Miracle-dz/UniFAR-FTUs huggingface dataset:
Entity Divider with Language Grounding in Multi-Agent Reinforcement Learning

We investigate the use of natural language to drive the generalization of policies in multi-agent settings. Unlike single-agent settings, the generalization of policies should also consider the influence of other agents. Besides, with the increasing number of entities in multi-agent settings, more agent-entity interactions are needed for language grounding, and the enormous search space could impede the learning process. Moreover, given a simple general instruction,e.g., beating all enemies, agents are required to decompose it into multiple subgoals and figure out the right one to focus on. Inspired by previous work, we try to address these issues at the entity level and propose a novel framework for language grounding in multi-agent reinforcement learning, entity divider (EnDi). EnDi enables agents to independently learn subgoal division at the entity level and act in the environment based on the associated entities. The subgoal division is regularized by opponent modeling to avoid subgoal conflicts and promote coordinated strategies. Empirically, EnDi demonstrates the strong generalization ability to unseen games with new dynamics and expresses the superiority over existing methods.
IELM: An Open Information Extraction Benchmark for Pre-Trained Language Models

We introduce a new open information extraction (OIE) benchmark for pre-trained language models (LM). Recent studies have demonstrated that pre-trained LMs, such as BERT and GPT, may store linguistic and relational knowledge. In particular, LMs are able to answer “fill-in-the-blank” questions when given a pre-defined relation category. Instead of focusing on pre-defined relations, we create an OIE benchmark aiming to fully examine the open relational information present in the pre-trained LMs. We accomplish this by turning pre-trained LMs into zero-shot OIE systems. Surprisingly, pre-trained LMs are able to obtain competitive performance on both standard OIE datasets (CaRB and Re-OIE2016) and two new large-scale factual OIE datasets (TAC KBP-OIE and Wikidata-OIE) that we establish via distant supervision. For instance, the zero-shot pre-trained LMs outperform the F1 score of the state-of-the-art supervised OIE methods on our factual OIE datasets without needing to use any training sets.
Model-based Evaluation of Driver Control Workloads in Haptic-based Driver Assistance Systems

This study presents a novel approach for modeling and simulating human-vehicle interactions in order to examine the effects of automated driving systems (ADS) on driving performance and driver control workload. Existing driver-ADS interaction studies have relied on simulated or real-world human driver experiments that are limited in providing objective evaluation of the dynamic interactions and control workloads on the driver. Our approach leverages an integrated human model-based active driving system (HuMADS) to simulate the dynamic interaction between the driver model and the haptic-based ADS during a vehicle overtaking task. Two driver arm-steering models were developed for both tense and relaxed human driver conditions and validated against experimental data. We conducted a simulation study to evaluate the effects of three different haptic shared control conditions (based on the presence and type of control conflict) on overtaking task performance and driver workloads. We found that No Conflict shared control scenarios result in improved driving performance and reduced control workloads, while Conflict scenarios result in unsafe maneuvers and increased workloads. These findings, which are consistent with experimental studies, demonstrate the potential for our approach to improving future ADS design for safer driver assistance systems.
Deconfounding Legal Judgment Prediction for European Court of Human Rights Cases Towards Better Alignment with Experts

This work demonstrates that Legal Judgement Prediction systems without expert-informed adjustments can be vulnerable to shallow, distracting surface signals that arise from corpus construction, case distribution, and confounding factors. To mitigate this, we use domain expertise to strategically identify statistically predictive but legally irrelevant information. We adopt adversarial training to prevent the system from relying on it. We evaluate our deconfounded models by employing interpretability techniques and comparing to expert annotations. Quantitative experiments and qualitative analysis show that our deconfounded model consistently aligns better with expert rationales than baselines trained for prediction only. We further contribute a set of reference expert annotations to the validation and testing partitions of an existing benchmark dataset of European Court of Human Rights cases.
Does Joint Training Really Help Cascaded Speech Translation?

Currently, in speech translation, the straightforward approach - cascading a recognition system with a translation system - delivers state-of-the-art results.However, fundamental challenges such as error propagation from the automatic speech recognition system still remain.To mitigate these problems, recently, people turn their attention to direct data and propose various joint training methods.In this work, we seek to answer the question of whether joint training really helps cascaded speech translation.We review recent papers on the topic and also investigate a joint training criterion by marginalizing the transcription posterior probabilities.Our findings show that a strong cascaded baseline can diminish any improvements obtained using joint training, and we suggest alternatives to joint training.We hope this work can serve as a refresher of the current speech translation landscape, and motivate research in finding more efficient and creative ways to utilize the direct data for speech translation.
Mixed-EVC: Mixed Emotion Synthesis and Control in Voice Conversion

