Miracle-dz/UniFAR-FTUs huggingface dataset:
New Self-similar Euler Flows: gradient catastrophe without shock formation

We consider self-similar solutions to the full compressible Euler system for an ideal gas in two and three space dimensions. The system admits a 2-parameter family of similarity solutions depending on parameters $\lambda$ and $\kappa$. Requiring locally finite amounts of mass, momentum, and energy imply certain constraints on $\lambda$ and $\kappa$. Further constraints are imposed for particular types of flows. E.g., Guderley's pioneering construction of an unbounded converging shock wave invading a quiescent fluid, requires $\kappa=0$ and $\lambda>1$. In this work we analyze the regime $0<\lambda<1$, which does not appear to have been addressed previously. Our findings include: (i) non-existence of Guderley shock solutions; (ii) existence of bounded and continuous incoming similarity flows in 3-d provided $\kappa$ takes the value $\hat\kappa=\frac{2(1-\lambda)}{\gamma-1}$, $\lambda$ is sufficiently small, and $\gamma$ is sufficiently large; (iii) continuation of the latter flows beyond collapse as globally defined and continuous similarity solutions. A key feature of these solutions is that they, in contrast to Guderley solutions, remain bounded at time of collapse, while the density, velocity, and sound speed all suffer gradient blowup. It is noteworthy that, notwithstanding infinite gradients at collapse, no shock wave appears. The analysis is based on a combination of analytical and numerical calculations.
Insufficient Gas Accretion Caused the Decline in Cosmic Star-formation Activity Eight Billion Years Ago

Measurements of the atomic hydrogen (Hi) properties of high-redshift galaxies are critical to understanding the decline in the star formation rate (SFR) density of the universe after its peak ≈8–11 Gyr ago. Here, we use ≈510 hr of observations with the upgraded Giant Metrewave Radio Telescope to measure the dependence of the average Hi mass of star-forming galaxies at z = 0.74–1.45 on their average stellar mass and redshift by stacking their Hi 21 cm emission signals. We divide our sample of 11,419 main-sequence galaxies at z = 0.74–1.45 into two stellar-mass (M *) subsamples, with M * > 1010 M ⊙ and M * < 1010 M ⊙, and obtain clear detections, at >4. 6σ significance, of the stacked Hi 21 cm emission in both subsamples. We find that galaxies with M * > 1010 M ⊙, which dominate the decline in the cosmic SFR density at z ≲ 1, have Hi reservoirs that can sustain their SFRs for only a short period, 0.86 ± 0.20 Gyr, unless their Hi is replenished via accretion. We also stack the Hi 21 cm emission from galaxies in two redshift subsamples, at z = 0.74–1.25 and z = 1.25–1.45, again obtaining clear detections of the stacked Hi 21 cm emission signals, at >5. 2σ significance in both subsamples. We find that the average Hi mass of galaxies with 〈M *〉 ≈ 1010 M ⊙ declines steeply over a period of ≈1 billion years, from (33.6 ± 6.4) × 109 M ⊙ at 〈z〉 ≈ 1.3 to (10.6 ± 1.9) × 109 M ⊙ at 〈z〉 ≈ 1.0, i.e., by a factor ≳3. We thus find direct evidence that accretion of Hi onto star-forming galaxies at z ≈ 1 is insufficient to replenish their Hi reservoirs and sustain their SFRs, thus resulting in the decline in the cosmic SFR density 8 billion years ago.
Searching for the Essence of Adversarial Perturbations

Neural networks have demonstrated state-of-the-art performance in various machine learning fields. However, the introduction of malicious perturbations in input data, known as adversarial examples, has been shown to deceive neural network predictions. This poses potential risks for real-world applications such as autonomous driving and text identification. In order to mitigate these risks, a comprehensive understanding of the mechanisms underlying adversarial examples is essential. In this study, we demonstrate that adversarial perturbations contain human-recognizable information, which is the key conspirator responsible for a neural network's incorrect prediction, in contrast to the widely held belief that human-unidentifiable characteristics play a critical role in fooling a network. This concept of human-recognizable characteristics enables us to explain key features of adversarial perturbations, including their existence, transferability among different neural networks, and increased interpretability for adversarial training. We also uncover two unique properties of adversarial perturbations that deceive neural networks: masking and generation. Additionally, a special class, the complementary class, is identified when neural networks classify input images. The presence of human-recognizable information in adversarial perturbations allows researchers to gain insight into the working principles of neural networks and may lead to the development of techniques for detecting and defending against adversarial attacks.
Accurate predictions from small boxes: variance suppression via the Zel'dovich approximation

