Miracle-dz/UniFAR-FTUs huggingface dataset:
StyleGAN-XL: Scaling StyleGAN to Large Diverse Datasets

Computer graphics has experienced a recent surge of data-centric approaches for photorealistic and controllable content creation. StyleGAN in particular sets new standards for generative modeling regarding image quality and controllability. However, StyleGAN’s performance severely degrades on large unstructured datasets such as ImageNet. StyleGAN was designed for controllability; hence, prior works suspect its restrictive design to be unsuitable for diverse datasets. In contrast, we find the main limiting factor to be the current training strategy. Following the recently introduced Projected GAN paradigm, we leverage powerful neural network priors and a progressive growing strategy to successfully train the latest StyleGAN3 generator on ImageNet. Our final model, StyleGAN-XL, sets a new state-of-the-art on large-scale image synthesis and is the first to generate images at a resolution of 10242 at such a dataset scale. We demonstrate that this model can invert and edit images beyond the narrow domain of portraits or specific object classes. Code, models, and supplementary videos can be found at https://sites.google. com/view/stylegan-xl/ .
Self-Adjusting Ego-Trees Topology for Reconfigurable Datacenter Networks

—State-of-the-art topologies for datacenters (DC) and high-performance computing (HPC) networks are demand-oblivious and static. Therefore, such network topologies are optimized for the worst-case trafﬁc scenarios and can’t take advantage of changing demand patterns when such exist. How- ever, recent optical switching technologies enable the concept of dynamically reconﬁguring circuit-switched topologies in real- time. This capability opens the door for the design of self-adjusting networks: networks with demand-aware and dynamic topologies in which links between nodes can be established and re-adjusted online and respond to evolving trafﬁc patterns. This paper studies a recently proposed model for optical leaf- spine reconﬁgurable networks. We present a novel algorithm, G REEDY E GO T REES , that dynamically changes the network topology. The algorithm greedily builds ego trees for nodes in the network, where nodes cooperate to help each other, taking into account the global needs of the network. We show that G REEDY E GO T REES has nice theoretical properties, outperforms other possible algorithms (like static expander and greedy dynamic matching) and can signiﬁcantly improve the average path length for real DC and HPC traces.
THE NUMERICAL STUDY OF TURBULENT STRUCTURE IN CURVED OPEN-CHANNEL FLOW

曲がり開水路流れには遠心力が作用し, かつ自由水面の存在よりその流動挙動, 乱流構造は複雑である. 本研究では直線開水路を出入口に有する曲がり開水路を対象に代数応力モデル, 境界適合座標系を用い解析を行った. 解析結果は実験と比較し本解析手法の有用性を検討するとともに, 直線開水路を含めた三次元開水路として計算を行い曲がり入口, 出口近傍における第一種から第二種へのあるいは逆の場合の二次流れの遷移挙動に関しても検討を加えた. その結果, 本解析手法は曲がり開水路流れ場の特徴的現象を比較的良好に予測するとともに, 曲がり開水路入口上流, 出口下流の直線開水路では曲がりの影響が二次流れに顕著に現れることを定量的に明らかにした.
A Data-Efﬁcient Deep Learning Training Strategy for Biomedical Ultrasound Imaging: Zone Training

—Deep learning (DL) powered biomedical ultrasound imaging is an emerging research ﬁeld where researchers adapt the image analysis capabilities of DL algorithms to biomedical ultrasound imaging settings. A major roadblock to wider adoption of DL powered biomedical ultrasound imaging is that acquiring large and diverse datasets is expensive in clinical settings, which is a requirement for successful DL implementation. Hence, there is a constant need for developing data-efﬁcient DL techniques to turn DL powered biomedical ultrasound imaging into reality. In this work, we develop a data-efﬁcient deep learning training strategy, which we named Zone Training . In Zone Training , we propose to divide the complete ﬁeld of view of an ultrasound image into multiple zones associated with different regions of a diffraction pattern and then, train separate DL networks for each zone. The main advantage of Zone Training is that it requires less training data to achieve high accuracy. In this work, three different tissue-mimicking phantoms were classiﬁed by a DL network. The results demonstrated that Zone Training required a factor of 2-5 less training data to achieve similar classiﬁcation accuracies compared to a conventional training strategy.
Arrhythmia Classification Using CGAN-Augmented ECG Signals*