Emotional voice conversion (EVC) traditionally targets the transformation of spoken utterances from one emotional state to another, with previous research mainly focusing on discrete emotion categories. This paper departs from the norm by introducing a novel perspective: a nuanced rendering of mixed emotions and enhancing control over emotional expression. To achieve this, we propose a novel EVC framework, Mixed-EVC, which only leverages discrete emotion training labels. We construct an attribute vector that encodes the relationships among these discrete emotions, which is predicted using a ranking-based support vector machine and then integrated into a sequence-to-sequence (seq2seq) EVC framework. Mixed-EVC not only learns to characterize the input emotional style but also quantifies its relevance to other emotions during training. As a result, users have the ability to assign these attributes to achieve their desired rendering of mixed emotions. Objective and subjective evaluations confirm the effectiveness of our approach in terms of mixed emotion synthesis and control while surpassing traditional baselines in the conversion of discrete emotions from one to another.
Discourse Context Predictability Effects in Hindi Word Order

We test the hypothesis that discourse predictability influences Hindi syntactic choice. While prior work has shown that a number of factors (e.g., information status, dependency length, and syntactic surprisal) influence Hindi word order preferences, the role of discourse predictability is underexplored in the literature. Inspired by prior work on syntactic priming, we investigate how the words and syntactic structures in a sentence influence the word order of the following sentences. Specifically, we extract sentences from the Hindi-Urdu Treebank corpus (HUTB), permute the preverbal constituents of those sentences, and build a classifier to predict which sentences actually occurred in the corpus against artificially generated distractors. The classifier uses a number of discourse-based features and cognitive features to make its predictions, including dependency length, surprisal, and information status. We find that information status and LSTM-based discourse predictability influence word order choices, especially for non-canonical object-fronted orders. We conclude by situating our results within the broader syntactic priming literature.
Adaptive Label Smoothing with Self-Knowledge in Natural Language Generation

Overconfidence has been shown to impair generalization and calibration of a neural network. Previous studies remedy this issue by adding a regularization term to a loss function, preventing a model from making a peaked distribution. Label smoothing smoothes target labels with a pre-defined prior label distribution; as a result, a model is learned to maximize the likelihood of predicting the soft label. Nonetheless, the amount of smoothing is the same in all samples and remains fixed in training. In other words, label smoothing does not reflect the change in probability distribution mapped by a model over the course of training. To address this issue, we propose a regularization scheme that brings dynamic nature into the smoothing parameter by taking model probability distribution into account, thereby varying the parameter per instance. A model in training self-regulates the extent of smoothing on the fly during forward propagation. Furthermore, inspired by recent work in bridging label smoothing and knowledge distillation, our work utilizes self-knowledge as a prior label distribution in softening target labels, and presents theoretical support for the regularization effect by knowledge distillation and the dynamic smoothing parameter. Our regularizer is validated comprehensively, and the result illustrates marked improvements in model generalization and calibration, enhancing robustness and trustworthiness of a model.
Origin of model fractional Chern insulators in all topological ideal flatbands: Explicit color-entangled wave function and exact density algebra

It is commonly believed that nonuniform Berry curvature destroys the Girvin-MacDonald-Platzman algebra and as a consequence destabilizes fractional Chern insulators. In this work we disprove this common lore by presenting a theory for all topological ideal flatbands with nonzero Chern number C. The smooth single-particle Bloch wavefunction is proved to admit an exact color-entangled form as a superposition of C lowest Landau level type wavefunctions distinguished by boundary conditions. Including repulsive interactions, Abelian and non-Abelian model fractional Chern insulators of Halperin type are stabilized as exact zero-energy ground states no matter how nonuniform Berry curvature is, as long as the quantum geometry is ideal and the repulsion is short-ranged. The key reason behind is the existence of an emergent Hilbert space in which Berry curvature can be exactly flattened by adjusting wavefunction's normalization. In such space, the flatband-projected density operator obeys a closed Girvin-MacDonald-Platzman type algebra, making exact mapping to C-layered Landau levels possible. In the end we discuss applications of the theory to moire flatband systems with a particular focus on the fractionalized phase and spontaneous symmetry breaking phase recently observed in graphene based twisted materials.
Chiral superconductivity in the doped triangular-lattice Fermi-Hubbard model in two dimensions

The triangular-lattice Fermi-Hubbard model has been extensively investigated in the literature due to its connection to chiral spin states and unconventional superconductivity. Previous simulations of the ground state of the doped system rely on quasi-one-dimensional lattices where true long-range order is forbidden. Here we simulate two-dimensional and quasi-one-dimensional triangular lattices using state-of-the-art Auxiliary-Field Quantum Monte Carlo. Upon doping a non-magnetic chiral spin state, we observe evidence of chiral superconductivity supported by long-range order in Cooper-pair correlation and a finite value of the chiral order parameter. With this aim, we first locate the transition from the metallic to the non-magnetic insulating phase and the onset of magnetic order. Our results pave the way towards a better understanding of strongly correlated lattice systems with magnetic frustration.
From colouring-in to pointillism: revisiting semantic segmentation supervision

