Miracle-dz/UniFAR-FTUs huggingface dataset:
A Comprehensive Survey on Data-Efficient GANs in Image Generation

Generative Adversarial Networks (GANs) have achieved remarkable achievements in image synthesis. These successes of GANs rely on large scale datasets, requiring too much cost. With limited training data, how to stable the training process of GANs and generate realistic images have attracted more attention. The challenges of Data-Efficient GANs (DE-GANs) mainly arise from three aspects: (i) Mismatch Between Training and Target Distributions, (ii) Overfitting of the Discriminator, and (iii) Imbalance Between Latent and Data Spaces. Although many augmentation and pre-training strategies have been proposed to alleviate these issues, there lacks a systematic survey to summarize the properties, challenges, and solutions of DE-GANs. In this paper, we revisit and define DE-GANs from the perspective of distribution optimization. We conclude and analyze the challenges of DE-GANs. Meanwhile, we propose a taxonomy, which classifies the existing methods into three categories: Data Selection, GANs Optimization, and Knowledge Sharing. Last but not the least, we attempt to highlight the current problems and the future directions.
Dynamic Position Encoding for Transformers

Recurrent models have been dominating the field of neural machine translation (NMT) for the past few years. Transformers have radically changed it by proposing a novel architecture that relies on a feed-forward backbone and self-attention mechanism. Although Transformers are powerful, they could fail to properly encode sequential/positional information due to their non-recurrent nature. To solve this problem, position embeddings are defined exclusively for each time step to enrich word information. However, such embeddings are fixed after training regardless of the task and word ordering system of the source and target languages. In this paper, we address this shortcoming by proposing a novel architecture with new position embeddings that take the order of the target words into consideration. Instead of using predefined position embeddings, our solution generates new embeddings to refine each word’s position information. Since we do not dictate the position of the source tokens and we learn them in an end-to-end fashion, we refer to our method as dynamic position encoding (DPE). We evaluated the impact of our model on multiple datasets to translate from English to German, French, and Italian and observed meaningful improvements in comparison to the original Transformer.
Targeted search for the stochastic gravitational-wave background from the galactic millisecond pulsar population

The millisecond pulsars, old-recycled objects spinning with high frequency $\mathcal{O}$ (kHz) sustaining the deformation from their spherical shape, may emit gravitational waves (GW). These are one of the potential candidates contributing to the anisotropic stochastic gravitational-wave background (SGWB) observable in the ground-based GW detectors. Here, we present the results from a likelihood-based targeted search for the SGWB due to millisecond pulsars in the Milky Way, by analyzing the data from the first three observing runs of Advanced LIGO and Advanced Virgo detector. We assume that the shape of SGWB power spectra and the sky distribution is known \textit{a priori} from the population synthesis model. The information of the ensemble source properties, i.e., the in-band number of pulsars, $N_{\textrm{obs}}$ and the averaged ellipticity, $\mu_\epsilon$ is encoded in the maximum likelihood statistic. We do not find significant evidence for the SGWB signal from the considered source population. The best Bayesian upper limit with $95\%$ confidence for the parameters are $N_{\textrm{obs}}\leq8.8\times10^{4}$ and $\mu_\epsilon\leq1.4\times10^{-6}$, which is comparable to the bounds on mean ellipticity with the GW observations of the individual pulsars. Finally, we show that for the plausible case of $N_{\textrm{obs}}=40,000$, with the one year of observations, the one-sigma sensitivity on $\mu_\epsilon$ might reach $1.5\times10^{-7}$ and $4.1\times10^{-8}$ for the second-generation detector network having A+ sensitivity and third-generation detector network, respectively.
Eigen mode selection in human subject game experiment

