Miracle-dz/UniFAR-FTUs huggingface dataset:
Circular average relative to fractal measures

<p style='text-indent:20px;'>We prove new <inline-formula><tex-math id="M1">\begin{document}$ L^p $\end{document}</tex-math></inline-formula>–<inline-formula><tex-math id="M2">\begin{document}$ L^q $\end{document}</tex-math></inline-formula> estimates for averages over dilates of the circle with respect to fractal measures, which unify different types of maximal estimates for the circular average. Our results are consequences of <inline-formula><tex-math id="M3">\begin{document}$ L^p $\end{document}</tex-math></inline-formula>–<inline-formula><tex-math id="M4">\begin{document}$ L^q $\end{document}</tex-math></inline-formula> smoothing estimates for the wave operator relative to fractal measures. We also discuss similar results concerning the spherical averages. </p>
Depth360: Monocular Depth Estimation using Learnable Axisymmetric Camera Model for Spherical Camera Image

—Self-supervised monocular depth estimation has been widely investigated to estimate depth images and relative poses from RGB images. This framework is attractive for researchers because the depth and pose networks can be trained from just time sequence images without the need for the ground truth depth and poses. In this work, we estimate the depth around a robot (360 ◦ view) using time sequence spherical camera images, from a camera whose parameters are unknown. We propose a learnable axisymmetric camera model which accepts distorted spherical camera images with two ﬁsheye camera images. In addition, we trained our models with a photo-realistic simulator to generate ground truth depth images to provide supervision. Moreover, we introduced loss functions to provide ﬂoor constraints to reduce artifacts that can result from reﬂective ﬂoor surfaces. We demonstrate the efﬁcacy of our method using the spherical camera images from the GO Stanford dataset and pinhole camera images from the KITTI dataset to compare our method’s performance with that of baseline method in learning the camera parameters.
Discounting the Past

Stochastic games with discounted payoff, introduced by Shapley, model adversarial interactions in stochastic environments where two players try to optimize a discounted sum of rewards. In this model, long-term weights are geometrically attenuated based on the delay in their occurrence. We propose a temporally dual notion -- called past-discounting -- where agents have geometrically decaying memory of the rewards encountered during a play of the game. We study objective functions based on past-discounted weight sequences and examine the corresponding stochastic games with liminf, discounted, and mean payoffs. For objectives specified as the limit inferior of past-discounted reward sequences, we show that positional determinacy fails and that optimal strategies may require unbounded memory. To overcome this obstacle, we study an approximate windowed objective based on the idea of using sliding windows of finite length to examine infinite plays. On the other hand, for objectives specified as the discounted and average limits of past-discounted reward sequences we establish determinacy in mixed stationary strategies in the setting of concurrent stochastic games and show how the values of these games may be computed via reductions to standard discounted and mean-payoff games.
Multi-Object Tracking and Segmentation with a Space-Time Memory Network

We propose a method for multi-object tracking and segmentation based on a novel memory-based mechanism to associate tracklets. The proposed tracker, MeNToS, addresses particularly the long-term data association problem, when objects are not observable for long time intervals. Indeed, the recently introduced HOTA metric (High Order Tracking Accuracy), which has a better alignment than the formerly established MOTA (Multiple Object Tracking Accuracy) with the human visual assessment of tracking, has shown that improvements are still needed for data association, despite the recent improvement in object detection. In MeNToS, after creating tracklets using instance segmentation and optical flow, the proposed method relies on a space-time memory network originally developed for one-shot video object segmentation to improve the association of sequence of detections (tracklets) with temporal gaps. We evaluate our tracker on KITTIMOTS and MOTSChallenge and we show the benefit of our data association strategy with the HOTA metric. Additional ablation studies demonstrate that our approach using a space-time memory network gives better and more robust long-term association than those based on a re-identification network. Our project page is at www.mehdimiah. com/mentos+.
Subspace Detours Meet Gromov-Wasserstein

In the context of optimal transport methods, the subspace detour approach was recently presented by Muzellec and Cuturi (2019). It consists in building a nearly optimal transport plan in the measures space from an optimal transport plan in a wisely chosen subspace, onto which the original measures are projected. The contribution of this paper is to extend this category of methods to the Gromov-Wasserstein problem, which is a particular type of transport distance involving the inner geometry of the compared distributions. After deriving the associated formalism and properties, we also discuss a specific cost for which we can show connections with the Knothe-Rosenblatt rearrangement. We finally give an experimental illustration on a shape matching problem.
Optimal Trading: A Model Predictive Control Approach