The prevailing paradigm for producing semantic segmentation training data relies on densely labelling each pixel of each image in the training set, akin to colouring-in books. This approach becomes a bottleneck when scaling up in the number of images, classes, and annotators. Here we propose instead a pointillist approach for semantic segmentation annotation, where only point-wise yes/no questions are answered. We explore design alternatives for such an active learning approach, measure the speed and consistency of human annotators on this task, show that this strategy enables training good segmentation models, and that it is suitable for evaluating models at test time. As concrete proof of the scalability of our method, we collected and released 22.6M point labels over 4,171 classes on the Open Images dataset. Our results enable to rethink the semantic segmentation pipeline of annotation, training, and evaluation from a pointillism point of view.
Subspace Recovery from Heterogeneous Data with Non-isotropic Noise

Recovering linear subspaces from data is a fundamental and important task in statistics and machine learning. Motivated by heterogeneity in Federated Learning settings, we study a basic formulation of this problem: the principal component analysis (PCA), with a focus on dealing with irregular noise. Our data come from $n$ users with user $i$ contributing data samples from a $d$-dimensional distribution with mean $\mu_i$. Our goal is to recover the linear subspace shared by $\mu_1,\ldots,\mu_n$ using the data points from all users, where every data point from user $i$ is formed by adding an independent mean-zero noise vector to $\mu_i$. If we only have one data point from every user, subspace recovery is information-theoretically impossible when the covariance matrices of the noise vectors can be non-spherical, necessitating additional restrictive assumptions in previous work. We avoid these assumptions by leveraging at least two data points from each user, which allows us to design an efficiently-computable estimator under non-spherical and user-dependent noise. We prove an upper bound for the estimation error of our estimator in general scenarios where the number of data points and amount of noise can vary across users, and prove an information-theoretic error lower bound that not only matches the upper bound up to a constant factor, but also holds even for spherical Gaussian noise. This implies that our estimator does not introduce additional estimation error (up to a constant factor) due to irregularity in the noise. We show additional results for a linear regression problem in a similar setup.
Fast and Low-Memory Deep Neural Networks Using Binary Matrix Factorization

—Despite the outstanding performance of deep neural networks in different applications, they remain computationally expensive and require a great amount of memory. This motivates more research on reducing the resources needed for implementing such networks. An efﬁcient approach addressed for this purpose is matrix factorization, which has been shown to be effective on different networks. In this paper, we design a training algorithm which utilizes binary matrix factorization and show its efﬁciency in reducing the required resources in deep neural networks. In effect, this technique leads to the fast and practical implementation of such networks.
Modern developments in fluid dynamics : an account of theory and experiment relating to boundary layers, turbulent motion and wakes

speaking countries. His most interesting introduction contains (Section VII) a series of suggestions for formulating' laws, which ought to be framed and exhibited in the offices of every legislative body; and he certainly carries out his own precepts that regulations should be brief, lucid, comprehensible even to those ignorant of law, presented in an orderly sequence, and not complicated by perpetual cross references. Legislative bodies in England might note his caustic reference to the Anglo-Saxon habit of publishing lengthy regulations " in which a number of heterogeneous provisions with no special inter-connection are thrown together in regrettable confusion. " Comprehensive references and a good index add to the value of this book.
In-context Reinforcement Learning with Algorithm Distillation

We propose Algorithm Distillation (AD), a method for distilling reinforcement learning (RL) algorithms into neural networks by modeling their training histories with a causal sequence model. Algorithm Distillation treats learning to reinforcement learn as an across-episode sequential prediction problem. A dataset of learning histories is generated by a source RL algorithm, and then a causal transformer is trained by autoregressively predicting actions given their preceding learning histories as context. Unlike sequential policy prediction architectures that distill post-learning or expert sequences, AD is able to improve its policy entirely in-context without updating its network parameters. We demonstrate that AD can reinforcement learn in-context in a variety of environments with sparse rewards, combinatorial task structure, and pixel-based observations, and find that AD learns a more data-efficient RL algorithm than the one that generated the source data.
Networked Signal and Information Processing: Learning by multiagent systems

This article reviews significant advances in networked signal and information processing (SIP), which have enabled in the last 25 years extending decision making and inference, optimization, control, and learning to the increasingly ubiquitous environments of distributed agents. As these interacting agents cooperate, new collective behaviors emerge from local decisions and actions. Moreover, and significantly, theory and applications show that networked agents, through cooperation and sharing, are able to match the performance of cloud or federated solutions while offering the potential for improved privacy, increased resilience, and conserved resources. A longer version of this manuscript, with examples and illustrative applications, is available at https://arxiv.org/abs/2210.13767.
Analyzing Privacy Leakage in Machine Learning via Multiple Hypothesis Testing: A Lesson From Fano

