-based deep classification models has not been thoroughly explored and kNN attack strategies are underdeveloped. In this paper, we first propose an Adversarial Soft kNN (ASK) loss for developing more effective kNN-based deep neural network attack strategies and designing better defense methods against them. Our ASK loss provides a differentiable surrogate of the expected kNN classification error. It is also interpretable as it preserves the mutual information between the perturbed input and the in-class-reference data. We use the ASK loss to design a novel attack method called the ASK-Attack (ASK-Atk), which shows superior attack efficiency and accuracy degradation relative to previous kNN attacks on hidden layers. We then derive an ASK-Defense (ASK-Def) method that optimizes the worst-case ASK training loss. Experiments on CIFAR-10 (ImageNet) show that (i) ASK-Atk achieves <inline-formula> <tex-math notation="LaTeX">$\geq 13\%$ </tex-math></inline-formula> (<inline-formula> <tex-math notation="LaTeX">$\geq 13\%$ </tex-math></inline-formula>) improvement in attack success rate over previous kNN attacks, and (ii) ASK-Def outperforms the conventional adversarial training method by <inline-formula> <tex-math notation="LaTeX">$\geq 6.9\%$ </tex-math></inline-formula> (<inline-formula> <tex-math notation="LaTeX">$\geq 3.5\%$ </tex-math></inline-formula>) in terms of robustness improvement. Relevant codes are available at <uri>https://github.com/wangren09/ASK</uri>.
Designing color symmetry in stigmergic art

Color symmetry is an extension of the symmetry imposed by isometric transformations and indicates that the colors of geometrical objects are assigned according to the symmetry properties of these objects. A color symmetry permutes the coloring of the objects consistently with their symmetry group. We apply this concept to bio-inspired generative art. Therefore, the geometrical objects are interpreted as motifs that may repeat themselves with a symmetry-consistent coloring. The motifs are obtained by design principles from stigmergy. We discuss the design procedure and present visual results.
A Theory-Driven Self-Labeling Refinement Method for Contrastive Representation Learning

For an image query, unsupervised contrastive learning labels crops of the same image as positives, and other image crops as negatives. Although intuitive, such a native label assignment strategy cannot reveal the underlying semantic similarity between a query and its positives and negatives, and impairs performance, since some negatives are semantically similar to the query or even share the same semantic class as the query. In this work, we first prove that for contrastive learning, inaccurate label assignment heavily impairs its generalization for semantic instance discrimination, while accurate labels benefit its generalization. Inspired by this theory, we propose a novel self-labeling refinement approach for contrastive learning. It improves the label quality via two complementary modules: (i) self-labeling refinery (SLR) to generate accurate labels and (ii) momentum mixup (MM) to enhance similarity between query and its positive. SLR uses a positive of a query to estimate semantic similarity between a query and its positive and negatives, and combines estimated similarity with vanilla label assignment in contrastive learning to iteratively generate more accurate and informative soft labels. We theoretically show that our SLR can exactly recover the true semantic labels of label-corrupted data, and supervises networks to achieve zero prediction error on classification tasks. MM randomly combines queries and positives to increase semantic similarity between the generated virtual queries and their positives so as to improves label accuracy. Experimental results on CIFAR10, ImageNet, VOC and COCO show the effectiveness of our method. PyTorch code and model will be released online.
On the matrices in B‐spline collocation methods for Riesz fractional equations and their spectral properties

