Miracle-dz/UniFAR-FTUs huggingface dataset:
Selection of Source Images Heavily Influences the Effectiveness of Adversarial Attacks

Although the adoption rate of deep neural networks (DNNs) has tremendously increased in recent years, a solution for their vulnerability against adversarial examples has not yet been found. As a result, substantial research efforts are dedicated to fix this weakness, with many studies typically using a subset of source images to generate adversarial examples, treating every image in this subset as equal. We demonstrate that, in fact, not every source image is equally suited for this kind of assessment. To do so, we devise a large-scale model-to-model transferability scenario for which we meticulously analyze the properties of adversarial examples, generated from every suitable source image in ImageNet by making use of three of the most frequently deployed attacks. In this transferability scenario, which involves seven distinct DNN models, including the recently proposed vision transformers, we reveal that it is possible to have a difference of up to $12.5\%$ in model-to-model transferability success, $1.01$ in average $L_2$ perturbation, and $0.03$ ($8/225$) in average $L_{\infty}$ perturbation when $1,000$ source images are sampled randomly among all suitable candidates. We then take one of the first steps in evaluating the robustness of images used to create adversarial examples, proposing a number of simple but effective methods to identify unsuitable source images, thus making it possible to mitigate extreme cases in experimentation and support high-quality benchmarking.
GigaSpeech: An Evolving, Multi-domain ASR Corpus with 10, 000 Hours of Transcribed Audio

This paper introduces GigaSpeech, an evolving, multi-domain English speech recognition corpus with 10,000 hours of high quality labeled audio suitable for supervised training, and 40,000 hours of total audio suitable for semi-supervised and unsupervised training. Around 40,000 hours of transcribed audio is first collected from audiobooks, podcasts and YouTube, covering both read and spontaneous speaking styles, and a variety of topics, such as arts, science, sports, etc. A new forced alignment and segmentation pipeline is proposed to create sentence segments suitable for speech recognition training, and to filter out segments with low-quality transcription. For system training, GigaSpeech provides five subsets of different sizes, 10h, 250h, 1000h, 2500h, and 10000h. For our 10,000-hour XL training subset, we cap the word error rate at 4% during the filtering/validation stage, and for all our other smaller training subsets, we cap it at 0%. The DEV and TEST evaluation sets, on the other hand, are re-processed by professional human transcribers to ensure high transcription quality. Baseline systems are provided for popular speech recognition toolkits, namely Athena, ESPnet, Kaldi and Pika.
Link Prediction with Persistent Homology: An Interactive View

Link prediction is an important learning task for graph-structured data. In this paper, we propose a novel topological approach to characterize interactions between two nodes. Our topological feature, based on the extended persistent homology, encodes rich structural information regarding the multi-hop paths connecting nodes. Based on this feature, we propose a graph neural network method that outperforms state-of-the-arts on different benchmarks. As another contribution, we propose a novel algorithm to more efficiently compute the extended persistence diagrams for graphs. This algorithm can be generally applied to accelerate many other topological methods for graph learning tasks.
Quantitative Understanding of VAE as a Non-linearly Scaled Isometric Embedding

Variational autoencoder (VAE) estimates the posterior parameters (mean and variance) of latent variables corresponding to each input data. While it is used for many tasks, the transparency of the model is still an underlying issue. This paper provides a quantitative understanding of VAE property through the differential geometric and information-theoretic interpretations of VAE. According to the Rate-distortion theory, the optimal transform coding is achieved by using an orthonormal transform with PCA basis where the transform space is isometric to the input. Considering the analogy of transform coding to VAE, we clarify theoretically and experimentally that VAE can be mapped to an implicit isometric embedding with a scale factor derived from the posterior parameter. As a result, we can estimate the data probabilities in the input space from the prior, loss metrics, and corresponding posterior parameters, and further, the quantitative importance of each latent variable can be evaluated like the eigenvalue of PCA.
Operator Splitting for a Homogeneous Embedding of the Linear Complementarity Problem

The linear complementarity problem (LCP) is a general set membership problem that includes quadratic cone programming as a special case. In this work we consider a homogeneous embedding of the LCP, which encodes both the optimality conditions of the original LCP as well as certificates of infeasibility. The resulting problem can be expressed as a monotone inclusion problem involving the sum of two maximal monotone operators, to which we apply Douglas-Rachford splitting. The resulting algorithm has almost identical per-iteration cost as applying the splitting to the LCP directly. Specifically, each iteration requires solving a linear system and projecting onto a convex cone. The resulting algorithm has no explicit hyper-parameters and is able to return a primal-dual solution should one exist or a certificate of infeasibility otherwise. If a sequence of related problems are solved it can easily be warm-started and make use of factorization caching of the linear system. We demonstrate on a range of public and synthetic datasets that for feasible problems our approach tends to be somewhat faster than applying operator splitting directly to the LCP, and in cases of infeasibility our approach can be significantly faster than alternative approaches based on diverging iterates. The algorithm we describe has been implemented in C and is available open-source as SCS-3.0.
Randomized Stochastic Variance-Reduced Methods for Multi-Task Stochastic Bilevel Optimization