Eigen mode selection ought to be a practical issue in some real game systems, as it is a practical issue in the dynamics behaviour of a building, bridge, or molecular, because of the mathematical similarity in theory. However, its reality and accuracy have not been known in real games. We design a 5-strategy game which, in the replicator dynamics theory, is predicted to exist two eigen modes. Further, in behaviour game theory, the game is predicted that the mode selection should depends on the game parameter. We conduct human subject game experiments by controlling the parameter. The data conﬁrm that, the predictions on the mode existence, as well as the mode selection are signiﬁcantly supported. This ﬁnding suggests that, like the equilibrium selection concept in classical game theory, eigen mode selection is an issue in game dynamics theory.
Interpretable Compositional Representations for Robust Few-Shot Generalization

We propose Recognition as Part Composition (RPC), an image encoding approach inspired by human cognition. It is based on the cognitive theory that humans recognize complex objects by components, and that they build a small compact vocabulary of concepts to represent each instance with. RPC encodes images by first decomposing them into salient parts, and then encoding each part as a mixture of a small number of prototypes, each representing a certain concept. We find that this type of learning inspired by human cognition can overcome hurdles faced by deep convolutional networks in low-shot generalization tasks, like zero-shot learning, few-shot learning and unsupervised domain adaptation. Furthermore, we find a classifier using an RPC image encoder is fairly robust to adversarial attacks, that deep neural networks are known to be prone to. Given that our image encoding principle is based on human cognition, one would expect the encodings to be interpretable by humans, which we find to be the case via crowd-sourcing experiments. Finally, we propose an application of these interpretable encodings in the form of generating synthetic attribute annotations for evaluating zero-shot learning methods on new datasets.
L3Cube-HingCorpus and HingBERT: A Code Mixed Hindi-English Dataset and BERT Language Models

Code-switching occurs when more than one language is mixed in a given sentence or a conversation. This phenomenon is more prominent on social media platforms and its adoption is increasing over time. Therefore code-mixed NLP has been extensively studied in the literature. As pre-trained transformer-based architectures are gaining popularity, we observe that real code-mixing data are scarce to pre-train large language models. We present L3Cube-HingCorpus, the first large-scale real Hindi-English code mixed data in a Roman script. It consists of 52.93M sentences and 1.04B tokens, scraped from Twitter. We further present HingBERT, HingMBERT, HingRoBERTa, and HingGPT. The BERT models have been pre-trained on codemixed HingCorpus using masked language modelling objectives. We show the effectiveness of these BERT models on the subsequent downstream tasks like code-mixed sentiment analysis, POS tagging, NER, and LID from the GLUECoS benchmark. The HingGPT is a GPT2 based generative transformer model capable of generating full tweets. Our models show significant improvements over currently available models pre-trained on multiple languages and synthetic code-mixed datasets. We also release L3Cube-HingLID Corpus, the largest code-mixed Hindi-English language identification(LID) dataset and HingBERT-LID, a production-quality LID model to facilitate capturing of more code-mixed data using the process outlined in this work. The dataset and models are available at https://github.com/l3cube-pune/code-mixed-nlp.
Empirical Evaluation and Theoretical Analysis for Representation Learning: A Survey

Representation learning enables us to automatically extract generic feature representations from a dataset to solve another machine learning task. Recently, extracted feature representations by a representation learning algorithm and a simple predictor have exhibited state-of-the-art performance on several machine learning tasks. Despite its remarkable progress, there exist various ways to evaluate representation learning algorithms depending on the application because of the flexibility of representation learning. To understand the current representation learning, we review evaluation methods of representation learning algorithms and theoretical analyses. On the basis of our evaluation survey, we also discuss the future direction of representation learning. Note that this survey is the extended version of Nozawa and Sato (2022).
GL-CLeF: A Global–Local Contrastive Learning Framework for Cross-lingual Spoken Language Understanding