Simulations have become an indispensable tool for accurate modelling of observables measured in galaxy surveys, but can be expensive if very large dynamic range in scale is required. We describe how to combine Lagrangian perturbation theory models with N-body simulations to reduce the effects of finite computational volume in the prediction of ensemble average properties in the simulations within the context of control variates. In particular we use the fact that Zel'dovich displacements, computed during initial condition generation for any simulation, correlate strongly with the final density field. Since all the correlators of biased tracers can be computed with arbitrary precision for these displacements, pairing the Zel'dovich `simulation' with the N-body realization allows hundredfold reductions in sample variance for power spectrum or correlation function estimation. Zel'dovich control variates can accurately extend matter or tracer field emulators to larger scales than previously possible, as well as improving measurements of statistics in simulations which are inherently limited to small volumes, such as hydrodynamical simulations of galaxy formation and reionization.
Pseudo-Data Based Self-Supervised Federated Learning for Classification of Histopathological Images

Computer-aided diagnosis (CAD) can help pathologists improve diagnostic accuracy together with consistency and repeatability for cancers. However, the CAD models trained with the histopathological images only from a single center (hospital) generally suffer from the generalization problem due to the straining inconsistencies among different centers. In this work, we propose a pseudo-data based self-supervised federated learning (FL) framework, named SSL-FT-BT, to improve both the diagnostic accuracy and generalization of CAD models. Specifically, the pseudo histopathological images are generated from each center, which contain both inherent and specific properties corresponding to the real images in this center, but do not include the privacy information. These pseudo images are then shared in the central server for self-supervised learning (SSL) to pre-train the backbone of global mode. A multi-task SSL is then designed to effectively learn both the center-specific information and common inherent representation according to the data characteristics. Moreover, a novel Barlow Twins based FL (FL-BT) algorithm is proposed to improve the local training for the CAD models in each center by conducting model contrastive learning, which benefits the optimization of the global model in the FL procedure. The experimental results on four public histopathological image datasets indicate the effectiveness of the proposed SSL-FL-BT on both diagnostic accuracy and generalization.
Wind speed estimation based variable speed wind power generation

Significant variation of the resource kinetic energy, in the form of wind speed, results in a substantially reduced energy capture in a fixed speed wind turbine. In order to increase the wind energy capture in the turbine, variable speed generation (VSG) strategies have been proposed and implemented. Typically, VSG strategies vary the rotational speed of the turbine, by controlling either a mechanical or electrical parameter to maintain a constant tip-speed ratio of the turbine. This is known as a maximum power point tracking (MPPT) controller. Traditionally, the MPPT controller is based upon a simple, but slow, search or perturbation algorithm to determine the optimal rotational speed of operation. In this paper, a wind speed estimation-based MPPT algorithm is proposed that employs a heuristic model of both the turbine and the generator to estimate the wind speed. A local search/perturbation of the rotational speed is employed to attain fine tuning of the desired optimal tip-speed ratio tracking. While this algorithm is more complicated than the search based method, it can still be implemented inexpensively. The ease of implementation guarantees fast control updates and ensures maximum power point operation for all wind speeds within the design limitations. The controller has been verified for efficacy on a 1.5 kW laboratory VSG wind generator.
Semi-Supervised Cross-Silo Advertising with Partial Knowledge Transfer

As an emerging secure learning paradigm in leveraging cross-silo private data, vertical federated learning (VFL) is expected to improve advertising models by enabling the joint learning of complementary user attributes privately owned by the ad-vertiser and the publisher. However, there are two key challenges in applying it to advertising systems: a) the limited scale of labeled overlapping samples , and b) the high cost of real-time cross-silo serving . In this paper, we propose a semi-supervised split distillation framework VFed-SSD to alleviate the two limitations. We identify that: i) there are massive unlabeled overlapped data available in advertising systems , and ii) we can keep a balance between model performance and inference cost by splitting up the federated model. Specifically, we develop a self-supervised task M atched P air D etection ( MPD ) to exploit the vertically partitioned unlabeled data and propose the Split K nowledge D istillation ( SplitKD ) schema to avoid cross-silo serving. Empirical studies on three industrial datasets 1 exhibit the effectiveness of our methods, with the median AUC over all datasets improved by 0 . 86 % and 2 . 6 % in the local and the federated deployment mode respectively. Overall, our framework provides an efﬁcient solution for cross-silo real-time advertising with minimal deploying cost and signiﬁcant performance lift.
The continuity of $p$-rationality and a lower bound for $p'$-degree characters of finite groups