In this paper, we consider non-convex stochastic bilevel optimization (SBO) problems that have many applications in machine learning. Although numerous studies have proposed stochastic algorithms for solving these problems, they are limited in two perspectives: (i) their sample complexities are high, which do not match the state-of-the-art result for non-convex stochastic optimization; (ii) their algorithms are tailored to problems with only one lower-level problem. When there are many lower-level problems, it could be prohibitive to process all these lower-level problems at each iteration. To address these limitations, this paper proposes fast randomized stochastic algorithms for non-convex SBO problems. First, we present a stochastic method for non-convex SBO with only one lower problem and establish its sample complexity of $O(1/\epsilon^3)$ for finding an $\epsilon$-stationary point under Lipschitz continuous conditions of stochastic oracles, matching the lower bound for stochastic smooth non-convex optimization. Second, we present a randomized stochastic method for non-convex SBO with $m>1$ lower level problems (multi-task SBO) by processing a constant number of lower problems at each iteration, and establish its sample complexity no worse than $O(m/\epsilon^3)$, which could be a better complexity than that of simply processing all $m$ lower problems at each iteration. Lastly, we establish even faster convergence results for gradient-dominant functions. To the best of our knowledge, this is the first work considering multi-task SBO and developing state-of-the-art sample complexity results.
A machine learning global analysis approach to enhance the precision of the Higgs decay branching fractions in electron-positron colliders

: Several high energy e + e − colliders are proposed as Higgs factories by the international high energy physics community. One of the most important goals of these projects is to study the Higgs properties, such as its couplings, mass, width, and production rate, with unprecedented precision. Precision studies of the Higgs boson should be the priority and drive the design and optimization of detectors. A global analysis approach based on the multinomial distribution and Machine Learning techniques is proposed to realize an “end-to-end” analysis and to enhance the precision of all accessible decay branching fractions of the Higgs signiﬁcantly. A proof-of-principle Monte Carlo simulation study is performed to show the feasibility. This approach shows that the statistical uncertainties of all branching fractions are proportional to a single parameter, which can be used as a metric to optimize the detector design, reconstruction algorithms, and data analyses. In the Higgs factories, the global analysis approach is valuable both to the Higgs measurements and detector R & D, because it has the potential for superior precision and makes detector optimization single-purpose.
PGDOT – Perturbed Gradient Descent Adapted with Occupation Time

This paper develops further the idea of perturbed gradient descent (PGD), by adapting perturbation with the history of states via the notion of occupation time. The proposed algorithm, perturbed gradient descent adapted with occupation time (PGDOT), is shown to converge at least as fast as the PGD algorithm and is guaranteed to avoid getting stuck at saddle points. The analysis is corroborated by empirical studies, in which a mini-batch version of PGDOT is shown to outperform alternatives such as mini-batch gradient descent, Adam, AMSGrad, and RMSProp in training multilayer perceptrons (MLPs). In particular, the mini-batch PGDOT manages to escape saddle points whereas these alternatives fail.
Robust Optimization for Multilingual Translation with Imbalanced Data

Multilingual models are parameter-efficient and especially effective in improving low-resource languages by leveraging crosslingual transfer. Despite recent advance in massive multilingual translation with ever-growing model and data, how to effectively train multilingual models has not been well understood. In this paper, we show that a common situation in multilingual training, data imbalance among languages, poses optimization tension between high resource and low resource languages where the found multilingual solution is often sub-optimal for low resources. We show that common training method which upsamples low resources can not robustly optimize population loss with risks of either underfitting high resource languages or overfitting low resource ones. Drawing on recent findings on the geometry of loss landscape and its effect on generalization, we propose a principled optimization algorithm, Curvature Aware Task Scaling (CATS), which adaptively rescales gradients from different tasks with a meta objective of guiding multilingual training to low-curvature neighborhoods with uniformly low loss for all languages. We ran experiments on common benchmarks (TED, WMT and OPUS-100) with varying degrees of data imbalance. CATS effectively improved multilingual optimization and as a result demonstrated consistent gains on low resources ($+0.8$ to $+2.2$ BLEU) without hurting high resources. In addition, CATS is robust to overparameterization and large batch size training, making it a promising training method for massive multilingual models that truly improve low resource languages.
Uncovering the Connections Between Adversarial Transferability and Knowledge Transferability