Due to high data demands of current methods, attention to zero-shot cross-lingual spoken language understanding (SLU) has grown, as such approaches greatly reduce human annotation effort. However, existing models solely rely on shared parameters, which can only perform implicit alignment across languages. We present Global-Local Contrastive Learning Framework (GL-CLeF) to address this shortcoming. Specifically, we employ contrastive learning, leveraging bilingual dictionaries to construct multilingual views of the same utterance, then encourage their representations to be more similar than negative example pairs, which achieves to explicitly align representations of similar sentences across languages. In addition, a key step in GL-CLeF is a proposed Local and Global component, which achieves a fine-grained cross-lingual transfer (i.e., sentence-level Local intent transfer, token-level Local slot transfer, and semantic-level Global transfer across intent and slot). Experiments on MultiATIS++ show that GL-CLeF achieves the best performance and successfully pulls representations of similar sentences across languages closer.
Multimodal Few-Shot Object Detection with Meta-Learning Based Cross-Modal Prompting

We study multi-modal few-shot object detection (FSOD) in this paper, using both few-shot visual examples and class semantic information for detection, which are complementary to each other by definition. Most of the previous works on multi-modal FSOD are fine-tuning-based which are inefficient for online applications. Moreover, these methods usually require expertise like class names to extract class semantic embedding, which are hard to get for rare classes. Our approach is motivated by the high-level conceptual similarity of (metric-based) meta-learning and prompt-based learning to learn generalizable few-shot and zero-shot object detection models respectively without fine-tuning. Specifically, we combine the few-shot visual classifier and text classifier learned via meta-learning and prompt-based learning respectively to build the multi-modal classifier and detection models. In addition, to fully exploit the pre-trained language models, we propose meta-learning-based cross-modal prompting to generate soft prompts for novel classes present in few-shot visual examples, which are then used to learn the text classifier. Knowledge distillation is introduced to learn the soft prompt generator without using human prior knowledge of class names, which may not be available for rare classes. Our insight is that the few-shot support images naturally include related context information and semantics of the class. We comprehensively evaluate the proposed multi-modal FSOD models on multiple few-shot object detection benchmarks, achieving promising results.
Towards Lightweight Transformer Via Group-Wise Transformation for Vision-and-Language Tasks

Despite the exciting performance, Transformer is criticized for its excessive parameters and computation cost. However, compressing Transformer remains as an open problem due to its internal complexity of the layer designs, i.e., Multi-Head Attention (MHA) and Feed-Forward Network (FFN). To address this issue, we introduce Group-wise Transformation towards a universal yet lightweight Transformer for vision-and-language tasks, termed as LW-Transformer. LW-Transformer applies Group-wise Transformation to reduce both the parameters and computations of Transformer, while also preserving its two main properties, i.e., the efficient attention modeling on diverse subspaces of MHA, and the expanding-scaling feature transformation of FFN. We apply LW-Transformer to a set of Transformer-based networks, and quantitatively measure them on three vision-and-language tasks and six benchmark datasets. Experimental results show that while saving a large number of parameters and computations, LW-Transformer achieves very competitive performance against the original Transformer networks for vision-and-language tasks. To examine the generalization ability, we apply LW-Transformer to the task of image classification, and build its network based on a recently proposed image Transformer called Swin-Transformer, where the effectiveness can be also confirmed.
End-to-end Weakly-supervised Multiple 3D Hand Mesh Reconstruction from Single Image

—In this paper, we consider the challenging task of simultaneously locating and recovering multiple hands from single 2D image. Previous studies either focus on single hand reconstruction or solve this problem in a multi-stage way. Moreover, the conventional two-stage pipeline ﬁrstly detects hand areas, and then estimates 3D hand pose from each cropped patch. To reduce the computational redundancy in preprocess- ing and feature extraction, we propose a concise but efﬁcient single-stage pipeline. Speciﬁcally, we design a multi-head auto- encoder structure for multi-hand reconstruction, where each head network shares the same feature map and outputs the hand center, pose and texture, respectively. Besides, we adopt a weakly-supervised scheme to alleviate the burden of expensive 3D real-world data annotations. To this end, we propose a series of losses optimized by a stage-wise training scheme, where a multi-hand dataset with 2D annotations is generated based on the publicly available single hand datasets. In order to further improve the accuracy of the weakly supervised model, we adopt several feature consistency constraints in both single and multiple hand settings. Speciﬁcally, the keypoints of each hand estimated from local features should be consistent with the re-projected points predicted from global features. Extensive experiments on public benchmarks including FreiHAND, HO3D, InterHand2.6M and RHD demonstrate that our method outperforms the state-of-the-art model-based methods in both weakly-supervised and fully-supervised manners.
Abadie's Kappa and Weighting Estimators of the Local Average Treatment Effect

