Miracle-dz/UniFAR-FTUs huggingface dataset:
The Roll-Out of Community Notes Did Not Reduce Engagement With Misinformation on Twitter

Developing interventions that successfully reduce engagement with misinformation on social media is challenging. One intervention that has recently gained great attention is Twitter's Community Notes (previously known as"Birdwatch"). Community Notes is a crowdsourced fact-checking approach that allows users to write textual notes to inform others about potentially misleading posts on Twitter. Yet, empirical evidence regarding its effectiveness in reducing engagement with misinformation on social media is missing. In this paper, we perform a large-scale empirical study to analyze whether the introduction of the Community Notes feature and its roll-out to users in the U. S. and around the world have reduced engagement with misinformation on Twitter in terms of retweet volume and likes. We employ Difference-in-Difference (DiD) models and Regression Discontinuity Design (RDD) to analyze a comprehensive dataset consisting of all fact-checking notes and corresponding source tweets since the launch of Community Notes in early 2021. Although we observe a significant increase in the volume of fact-checks carried out via Community Notes, particularly for tweets from verified users with many followers, we find no evidence that the introduction of Community Notes significantly reduced engagement with misleading tweets on Twitter. Rather, our findings suggest that Community Notes might be too slow to effectively reduce engagement with misinformation in the early (and most viral) stage of diffusion. Our work emphasizes the importance of evaluating fact-checking interventions in the field and offers important implications to enhance crowdsourced fact-checking strategies on social media.
Instruction : Translate the phrase ” Bonne chance ” into English Response : Good Luck

Large language models (LLMs) strengthen instruction-following capability through instruction-finetuning (IFT) on supervised instruction/response data. However, widely used IFT datasets (e.g., ALPACA’s 52k data) surprisingly contain many lowquality instances with incorrect or irrelevant responses, which are misleading and detrimental to IFT. In this paper, we propose a simple and effective data selection strategy that automatically identifies and filters out low-quality data using a strong LLM (e.g., ChatGPT). To this end, we introduce ALPAGASUS1, which is finetuned on only 9k high-quality data filtered from the 52k ALPACA data. ALPAGASUS significantly outperforms the original ALPACA as evaluated by GPT-4 on multiple test sets and the controlled human evaluation. Its 13B variant matches > 90% performance of its teacher LLM (i.e., Text-Davinci-003 generating the 52k data) on test tasks. It also provides 5.7x faster training, reducing the training time for a 7B variant from 80 minutes (for ALPACA) to 14 minutes 2. Moreover, the experiments prove the efficacy of our method across diverse datasets, base models, and LLM filters. Overall, ALPAGASUS demonstrates a novel data-centric IFT paradigm that can be generally applied to instruction-tuning data, leading to faster training and better instruction-following models. Our project page is available at: https://lichang-chen.github. io/AlpaGasus/.
Three-dimensional analysis of vortex-lattice formation in rotating Bose–Einstein condensates using smoothed-particle hydrodynamics

Recently, we presented a new numerical scheme for vortex lattice formation in a rotating Bose–Einstein condensate (BEC) using smoothed particle hydrodynamics (SPH) with an explicit time-integrating scheme; our SPH scheme could reproduce the vortex lattices and their formation processes in rotating quasi-two-dimensional (2D) BECs trapped in a 2D harmonic potential. In this study, we have successfully demonstrated a simulation of rotating 3D BECs trapped in a 3D harmonic potential forming ‘cigar-shaped’ condensates. We have found that our scheme can reproduce the following typical behaviors of rotating 3D BECs observed in the literature: (i) the characteristic shape of the lattice formed in the cross-section at the origin and its formation process, (ii) the stable existence of vortex lines along the vertical axis after reaching the steady state, (iii) a ‘cookie-cutter’ shape, with a similar lattice shape observed wherever we cut the condensate in a certain range in the vertical direction, (iv) the bending of vortex lines when approaching the inner edges of the condensate, and (v) the formation of vortex lattices by vortices entering from outside the condensate. Therefore, we further validated our scheme by simulating rotating 3D BECs.
Does Double Descent Occur in Self-Supervised Learning?

