Miracle-dz/UniFAR-FTUs huggingface dataset:
eDiff-I: Text-to-Image Diffusion Models with an Ensemble of Expert Denoisers

Large-scale diffusion-based generative models have led to breakthroughs in text-conditioned high-resolution image synthesis. Starting from random noise, such text-to-image diffusion models gradually synthesize images in an iterative fashion while conditioning on text prompts. We find that their synthesis behavior qualitatively changes throughout this process: Early in sampling, generation strongly relies on the text prompt to generate text-aligned content, while later, the text conditioning is almost entirely ignored. This suggests that sharing model parameters throughout the entire generation process may not be ideal. Therefore, in contrast to existing works, we propose to train an ensemble of text-to-image diffusion models specialized for different synthesis stages. To maintain training efficiency, we initially train a single model, which is then split into specialized models that are trained for the specific stages of the iterative generation process. Our ensemble of diffusion models, called eDiff-I, results in improved text alignment while maintaining the same inference computation cost and preserving high visual quality, outperforming previous large-scale text-to-image diffusion models on the standard benchmark. In addition, we train our model to exploit a variety of embeddings for conditioning, including the T5 text, CLIP text, and CLIP image embeddings. We show that these different embeddings lead to different behaviors. Notably, the CLIP image embedding allows an intuitive way of transferring the style of a reference image to the target text-to-image output. Lastly, we show a technique that enables eDiff-I's"paint-with-words"capability. A user can select the word in the input text and paint it in a canvas to control the output, which is very handy for crafting the desired image in mind. The project page is available at https://deepimagination.cc/eDiff-I/
MemoNet: Memorizing All Cross Features' Representations Efficiently via Multi-Hash Codebook Network for CTR Prediction

New findings in natural language processing (NLP) demonstrate that the strong memorization capability contributes a lot to the success of Large Language Models (LLM). This inspires us to explicitly bring an independent memory mechanism into CTR ranking model to learn and memorize cross features' representations. In this paper, we propose multi-Hash Codebook NETwork (HCNet) as the memory mechanism for efficiently learning and memorizing representations of cross features in CTR tasks. HCNet uses a multi-hash codebook as the main memory place and the whole memory procedure consists of three phases: multi-hash addressing, memory restoring, and feature shrinking. We also propose a new CTR model named MemoNet which combines HCNet with a DNN backbone. Extensive experimental results on three public datasets and online test show that MemoNet reaches superior performance over state-of-the-art approaches. Besides, MemoNet shows scaling law of large language model in NLP, which means we can enlarge the size of the codebook in HCNet to sustainably obtain performance gains. Our work demonstrates the importance and feasibility of learning and memorizing representations of cross features, which sheds light on a new promising research direction. The source code is in https://github.com/ptzhangAlg/RecAlg.
PLATO-K: Internal and External Knowledge Enhanced Dialogue Generation

Recently, the practical deployment of open-domain dialogue systems has been plagued by the knowledge issue of information deficiency and factual inaccuracy. To this end, we introduce PLATO-K based on two-stage dialogic learning to strengthen internal knowledge memorization and external knowledge exploitation. In the first stage, PLATO-K learns through massive dialogue corpora and memorizes essential knowledge into model parameters. In the second stage, PLATO-K mimics human beings to search for external information and to leverage the knowledge in response generation. Extensive experiments reveal that the knowledge issue is alleviated significantly in PLATO-K with such comprehensive internal and external knowledge enhancement. Compared to the existing state-of-the-art Chinese dialogue model, the overall engagingness of PLATO-K is improved remarkably by 36.2% and 49.2% on chit-chat and knowledge-intensive conversations.
On the local convergence of the semismooth Newton method for composite optimization

In this paper, we consider a large class of nonlinear equations derived from first-order type methods for solving composite optimization problems. Traditional approaches to establishing superlinear convergence rates of semismooth Newton-type methods for solving nonlinear equations usually postulate either nonsingularity of the B-Jacobian or smoothness of the equation. We investigate the feasibility of both conditions. For the nonsingularity condition, we present equivalent characterizations in broad generality, and illustrate that they are easy-to-check criteria for some examples. For the smoothness condition, we show that it holds locally for a large class of residual mappings derived from composite optimization problems. Furthermore, we investigate a relaxed version of the smoothness condition - smoothness restricted to certain active manifolds. We present a conceptual algorithm utilizing such structures and prove that it has a superlinear convergence rate.
Inference and Denoise: Causal Inference-Based Neural Speech Enhancement