Differential privacy (DP) is by far the most widely accepted framework for mitigating privacy risks in machine learning. However, exactly how small the privacy parameter $\epsilon$ needs to be to protect against certain privacy risks in practice is still not well-understood. In this work, we study data reconstruction attacks for discrete data and analyze it under the framework of multiple hypothesis testing. We utilize different variants of the celebrated Fano's inequality to derive upper bounds on the inferential power of a data reconstruction adversary when the model is trained differentially privately. Importantly, we show that if the underlying private data takes values from a set of size $M$, then the target privacy parameter $\epsilon$ can be $O(\log M)$ before the adversary gains significant inferential power. Our analysis offers theoretical evidence for the empirical effectiveness of DP against data reconstruction attacks even at relatively large values of $\epsilon$.
Properties of RR Lyrae stars in the inner regions of the Large Magellanic Cloud. II.The extended sample.

Context. All galaxies that have been adequately examined so far have shown an extended stellar halo. Aims. To search for such a halo in the LMC we have obtained low-resolution spectra for 100 LMC RR Lyrae stars, of which 87 are in the field and 13 in the clusters NGC 1835 and NGC 2019. Methods. We measured radial velocities for 87 LMC RR Lyrae stars, and metallicities for 78 RR Lyrae stars, nearly tripling the previous sample. These targets are located in 10 fields covering a wide range of distances, out to 2.5 degrees from the center of the LMC. Results. Our main result is that the mean velocity dispersion for the LMC RR Lyrae stars is $\sigma_{RV} = 50 \pm 2$ km s -1 . This quantity does not appear to vary with distance from the LMC center. The metallicity shows a Gaussian distribution, with mean [Fe/H] = –$1.53 \pm 0.02$ dex, and dispersion $\sigma_{\rm [Fe/H]} = 0.20\pm 0.02$ dex in the Harris metallicity scale, confirming that they represent a very homogeneous metal-poor population. There is no dependence between the kinematics and metallicity of the field RR Lyrae star population. Conclusions. Using good quality low-resolution spectra from FORS1, FORS2 and GEMINI-GMOS we have found that field RR Lyrae stars in the LMC show a large velocity dispersion and that this indicate the presence of old and metal-poor stellar halo. All the evidence so far for the halo, however, is from the spectroscopy of the inner LMC regions, similar to the inner flattened halo in our Galaxy. Further study is necessary to confirm this important result.
Better Few-Shot Relation Extraction with Label Prompt Dropout

Few-shot relation extraction aims to learn to identify the relation between two entities based on very limited training examples. Recent efforts found that textual labels (i.e., relation names and relation descriptions) could be extremely useful for learning class representations, which will benefit the few-shot learning task. However, what is the best way to leverage such label information in the learning process is an important research question. Existing works largely assume such textual labels are always present during both learning and prediction. In this work, we argue that such approaches may not always lead to optimal results. Instead, we present a novel approach called label prompt dropout, which randomly removes label descriptions in the learning process. Our experiments show that our approach is able to lead to improved class representations, yielding significantly better results on the few-shot relation extraction task.
Lottery Aware Sparsity Hunting: Enabling Federated Learning on Resource-Limited Edge

Limited computation and communication capabilities of clients pose signiﬁcant challenges in federated learning (FL) over resource-limited edge nodes. A potential solution to this problem is to deploy oﬀ-the-shelf sparse learning algorithms that train a binary sparse mask on each client with the expectation of training a consistent sparse server mask yielding sparse weight tensors. However, as we investigate in this paper, such naive deployments result in a signiﬁcant drop in accuracy compared to FL with dense models, especially for clients with limited resource budgets. In particular, our investigations reveal a serious lack of consensus among the trained sparsity masks on clients, which prevents convergence for the server mask and potentially leads to a substantial drop in model performance. Based on such key observations, we propose federated lottery aware sparsity hunting (FLASH), a uniﬁed sparse learning framework to make the server win a lottery in terms of yielding a sparse sub-model, able to maintain classiﬁcation performance under highly resource-limited client settings. Moreover, to support FL on diﬀerent devices requiring diﬀerent parameter density, we leverage our ﬁndings to present hetero-FLASH , where clients can have diﬀerent target sparsity budgets based on their device resource limits. Experimental evaluations with multiple models on various datasets (both IID and non-IID) show superiority of our models in closing the gap with unpruned baseline while yielding up to ∼ 10 . 1% improved accuracy with ∼ 10 . 26 × fewer communication costs, compared to existing alternatives, at similar hyperparameter settings.