Miracle-dz/UniFAR-FTUs huggingface dataset:
A unified framework for Simplicial Kuramoto models

Simplicial Kuramoto models have emerged as a diverse and intriguing class of models describing oscillators on simplices rather than nodes. In this paper, we present a unified framework to describe different variants of these models, categorized into three main groups:"simple"models,"Hodge-coupled"models, and"order-coupled"(Dirac) models. Our framework is based on topology, discrete differential geometry as well as gradient flows and frustrations, and permits a systematic analysis of their properties. We establish an equivalence between the simple simplicial Kuramoto model and the standard Kuramoto model on pairwise networks under the condition of manifoldness of the simplicial complex. Then, starting from simple models, we describe the notion of simplicial synchronization and derive bounds on the coupling strength necessary or sufficient for achieving it. For some variants, we generalize these results and provide new ones, such as the controllability of equilibrium solutions. Finally, we explore a potential application in the reconstruction of brain functional connectivity from structural connectomes and find that simple edge-based Kuramoto models perform competitively or even outperform complex extensions of node-based models.
Electronic structure of nitrogen-doped lutetium hydrides

First-principles density functional theory (DFT) calculations of Lu-H-N compounds reveal low-energy configurations of Fm$\overline{3}$m Lu$_{8}$H$_{23-x}$N structures that exhibit novel electronic properties such as flat bands, sharply peaked densities of states (van Hove singularities, vHs), and intersecting Dirac cones near the Fermi energy (E$_F$). These N-doped LuH$_3$-based structures also exhibit an interconnected metallic hydrogen network, which is a common feature of high-T$_c$ hydride superconductors. Electronic property systematics give estimates of T$_c$ for optimally ordered structures that are well above the critical temperatures predicted for structures considered previously. The vHs and flat bands near E$_F$ are enhanced in DFT+U calculations, implying strong correlation physics should also be considered for first-principles studies of these materials. These results provide a basis for understanding the novel electronic properties observed for nitrogen-doped lutetium hydride.
DPHuBERT: Joint Distillation and Pruning of Self-Supervised Speech Models

Self-supervised learning (SSL) has achieved notable success in many speech processing tasks, but the large model size and heavy computational cost hinder the deployment. Knowledge distillation trains a small student model to mimic the behavior of a large teacher model. However, the student architecture usually needs to be manually designed and will remain fixed during training, which requires prior knowledge and can lead to suboptimal performance. Inspired by recent success of task-specific structured pruning, we propose DPHuBERT, a novel task-agnostic compression method for speech SSL based on joint distillation and pruning. Experiments on SUPERB show that DPHuBERT outperforms pure distillation methods in almost all tasks. Moreover, DPHuBERT requires little training time and performs well with limited training data, making it suitable for resource-constrained applications. Our method can also be applied to various speech SSL models. Our code and models will be publicly available.
Robust Natural Language Understanding with Residual Attention Debiasing

Natural language understanding (NLU) models often suffer from unintended dataset biases. Among bias mitigation methods, ensemble-based debiasing methods, especially product-of-experts (PoE), have stood out for their impressive empirical success. However, previous ensemble-based debiasing methods typically apply debiasing on top-level logits without directly addressing biased attention patterns. Attention serves as the main media of feature interaction and aggregation in PLMs and plays a crucial role in providing robust prediction. In this paper, we propose REsidual Attention Debiasing (READ), an end-to-end debiasing method that mitigates unintended biases from attention. Experiments on three NLU tasks show that READ significantly improves the performance of BERT-based models on OOD data with shortcuts removed, including +12.9% accuracy on HANS, +11.0% accuracy on FEVER-Symmetric, and +2.7% F1 on PAWS. Detailed analyses demonstrate the crucial role of unbiased attention in robust NLU models and that READ effectively mitigates biases in attention. Code is available at https://github.com/luka-group/READ.
NaturalFinger: Generating Natural Fingerprint with Generative Adversarial Networks

Deep neural network (DNN) models have become a critical asset of the model owner as training them requires a large amount of resource (i.e. labeled data). Therefore, many fingerprinting schemes have been proposed to safeguard the intellectual property (IP) of the model owner against model extraction and illegal redistribution. However, previous schemes adopt unnatural images as the fingerprint, such as adversarial examples and noisy images, which can be easily perceived and rejected by the adversary. In this paper, we propose NaturalFinger which generates natural fingerprint with generative adversarial networks (GANs). Besides, our proposed NaturalFinger fingerprints the decision difference areas rather than the decision boundary, which is more robust. The application of GAN not only allows us to generate more imperceptible samples, but also enables us to generate unrestricted samples to explore the decision boundary.To demonstrate the effectiveness of our fingerprint approach, we evaluate our approach against four model modification attacks including adversarial training and two model extraction attacks. Experiments show that our approach achieves 0.91 ARUC value on the FingerBench dataset (154 models), exceeding the optimal baseline (MetaV) over 17\%.
Self-Supervised Learning of Action Affordances as Interaction Modes