This study addresses the speech enhancement (SE) task within the causal inference paradigm by modeling the noise presence as an intervention. Based on the potential outcome framework, the proposed causal inference-based speech enhancement (CISE) separates clean and noisy frames in an intervened noisy speech using a noise detector and assigns both sets of frames to two mask-based enhancement modules (EMs) to perform noise-conditional SE. Specifically, we use the presence of noise as guidance for EM selection during training, and the noise detector selects the enhancement module according to the prediction of the presence of noise for each frame. Moreover, we derived an SE-specific average treatment effect to quantify the causal effect adequately. Experimental evidence demonstrates that CISE outperforms a non-causal mask-based SE approach in the studied settings and has better performance and efficiency than more complex SE models. Please find our implementation on GitHub.
Using binary statistics in Taurus-Auriga to distinguish between brown dwarf formation processes

Whether BDs form as stars through gravitational collapse ("star-like") or BDs and some very low-mass stars constitute a separate population which form alongside stars comparable to the population of planets, e.g. through circumstellar disk ("peripheral") fragmentation, is one of the key questions of the star-formation problem. For young stars in Taurus-Auriga the binary fraction is large with little dependence on primary mass above ~0.2Msun, while for BDs it is <10%. We investigate a case in which BDs in Taurus formed dominantly through peripheral fragmentation. The decline of the binary frequency in the transition region between star-like and peripheral formation is modelled. A dynamical population synthesis model is employed in which stellar binary formation is universal. Peripheral objects form separately in circumstellar disks with a distinctive initial mass function (IMF), own orbital parameter distributions for binaries and a low binary fraction. A small amount of dynamical processing of the stellar component is accounted for as appropriate for the low-density Taurus-Auriga embedded clusters. The binary fraction declines strongly between the mass-limits for star-like and peripheral formation. The location of characteristic features and the steepness depend on these mass-limits. Such a trend might be unique to low density regions hosting dynamically unprocessed binary populations. The existence of a strong decline in the binary fraction -- primary mass diagram will become verifiable in future surveys on BD and VLMS binarity in the Taurus-Auriga star forming region. It is a test of the (non-)continuity of star formation along the mass-scale, the separateness of the stellar and BD populations and the dominant formation channel for BDs and BD binaries in regions of low stellar density hosting dynamically unprocessed populations.
ALMA Observations of CO Emission from Luminous Lyman-break Galaxies at z = 6.0293–6.2037

We present our new Atacama Large Millimeter/submillimeter Array (ALMA) observations targeting CO(6–5) emission from three luminous Lyman-break galaxies (LBGs) at z spec = 6.0293–6.2037 found in the Subaru/Hyper Suprime-Cam survey, whose [O iii] 88 μm and [C ii] 158 μm emissions have been detected with ALMA. We find a marginal detection of the CO(6–5) line from one of our LBGs, J0235–0532, at the ≃4σ significance level and obtain upper limits for the other two LBGs, J1211–0118 and J0217–0208. Our z = 6 luminous LBGs are consistent with the previously found correlation between the CO luminosity and the infrared luminosity. The unique ensemble of the multiple far-infrared emission lines and underlying continuum fed to a photodissociation region model reveals that J0235–0532 has a relatively high density of hydrogen nuclei n H that is comparable to those of low-z (U)LIRGs, quasars, and Galactic star-forming regions with high n H values, while the other two LBGs have lower n H consistent with local star-forming galaxies. By carefully taking account of various uncertainties, we obtain constraints on total gas mass and gas surface density from their CO luminosity measurements. We find that J0235–0532 is located below the Kennicutt–Schmidt (KS) relation, comparable to the z = 5.7 LBG, HZ10, previously detected with CO(2–1). Combined with previous results for dusty starbursts at similar redshifts, the KS relation at z = 5–6 is on average consistent with the local one.
Quantum City: simulation of a practical near-term metropolitan quantum network

We present the architecture and analyze the applications of a metropolitan-scale quantum network that requires only limited hardware resources for end users. Using NetSquid, a quantum network simulation tool based on discrete events, we assess the performance of several quantum network protocols involving two or more users in various configurations in terms of topology, hardware and trust choices. Our analysis takes losses and errors into account and considers realistic parameters corresponding to present or near-term technology. Our results show that practical quantum-enhanced network functionalities are within reach today and can prepare the ground for further applications when more advanced technology becomes available.
Adversarial Auto-Augment with Label Preservation: A Representation Learning Principle Guided Approach

Data augmentation is a critical contributing factor to the success of deep learning but heavily relies on prior domain knowledge which is not always available. Recent works on automatic data augmentation learn a policy to form a sequence of augmentation operations, which are still pre-defined and restricted to limited options. In this paper, we show that a prior-free autonomous data augmentation's objective can be derived from a representation learning principle that aims to preserve the minimum sufficient information of the labels. Given an example, the objective aims at creating a distant"hard positive example"as the augmentation, while still preserving the original label. We then propose a practical surrogate to the objective that can be optimized efficiently and integrated seamlessly into existing methods for a broad class of machine learning tasks, e.g., supervised, semi-supervised, and noisy-label learning. Unlike previous works, our method does not require training an extra generative model but instead leverages the intermediate layer representations of the end-task model for generating data augmentations. In experiments, we show that our method consistently brings non-trivial improvements to the three aforementioned learning tasks from both efficiency and final performance, either or not combined with strong pre-defined augmentations, e.g., on medical images when domain knowledge is unavailable and the existing augmentation techniques perform poorly. Code is available at: https://github.com/kai-wen-yang/LPA3}{https://github.com/kai-wen-yang/LPA3.
Theory of asymptotic operation. a summary of basic principles