Recent research has demonstrated the importance of flexibly controlling for covariates in instrumental variables estimation. In this paper we study the finite sample and asymptotic properties of various weighting estimators of the local average treatment effect (LATE), motivated by Abadie's (2003) kappa theorem and offering the requisite flexibility relative to standard practice. We argue that two of the estimators under consideration, which are weight normalized, are generally preferable. Several other estimators, which are unnormalized, do not satisfy the properties of scale invariance with respect to the natural logarithm and translation invariance, thereby exhibiting sensitivity to the units of measurement when estimating the LATE in logs and the centering of the outcome variable more generally. We also demonstrate that, when noncompliance is one sided, certain weighting estimators have the advantage of being based on a denominator that is strictly greater than zero by construction. This is the case for only one of the two normalized estimators, and we recommend this estimator for wider use. We illustrate our findings with a simulation study and three empirical applications, which clearly document the sensitivity of unnormalized estimators to how the outcome variable is coded. We implement the proposed estimators in the Stata package kappalate.
Sources of Irreproducibility in Machine Learning: A Review

Background: Many published machine learning studies are irreproducible. Issues with methodology and not properly accounting for variation introduced by the algorithm themselves or their implementations are attributed as the main contributors to the irreproducibility.Problem: There exist no theoretical framework that relates experiment design choices to potential effects on the conclusions. Without such a framework, it is much harder for practitioners and researchers to evaluate experiment results and describe the limitations of experiments. The lack of such a framework also makes it harder for independent researchers to systematically attribute the causes of failed reproducibility experiments. Objective: The objective of this paper is to develop a framework that enable applied data science practitioners and researchers to understand which experiment design choices can lead to false findings and how and by this help in analyzing the conclusions of reproducibility experiments. Method: We have compiled an extensive list of factors reported in the literature that can lead to machine learning studies being irreproducible. These factors are organized and categorized in a reproducibility framework motivated by the stages of the scientific method. The factors are analyzed for how they can affect the conclusions drawn from experiments. A model comparison study is used as an example. Conclusion: We provide a framework that describes machine learning methodology from experimental design decisions to the conclusions inferred from them.
A New Method for Direct Measurement of Isotopologue Ratios in Protoplanetary Disks: A Case Study of the 12CO/13CO Ratio in the TW Hya Disk

Planetary systems are thought to be born in protoplanetary disks. Isotope ratios are a powerful tool for investigating the material origin and evolution from molecular clouds to planetary systems via protoplanetary disks. However, it is challenging to measure the isotope (isotopologue) ratios, especially in protoplanetary disks, because the emission lines of major species are saturated. We developed a new method to overcome these challenges by using optically thin line wings induced by thermal broadening. As a first application of the method, we analyzed two carbon monoxide isotopologue lines, 12CO 3–2 and 13CO 3–2, from archival observations of a protoplanetary disk around TW Hya with the Atacama Large Millimeter/submillimeter Array. The 12CO/13CO ratio was estimated to be 21 ± 5 at disk radii of 70–110 au, which is significantly smaller than the value observed in the local interstellar medium, ∼69. It implies that an isotope exchange reaction occurs in a low-temperature environment with C/O > 1. In contrast, it is suggested that 12CO/13CO is higher than ∼84 in the outer disk (r > 130 au), which can be explained by the difference in the binding energy of the isotopologues on dust grains and the CO gas depletion processes. Our results imply that the gas-phase 12CO/13CO can vary by a factor of >4 even inside a protoplanetary disk and therefore can be used to trace material evolution in disks.
TranS: Transition-based Knowledge Graph Embedding with Synthetic Relation Representation