In this work, we focus on a fractional differential equation in Riesz form discretized by a polynomial B‐spline collocation method. For an arbitrary polynomial degree p$$ p $$ , we show that the resulting coefficient matrices possess a Toeplitz‐like structure. We investigate their spectral properties via their symbol and we prove that, like for second order differential problems, the given matrices are ill‐conditioned both in the low and high frequencies for large p$$ p $$ . More precisely, in the fractional scenario the symbol vanishes at 0 with order α$$ \alpha $$ , the fractional derivative order that ranges from 1 to 2, and it decays exponentially to zero at π$$ \pi $$ for increasing p$$ p $$ at a rate that becomes faster as α$$ \alpha $$ approaches 1. This translates into a mitigated conditioning in the low frequencies and into a deterioration in the high frequencies when compared to second order problems. Furthermore, the derivation of the symbol reveals another similarity of our problem with a classical diffusion problem. Since the entries of the coefficient matrices are defined as evaluations of fractional derivatives of the B‐spline basis at the collocation points, we are able to express the central entries of the coefficient matrix as inner products of two fractional derivatives of cardinal B‐splines. Finally, we perform a numerical study of the approximation behavior of polynomial B‐spline collocation. This study suggests that, in line with nonfractional diffusion problems, the approximation order for smooth solutions in the fractional case is p+2−α$$ p+2-\alpha $$ for even p$$ p $$ , and p+1−α$$ p+1-\alpha $$ for odd p$$ p $$ .
One-Shot Affordance Detection

Affordance detection refers to identifying the potential action possibilities of objects in an image, which is an important ability for robot perception and manipulation. To empower robots with this ability in unseen scenarios, we consider the challenging one-shot affordance detection problem in this paper, i.e., given a support image that depicts the action purpose, all objects in a scene with the common affordance should be detected. To this end, we devise a One-Shot Affordance Detection (OS-AD) network that firstly estimates the purpose and then transfers it to help detect the common affordance from all candidate images. Through collaboration learning, OS-AD can capture the common characteristics between objects having the same underlying affordance and learn a good adaptation capability for perceiving unseen affordances. Besides, we build a Purpose-driven Affordance Dataset (PAD) by collecting and labeling 4k images from 31 affordance and 72 object categories. Experimental results demonstrate the superiority of our model over previous representative ones in terms of both objective metrics and visual quality. The benchmark suite is at ProjectPage.
Building Bridges: Generative Artworks to Explore AI Ethics

In recent years, there has been an increased emphasis on understanding and mitigating adverse impacts of artificial intelligence (AI) technologies on society. Across academia, industry, and government bodies, a variety of endeavours are being pursued towards enhancing AI ethics. A significant challenge in the design of ethical AI systems is that there are multiple stakeholders in the AI pipeline, each with their own set of constraints and interests. These different perspectives are often not understood, due in part to communication gaps. For example, AI researchers who design and develop AI models are not necessarily aware of the instability induced in consumers’ lives by the compounded effects of AI decisions. Educating different stakeholders about their roles and responsibilities in the broader context becomes necessary. In this position paper, we outline some potential ways in which generative artworks can play this role by serving as accessible and powerful educational tools for surfacing different perspectives. We hope to spark interdisciplinary discussions about computational creativity broadly as a tool for enhancing AI ethics.
A unified multi-phase and multi-material formulation for combustion modeling

The motivation of this work is to produce an integrated formulation for material response (e.g. elastoplastic, viscous, viscoplastic etc.) due to detonation wave loading. Here, we focus on elastoplastic structural response. In particular, we are interested to capture miscible and immiscible behaviour within condensed-phase explosives arising from the co-existence of a reactive carrier mixture of miscible materials, and several material interfaces due to the presence of immiscible impurities such as particles or cavities. The dynamic and thermodynamic evolution of the explosive is communicated to one or more inert confiners through their shared interfaces, which may undergo severe topological change. We also wish to consider elastic and plastic structural response of the confiners, rather than make a hydrodynamic assumption for their behaviour. Previous work by these authors has met these requirements by means of the simultaneous solution of appropriate systems of equations for the behaviour of the condensed-phase explosive and the elastoplastic behaviour of the confiners. To that end, both systems were written in the same mathematical form as a system of inhomogeneous hyperbolic partial differential equations which were solved on the same discrete space using the same algorithms, as opposed to coupling fluid and solid algorithms (co-simulation). In the present work, we employ a single system of partial differential equations (PDEs) proposed by Peshkov and Romenski, which is able to account for different states of matter by means of generalising the concept of distortion tensors beyond solids. We amalgamate that formulation with a single system of PDEs which meets the requirement of co-existing miscible and immiscible explosive mixtures. We present the mathematical derivation and construct appropriate algorithms for its solution. The resulting model is validated against exact solutions for several use-cases, including mechanicallyand thermally-induced, inviscid and viscous detonations. Results indicate that the model can accurately simulate a very broad range of problems involving the nonlinear interaction between reactive and inert materials within a single framework.
R-Drop: Regularized Dropout for Neural Networks

