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Data heterogeneity is one of the key challenges in federated learning, and many efforts have been devoted to tackling this problem. However, distributed concept drift with data heterogeneity, where clients may additionally experience different concept drifts, is a largely unexplored area. In this work, we focus on real drift, where the conditional distribution $P(Y|X)$ changes. We first study how distributed concept drift affects the model training and find that local classifier plays a critical role in drift adaptation. Moreover, to address data heterogeneity, we study the feature alignment under distributed concept drift, and find two factors that are crucial for feature alignment: the conditional distribution $P(Y|X)$ and the degree of data heterogeneity. Motivated by the above findings, we propose FedCCFA, a federated learning framework with classifier clustering and feature alignment. To enhance collaboration under distributed concept drift, FedCCFA clusters local classifiers at class-level and generates clustered feature anchors according to the clustering results. Assisted by these anchors, FedCCFA adaptively aligns clients' feature spaces based on the entropy of label distribution $P(Y)$, alleviating the inconsistency in feature space. Our results demonstrate that FedCCFA significantly outperforms existing methods under various concept drift settings. Code is available at https://github.com/Chen-Junbao/FedCCFA.

The recently identified intercept $a_B$ tension of supernova (SN) magnitude-redshift relation between local ($z<0.0233$) and late-time ($z>0.0233$) Universe hints for either local-scale new physics or systematics. By comparing the intercepts of different SN groups in the PantheonPlus sample, we find the supernovae (SNe) in the third-rung distance ladder maintain a very stable $a_B$ over a wide redshift range ($0.00372\;\mathrm{km/s/Mpc}$. Therefore, the Hubble tension can be narrowed down to a tension between the Planck-CMB global constraint and the first two-rung local distance ladders, ruling out the third-rung SN systematics like a late-time transition in the SN absolute magnitude., Comment: two columns, 13 pages, 2 tables, 6 figures

The paper introduces an asymptotically optimal lifelong sampling-based path planning algorithm that combines the merits of lifelong planning algorithms and lazy search algorithms for rapid replanning in dynamic environments where edge evaluation is expensive. By evaluating only sub-path candidates for the optimal solution, the algorithm saves considerable evaluation time and thereby reduces the overall planning cost. It employs a novel informed rewiring cascade to efficiently repair the search tree when the underlying search graph changes. Simulation results demonstrate that the algorithm outperforms various state-of-the-art sampling-based planners in addressing both static and dynamic motion planning problems.

Preheating at the end of inflation is a violent nonlinear process that efficiently transfers the energy of the inflaton to a second field, the preheat field. When the preheat field is light during inflation and its background value modulates the preheating process, the superhorizon isocurvature perturbations of the preheat field may be converted to curvature perturbations that leave an imprint on the cosmic microwave background and the large-scale structure of the universe. We use high-precision lattice simulations to study kinetic preheating after $\alpha$-attractor inflation, a case where the effective mass of the preheat field is naturally suppressed during inflation. By comparing the expansion e-folds between different Hubble patches, we find that the conversion from isocurvature perturbations to curvature perturbations is very inefficient and can hardly be detected by cosmological observations., Comment: MEET-U project II

Modern automatic speech recognition (ASR) model is required to accurately transcribe diverse speech signals (from different domains, languages, accents, etc) given the specific contextual information in various application scenarios. Classic end-to-end models fused with extra language models perform well, but mainly in data matching scenarios and are gradually approaching a bottleneck. In this work, we introduce Seed-ASR, a large language model (LLM) based speech recognition model. Seed-ASR is developed based on the framework of audio conditioned LLM (AcLLM), leveraging the capabilities of LLMs by inputting continuous speech representations together with contextual information into the LLM. Through stage-wise large-scale training and the elicitation of context-aware capabilities in LLM, Seed-ASR demonstrates significant improvement over end-to-end models on comprehensive evaluation sets, including multiple domains, accents/dialects and languages. Additionally, Seed-ASR can be further deployed to support specific needs in various scenarios without requiring extra language models. Compared to recently released large ASR models, Seed-ASR achieves 10%-40% reduction in word (or character, for Chinese) error rates on Chinese and English public test sets, further demonstrating its powerful performance.

Quantitative $T_1$ mapping by MRI is an increasingly important tool for clinical assessment of cardiovascular diseases. The cardiac $T_1$ map is derived by fitting a known signal model to a series of baseline images, while the quality of this map can be deteriorated by involuntary respiratory and cardiac motion. To correct motion, a template image is often needed to register all baseline images, but the choice of template is nontrivial, leading to inconsistent performance sensitive to image contrast. In this work, we propose a novel deep-learning-based groupwise registration framework, which omits the need for a template, and registers all baseline images simultaneously. We design two groupwise losses for this registration framework: the first is a linear principal component analysis (PCA) loss that enforces alignment of baseline images irrespective of the intensity variation, and the second is an auxiliary relaxometry loss that enforces adherence of intensity profile to the signal model. We extensively evaluated our method, termed ``PCA-Relax'', and other baseline methods on an in-house cardiac MRI dataset including both pre- and post-contrast $T_1$ sequences. All methods were evaluated under three distinct training-and-evaluation strategies, namely, standard, one-shot, and test-time-adaptation. The proposed PCA-Relax showed further improved performance of registration and mapping over well-established baselines. The proposed groupwise framework is generic and can be adapted to applications involving multiple images., Comment: MICCAI 2024. Contents may slightly differ from the camera-ready version