We develop a dynamic trading strategy in the Linear Quadratic Regulator (LQR) framework. By including a price mean-reversion signal into the optimization program, in a trading environment where market impact is linear and stage costs are quadratic, we obtain an optimal trading curve that reacts opportunistically to price changes while retaining its ability to satisfy smooth or hard completion constraints. The optimal allocation is affine in the spot price and in the number of outstanding shares at any time, and it can be fully derived iteratively. It is also aggressive in the money, meaning that it accelerates whenever the price is favorable, with an intensity that can be calibrated by the practitioner. Since the LQR may yield locally negative participation rates (i.e round trip trades) which are often undesirable, we show that the aforementioned optimization problem can be improved and solved under positivity constraints following a Model Predictive Control (MPC) approach. In particular, it is smoother and more consistent with the completion constraint than putting a hard floor on the participation rate. We finally examine how the LQR can be simplified in the continuous trading context, which allows us to derive a closed formula for the trading curve under further assumptions, and we document a two-step strategy for the case where trades can also occur in an additional dark pool.
Gauge field production and Schwinger reheating in runaway axion inflation

In a class of (pseudoscalar) inflation, inflationary phase is followed by a kination phase, where the Universe is dominated by the kinetic energy of the inflaton that runs away in a vanishing scalar potential. In this class of postinflationary evolution of the Universe, reheating of the Universe cannot be achieved by the inflaton particle decay, which requires its coherent oscillation in a quadratic potential. In this study, we explore the U(1) gauge field production through the Chern-Simons coupling between the pseudoscalar inflaton and the gauge field during the kination era and examine the subsequent pair-particle production induced by the amplified gauge field known as the Schwinger effect, which can lead to reheating of the Universe. We find that with a rough estimate of the Schwinger effect for the Standard Model hyper U(1) gauge field and subsequent thermalization of the pair-produced particles, a successful reheating of the Universe can be achieved by their eventual domination over the kinetic energy of the inflaton, with some reasonable parameter sets. This can be understood as a concrete realization of the “Schwinger reheating”. Constraints from the later-time cosmology are also discussed.
Multilingual Unsupervised Neural Machine Translation with Denoising Adapters

We consider the problem of multilingual unsupervised machine translation, translating to and from languages that only have monolingual data by using auxiliary parallel language pairs. For this problem the standard procedure so far to leverage the monolingual data is _back-translation_, which is computationally costly and hard to tune. In this paper we propose instead to use _denoising adapters_, adapter layers with a denoising objective, on top of pre-trained mBART-50. In addition to the modularity and flexibility of such an approach we show that the resulting translations are on-par with back-translating as measured by BLEU, and furthermore it allows adding unseen languages incrementally.
Lewontin (1972)

This series explores significant developments in the life sciences from historical and philosophical perspectives. Historical episodes include Aristote-lian biology, Greek and Islamic biology and medicine, Renaissance biology, natural history, Darwinian evolution, Nineteenth-century physiology and cell theory, Twentieth-century genetics, ecology, and systematics, and the biological theories and practices of non-Western perspectives. Philosophical topics include individuality, reductionism and holism, fitness, levels of selection, mechanism and teleology, and the nature-nurture debates, as well as explanation, confirmation, inference, experiment, scientific practice, and models and theories vis-à-vis the biological sciences.
Trash or Treasure? An Interactive Dual-Stream Strategy for Single Image Reflection Separation

Single image reflection separation (SIRS), as a representative blind source separation task, aims to recover two layers, $\textit{i.e.}$, transmission and reflection, from one mixed observation, which is challenging due to the highly ill-posed nature. Existing deep learning based solutions typically restore the target layers individually, or with some concerns at the end of the output, barely taking into account the interaction across the two streams/branches. In order to utilize information more efficiently, this work presents a general yet simple interactive strategy, namely $\textit{your trash is my treasure}$ (YTMT), for constructing dual-stream decomposition networks. To be specific, we explicitly enforce the two streams to communicate with each other block-wisely. Inspired by the additive property between the two components, the interactive path can be easily built via transferring, instead of discarding, deactivated information by the ReLU rectifier from one stream to the other. Both ablation studies and experimental results on widely-used SIRS datasets are conducted to demonstrate the efficacy of YTMT, and reveal its superiority over other state-of-the-art alternatives. The implementation is quite simple and our code is publicly available at $\href{https://github.com/mingcv/YTMT-Strategy}{\textit{https://github.com/mingcv/YTMT-Strategy}}$.
Isospin magnetism and spin-polarized superconductivity in Bernal bilayer graphene