This summary of several talks given in 1990-1993 discusses the problem of asymptotic expansions of multiloop Feynman diagrams in masses and external momenta - a central problem in perturbative quantum field theory. Basic principles of the theory of asymptotic operation -- the most powerful tool for that purpose -- are discussed. Its connection with the conventional methods is explained (the BPHZ theory, the method of leading logarithmic approximation etc.). The problem of non-euclidean asymptotic regimes is discussed as well as ways of its solution.
Parameter estimation methods for analyzing overlapping gravitational wave signals in the third-generation detector era

In the coming years, third-generation detectors such as Einstein Telescope and Cosmic Explorer will enter the network of ground-based gravitational-wave detectors. Their current design predicts a significantly improved sensitivity band with a lower minimum frequency than existing detectors. This, combined with the increased arm length, leads to two major effects: the detection of more signals and the detection of longer signals. Both will result in a large number of overlapping signals. It has been shown that such overlapping signals can lead to biases in the recovered parameters, which would adversely affect the science extracted from the observed binary merger signals. In this work, we analyze overlapping binary black hole coalescences with two methods to analyze multi-signal observations: \textit{hierarchical subtraction} and \textit{joint parameter estimation}. We find that these methods enable a reliable parameter extraction in most cases and that joint parameter estimation is usually more precise but comes with higher computational costs.
Large deviations rates for stochastic gradient descent with strongly convex functions

Recent works have shown that high probability metrics with stochastic gradient descent (SGD) exhibit informativeness and in some cases advantage over the commonly adopted mean-square error-based ones. In this work we provide a formal framework for the study of general high probability bounds with SGD, based on the theory of large deviations. The framework allows for a generic (not-necessarily bounded) gradient noise satisfying mild technical assumptions, allowing for the dependence of the noise distribution on the current iterate. Under the preceding assumptions, we find an upper large deviations bound for SGD with strongly convex functions. The corresponding rate function captures analytical dependence on the noise distribution and other problem parameters. This is in contrast with conventional mean-square error analysis that captures only the noise dependence through the variance and does not capture the effect of higher order moments nor interplay between the noise geometry and the shape of the cost function. We also derive exact large deviation rates for the case when the objective function is quadratic and show that the obtained function matches the one from the general upper bound hence showing the tightness of the general upper bound. Numerical examples illustrate and corroborate theoretical findings.
TSAA: A Two-Stage Anchor Assignment Method towards Anchor Drift in Crowded Object Detection

Among current anchor-based detectors, a positive anchor box will be intuitively assigned to the object that overlaps it the most. The assigned label to each anchor will directly determine the optimization direction of the corresponding prediction box, including the direction of box regression and category prediction. In our practice of crowded object detection, however, the results show that a positive anchor does not always regress toward the object that overlaps it the most when multiple objects overlap. We name it anchor drift. The anchor drift reflects that the anchor-object matching relation, which is determined by the degree of overlap between anchors and objects, is not always optimal. Conflicts between the fixed matching relation and learned experience in the past training process may cause ambiguous predictions and thus raise the false-positive rate. In this paper, a simple but efficient adaptive two-stage anchor assignment (TSAA) method is proposed. It utilizes the final prediction boxes rather than the fixed anchors to calculate the overlap degree with objects to determine which object to regress for each anchor. The participation of the prediction box makes the anchor-object assignment mechanism adaptive. Extensive experiments are conducted on three classic detectors RetinaNet, Faster-RCNN and YOLOv3 on CrowdHuman and COCO to evaluate the effectiveness of TSAA. The results show that TSAA can significantly improve the detectors' performance without additional computational costs or network structure changes.
Attention-based Neural Cellular Automata