Most investigations into double descent have focused on supervised models while the few works studying self-supervised settings find a surprising lack of the phenomenon. These results imply that double descent may not exist in self-supervised models. We show this empirically using a standard and linear autoencoder, two previously unstudied settings. The test loss is found to have either a classical U-shape or to monotonically decrease instead of exhibiting a double-descent curve. We hope that further work on this will help elucidate the theoretical underpinnings of this phenomenon.
Flow-Guided Controllable Line Drawing Generation

In this paper, we investigate the problem of automatically controllable artistic character line drawing generation from photographs by proposing a Vector Flow Aware and Line Controllable Image-to-Image Translation architecture, which can be viewed as an appealing intersection between Artificial Intelligence and Arts. Specifically, we first present an Image-to-Flow network (I2FNet) to efficiently and robustly create the vector flow field in a learning-based manner, which can provide a direction guide for drawing lines. Then, we introduce our well-designed Double Flow Generator (DFG) framework to fuse features from learned vector flow and input image flow guaranteeing the spatial coherence of lines. Meanwhile, in order to allow for controllable character line drawing generation, we integrate a Line Control Matrix (LCM) into DFG and train a Line Control Regressor (LCR) to synthesize drawings with different styles by elaborately controlling the level of details, such as thickness, smoothness, and continuity, of lines. Finally, we design a Fourier Transformation Loss to further constrain the character line generation from the frequency domain view of the point. Quantitative and qualitative experiments demonstrate that our approach can obtain superior performance in producing high-resolution character line-drawing images with perceptually realistic characteristics.
The electrostatic fate of $N$-layer moir\'e graphene

Twisted $N$-layer graphene (TNG) moir\'e structures have recently been shown to exhibit robust superconductivity similar to twisted bilayer graphene (TBG). In particular for $N=4$ and $N=5$, the phase diagram features a superconducting pocket that extends beyond the nominal full filling of the flat band. These observations are seemingly at odds with the canonical understanding of the low-energy theory of TNG, wherein the TNG Hamiltonian consists of one flat-band sector and accompanying dispersive bands. Using a self-consistent Hartee-Fock treatment, we explain how the phenomenology of TNG can be understood through an interplay of in-plane Hartree and inhomogeneous layer potentials, which cause a reshuffling of electronic bands. We extend our understanding beyond the case of N = 5 realized in experiment so far. We decribe how the Hartree and layer potentials control the phase diagram for devices with N $>$ 5 and tend to preclude exchange-driven correlated phenomena in this limit. To circumvent these electrostatic constraints, we propose a new flat-band paradigm that could be realized in large-N devices by taking advantage of two nearly flat sectors acting together to enhance the importance of exchange effects.
Optimal Compression of Unit Norm Vectors in the High Distortion Regime

Motivated by the need for communication-efficient distributed learning, we investigate the method for compressing a unit norm vector into the minimum number of bits, while still allowing for some acceptable level of distortion in recovery. This problem has been explored in the rate-distortion/covering code literature, but our focus is exclusively on the "high-distortion" regime. We approach this problem in a worst-case scenario, without any prior information on the vector, but allowing for the use of randomized compression maps. Our study considers both biased and unbiased compression methods and determines the optimal compression rates. It turns out that simple compression schemes are nearly optimal in this scenario. While the results are a mix of new and known, they are compiled in this paper for completeness.
Pseudolocality and completeness for nonnegative Ricci curvature limits of 3D singular Ricci flows

Lai (2021) used singular Ricci flows, introduced by Kleiner and Lott (2017), to construct a nonnegative Ricci curvature Ricci flow $g(t)$ emerging from an arbitrary 3D complete noncompact Riemannian manifold $(M^3, g_0)$ which has nonnegative Ricci curvature. We show $g(t)$ is complete for positive times provided $g_0$ satisfies a volume ratio lower bound that approaches zero at spatial infinity. Our proof combines a pseudolocality result of Lai (2021) for singular flows, together with a pseudolocality result of Hochard (2016) and Simon and Topping (2022) for nonsingular flows. We also show that the construction of complete nonnegative complex sectional curvature flows by Cabezas-Rivas and Wilking (2015) can be adapted here to show $g(t)$ is complete for positive times provided $g_0$ is a compactly supported perturbation of a nonnegative sectional curvature metric on $\mathbb{R}^3$.
Unifying Token- and Span-level Supervisions for Few-shot Sequence Labeling

Few-shot sequence labeling aims to identify novel classes based on only a few labeled samples. Existing methods solve the data scarcity problem mainly by designing token-level or span-level labeling models based on metric learning. However, these methods are only trained at a single granularity (i.e., either token-level or span-level) and have some weaknesses of the corresponding granularity. In this article, we first unify token- and span-level supervisions and propose a Consistent Dual Adaptive Prototypical (CDAP) network for few-shot sequence labeling. CDAP contains the token- and span-level networks, jointly trained at different granularities. To align the outputs of two networks, we further propose a consistent loss to enable them to learn from each other. During the inference phase, we propose a consistent greedy inference algorithm that first adjusts the predicted probability and then greedily selects non-overlapping spans with maximum probability. Extensive experiments show that our model achieves new state-of-the-art results on three benchmark datasets. All the code and data of this work will be released at https://github.com/zifengcheng/CDAP.
Generalization Bounds for Few-Shot Transfer Learning with Pretrained Classifiers