In conventional superconductors, Cooper pairing occurs between electrons of opposite spin. We observe spin-polarized superconductivity in Bernal bilayer graphene when doped to a saddle-point van Hove singularity generated by a large applied perpendicular electric field. We observe a cascade of electrostatic gate-tuned transitions between electronic phases distinguished by their polarization within the isospin space defined by the combination of the spin and momentum-space valley degrees of freedom. Although all of these phases are metallic at zero magnetic field, we observe a transition to a superconducting state at finite magnetic field B‖ ≈ 150 milliteslas applied parallel to the two-dimensional sheet. Superconductivity occurs near a symmetry-breaking transition and exists exclusively above the B‖ limit expected of a paramagnetic superconductor with the observed transition critical temperature TC ≈ 30 millikelvins, consistent with a spin-triplet order parameter. Description Hiding in plain sight Superconductivity observed in “twisted” bilayers of the two-dimensional (2D) material graphene requires the layers to be carefully stacked on top of each other at a very precise angle. Zhou et al. found superconductivity in the most common form of bilayer graphene, called Bernal, which does not require such precise manipulation (see the Perspective by Heikkilä). The researchers subjected the samples to an external electric field, but superconductivity emerged only after they also turned on an in-plane magnetic field. This unusual effect of a magnetic field, which normally acts to suppress superconductivity, is suggestive of exotic, spin-triplet pairing in Bernal-stacked bilayer graphene. —JS Superconductivity emerges in a stable form of bilayer graphene upon the application of an in-plane magnetic field.
Generation of perfect-cavity-enhanced atom-photon entanglement with a millisecond lifetime via a spatially-multiplexed cavity

A qubit memory is the building block for quantum information. Cavity-enhanced spin-wave-photon entanglement has been achieved by applying dual-control modes. However, owing to cross readouts between the modes, the qubit retrieval efficiency is about one quarter lower than that for a single spin-wave mode at all storage times. Here, we overcome cross readouts using a multiplexed ring cavity. The cavity is embedded with a polarization interferometer, and we create a write-out photonic qubit entangled with a magnetic-field-insensitive spin-wave qubit by applying a single-mode write-laser beam to cold atoms. The spin-wave qubit is retrieved with a single-mode read-laser beam, and the quarter retrieval-efficiency loss is avoided at all storage times. Our experiment demonstrates 50% intrinsic retrieval efficiency for 540 microsecond storage time, which is 13.5 times longer than the best reported result. Importantly, our multiplexed-cavity scheme paves one road to generate perfect-cavity-enhanced and large-scale multiplexed spin-wave-photon entanglement with a long lifetime.
Behavioral Experiments for Understanding Catastrophic Forgetting

In this paper we explore whether the fundamental tool of experimental psychology, the behavioral experiment, has the power to generate insight not only into humans and animals, but artificial systems too. We apply the techniques of experimental psychology to investigating catastrophic forgetting in neural networks. We present a series of controlled experiments with two-layer ReLU networks, and exploratory results revealing a new understanding of the behavior of catastrophic forgetting. Alongside our empirical findings, we demonstrate an alternative, behavior-first approach to investigating neural network phenomena.
Long Random Matrices and Tensor Unfolding

In this paper, we consider the singular values and singular vectors of low rank perturbations of large rectangular random matrices, in the regime the matrix is"long": we allow the number of rows (columns) to grow polynomially in the number of columns (rows). We prove there exists a critical signal-to-noise ratio (depending on the dimensions of the matrix), and the extreme singular values and singular vectors exhibit a BBP type phase transition. As a main application, we investigate the tensor unfolding algorithm for the asymmetric rank-one spiked tensor model, and obtain an exact threshold, which is independent of the procedure of tensor unfolding. If the signal-to-noise ratio is above the threshold, tensor unfolding detects the signals; otherwise, it fails to capture the signals.
Variable Ion Compositions of Solar Energetic Particle Events in the Inner Heliosphere: A Field Line Braiding Model with Compound Injections

We propose a model for interpreting highly variable ion composition ratios in solar energetic particle (SEP) events recently observed by the Parker Solar Probe (PSP) at 0.3–0.45 au. We use numerical simulations to calculate SEP propagation in a turbulent interplanetary magnetic field with a Kolmogorov power spectrum from large scales down to the gyration scale of energetic particles. We show that when the source regions of different species are offset by a distance comparable to the size of the source regions, the observed energetic particle composition He/H can be strongly variable over more than two orders of magnitude, even if the source ratio is at the nominal value. Assuming a 3He/4He source ratio of 10% in impulsive 3He-rich events and the same spatial offset of the source regions, the 3He/4He ratio at observation sites also vary considerably. The variability of the ion composition ratios depends on the radial distance, which can be tested by observations made at different radial locations. We discuss the implications of these results on the variability of ion composition of impulsive events and on further PSP and Solar Orbiter observations close to the Sun.
StyleAlign: Analysis and Applications of Aligned StyleGAN Models