ECG databases are usually highly imbalanced due to the abundance of Normal ECG and scarcity of abnormal cases. As such, deep learning classifiers trained on imbalanced datasets usually perform poorly, especially on minor classes. One solution is to generate realistic synthetic ECG signals using Generative Adversarial Networks (GAN) to augment imbalanced datasets. In this study, we combined conditional GAN with WGAN-GP and developed AC-WGAN-GP in 1D form for the first time to be applied on MIT-BIH Arrhythmia dataset. We investigated the impact of data augmentation on arrhythmia classification. Two models were employed for ECG generation: (i) unconditional GAN; Wasserstein GAN with gradient penalty (WGAN-GP) is trained on each class individually; (ii) conditional GAN; one Auxiliary Classifier WGAN-GP (AC-WGAN-GP) model is trained on all classes and then used to generate synthetic beats in all classes. Two scenarios are defined for each case: (a) unscreened; all the generated synthetic beats were used, and (b) screened; only high-quality beats are selected and used, based on their Dynamic Time Warping (DTW) distance to a designated template. The state-of-the-art ResNet classifier (EcgResNet34) is trained on each of the four augmented datasets and the performance metrics (precision/recall/F1-Score micro- and macro-averaged, confusion matrices, multiclass precision-recall curves) were compared with those of the original imbalanced case. We also used a simple metric Net Improvement. All the three metrics show consistently that unconditional GAN with raw generated data creates the best improvements.
Rethinking the notion of oracle: A link between synthetic descriptive set theory and effective topos theory

We present three diﬀerent perspectives of oracle. First, an oracle is a blackbox; second, an oracle is an endofunctor on the category of represented spaces; and third, an oracle is an operation on the object of truth-values. These three perspectives create a link between the three ﬁelds, computability theory, synthetic descriptive set theory, and eﬀective topos theory.
Examining Scaling and Transfer of Language Model Architectures for Machine Translation

Natural language understanding and generation models follow one of the two dominant architectural paradigms: language models (LMs) that process concatenated sequences in a single stack of layers, and encoder-decoder models (EncDec) that utilize separate layer stacks for input and output processing. In machine translation, EncDec has long been the favoured approach, but with few studies investigating the performance of LMs. In this work, we thoroughly examine the role of several architectural design choices on the performance of LMs on bilingual, (massively) multilingual and zero-shot translation tasks, under systematic variations of data conditions and model sizes. Our results show that: (i) Different LMs have different scaling properties, where architectural differences often have a significant impact on model performance at small scales, but the performance gap narrows as the number of parameters increases, (ii) Several design choices, including causal masking and language-modeling objectives for the source sequence, have detrimental effects on translation quality, and (iii) When paired with full-visible masking for source sequences, LMs could perform on par with EncDec on supervised bilingual and multilingual translation tasks, and improve greatly on zero-shot directions by facilitating the reduction of off-target translations.
Estimation of Impulse-Response Functions with Dynamic Factor Models: A New Parametrization

We propose a new parametrization for the estimation and identification of the impulseresponse functions (IRFs) of dynamic factor models (DFMs). The theoretical contribution of this paper concerns the problem of observational equivalence between different IRFs, which implies non-identification of the IRF parameters without further restrictions. We show how the minimal identification conditions proposed by Bai and Wang (2015) are nested in the proposed framework and can be further augmented with overidentifying restrictions leading to efficiency gains. The current standard practice for the IRF estimation of DFMs is based on principal components, compared to which the new parametrization is less restrictive and allows for modelling richer dynamics. As the empirical contribution of the paper, we develop an estimation method based on the EM algorithm, which incorporates the proposed identification restrictions. In the empirical application, we use a standard high-dimensional macroeconomic dataset to estimate the effects of a monetary policy shock. We estimate a strong reaction of the macroeconomic variables, while the benchmark models appear to give qualitatively counterintuitive results. The estimation methods are implemented in the accompanying R package.
Finding Directions in GAN's Latent Space for Neural Face Reenactment