Dropout is a powerful and widely used technique to regularize the training of deep neural networks. In this paper, we introduce a simple regularization strategy upon dropout in model training, namely R-Drop, which forces the output distributions of different sub models generated by dropout to be consistent with each other. Specifically, for each training sample, R-Drop minimizes the bidirectional KL-divergence between the output distributions of two sub models sampled by dropout. Theoretical analysis reveals that R-Drop reduces the freedom of the model parameters and complements dropout. Experiments on $\bf{5}$ widely used deep learning tasks ($\bf{18}$ datasets in total), including neural machine translation, abstractive summarization, language understanding, language modeling, and image classification, show that R-Drop is universally effective. In particular, it yields substantial improvements when applied to fine-tune large-scale pre-trained models, e.g., ViT, RoBERTa-large, and BART, and achieves state-of-the-art (SOTA) performances with the vanilla Transformer model on WMT14 English$\to$German translation ($\bf{30.91}$ BLEU) and WMT14 English$\to$French translation ($\bf{43.95}$ BLEU), even surpassing models trained with extra large-scale data and expert-designed advanced variants of Transformer models. Our code is available at GitHub{\url{https://github.com/dropreg/R-Drop}}.
Speech2Properties2Gestures: Gesture-Property Prediction as a Tool for Generating Representational Gestures from Speech

We propose a new framework for gesture generation, aiming to allow data-driven approaches to produce more semantically rich gestures. Our approach first predicts whether to gesture, followed by a prediction of the gesture properties. Those properties are then used as conditioning for a modern probabilistic gesture-generation model capable of high-quality output. This empowers the approach to generate gestures that are both diverse and representational. Follow-ups and more information can be found on the project page: https://svito-zar.github. io/speech2properties2gestures/
Exoplanet X-ray irradiation and evaporation rates with eROSITA

High-energy irradiation is a driver for atmospheric evaporation and mass loss in exoplanets. This work is based on data from eROSITA, the soft X-ray instrument aboard SRG (Spectrum Roentgen Gamma) mission, as well as archival data from other missions, we aim to characterise the high-energy environment of known exoplanets and estimate their mass loss rates. We use X-ray source catalogues from eROSITA, XMM-Newton, Chandra and ROSAT to derive X-ray luminosities of exoplanet host stars in the 0.2-2 keV energy band with an underlying coronal, i.e. optically thin thermal spectrum. We present a catalogue of stellar X-ray and EUV luminosities, exoplanetary X-ray and EUV irradiation fluxes and estimated mass loss rates for a total of 287 exoplanets, 96 among them being characterised for the first time from new eROSITA detections. We identify 14 first time X-ray detections of transiting exoplanets that are subject to irradiation levels known to cause observable evaporation signatures in other exoplanets, which makes them suitable targets for follow-up observations.
Early Convolutions Help Transformers See Better