In this paper, we perform an in-depth study of the properties and applications of aligned generative models. We refer to two models as aligned if they share the same architecture, and one of them (the child) is obtained from the other (the parent) via fine-tuning to another domain, a common practice in transfer learning. Several works already utilize some basic properties of aligned StyleGAN models to perform image-to-image translation. Here, we perform the first detailed exploration of model alignment, also focusing on StyleGAN. First, we empirically analyze aligned models and provide answers to important questions regarding their nature. In particular, we find that the child model's latent spaces are semantically aligned with those of the parent, inheriting incredibly rich semantics, even for distant data domains such as human faces and churches. Second, equipped with this better understanding, we leverage aligned models to solve a diverse set of tasks. In addition to image translation, we demonstrate fully automatic cross-domain image morphing. We further show that zero-shot vision tasks may be performed in the child domain, while relying exclusively on supervision in the parent domain. We demonstrate qualitatively and quantitatively that our approach yields state-of-the-art results, while requiring only simple fine-tuning and inversion.
SLAM: A Unified Encoder for Speech and Language Modeling via Speech-Text Joint Pre-Training

Unsupervised pre-training is now the predominant approach for both text and speech understanding. Self-attention models pre-trained on large amounts of unannotated data have been hugely successful when fine-tuned on downstream tasks from a variety of domains and languages. This paper takes the universality of unsupervised language pre-training one step further, by unifying speech and text pre-training within a single model. We build a single encoder with the BERT objective on unlabeled text together with the w2v-BERT objective on unlabeled speech. To further align our model representations across modalities, we leverage alignment losses, specifically Translation Language Modeling (TLM) and Speech Text Matching (STM) that make use of supervised speech-text recognition data. We demonstrate that incorporating both speech and text data during pre-training can significantly improve downstream quality on CoVoST~2 speech translation, by around 1 BLEU compared to single-modality pre-trained models, while retaining close to SotA performance on LibriSpeech and SpeechStew ASR tasks. On four GLUE tasks and text-normalization, we observe evidence of capacity limitations and interference between the two modalities, leading to degraded performance compared to an equivalent text-only model, while still being competitive with BERT. Through extensive empirical analysis we also demonstrate the importance of the choice of objective function for speech pre-training, and the beneficial effect of adding additional supervised signals on the quality of the learned representations.
SciXGen: A Scientific Paper Dataset for Context-Aware Text Generation

Generating texts in scientific papers requires not only capturing the content contained within the given input but also frequently acquiring the external information called \textit{context}. We push forward the scientific text generation by proposing a new task, namely \textbf{context-aware text generation} in the scientific domain, aiming at exploiting the contributions of context in generated texts. To this end, we present a novel challenging large-scale \textbf{Sci}entific Paper Dataset for Conte\textbf{X}t-Aware Text \textbf{Gen}eration (SciXGen), consisting of well-annotated 205,304 papers with full references to widely-used objects (e.g., tables, figures, algorithms) in a paper. We comprehensively benchmark, using state-of-the-arts, the efficacy of our newly constructed SciXGen dataset in generating description and paragraph. Our dataset and benchmarks will be made publicly available to hopefully facilitate the scientific text generation research.
Minibatch vs Local SGD with Shuffling: Tight Convergence Bounds and Beyond

In distributed learning, local SGD (also known as federated averaging) and its simple baseline minibatch SGD are widely studied optimization methods. Most existing analyses of these methods assume independent and unbiased gradient estimates obtained via with-replacement sampling. In contrast, we study shuffling-based variants: minibatch and local Random Reshuffling, which draw stochastic gradients without replacement and are thus closer to practice. For smooth functions satisfying the Polyak-{\L}ojasiewicz condition, we obtain convergence bounds (in the large epoch regime) which show that these shuffling-based variants converge faster than their with-replacement counterparts. Moreover, we prove matching lower bounds showing that our convergence analysis is tight. Finally, we propose an algorithmic modification called synchronized shuffling that leads to convergence rates faster than our lower bounds in near-homogeneous settings.
A Strong Baseline for Semi-Supervised Incremental Few-Shot Learning

Few-shot learning (FSL) aims to learn models that generalize to novel classes with limited training samples. Recent works advance FSL towards a scenario where unlabeled examples are also available and propose semi-supervised FSL methods. Another line of methods also cares about the performance of base classes in addition to the novel ones and thus establishes the incremental FSL scenario. In this paper, we generalize the above two under a more realistic yet complex setting, named by Semi-Supervised Incremental Few-Shot Learning (S2 I-FSL). To tackle the task, we propose a novel paradigm containing two parts: (1) a well-designed meta-training algorithm for mitigating ambiguity between base and novel classes caused by unreliable pseudo labels and (2) a model adaptation mechanism to learn discriminative features for novel classes while preserving base knowledge using few labeled and all the unlabeled data. Extensive experiments on standard FSL, semi-supervised FSL, incremental FSL, and the firstly built S2 I-FSL benchmarks demonstrate the effectiveness of our proposed method.