This paper is on face/head reenactment where the goal is to transfer the facial pose (3D head orientation and expression) of a target face to a source face. Previous methods focus on learning embedding networks for identity and pose disentanglement which proves to be a rather hard task, degrading the quality of the generated images. We take a different approach, bypassing the training of such networks, by using (fine-tuned) pre-trained GANs which have been shown capable of producing high-quality facial images. Because GANs are characterized by weak controllability, the core of our approach is a method to discover which directions in latent GAN space are responsible for controlling facial pose and expression variations. We present a simple pipeline to learn such directions with the aid of a 3D shape model which, by construction, already captures disentangled directions for facial pose, identity and expression. Moreover, we show that by embedding real images in the GAN latent space, our method can be successfully used for the reenactment of real-world faces. Our method features several favorable properties including using a single source image (one-shot) and enabling cross-person reenactment. Our qualitative and quantitative results show that our approach often produces reenacted faces of significantly higher quality than those produced by state-of-the-art methods for the standard benchmarks of VoxCeleb1&2. Source code is available at: https://github.com/StelaBou/stylegan_directions_face_reenactment
Finding lost DG: Explaining domain generalization via model complexity

The domain generalization (DG) problem setting challenges a model trained on multiple known data distributions to generalise well on unseen data distributions. Due to its practical importance, a large number of methods have been proposed to address this challenge. However much of the work in general purpose DG is heuristically motivated, as the DG problem is hard to model formally; and recent evaluations have cast doubt on existing methods' practical efficacy -- in particular compared to a well tuned empirical risk minimisation baseline. We present a novel learning-theoretic generalisation bound for DG that bounds unseen domain generalisation performance in terms of the model's Rademacher complexity. Based on this, we conjecture that existing methods' efficacy or lack thereof is largely determined by an empirical risk vs predictor complexity trade-off, and demonstrate that their performance variability can be explained in these terms. Algorithmically, this analysis suggests that domain generalisation should be achieved by simply performing regularised ERM with a leave-one-domain-out cross-validation objective. Empirical results on the DomainBed benchmark corroborate this.
Deep-Disaster: Unsupervised Disaster Detection and Localization Using Visual Data

Social media plays a significant role in sharing essential information, which helps humanitarian organizations in rescue operations during and after disaster incidents. However, developing an efficient method that can provide rapid analysis of social media images in the early hours of disasters is still largely an open problem, mainly due to the lack of suitable datasets and the sheer complexity of this task. In addition, supervised methods can not generalize well to novel disaster incidents. In this paper, inspired by the success of Knowledge Distillation (KD) methods, we propose an unsupervised deep neural network to detect and localize damages in social media images. Our proposed KD architecture is a feature-based distillation approach that comprises a pre-trained teacher and a smaller student network, with both networks having similar GAN architecture containing a generator and a discriminator. The student network is trained to emulate the teacher’s behavior on training input samples, which, in turn, contain images that do not include any damaged regions. Therefore, the student network only learns the distribution of no damage data and would have different behavior from the teacher network facing damages. To detect damage, we utilize the difference between features generated by two networks using a defined score function that demonstrates the probability of damages occurring. Our experimental results on the benchmark dataset confirm that our approach outperforms state-of-the-art methods in detecting and localizing the damaged areas, especially for novel disaster types1.
Emission-line Variability during a Nonthermal Outburst in the Gamma-Ray Bright Quasar 1156+295