Vision transformer (ViT) models exhibit substandard optimizability. In particular, they are sensitive to the choice of optimizer (AdamW vs. SGD), optimizer hyperparameters, and training schedule length. In comparison, modern convolutional neural networks are easier to optimize. Why is this the case? In this work, we conjecture that the issue lies with the patchify stem of ViT models, which is implemented by a stride-p p*p convolution (p=16 by default) applied to the input image. This large-kernel plus large-stride convolution runs counter to typical design choices of convolutional layers in neural networks. To test whether this atypical design choice causes an issue, we analyze the optimization behavior of ViT models with their original patchify stem versus a simple counterpart where we replace the ViT stem by a small number of stacked stride-two 3*3 convolutions. While the vast majority of computation in the two ViT designs is identical, we find that this small change in early visual processing results in markedly different training behavior in terms of the sensitivity to optimization settings as well as the final model accuracy. Using a convolutional stem in ViT dramatically increases optimization stability and also improves peak performance (by ~1-2% top-1 accuracy on ImageNet-1k), while maintaining flops and runtime. The improvement can be observed across the wide spectrum of model complexities (from 1G to 36G flops) and dataset scales (from ImageNet-1k to ImageNet-21k). These findings lead us to recommend using a standard, lightweight convolutional stem for ViT models in this regime as a more robust architectural choice compared to the original ViT model design.
Decentralized Composite Optimization in Stochastic Networks: A Dual Averaging Approach With Linear Convergence

Decentralized optimization, particularly the class of decentralized composite convex optimization (DCCO) problems, has found many applications. Due to ubiquitous communication congestion and random dropouts in practice, it is highly desirable to design decentralized algorithms that can handle stochastic communication networks. However, most existing algorithms for DCCO only work in networks that are deterministically connected during bounded communication rounds, and therefore, cannot be extended to stochastic networks. In this article, we propose a new decentralized dual averaging (DDA) algorithm that can solve DCCO in stochastic networks. Under a rather mild condition on stochastic networks, we show that the proposed algorithm attains global linear convergence if each local objective function is strongly convex. Our algorithm substantially improves the existing DDA-type algorithms as the latter were only known to converge sublinearly prior to our work. The key to achieving the improved rate is the design of a novel dynamic averaging consensus protocol for DDA, which intuitively leads to more accurate local estimates of the global dual variable. To the best of our knowledge, this is the first linearly convergent DDA-type decentralized algorithm and also the first algorithm that attains global linear convergence for solving DCCO in stochastic networks. Numerical results are also presented to support our design and analysis.
A2A: 21,000 bulge stars from the ARGOS survey with stellar parameters on the APOGEE scale

Aims. Spectroscopic surveys have by now collectively observed tens of thousands of stars in the bulge of our Galaxy. However, each of these surveys had unique observing and data processing strategies that led to distinct stellar parameter and abundance scales. Because of this, stellar samples from different surveys cannot be directly combined. Methods. Here we use the data-driven method, The Cannon, to bring 21 000 stars from the argos bulge survey, including 10 000 red clump stars, onto the parameter and abundance scales of the cross-Galactic survey, apogee, obtaining rms precisions of 0.10 dex, 0.07 dex, 74 K, and 0.18 dex for [Fe/H], [Mg/Fe], Teff , and log(g), respectively. The re-calibrated argos survey – which we refer to as the A2A survey – is combined with the APOGEE survey to investigate the abundance structure of the Galactic bulge. Results. We find X-shaped [Fe/H] and [Mg/Fe] distributions in the bulge that are more pinched than the bulge density, a signature of its disk origin. The mean abundance along the major axis of the bar varies such that the stars are more [Fe/H]-poor and [Mg/Fe]-rich near the Galactic centre than in the outer bulge and the long bar region. The vertical [Fe/H] and [Mg/Fe] gradients vary between the inner bulge and the long bar, with the inner bulge showing a flattening near the plane that is absent in the long bar. The [Fe/H]−[Mg/Fe] distribution shows two main maxima, an ‘[Fe/H]-poor [Mg/Fe]rich’ maximum and an ‘[Fe/H]-rich [Mg/Fe]poor’ maximum, that vary in strength with position in the bulge. In particular, the outer long bar close to the Galactic plane is dominated by super-solar [Fe/H], [Mg/Fe]-normal stars. Stars composing the [Fe/H]-rich maximum show little kinematic dependence on [Fe/H], but for lower [Fe/H] the rotation and dispersion of the bulge increase slowly. Stars with [Fe/H] < −1 dex have a very different kinematic structure than stars with higher [Fe/H]. Conclusions. Comparing with recent models for the Galactic boxy-peanut bulge, the abundance gradients and distribution, and the relation between [Fe/H] and kinematics suggests that the stars comprising each maximum have separate disk origins with the ‘[Fe/H]poor [Mg/Fe]-rich’ stars originating from a thicker disk than the ‘[Fe/H]-rich [Mg/Fe]-poor’ stars.
A game theoretical approximation for a parabolic/elliptic system with different operators