Recent extensions of Cellular Automata (CA) have incorporated key ideas from modern deep learning, dramatically extending their capabilities and catalyzing a new family of Neural Cellular Automata (NCA) techniques. Inspired by Transformer-based architectures, our work presents a new class of $\textit{attention-based}$ NCAs formed using a spatially localized$\unicode{x2014}$yet globally organized$\unicode{x2014}$self-attention scheme. We introduce an instance of this class named $\textit{Vision Transformer Cellular Automata}$ (ViTCA). We present quantitative and qualitative results on denoising autoencoding across six benchmark datasets, comparing ViTCA to a U-Net, a U-Net-based CA baseline (UNetCA), and a Vision Transformer (ViT). When comparing across architectures configured to similar parameter complexity, ViTCA architectures yield superior performance across all benchmarks and for nearly every evaluation metric. We present an ablation study on various architectural configurations of ViTCA, an analysis of its effect on cell states, and an investigation on its inductive biases. Finally, we examine its learned representations via linear probes on its converged cell state hidden representations, yielding, on average, superior results when compared to our U-Net, ViT, and UNetCA baselines.
A Simple and Optimal Policy Design with Safety against Heavy-tailed Risk for Stochastic Bandits

We study the stochastic multi-armed bandit problem and design new policies that enjoy both worst-case optimality for expected regret and light-tailed risk for regret distribution. Starting from the two-armed bandit setting with time horizon $T$, we propose a simple policy and prove that the policy (i) enjoys the worst-case optimality for the expected regret at order $O(\sqrt{T\ln T})$ and (ii) has the worst-case tail probability of incurring a linear regret decay at an exponential rate $\exp(-\Omega(\sqrt{T}))$, a rate that we prove to be best achievable for all worst-case optimal policies. Briefly, our proposed policy achieves a delicate balance between doing more exploration at the beginning of the time horizon and doing more exploitation when approaching the end, compared to the standard Successive Elimination policy and Upper Confidence Bound policy. We then improve the policy design and analysis to work for the general $K$-armed bandit setting. Specifically, the worst-case probability of incurring a regret larger than any $x>0$ is upper bounded by $\exp(-\Omega(x/\sqrt{KT}))$. We then enhance the policy design to accommodate the"any-time"setting where $T$ is not known a priori, and prove equivalently desired policy performances as compared to the"fixed-time"setting with known $T$. A brief account of numerical experiments is conducted to illustrate the theoretical findings. We conclude by extending our proposed policy design to the general stochastic linear bandit setting and proving that the policy leads to both worst-case optimality in terms of expected regret order and light-tailed risk on the regret distribution.
Detectability of Varied Hybridization Scenarios using Genome-Scale Hybrid Detection Methods

Hybridization events complicate the accurate reconstruction of phylogenies, as they lead to patterns of genetic heritability that are unexpected under traditional, bifurcating models of species trees. This has led to the development of methods to infer these varied hybridization events, both methods that reconstruct networks directly, and summary methods that predict individual hybridization events. However, a lack of empirical comparisons between methods - especially pertaining to large networks with varied hybridization scenarios - hinders their practical use. Here, we provide a comprehensive review of popular summary methods: TICR, MSCquartets, HyDe, Patterson's D-Statistic (ABBA-BABA), D3, and Dp. TICR and MSCquartets are based on quartet concordance factors gathered from gene tree topologies and Patterson's D-Statistic, D3, and Dp use site pattern frequencies to identify hybridization events. We then use simulated data to address questions of method accuracy and ideal use scenarios by testing methods against complex networks which depict gene flow events that differ in depth (timing), quantity (single vs. multiple, overlapping hybridizations), and rate of gene flow. We find that deeper or multiple hybridization events may introduce noise and weaken the signal of hybridization, leading to higher false negative rates across methods. Despite some forms of hybridization eluding quartet-based detection methods, MSCquartets displays high precision in most scenarios. While HyDe results in high false negative rates when tested on hybridizations involving ghost lineages, HyDe is the only method to be able to separate hybrid vs parent signals. Lastly, we test the methods on ultraconserved elements from the bee subfamily Nomiinae, finding the possibility of hybridization events between clades which correspond to regions of poor support in the species tree estimated in the original study.
Privacy Induces Robustness: Information-Computation Gaps and Sparse Mean Estimation

We establish a simple connection between robust and differentially-private algorithms: private mechanisms which perform well with very high probability are automatically robust in the sense that they retain accuracy even if a constant fraction of the samples they receive are adversarially corrupted. Since optimal mechanisms typically achieve these high success probabilities, our results imply that optimal private mechanisms for many basic statistics problems are robust. We investigate the consequences of this observation for both algorithms and computational complexity across different statistical problems. Assuming the Brennan-Bresler secret-leakage planted clique conjecture, we demonstrate a fundamental tradeoff between computational efficiency, privacy leakage, and success probability for sparse mean estimation. Private algorithms which match this tradeoff are not yet known -- we achieve that (up to polylogarithmic factors) in a polynomially-large range of parameters via the Sum-of-Squares method. To establish an information-computation gap for private sparse mean estimation, we also design new (exponential-time) mechanisms using fewer samples than efficient algorithms must use. Finally, we give evidence for privacy-induced information-computation gaps for several other statistics and learning problems, including PAC learning parity functions and estimation of the mean of a multivariate Gaussian.