When humans perform a task with an articulated object, they interact with the object only in a handful of ways, while the space of all possible interactions is nearly endless. This is because humans have prior knowledge about what interactions are likely to be successful, i.e., to open a new door we first try the handle. While learning such priors without supervision is easy for humans, it is notoriously hard for machines. In this work, we tackle unsupervised learning of priors of useful interactions with articulated objects, which we call interaction modes. In contrast to the prior art, we use no supervision or privileged information; we only assume access to the depth sensor in the simulator to learn the interaction modes. More precisely, we define a successful interaction as the one changing the visual environment substantially and learn a generative model of such interactions, that can be conditioned on the desired goal state of the object. In our experiments, we show that our model covers most of the human interaction modes, outperforms existing state-of-the-art methods for affordance learning, and can generalize to objects never seen during training. Additionally, we show promising results in the goal-conditional setup, where our model can be quickly fine-tuned to perform a given task. We show in the experiments that such affordance learning predicts interaction which covers most modes of interaction for the querying articulated object and can be fine-tuned to a goal-conditional model. For supplementary: https://actaim.github. io/.
Fate of the vacuum point and of gray solitons in dispersive quantum shock waves in a one-dimensional Bose gas

We continue the study of dispersive quantum shock waves in a one-dimensional Bose gas beyond the mean-field approximation. In a recent work by Simmons et al. [Phys. Rev. Let. 125, 180401 (2020)], the oscillatory shock wave train developing in this system from an initial localized density bump on a uniform background was interpreted as a result of quantum mechanical self-interference, wherein the interference contrast would diminish with the loss of matter-wave phase coherence. Such loss of coherence, relative to the mean-field Gross-Pitaevskii description, occurs due to either quantum or thermal fluctuations, as well as in the strongly interacting regime. In this work, we extend the analysis of dispersive quantum shock waves in this context to other dynamical scenarios. More specifically, the scenarios studied include evolution of a sufficiently high density bump, known to lead to the so-called ``vacuum point'' in the mean-field description, and evolution of an initial density dip, known to shed a train of grey solitons in the same mean-field approximation. We study the fate of these nonlinear wave structures in the presence of quantum and thermal fluctuations, as well as at intermediate and strong interactions, and show that both the vacuum point and grey solitons cease to manifest themselves beyond the mean-field approach. On the other hand, we find that a vacuum point can occur in an ideal (noninteracting) Bose gas evolving from a ground state of a localized dimple potential. Due to the ubiquity of dispersive shock waves in nature, our results should provide useful insights and perspectives for a variety of other physical systems known to display nonlinear wave phenomena.
Approximation-Generalization Trade-offs under (Approximate) Group Equivariance

The explicit incorporation of task-specific inductive biases through symmetry has emerged as a general design precept in the development of high-performance machine learning models. For example, group equivariant neural networks have demonstrated impressive performance across various domains and applications such as protein and drug design. A prevalent intuition about such models is that the integration of relevant symmetry results in enhanced generalization. Moreover, it is posited that when the data and/or the model may only exhibit $\textit{approximate}$ or $\textit{partial}$ symmetry, the optimal or best-performing model is one where the model symmetry aligns with the data symmetry. In this paper, we conduct a formal unified investigation of these intuitions. To begin, we present general quantitative bounds that demonstrate how models capturing task-specific symmetries lead to improved generalization. In fact, our results do not require the transformations to be finite or even form a group and can work with partial or approximate equivariance. Utilizing this quantification, we examine the more general question of model mis-specification i.e. when the model symmetries don't align with the data symmetries. We establish, for a given symmetry group, a quantitative comparison between the approximate/partial equivariance of the model and that of the data distribution, precisely connecting model equivariance error and data equivariance error. Our result delineates conditions under which the model equivariance error is optimal, thereby yielding the best-performing model for the given task and data.
PFNs Are Flexible Models for Real-World Bayesian Optimization

In this paper, we use Prior-data Fitted Networks (PFNs) as a flexible surrogate for Bayesian Optimization (BO). PFNs are neural processes that are trained to approximate the posterior predictive distribution (PPD) for any prior distribution that can be efficiently sampled from. We describe how this flexibility can be exploited for surrogate modeling in BO. We use PFNs to mimic a naive Gaussian process (GP), an advanced GP, and a Bayesian Neural Network (BNN). In addition, we show how to incorporate further information into the prior, such as allowing hints about the position of optima (user priors), ignoring irrelevant dimensions, and performing non-myopic BO by learning the acquisition function. The flexibility underlying these extensions opens up vast possibilities for using PFNs for BO. We demonstrate the usefulness of PFNs for BO in a large-scale evaluation on artificial GP samples and three different hyperparameter optimization testbeds: HPO-B, Bayesmark, and PD1. We publish code alongside trained models at github.com/automl/PFNs4BO.
Fabric defect detection algorithm using local statistic features and global saliency analysis