Knowledge transferability, or transfer learning, has been widely adopted to allow a pre-trained model in the source domain to be effectively adapted to downstream tasks in the target domain. It is thus important to explore and understand the factors affecting knowledge transferability. In this paper, as the first work, we analyze and demonstrate the connections between knowledge transferability and another important phenomenon--adversarial transferability, \emph{i.e.}, adversarial examples generated against one model can be transferred to attack other models. Our theoretical studies show that adversarial transferability indicates knowledge transferability and vice versa. Moreover, based on the theoretical insights, we propose two practical adversarial transferability metrics to characterize this process, serving as bidirectional indicators between adversarial and knowledge transferability. We conduct extensive experiments for different scenarios on diverse datasets, showing a positive correlation between adversarial transferability and knowledge transferability. Our findings will shed light on future research about effective knowledge transfer learning and adversarial transferability analyses.
A Query-Driven Topic Model

Topic modeling is an unsupervised method for revealing the hidden semantic structure of a corpus. It has been increasingly widely adopted as a tool in the social sciences, including political science, digital humanities and sociological research in general. One desirable property of topic models is to allow users to find topics describing a specific aspect of the corpus. A possible solution is to incorporate domain-specific knowledge into topic modeling, but this requires a specification from domain experts. We propose a novel query-driven topic model that allows users to specify a simple query in words or phrases and return query-related topics, thus avoiding tedious work from domain experts. Our proposed approach is particularly attractive when the user-specified query has a low occurrence in a text corpus, making it difficult for traditional topic models built on word cooccurrence patterns to identify relevant topics. Experimental results demonstrate the effectiveness of our model in comparison with both classical topic models and neural topic models.
Low-Rank Projections of GCNs Laplacian

In this work, we study the behavior of standard models for community detection under spectral manipulations. Through various ablation experiments, we evaluate the impact of bandpass filtering on the performance of a GCN: we empirically show that most of the necessary and used information for nodes classification is contained in the low-frequency domain, and thus contrary to images, high frequencies are less crucial to community detection. In particular, it is sometimes possible to obtain accuracies at a state-of-the-art level with simple classifiers that rely only on a few low frequencies.
A Shuffling Framework for Local Differential Privacy

LDP deployments are vulnerable to inference attacks as an adversary can link the noisy responses to their identity and subsequently, auxiliary information using the order of the data. An alternative model, shuffle DP , prevents this by shuffling the noisy responses uniformly at random. However, this limits the data learnability – only symmetric functions (input order agnostic) can be learned. In this paper, we strike a balance and propose a generalized shuffling framework that interpolates between the two deployment models. We show that systematic shuffling of the noisy responses can thwart specific inference attacks while retaining some meaningful data learnability. To this end, we propose a novel privacy guarantee, 𝑑 𝜎 -privacy, that captures the privacy of the order of a data sequence. 𝑑 𝜎 -privacy allows tuning the granularity at which the ordinal information is maintained, which formalizes the degree the resistance to inference attacks trading it off with data learnability. Additionally, we propose a novel shuffling mechanism that can achieve 𝑑 𝜎 -privacy and demonstrate the practicality of our mechanism via evaluation on real-world datasets.
Quantum Kibble-Zurek mechanism: Kink correlations after a quench in the quantum Ising chain

The transverse field in the quantum Ising chain is linearly ramped from the parato the ferromagnetic phase across the quantum critical point at a rate characterized by a quench time τQ. We calculate a connected kinkkink correlator in the final state at zero transverse field. The correlator is a sum of two terms: a negative (anti-bunching) Gaussian that depends on the Kibble-Zurek (KZ) correlation length only and a positive term that depends on a second longer scale of length. The second length is made longer by dephasing of the state excited near the critical point during the following ramp across the ferromagnetic phase. This interpretation is corroborated by considering a linear ramp that is halted in the ferromagnetic phase for a finite waiting time and then continued at the same rate as before the halt. The extra time available for dephasing increases the second scale of length that asymptotically grows linearly with the waiting time. The dephasing also suppresses magnitude of the second term making it negligible for waiting times much longer than τQ. The same dephasing can be obtained with a smooth ramp that slows down in the ferromagnetic phase. Assuming sufficient dephasing we obtain also higher order kink correlators and the ferromagnetic correlation function.
Learning Intrusion Prevention Policies through Optimal Stopping

We study automated intrusion prevention using reinforcement learning. In a novel approach, we formulate the problem of intrusion prevention as an optimal stopping problem. This formulation allows us insight into the structure of the optimal policies, which turn out to be threshold based. Since the computation of the optimal defender policy using dynamic programming is not feasible for practical cases, we approximate the optimal policy through reinforcement learning in a simulation environment. To define the dynamics of the simulation, we emulate the target infrastructure and collect measurements. Our evaluations show that the learned policies are close to optimal and that they indeed can be expressed using thresholds.
Tahinde Fiziksel-Kimyasal Analizler ve Yağ Asitleri Bileşiminin Belirlenmesi