We study the ability of foundation models to learn representations for classification that are transferable to new, unseen classes. Recent results in the literature show that representations learned by a single classifier over many classes are competitive on few-shot learning problems with representations learned by special-purpose algorithms designed for such problems. We offer a theoretical explanation for this behavior based on the recently discovered phenomenon of class-feature-variability collapse, that is, that during the training of deep classification networks the feature embeddings of samples belonging to the same class tend to concentrate around their class means. More specifically, we show that the few-shot error of the learned feature map on new classes (defined as the classification error of the nearest class-center classifier using centers learned from a small number of random samples from each new class) is small in case of class-feature-variability collapse, under the assumption that the classes are selected independently from a fixed distribution. This suggests that foundation models can provide feature maps that are transferable to new downstream tasks, even with very few samples; to our knowledge, this is the first performance bound for transfer-learning that is non-vacuous in the few-shot setting.
Understanding and Mitigating Spurious Correlations in Text Classification with Neighborhood Analysis

Recent research has revealed that machine learning models have a tendency to leverage spurious correlations that exist in the training set but may not hold true in general circumstances. For instance, a sentiment classifier may erroneously learn that the token “performances” is commonly associated with positive movie reviews.Relying on these spurious correlations degrades the classifier’s performance when it deploys on out-of-distribution data.In this paper, we examine the implications of spurious correlations through a novel perspective called neighborhood analysis. The analysis uncovers how spurious correlations lead unrelated words to erroneously cluster together in the embedding space. Driven by the analysis, we design a metric to detect spurious tokens and also propose a family of regularization methods, NFL (doN’t Forget your Language) to mitigate spurious correlations in text classification.Experiments show that NFL can effectively prevent erroneous clusters and significantly improve the robustness of classifiers without auxiliary data. The code is publicly available at https://github.com/oscarchew/doNt-Forget-your-Language.
Unraveling Coordination Problems

Strategic uncertainty complicates policy design in coordination games. To rein in strategic uncertainty, the Planner in this paper connects the problem of policy design to that of equilibrium selection. We characterize the subsidy scheme that induces coordination on a given outcome of the game as its unique equilibrium. Optimal subsidies are unique, symmetric for identical players, continuous functions of model parameters, and do not make the targeted strategies strictly dominant for any one player; these properties differ starkly from canonical results in the literature. Uncertainty about payoffs impels policy moderation as overly aggressive intervention might itself induce coordination failure. JEL codes: D81, D82, D83, D86, H20. Keywords: mechanism design, global games, contracting with externalities, unique implementation.
On posterior consistency of data assimilation with Gaussian process priors: the 2D Navier-Stokes equations

We consider a non-linear Bayesian data assimilation model for the periodic two-dimensional Navier-Stokes equations with initial condition modelled by a Gaussian process prior. We show that if the system is updated with sufficiently many discrete noisy measurements of the velocity field, then the posterior distribution eventually concentrates near the ground truth solution of the time evolution equation, and in particular that the initial condition is recovered consistently by the posterior mean vector field. We further show that the convergence rate can in general not be faster than inverse logarithmic in sample size, but describe specific conditions on the initial conditions when faster rates are possible. In the proofs we provide an explicit quantitative estimate for backward uniqueness of solutions of the two-dimensional Navier-Stokes equations.
Treatments and placebos for the pathologies of effective field theories

We demonstrate some shortcomings of"fixing the equations,"an increasingly popular remedy for time evolution problems of effective field theories (EFTs). We compare the EFTs and their"fixed"versions to the UV theories from which they can be derived in two cases: K-essence and nonlinear Proca theory. We find that when an EFT breaks down due to loss of hyperbolicity, fixing does not approximate the UV theory well if the UV solution does not quickly settle down to vacuum. We argue that this can be related to the EFT approximation itself becoming invalid, which cannot be rectified by fixing.
Large-Scale Person Detection and Localization using Overhead Fisheye Cameras

Location determination finds wide applications in daily life. Instead of existing efforts devoted to localizing tourist photos captured by perspective cameras, in this article, we focus on devising person positioning solutions using overhead fisheye cameras. Such solutions are advantageous in large field of view (FOV), low cost, anti-occlusion, and unaggressive work mode (without the necessity of cameras carried by persons). However, related studies are quite scarce, due to the paucity of data. To stimulate research in this exciting area, we present LOAF, the first large-scale overhead fisheye dataset for person detection and localization. LOAF is built with many essential features, e.g., i) the data cover abundant diversities in scenes, human pose, density, and location; ii) it contains currently the largest number of annotated pedestrian, i.e., 457K bounding boxes with groundtruth location information; iii) the body-boxes are labeled as radius-aligned so as to fully address the positioning challenge. To approach localization, we build a fisheye person detection network, which exploits the fisheye distortions by a rotation-equivariant training strategy and predict radius-aligned human boxes end-to-end. Then, the actual locations of the detected persons are calculated by a numerical solution on the fisheye model and camera altitude data. Extensive experiments on LOAF validate the superiority of our fisheye detector w.r. t. previous methods, and show that our whole fisheye positioning solution is able to locate all persons in FOV with an accuracy of0.5 m, within 0.1 s.
New Bounds for Matrix Multiplication: from Alpha to Omega