The unified dark fluid model unifies dark matter and dark energy into a single component, providing an alternative and more concise framework for interpreting cosmological observations. We introduce a PAge-like Unified Dark Fluid (PUDF) model based on the PAge approximation (Huang 2020), which is parameterized by the age of the universe and an $\eta$ parameter indicating the deviation from Einstein-De Sitter Universe. The PUDF model shares many similar features of the standard Lambda cold dark matter ($\Lambda$CDM) model and can effectively describe the large-scale structure formation and late-time cosmic acceleration. We constrain the PUDF model with the Planck 2018 cosmic microwave background anisotropies, baryon acoustic oscillation measurements including those from the most recent DESI 2024, the Pantheon+ sample of Type Ia supernovae, and the Cosmic Chronometers compilation. Although the PUDF performs well in fitting all the cosmological datasets, the joint analysis of the data still favors the $\Lambda$CDM model over the PUDF model, according to the Bayesian evidence of model comparison., Comment: 6 pages, 4 figures, 1 table. Accepted for publication in the Journal of Cosmology and Astroparticle Physics

In this work, we study the critical long-range percolation on $\mathbb{Z}$, where an edge connects $i$ and $j$ independently with probability $1-\exp\{-\beta |i-j|^{-2}\}$ for some fixed $\beta>0$. Viewing this as a random electric network where each edge has a unit conductance, we show that with high probability the effective resistances from the origin 0 to $[-N, N]^c$ and from the interval $[-N,N]$ to $[-2N,2N]^c$ (conditioned on no edge joining $[-N,N]$ and $[-2N,2N]^c$) both have a polynomial lower bound in $N$. Our bound holds for all $\beta>0$ and thus rules out a potential phase transition (around $\beta = 1$) which seemed to be a reasonable possibility., Comment: 26 pages, 10 figures

The standard model of modern cosmology might be cracked by the recent persistent hot debate on the Hubble-constant ($H_0$) tension, which manifests itself as the sound-horizon ($r_s$) tension or absolute-magnitude ($M_B$) tension if deeming the origin of the Hubble tension from modifying the early or late Universe, respectively. In this paper, we achieve a fully model-independent constraint (fitting a model-independent global parameterization to a model-independent inverse distant ladder with a model-independent high-redshift calibration) on late-time models with strong evidence against homogeneous new physics over the $\Lambda$-cold-dark-matter ($\Lambda$CDM) model. Further using this model-independent constraint to calibrate sufficiently local supernovae with corresponding late-time models extrapolated below the homogeneity scale, we find surprisingly that, although both $H_0$ tension and $M_B$ tension are absent in our local Universe, a combination of $H_0$ and $M_B$ as the intercept $a_B$ of the magnitude-redshift relation exhibits $3\sim 7\sigma$ tension even for the $\Lambda$CDM model. This $a_B$ tension seems to call for local-scale inhomogeneous new physics disguised as local observational systematics., Comment: v2, 14 pages, 6 tables and 4 figures, extended details, version accepted for publication in Sci. China Phys. Mech. Astron

In this paper, we provide the sufficient and necessary conditions for the symmetry of the following stable L\'evy-type operator $\mathcal{L}$ on $\mathbb{R}$: $$\mathcal{L}=a(x){\Delta^{\alpha/2}}+b(x)\frac{\d}{\d x},$$ where $a,b$ are the continuous positive and differentiable functions, respectively. Under the assumption of symmetry, we further study the criteria for functional inequalities, including Poincar\'e inequalities, logarithmic Sobolev inequalities and Nash inequalities. Our proofs rely on the Orlicz space theory and the estimates of the Green functions.

Deep biasing for the Transducer can improve the recognition performance of rare words or contextual entities, which is essential in practical applications, especially for streaming Automatic Speech Recognition (ASR). However, deep biasing with large-scale rare words remains challenging, as the performance drops significantly when more distractors exist and there are words with similar grapheme sequences in the bias list. In this paper, we combine the phoneme and textual information of rare words in Transducers to distinguish words with similar pronunciation or spelling. Moreover, the introduction of training with text-only data containing more rare words benefits large-scale deep biasing. The experiments on the LibriSpeech corpus demonstrate that the proposed method achieves state-of-the-art performance on rare word error rate for different scales and levels of bias lists., Comment: Submitted to ASRU 2023