Let $p$ be a prime and $G$ a finite group. We propose a strong bound for the number of $p'$-degree irreducible characters of $G$ in terms of the commutator factor group of a Sylow $p$-subgroup of $G$. The bound arises from a recent conjecture of Navarro and Tiep [NT21] on fields of character values and a phenomenon called the continuity of $p$-rationality level of $p'$-degree characters. This continuity property in turn is predicted by the celebrated McKay-Navarro conjecture [Nav04]. We achieve both the bound and the continuity property for $p=2$.
Minimax Optimal Online Imitation Learning via Replay Estimation

Online imitation learning is the problem of how best to mimic expert demonstrations, given access to the environment or an accurate simulator. Prior work has shown that in the infinite sample regime, exact moment matching achieves value equivalence to the expert policy. However, in the finite sample regime, even if one has no optimization error, empirical variance can lead to a performance gap that scales with $H^2 / N$ for behavioral cloning and $H / \sqrt{N}$ for online moment matching, where $H$ is the horizon and $N$ is the size of the expert dataset. We introduce the technique of replay estimation to reduce this empirical variance: by repeatedly executing cached expert actions in a stochastic simulator, we compute a smoother expert visitation distribution estimate to match. In the presence of general function approximation, we prove a meta theorem reducing the performance gap of our approach to the parameter estimation error for offline classification (i.e. learning the expert policy). In the tabular setting or with linear function approximation, our meta theorem shows that the performance gap incurred by our approach achieves the optimal $\widetilde{O} \left( \min({H^{3/2}} / {N}, {H} / {\sqrt{N}} \right)$ dependency, under significantly weaker assumptions compared to prior work. We implement multiple instantiations of our approach on several continuous control tasks and find that we are able to significantly improve policy performance across a variety of dataset sizes.
From Keypoints to Object Landmarks via Self-Training Correspondence: A Novel Approach to Unsupervised Landmark Discovery

This paper proposes a novel paradigm for the unsupervised learning of object landmark detectors. Contrary to existing methods that build on auxiliary tasks such as image generation or equivariance, we propose a self-training approach where, departing from generic keypoints, a landmark detector and descriptor is trained to improve itself, tuning the keypoints into distinctive landmarks. To this end, we propose an iterative algorithm that alternates between producing new pseudo-labels through feature clustering and learning distinctive features for each pseudo-class through contrastive learning. With a shared backbone for the landmark detector and descriptor, the keypoint locations progressively converge to stable landmarks, filtering those less stable. Compared to previous works, our approach can learn points that are more flexible in terms of capturing large viewpoint changes. We validate our method on a variety of difficult datasets, including LS3D, BBCPose, Human3.6M and PennAction, achieving new state of the art results. Code and models can be found at https://github.com/dimitrismallis/KeypointsToLandmarks/.
Doubly Robust Estimation for Correcting Position Bias in Click Feedback for Unbiased Learning to Rank

Clicks on rankings suffer from position bias: generally items on lower ranks are less likely to be examined—and thus clicked—by users, in spite of their actual preferences between items. The prevalent approach to unbiased click-based learning-to-rank (LTR) is based on counterfactual inverse-propensity-scoring (IPS) estimation. In contrast with general reinforcement learning, counterfactual doubly robust (DR) estimation has not been applied to click-based LTR in previous literature. In this article, we introduce a novel DR estimator that is the first DR approach specifically designed for position bias. The difficulty with position bias is that the treatment—user examination—is not directly observable in click data. As a solution, our estimator uses the expected treatment per rank, instead of the actual treatment that existing DR estimators use. Our novel DR estimator has more robust unbiasedness conditions than the existing IPS approach, and in addition, provides enormous decreases in variance: our experimental results indicate it requires several orders of magnitude fewer datapoints to converge at optimal performance. For the unbiased LTR field, our DR estimator contributes both increases in state-of-the-art performance and the most robust theoretical guarantees of all known LTR estimators.
Polymer dynamics of isotropic universe in Ashtekar and in volume variables

: We analyze the semiclassical and quantum polymer dynamics of the isotropic Universe in terms of both the standard Ashtekar-Barbero-Immirzi connection and its conjugate momentum and also of the new generalized coordinate conjugate to the Universe volume. We study the properties of the resulting bouncing cosmology that emerges in both the representations and we show that the Big Bounce is an intrinsic cut-off on the cosmological dynamics only when the volume variable is implemented, while in terms of the standard connection the Universe Bounce energy density is ﬁxed by the initial conditions on the prepared wavepacket. As a phenomenological implication, we introduce particle creation as a dissipative term and study the production of entropy in the two formulations. Then, we compare the obtained dynamics with what emerges in Loop Quantum Cosmology, where the same difference in the nature of the Big Bounce is associated to ﬁxing a minimum area eigenvalue in a comoving or in a physical representation. We conclude that the privileged character of the Ashtekar-Barbero-Immirzi connection suggests that the natural scenario in the polymer framework is a Big Bounce that is not a Universal cut-off. However, by a parallelism between the polymer and Loop Quantum Cosmology properties of the basic operators, we also develop some considerations in favour of the viability of the ¯ µ scheme of Loop Quantum Cosmology on a semiclassical level.
SOM-CPC: Unsupervised Contrastive Learning with Self-Organizing Maps for Structured Representations of High-Rate Time Series