In order to efficiently detect defect for a fabric image with complex texture and variety of defects,this paper proposed a novel defect detection algorithm based on local statistical features and global saliency analysis.In the proposed algorithm,the target image is firstly divided into blocks with the same size,then the local binary pattern(LBP) technique is used to extract the texture features of the blocks and the histogram technique is used to extract the grayscale statistical features of the blocks.Secondly,for a given image block,K blocks are randomly chosen for calculating the LBP feature contrast and grayscale histogram feature contrast between the given block and the randomly-chosen one.Based on the obtained global contrast information,a saliency map is produced.Finally,the saliency map is segmented by using an optimal threshold,which is obtained by an iterative approach.Through these procedures,the detection result is obtained.The experimental results demonstrate that the proposed algorithm,integrating the local textual and grayscale statistical features and the global saliency analysis can detect the fabric defections effectively.
Direct Preference Optimization: Your Language Model is Secretly a Reward Model

While large-scale unsupervised language models (LMs) learn broad world knowledge and some reasoning skills, achieving precise control of their behavior is difficult due to the completely unsupervised nature of their training. Existing methods for gaining such steerability collect human labels of the relative quality of model generations and fine-tune the unsupervised LM to align with these preferences, often with reinforcement learning from human feedback (RLHF). However, RLHF is a complex and often unstable procedure, first fitting a reward model that reflects the human preferences, and then fine-tuning the large unsupervised LM using reinforcement learning to maximize this estimated reward without drifting too far from the original model. In this paper we introduce a new parameterization of the reward model in RLHF that enables extraction of the corresponding optimal policy in closed form, allowing us to solve the standard RLHF problem with only a simple classification loss. The resulting algorithm, which we call Direct Preference Optimization (DPO), is stable, performant, and computationally lightweight, eliminating the need for sampling from the LM during fine-tuning or performing significant hyperparameter tuning. Our experiments show that DPO can fine-tune LMs to align with human preferences as well as or better than existing methods. Notably, fine-tuning with DPO exceeds PPO-based RLHF in ability to control sentiment of generations, and matches or improves response quality in summarization and single-turn dialogue while being substantially simpler to implement and train.
Generating EDU Extracts for Plan-Guided Summary Re-Ranking

Two-step approaches, in which summary candidates are generated-then-reranked to return a single summary, can improve ROUGE scores over the standard single-step approach. Yet, standard decoding methods (i.e., beam search, nucleus sampling, and diverse beam search) produce candidates with redundant, and often low quality, content. In this paper, we design a novel method to generate candidates for re-ranking that addresses these issues. We ground each candidate abstract on its own unique content plan and generate distinct plan-guided abstracts using a model’s top beam. More concretely, a standard language model (a BART LM) auto-regressively generates elemental discourse unit (EDU) content plans with an extractive copy mechanism. The top K beams from the content plan generator are then used to guide a separate LM, which produces a single abstractive candidate for each distinct plan. We apply an existing re-ranker (BRIO) to abstractive candidates generated from our method, as well as baseline decoding methods. We show large relevance improvements over previously published methods on widely used single document news article corpora, with ROUGE-2 F1 gains of 0.88, 2.01, and 0.38 on CNN / Dailymail, NYT, and Xsum, respectively. A human evaluation on CNN / DM validates these results. Similarly, on 1k samples from CNN / DM, we show that prompting GPT-3 to follow EDU plans outperforms sampling-based methods by by 1.05 ROUGE-2 F1 points. Code to generate and realize plans is available at https://github.com/griff4692/edu-sum.
Benchmarking Diverse-Modal Entity Linking with Generative Models

Entities can be expressed in diverse formats, such as texts, images, or column names and cell values in tables. While existing entity linking (EL) models work well on per modality configuration, such as text-only EL, visual grounding, or schema linking, it is more challenging to design a unified model for diverse modality configurations. To bring various modality configurations together, we constructed a benchmark for diverse-modal EL (DMEL) from existing EL datasets, covering all three modalities including text, image, and table. To approach the DMEL task, we proposed a generative diverse-modal model (GDMM) following a multimodal-encoder-decoder paradigm. Pre-training \Model with rich corpora builds a solid foundation for DMEL without storing the entire KB for inference. Fine-tuning GDMM builds a stronger DMEL baseline, outperforming state-of-the-art task-specific EL models by 8.51 F1 score on average. Additionally, extensive error analyses are conducted to highlight the challenges of DMEL, facilitating future research on this task.
ContrastNER: Contrastive-based Prompt Tuning for Few-shot NER