We develop a Python tool to estimate the tail distribution of the number of dark matter halos beyond a mass threshold and in a given volume in a light-cone. The code is based on the extended Press-Schechter model and is computationally efficient, typically taking a few seconds on a personal laptop for a given set of cosmological parameters. The high efficiency of the code allows a quick estimation of the tension between cosmological models and the red candidate massive galaxies released by the James Webb Space Telescope, as well as scanning the theory space with the Markov Chain Monte Carlo method. As an example application, we use the tool to study the cosmological implication of the candidate galaxies presented in Labb\'e et al. (2023). The standard $\Lambda$ cold dark matter ($\Lambda$CDM) model is well consistent with the data if the star formation efficiency can reach $\sim 0.3$ at high redshift. For a low star formation efficiency $\epsilon \sim 0.1$, $\Lambda$CDM model is disfavored at $\sim 2\sigma$-$3\sigma$ confidence level., Comment: 6 pages, 3 figures

In this work, we study the random metric for the critical long-range percolation on $\mathbb{Z}^d$. A recent work by B\"aumler [3] implies the subsequential scaling limit, and our main contribution is to prove that the subsequential limit is uniquely characterized by a natural list of axioms. Our proof method is hugely inspired by recent works of Gwynne and Miller [42], and Ding and Gwynne [25] on the uniqueness of Liouville quantum gravity metrics., Comment: 100 pages,17 figures

Domain adaptation using text-only corpus is challenging in end-to-end(E2E) speech recognition. Adaptation by synthesizing audio from text through TTS is resource-consuming. We present a method to learn Unified Speech-Text Representation in Conformer Transducer(USTR-CT) to enable fast domain adaptation using the text-only corpus. Different from the previous textogram method, an extra text encoder is introduced in our work to learn text representation and is removed during inference, so there is no modification for online deployment. To improve the efficiency of adaptation, single-step and multi-step adaptations are also explored. The experiments on adapting LibriSpeech to SPGISpeech show the proposed method reduces the word error rate(WER) by relatively 44% on the target domain, which is better than those of TTS method and textogram method. Also, it is shown the proposed method can be combined with internal language model estimation(ILME) to further improve the performance., Comment: Submitted to Interspeech 2023

Much of the research in supernova cosmology is based on an assumption that the peak luminosity of type Ia supernovae (SNe Ia), after a standardization process, is independent of the galactic environment. A series of recent studies suggested that there is a significant correlation between the standardized luminosity and the progenitor age of SNe Ia. The correlation found in the most recent work by Lee et al. is strong enough to explain the extra dimming of distant SNe Ia and therefore casts doubts on the direct evidence of cosmic acceleration. The present work incorporates the uncertainties of progenitor ages, which were ignored in Lee et al., into a fully Bayesian inference framework. We find a weaker dependence of supernova standardized luminosity on the progenitor age, but the detection of correlation remains significant (3.5$\sigma$). Assuming that such correlation can be extended to high redshift and applying it to the Pantheon SN Ia data set, we confirm that when the Hubble residual does not include intrinsic scatter, the age-bias could be the primary cause of the observed extra dimming of distant SNe Ia. Furthermore, we use the PAge formalism, which is a good approximation to many dark energy and modified gravity models, to do a model comparison. We find that if intrinsic scatter is included in the Hubble residual, the Lambda cold dark matter model remains a good fit. However, in a scenario without intrinsic scatter, the Lambda cold dark matter model faces a challenge., Comment: 8 pages, 6 figures, 2 tables. Published in SCPMA

Time-ordered data (TOD) from ground-based CMB experiments are generally filtered before map-making to remove or reduce the contamination from the ground and the atmospheric emissions. However, when the observation region contains strong point sources, the filtering process will result in considerable leakage around the point sources in a measured CMB map, and leave spurious polarization signals. Therefore, such signals need to be assessed and removed before CMB science exploitation. In this work, we present a new method that we call "template fitting" and can effectively remove these leakage signals in pixel domain, not only satisfying the requirement for measuring primordial gravitational waves from CMB-$B$ modes, but also avoiding time-consuming operations on TOD., Comment: 13pages, 6 figures, 5 tables

The standard $\Lambda$CDM model is recently reported to deviate from the high-redshift Hubble diagram of type Ia supernovae (SNe) and quasars (QSOs) at $\sim4\sigma$ confidence level. In this work, we combine the PAge approximation (a nearly model-independent parameterization) and a high-quality QSO sample to search for the origins of the deviation. By visualizing the $\Lambda$CDM model and the marginalized $3\sigma$ constraints of SNe+QSOs into PAge space, we confirm that the SNe+QSOs constraints in both flat and non-flat PAge cases are in remarkable tension with the standard $\Lambda$CDM cosmology. Next, we investigate the tension from the perspective of redshift-evolution effects. We find that the QSO correlation coefficient $\gamma$ calibrated by SNe+low-z QSOs and SNe+high-z QSOs shows $\sim2.7\sigma$ and $\sim4\sigma$ tensions in flat and non-flat universes, respectively. The tensions for intrinsic dispersion $\delta$ between different data sets are found to be $>4\sigma$ in both flat and non-flat cases. These results indicate that the QSO luminosity correlation suffers from significant redshift evolution and non-universal intrinsic dispersion. Using a redshift-dependence correlation to build QSO Hubble diagram could lead to biases. Thus, the $\sim4\sigma$ deviation from the standard $\Lambda$CDM probably originates from the redshift-evolution effects and non-universal dispersion of the QSO luminosity correlation rather than new physics., Comment: 6 pages, 3 figures