In this paper we find viscosity solutions to a coupled system composed by two equations, the first one is parabolic and driven by the infinity Laplacian while the second one is elliptic and involves the usual Laplacian. We prove that there is a two-player zero-sum game played in two different boards with different rules in each board (in the first one we play a Tug-of-War game taking the number of plays into consideration and in the second board we move at random) whose value functions converge uniformly to a viscosity solution to the PDE system.
Ultraspinning Chow’s black holes in six-dimensional gauged supergravity and their thermodynamical properties

By taking the ultraspinning limit as a simple generating solution trick, we construct a novel class of two equal-charge ultraspinning black hole solutions from Chow’s rotating charged black hole in six-dimensional N = 4 gauged supergravity theory. We investigate their thermodynamical properties and then demonstrate that all thermodynamical quantities completely obey both the differential first law and the Bekenstein-Smarr mass formula. For the six-dimensional singlyrotating ultraspinning Chow’s black hole, we show that it is always obey the reverse isoperimetric inequality (RII), thus it is always subentropic. This property is obviously different from that of the six-dimensional singly-rotating Kerr-anti de Sitter (AdS) superentropic black hole, which always strictly violates the RII. For the six-dimensional double-rotating ultraspinning Chow’s black hole, we indicate that it is violates the RII when its mass and electric parameters both very small, thus it is superentropic in these cases.
Properties of the Environment of Galaxies in Clusters of Galaxies CL 0024+1654 and RX J0152.7−1357

We report the results of combined analyses of X-ray and optical data of two galaxy clusters, CL 0024+1654 and RX J0152.7−1357 at redshift z = 0.395 and z = 0.830, respectively, offering a holistic physical description of the two clusters. Our X-ray analysis yielded temperature and density profiles of the gas in the intra-cluster medium (ICM). Using optical photometric and spectroscopic data, complemented with mass distribution from a gravitational lensing study, we investigated any possible correlation between the physical properties of the galaxy members, i.e. their color, morphology, and star formation rate (SFR), and their environments. We quantified the properties of the environment around each galaxy by galaxy number density, ICM temperature, and mass density. Although our results show that the two clusters exhibit a weaker correlation compared to relaxed clusters, it still confirms the significant effect of the ICM on the SFR in the galaxies. The close relation between the physical properties of galaxies and the condition of their immediate environment found in this work indicates the locality of galaxy evolution, even within a larger bound system such as a cluster. Various physical mechanisms are suggested to explain the relation between the properties of galaxies and their environment.
Wick rotation for spin foam quantum gravity

We show that the Euclidean and Lorentzian EPRL vertex amplitudes of covariant Loop Quantum Gravity are related through a ``Wick rotation'' of the real Immirzi parameter to purely imaginary values. Our result follows from the simultaneous analytic continuation of the algebras, group elements and unitary irreducible representations of the gauge groups $Spin(4)$ and $SL(2,\mathbb{C})$, applied to the decomposition of the two models in terms of $SU(2)$ invariants and booster functions.
Who is Responsible for Adversarial Defense?

We have seen a surge in research aims toward adversarial attacks and defenses in AI/ML systems. While it is crucial to formulate new attack methods and devise novel defense strategies for robustness, it is also imperative to recognize who is responsible for implementing, validating, and justifying the necessity of these defenses. In particular, which components of the system are vulnerable to what type of adversarial attacks, and the expertise needed to realize the severity of adversarial attacks. Also how to evaluate and address the adversarial challenges in order to recommend defense strategies for different applications. This paper opened a discussion on who should examine and implement the adversarial defenses and the reason behind such efforts.