Continuous monitoring with an ever-increasing number of sensors has become ubiquitous across many application domains. However, acquired time series are typically high-dimensional and difficult to interpret. Expressive deep learning (DL) models have gained popularity for dimensionality reduction, but the resulting latent space often remains difficult to interpret. In this work we propose SOM-CPC, a model that visualizes data in an organized 2D manifold, while preserving higher-dimensional information. We address a largely unexplored and challenging set of scenarios comprising high-rate time series, and show on both synthetic and real-life data (physiological data and audio recordings) that SOM-CPC outperforms strong baselines like DL-based feature extraction, followed by conventional dimensionality reduction techniques, and models that jointly optimize a DL model and a Self-Organizing Map (SOM). SOM-CPC has great potential to acquire a better understanding of latent patterns in high-rate data streams.
Self-Supervised Learning for Building Damage Assessment from Large-scale xBD Satellite Imagery Benchmark Datasets

In the field of post-disaster assessment, for timely and accurate rescue and localization after a disaster, people need to know the location of damaged buildings. In deep learning, some scholars have proposed methods to make automatic and highly accurate building damage assessments by remote sensing images, which are proved to be more efficient than assessment by domain experts. However, due to the lack of a large amount of labeled data, these kinds of tasks can suffer from being able to do an accurate assessment, as the efficiency of deep learning models relies highly on labeled data. Although existing semi-supervised and unsupervised studies have made breakthroughs in this area, none of them has completely solved this problem. Therefore, we propose adopting a self-supervised comparative learning approach to address the task without the requirement of labeled data. We constructed a novel asymmetric twin network architecture and tested its performance on the xBD dataset. Experiment results of our model show the improvement compared to baseline and commonly used methods. We also demonstrated the potential of self-supervised methods for building damage recognition awareness.
CropMix: Sampling a Rich Input Distribution via Multi-Scale Cropping

We present a simple method, CropMix, for the purpose of producing a rich input distribution from the original dataset distribution. Unlike single random cropping, which may inadvertently capture only limited information, or irrelevant information, like pure background, unrelated objects, etc, we crop an image multiple times using distinct crop scales, thereby ensuring that multi-scale information is captured. The new input distribution, serving as training data, useful for a number of vision tasks, is then formed by simply mixing multiple cropped views. We first demonstrate that CropMix can be seamlessly applied to virtually any training recipe and neural network architecture performing classification tasks. CropMix is shown to improve the performance of image classifiers on several benchmark tasks across-the-board without sacrificing computational simplicity and efficiency. Moreover, we show that CropMix is of benefit to both contrastive learning and masked image modeling towards more powerful representations, where preferable results are achieved when learned representations are transferred to downstream tasks. Code is available at GitHub.
Cluster-based Evaluation of Automatically Generated Text

While probabilistic language generators have improved dramatically over the last few years, the automatic evaluation metrics used to assess them have not kept pace with this progress. In the domain of language generation, a good metric must correlate highly with human judgements. Yet, with few exceptions, there is a lack of such metrics in the literature. In this work, we analyse the general paradigm of language generator evaluation. We ﬁrst discuss the computational and qualitative issues with using automatic evaluation metrics that operate on probability distributions over strings, the backbone of most language generators. We then propose the use of distributions over clusters instead, where we cluster strings based on their text embeddings (obtained from a pretrained language model). While we ﬁnd the biases introduced by this substitution to be quite strong, we observe that, empirically, this methodology leads to metric estimators with higher correlation with human judgements, while simultaneously reducing estimator variance. We ﬁnish the paper with a probing analysis, which leads us to conclude that—by encoding syntactic- and coherence-level features of text, while ignoring surface-level features—these clusters may simply be better equipped to evaluate state-of-the-art language models.
Conversational Speech Separation: an Evaluation Study for Streaming Applications

Continuous speech separation (CSS) is a recently proposed framework which aims at separating each speaker from an input mixture signal in a streaming fashion. Hereafter we perform an evaluation study on practical design considerations for a CSS system, addressing important aspects which have been neglected in recent works. In particular, we focus on the trade-off between separation performance, computational requirements and output latency showing how an offline separation algorithm can be used to perform CSS with a desired latency. We carry out an extensive analysis on the choice of CSS processing window size and hop size on sparsely overlapped data. We find out that the best trade-off between computational burden and performance is obtained for a window of 5 s.