Degisik kaynaklardan saglanan 11 tahin orneginde su, kul, ham protein, ham yag, ham seluloz ve tuz miktarlari belirlenmistir. Orneklerde Na, K, P, Cu, Fe, Mn ve Zn miktarlari da tespit edilmistir. Tahin yaglarinda nispi yogunluk, kirilma indisi, serbest yag asitleri, iyot sayisi, sabunlasma sayisi ve sabunlasmayan madde tayin edilmistir. Gaz kromatografisi yontemiyle, tahin yaglarinda baslica ve beslenme acisindan onemli yag asitleri olarak %9.55-10.32 palmitik, %37.42-45.04 oleik ve %43.25-52.34 linoleik asit saptanmistir.
On two approaches to the third-order solution of surface gravity waves

Third-order approximate solutions for surface gravity waves in the finite water depth are studied in the context of potential flow theory. This solution provides explicit expressions for the surface elevation, free-surface velocity potential and velocity potential. The amplitude dispersion relation is also provided. Two approaches are used to derive the third order analytical solution, resulting in two types of approximate solutions: the perturbation solution and the Hamiltonian solution. The perturbation solution is obtained by classical perturbation technique in which the time variable is expanded in multiscale to eliminate secular terms. The Hamiltonian solution is derived from the canonical transformation in the Hamiltonian theory of water waves. By comparing the two types of solutions, it is found that they are completely equivalent for the first to second order solutions and the nonlinear dispersion, but for the third order part only the sum-sum terms are the same. Due to the canonical transformation that could completely separate the dynamic and bound harmonics, the Hamiltonian solutions break through the difficulty that the perturbation theory breaks down due to singularities in the transfer functions when quartet resonance criterion is satisfied. Furthermore, it is also found that some time-averaged quantities based on the Hamiltonian solution, such as mean potential energy and mean kinetic energy, are equal to those in the initial state in which sea surface is assumed to be a Gaussian random process. This is because there are associated conserved quantities in the Hamiltonian form. All of these show that the Hamiltonian solution is more reasonable and accurate to describe the third order steady-state wave field. Finally, based on the Hamiltonian solution, some statistics are given such as the volume flux, skewness, and excess kurtosis.
Systematic Perturbations of the Thermodynamic Properties in Cool Cores of HIFLUGCS Galaxy Clusters

We present an ensemble X-ray analysis of systematic perturbations in the central hot gas properties for a sample of 28 nearby strong cool-core systems, selected from the HIghest X-ray FLUx Galaxy Cluster Sample (HIFLUGCS). We analyze their cool-core features observed with the Chandra X-ray Observatory. All individual systems in our sample exhibit at least a pair of positive and negative excess perturbations in the X-ray residual image after subtracting the global brightness profile. We extract and analyze X-ray spectra of the intracluster medium (ICM) in the detected perturbed regions. To investigate possible origins of the gas perturbations, we characterize thermodynamic properties of the ICM in the perturbed regions and characterize their correlations between positive and negative excess regions. The best-fit relations for temperature and entropy show a clear offset from the one-to-one relation, Tneg/Tpos=1.20−0.03+0.04 and K neg/K pos = 1.43 ± 0.07, whereas the best-fit relation for pressure is found to be remarkably consistent with the one-to-one relation P neg = P pos, indicating that the ICM in the perturbed regions is in pressure equilibrium. These observed features in the HIFLUGCS sample are in agreement with the hypothesis that the gas perturbations in cool cores are generated by gas sloshing. We also analyze synthetic observations of perturbed cluster cores created from binary merger simulations, finding that the observed temperature ratio agrees with the simulations, T neg/T pos ∼ 1.3. We conclude that gas sloshing induced by infalling substructures plays a major role in producing the characteristic gas perturbations in cool cores. The ubiquitous presence of gas perturbations in cool cores may suggest a significant contribution of gas sloshing to suppressing runaway cooling of the ICM.
Attention mechanisms and deep learning for machine vision: A survey of the state of the art

With the advent of state of the art nature-inspired pure attention based models i.e. transformers, and their success in natural language processing (NLP), their extension to machine vision (MV) tasks was inevitable and much felt. Subsequently, vision transformers (ViTs) were introduced which are giving quite a challenge to the established deep learning based machine vision techniques. However, pure attention based models/architectures like transformers require huge data, large training times and large computational resources. Some recent works suggest that combinations of these two varied fields can prove to build systems which have the advantages of both these fields. Accordingly, this state of the art survey paper is introduced which hopefully will help readers get useful information about this interesting and potential research area. A gentle introduction to attention mechanisms is given, followed by a discussion of the popular attention based deep architectures. Subsequently, the major categories of the intersection of attention mechanisms and deep learning for machine vision (MV) based are discussed. Afterwards, the major algorithms, issues and trends within the scope of the paper are discussed.