We present multi-epoch optical spectra of the γ-ray bright blazar 1156+295 (4C +29.45, Ton 599) obtained with the 4.3 m Lowell Discovery Telescope. During a multiwavelength outburst in late 2017, when the γ-ray flux increased to 2.5 × 10−6 phot cm−2 s−1 and the quasar was first detected at energies ≥100 GeV, the flux of the Mg ii λ2798 emission line changed, as did that of the Fe emission complex at shorter wavelengths. These emission-line fluxes increased along with the highly polarized optical continuum flux, which is presumably synchrotron radiation from the relativistic jet, with a relative time delay of ≲2 weeks. This implies that the line-emitting clouds lie near the jet, which points almost directly toward the line of sight. The emission-line radiation from such clouds, which are located outside the canonical accretion-disk related broad-line region, may be a primary source of seed photons that are up-scattered to γ-ray energies by relativistic electrons in the jet.
Partial Sum Processes of Residual-Based and Wald-type Break-Point Statistics in Time Series Regression Models

We revisit classical asymptotics when testing for a structural break in linear regression models by obtaining the limit theory of residual-based and Wald-type processes. First, we establish the Brownian bridge limiting distribution of these test statistics. Second, we study the asymptotic behaviour of the partial-sum processes in nonstationary (linear) time series regression models. Although, the particular comparisons of these two different modelling environments is done from the perspective of the partial-sum processes, it emphasizes that the presence of nuisance parameters can change the asymptotic behaviour of the functionals under consideration. Simulation experiments verify size distortions when testing for a break in nonstationary time series regressions which indicates that the normalized Brownian bridge limit cannot provide a suitable asymptotic approximation in this case. Further research is required to establish the cause of size distortions under the null hypothesis of parameter stability.
Federated Active Learning (F-AL): An Efficient Annotation Strategy for Federated Learning

Federated learning (FL) has been intensively investigated in terms of communication efficiency, privacy, and fairness. However, efficient annotation, which is a pain point in real-world FL applications, is less studied. In this project, we propose to apply active learning (AL) to the FL framework to reduce the annotation workload. We expect that the AL and FL can improve the performance of each other complementarily. In our proposed federated active learning (F-AL) method, the clients collaboratively execute the AL to obtain the instances which are considered informative to FL in a distributed optimization manner. We compare the test accuracies of the global FL models using the conventional random sampling strategy, client-level separate AL (S-AL), and the proposed F-AL. We empirically demonstrate that the F-AL outperforms baseline methods in image classification tasks.
Leveraging Bitstream Metadata for Fast and Accurate Video Compression Correction

Video compression is a central feature of the modern internet powering technologies from social media to video conferencing. While video compression continues to mature, for many, and particularly for extreme, compression settings, quality loss is still noticeable. These extreme settings nevertheless have important applications to the efﬁ-cient transmission of videos over bandwidth constrained or otherwise unstable connections. In this work, we develop a deep learning architecture capable of restoring detail to compressed videos which leverages the underlying structure and motion information embedded in the video bitstream. We show that this improves restoration accuracy compared to prior compression correction methods and is competitive when compared with recent deep-learning-based video compression methods on rate-distortion while achieving higher throughput.
TopoOpt: Optimizing the Network Topology for Distributed DNN Training

We explore a novel approach for building DNN training clusters using commodity optical devices. Our proposal, called T OPO O PT ,co-optimizes the distributedtraining process across three dimensions: computation, communication, and network topology. T OPO O PT uses a novel alternating optimization technique and a group theory-inspired algorithm to ﬁnd the best network topology and routing plan, together with parallelization strategy, for distributed DNN training. To motivate our proposal, we measure the communication patterns of distributed DNN workloads at a large online service provider. Experiments with a 12-node prototype demonstrate the feasibility of T OPO O PT . Simulations on real distributed training models show that, compared to similar-cost Fat-tree intercon-nects, T OPO O PT reduces DNN training time by up to 3 × .
Is the Performance of My Deep Network Too Good to Be True? A Direct Approach to Estimating the Bayes Error in Binary Classification