Prompt-based language models have produced encouraging results in numerous applications, including Named Entity Recognition (NER) tasks. NER aims to identify entities in a sentence and provide their types. However, the strong performance of most available NER approaches is heavily dependent on the design of discrete prompts and a verbalizer to map the model-predicted outputs to entity categories, which are complicated undertakings. To address these challenges, we present ContrastNER, a prompt-based NER framework that employs both discrete and continuous tokens in prompts and uses a contrastive learning approach to learn the continuous prompts and forecast entity types. The experimental results demonstrate that ContrastNER obtains competitive performance to the state-of-the-art NER methods in high-resource settings and outperforms the state-of-the-art models in low-resource circumstances without requiring extensive manual prompt engineering and verbalizer design.
Evolution from quantum anomalous Hall insulator to heavy-fermion semimetal in magic-angle twisted bilayer graphene

The ground states of twisted bilayer graphene (TBG) at chiral and flat-band limit with integer fillings are known from exact solutions, while their dynamical and thermodynamical properties are revealed by unbiased quantum Monte Carlo (QMC) simulations. However, to elucidate experimental observations of correlated metallic, insulating and superconducting states and their transitions, investigations on realistic, or non-chiral cases are vital. Here we employ momentum-space QMC method to investigate the evolution of correlated states in magic-angle TBG away from chiral limit at charge neutrality with polarized spin/valley, which approximates to an experimental case with filling factor $\nu=-3$. We find that the ground state evolves from quantum anomalous Hall insulator into an intriguing correlated semimetallic state possessing heavy-fermion features as AA hopping strength reaches experimental values. Such a state resembles the recently proposed heavy-fermion representations with localized electrons residing at AA stacking regions and delocalized electrons itinerating via AB/BA stacking regions. The spectral signatures of the localized and itinerant electrons in the heavy-fermion semimetal phase are revealed, with the connection to experimental results being discussed.
Electronic and vibrational excitations on the surface of the three-dimensional topological insulator 
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We study surface states in the three-dimensional topological insulators Bi$_2$Te$_{3-x}$Se$_{x}$ (x = 0, 2, 3) by polarization resolved resonant Raman spectroscopy. By tracking the spectral intensity of the surface phonon modes with respect to the incident photon energy, we show that the surface phonons are qualitatively similar to their bulk counterparts. Using the resonant Raman excitation profile, we estimated the binding energy of the surface conduction bands relative to bulk conduction bands. In addition, we selectively excite the surface-to-bulk electronic continuum near the Fermi energy in Bi$_2$Se$_3$ to determine the strength of Fano interaction between the most prominent surface phonon and the surface-to-bulk continuum.
Translatotron 3: Speech to Speech Translation with Monolingual Data

This paper presents Translatotron 3, a novel approach to unsupervised direct speech-to-speech translation from monolingual speech-text datasets by combining masked autoencoder, unsupervised embedding mapping, and back-translation. Experimental results in speech-to-speech translation tasks between Spanish and English show that Translatotron 3 outperforms a baseline cascade system, reporting $18.14$ BLEU points improvement on the synthesized Unpaired-Conversational dataset. In contrast to supervised approaches that necessitate real paired data, or specialized modeling to replicate para-/non-linguistic information such as pauses, speaking rates, and speaker identity, Translatotron 3 showcases its capability to retain it. Audio samples can be found at http://google-research.github. io/lingvo-lab/translatotron3
Causal Component Analysis

Independent Component Analysis (ICA) aims to recover independent latent variables from observed mixtures thereof. Causal Representation Learning (CRL) aims instead to infer causally related (thus often statistically dependent) latent variables, together with the unknown graph encoding their causal relationships. We introduce an intermediate problem termed Causal Component Analysis (CauCA). CauCA can be viewed as a generalization of ICA, modelling the causal dependence among the latent components, and as a special case of CRL. In contrast to CRL, it presupposes knowledge of the causal graph, focusing solely on learning the unmixing function and the causal mechanisms. Any impossibility results regarding the recovery of the ground truth in CauCA also apply for CRL, while possibility results may serve as a stepping stone for extensions to CRL. We characterize CauCA identifiability from multiple datasets generated through different types of interventions on the latent causal variables. As a corollary, this interventional perspective also leads to new identifiability results for nonlinear ICA -- a special case of CauCA with an empty graph -- requiring strictly fewer datasets than previous results. We introduce a likelihood-based approach using normalizing flows to estimate both the unmixing function and the causal mechanisms, and demonstrate its effectiveness through extensive synthetic experiments in the CauCA and ICA setting.