The main contribution of this paper is a new improved variant of the laser method for designing matrix multiplication algorithms. Building upon the recent techniques of [Duan, Wu, Zhou FOCS'2023], the new method introduces several new ingredients that not only yield an improved bound on the matrix multiplication exponent $\omega$, but also improves the known bounds on rectangular matrix multiplication by [Le Gall and Urrutia SODA'2018]. In particular, the new bound on $\omega$ is $\omega \le 2.371552$ (improved from $\omega \le 2.371866$). For the dual matrix multiplication exponent $\alpha$ defined as the largest $\alpha$ for which $\omega(1, \alpha, 1) = 2$, we obtain the improvement $\alpha \ge 0.321334$ (improved from $\alpha \ge 0.31389$). Similar improvements are obtained for various other exponents for multiplying rectangular matrices.
Efficient Computation of Counterfactual Bounds

We assume to be given structural equations over discrete variables inducing a directed acyclic graph, namely, a structural causal model, together with data about its internal nodes. The question we want to answer is how we can compute bounds for partially identifiable counterfactual queries from such an input. We start by giving a map from structural casual models to credal networks. This allows us to compute exact counterfactual bounds via algorithms for credal nets on a subclass of structural causal models. Exact computation is going to be inefficient in general given that, as we show, causal inference is NP-hard even on polytrees. We target then approximate bounds via a causal EM scheme. We evaluate their accuracy by providing credible intervals on the quality of the approximation; we show through a synthetic benchmark that the EM scheme delivers accurate results in a fair number of runs. In the course of the discussion, we also point out what seems to be a neglected limitation to the trending idea that counterfactual bounds can be computed without knowledge of the structural equations. We also present a real case study on palliative care to show how our algorithms can readily be used for practical purposes.
The maximum growth rate of life on Earth

Abstract Life on Earth spans a range of temperatures and exhibits biological growth rates that are temperature dependent. While the observation that growth rates are temperature dependent is well known, we have recently shown that the statistical distribution of specific growth rates for life on Earth is a function of temperature (Corkrey et al., 2016). The maximum rates of growth of all life have a distinct limit, even when grown under optimal conditions, and which vary predictably with temperature. We term this distribution of growth rates the biokinetic spectrum for temperature (BKST). The BKST possibly arises from a trade-off between catalytic activity and stability of enzymes involved in a rate-limiting Master Reaction System (MRS) within the cell. We develop a method to extrapolate quantile curves for the BKST to obtain the posterior probability of the maximum rate of growth of any form of life on Earth. The maximum rate curve conforms to the observed data except below 0°C and above 100°C where the predicted value may be positively biased. The deviation below 0°C may arise from the bulk properties of water, while the degradation of biomolecules may be important above 100°C. The BKST has potential application in astrobiology by providing an estimate of the maximum possible growth rate attainable by terrestrial life and perhaps life elsewhere. We suggest that the area under the maximum growth rate curve and the peak rate may be useful characteristics in considerations of habitability. The BKST can serve as a diagnostic for unusual life, such as second biogenesis or non-terrestrial life. Since the MRS must have been heavily conserved the BKST may contain evolutionary relics. The BKST can serve as a signature summarizing the nature of life in environments beyond Earth, or to characterize species arising from a second biogenesis on Earth.
Image Captions are Natural Prompts for Text-to-Image Models

With the rapid development of Artificial Intelligence Generated Content (AIGC), it has become common practice in many learning tasks to train or fine-tune large models on synthetic data due to the data-scarcity and privacy leakage problems. Albeit promising with unlimited data generation, owing to massive and diverse information conveyed in real images, it is challenging for text-to-image generative models to synthesize informative training data with hand-crafted prompts, which usually leads to inferior generalization performance when training downstream models. In this paper, we theoretically analyze the relationship between the training effect of synthetic data and the synthetic data distribution induced by prompts. Then we correspondingly propose a simple yet effective method that prompts text-to-image generative models to synthesize more informative and diverse training data. Specifically, we caption each real image with the advanced captioning model to obtain informative and faithful prompts that extract class-relevant information and clarify the polysemy of class names. The image captions and class names are concatenated to prompt generative models for training image synthesis. Extensive experiments on ImageNette, ImageNet-100, and ImageNet-1K verify that our method significantly improves the performance of models trained on synthetic training data, i.e., 10% classification accuracy improvements on average.