There is a fundamental limitation in the prediction performance that a machine learning model can achieve due to the inevitable uncertainty of the prediction target. In classification problems, this can be characterized by the Bayes error, which is the best achievable error with any classifier. The Bayes error can be used as a criterion to evaluate classifiers with state-of-the-art performance and can be used to detect test set overfitting. We propose a simple and direct Bayes error estimator, where we just take the mean of the labels that show \emph{uncertainty} of the class assignments. Our flexible approach enables us to perform Bayes error estimation even for weakly supervised data. In contrast to others, our method is model-free and even instance-free. Moreover, it has no hyperparameters and gives a more accurate estimate of the Bayes error than several baselines empirically. Experiments using our method suggest that recently proposed deep networks such as the Vision Transformer may have reached, or is about to reach, the Bayes error for benchmark datasets. Finally, we discuss how we can study the inherent difficulty of the acceptance/rejection decision for scientific articles, by estimating the Bayes error of the ICLR papers from 2017 to 2023.
When RIS Meets GEO Satellite Communications: A New Optimization Framework in 6G

—Reﬂecting intelligent surfaces (RIS) is a low-cost and energy-efﬁcient solution to achieve high spectral efﬁciency in sixth-generation (6G) networks. The basic idea of RIS is to smartly reconﬁgure the signal propagation by using passive reﬂecting elements. On the other side, the demand of high throughput geostationary (GEO) satellite communications (SatCom) is rapidly growing to deliver broadband services in inaccessible/insufﬁcient covered areas of terrestrial networks. This paper proposes a GEO SatCom network, where a satellite transmits the signal to a ground mobile terminal using multicar-rier communications. To enhance the effective gain, the signal delivery from satellite to the ground mobile terminal is also assisted by RIS which smartly shift the phase of the signal towards ground terminal. We consider that RIS is mounted on a high building and equipped with multiple re-conﬁgurable passive elements along with smart controller. We jointly optimize the power allocation and phase shift design to maximize the channel capacity of the system. The joint optimization problem is formulated as nonconvex due to coupled variables which is hard to solve through traditional convex optimization methods. Thus, we propose a new (cid:15) − optimal algorithm which is based on Mesh Adaptive Direct Search to obtain an efﬁcient solution. Simulation results unveil the beneﬁts of RIS-assisted SatCom in terms of system channel capacity.
Learning Infinite-Horizon Average-Reward Markov Decision Processes with Constraints

We study regret minimization for infinite-horizon average-reward Markov Decision Processes (MDPs) under cost constraints. We start by designing a policy optimization algorithm with carefully designed action-value estimator and bonus term, and show that for ergodic MDPs, our algorithm ensures $\widetilde{O}(\sqrt{T})$ regret and constant constraint violation, where $T$ is the total number of time steps. This strictly improves over the algorithm of (Singh et al., 2020), whose regret and constraint violation are both $\widetilde{O}(T^{2/3})$. Next, we consider the most general class of weakly communicating MDPs. Through a finite-horizon approximation, we develop another algorithm with $\widetilde{O}(T^{2/3})$ regret and constraint violation, which can be further improved to $\widetilde{O}(\sqrt{T})$ via a simple modification, albeit making the algorithm computationally inefficient. As far as we know, these are the first set of provable algorithms for weakly communicating MDPs with cost constraints.
CIC: Contrastive Intrinsic Control for Unsupervised Skill Discovery

We introduce Contrastive Intrinsic Control (CIC), an algorithm for unsupervised skill discovery that maximizes the mutual information between state-transitions and latent skill vectors. CIC utilizes contrastive learning between state-transitions and skills to learn behavior embeddings and maximizes the entropy of these embeddings as an intrinsic reward to encourage behavioral diversity. We evaluate our algorithm on the Unsupervised Reinforcement Learning Benchmark, which consists of a long reward-free pre-training phase followed by a short adaptation phase to downstream tasks with extrinsic rewards. CIC substantially improves over prior methods in terms of adaptation efficiency, outperforming prior unsupervised skill discovery methods by 1.79x and the next leading overall exploration algorithm by 1.18x.