Knowledge graph embedding (KGE) aims to learn continuous vectors of relations and entities in knowledge graph. Recently, transition-based KGE methods have achieved promising performance, where the single relation vector learns to translate head entity to tail entity. However, this scoring pattern is not suitable for complex scenarios where the same entity pair has different relations. Previous models usually focus on the improvement of entity representation for 1-to-N, N-to-1 and N-to-N relations, but ignore the single relation vector. In this paper, we propose a novel transition-based method, TranS, for knowledge graph embedding. The single relation vector in traditional scoring patterns is replaced with synthetic relation representation, which can solve these issues effectively and efficiently. Experiments on a large knowledge graph dataset, ogbl-wikikg2, show that our model achieves state-of-the-art results.
Time dependent correlators of holographic EPR pairs

We study the ﬁeld correlators between the quark and anti-quark EPR pair in the super Yang-Mills theory using the holographic description, which is a string in AdS space with its two ends anchored on the boundary. We consider the cases that the end points of the string are static, and that end points are uniformly accelerated in opposite direction where the exact solutions for the string’s proﬁles are available. In both cases, the two-points correlators of the boundary ﬁelds which are described by the linearized perturbations in the worldsheet can also be derived exactly and the all-time evolution of the correlators are obtained. In the case of the accelerating string, the induced geometry on the string worldsheet has the causal structure of a two-sided AdS black hole with a wormhole connects two causally disconnected boundaries, which is a realization of the ER=EPR conjecture. We ﬁnd that the causality plays an crucial role in determining the nature of the dispersion relation of the particles anchoring at the end points of the string, and the feature of the induced mutual interaction between two particles. In the case of the causally disconnected two ends, the induced eﬀect from the ﬁeld gives the dissipative dynamics of each particles with no dependence of the distance between two ends, and the induced mutual coupling between two particles vanishes in the late times, following a power law. When two ends are causally connected, the induced dispersion relation becomes non-dissipative at the late times. Here we will also comment on the implication of our ﬁndings in the entangled particle dynamics and the ER=EPR conjecture.
Joule-Thomson expansion of d-dimensional charged AdS black holes with cloud of strings and quintessence

Herein, we focus on the study of Joule-Thomson expansion corresponding to a d-dimensional charged AdS black hole with cloud of strings and quintessence. Then its relevant solution and some thermodynamic properties are investigated. Speciﬁcally, we evaluate its Joule-Thomson expansion from four important aspects, including the Joule-Thomson coeﬃcient, inversion curve, isenthalpic curve, and ratio T mini T c . After analysis, diﬀerent dimensions with strings of cloud and quintessence parameters have diﬀerent eﬀects on the Joule-Thomson coeﬃcient (the same situation are found for the inversion curve, isenthalpic curve, and ratio T mini T c ).
Zero-Shot Program Representation Learning

Learning program representations has been the core prerequisite of code intelligence tasks (e.g., code search and code clone detection). The state-of-the-art pre-trained models such as CodeBERT require the availability of large-scale code corpora. However, gathering training samples can be costly and infeasible for domain-specific languages such as Solidity for smart contracts. In this paper, we propose Zecoler, a zero-shot learning approach for code representations. Zecoler is built upon a pre-trained programming language model. In order to elicit knowledge from the pre-trained models efficiently, Zecoler casts the downstream tasks to the same form of pre-training tasks by inserting trainable prompts into the original input. Then, it employs the prompt learning technique to optimize the pre-trained model by merely adjusting the original input. This enables the representation model to efficiently fit the scarce task-specific data while reusing pre-trained knowledge. We evaluate Zecoler in three code intelligence tasks in two programming languages that have no training samples, namely, Solidity and Go, with model trained in corpora of common languages such as Java. Experimental results show that our approach significantly outperforms baseline models in both zero-shot and few-shot settings.