Miracle-dz/UniFAR-FTUs huggingface dataset:
AlloST: Low-resource Speech Translation without Source Transcription

The end-to-end architecture has made promising progress in speech translation (ST). However, the ST task is still challenging under low-resource conditions. Most ST models have shown unsatisfactory results, especially in the absence of word information from the source speech utterance. In this study, we survey methods to improve ST performance without using source transcription, and propose a learning framework that utilizes a language-independent universal phone recognizer. The framework is based on an attention-based sequence-to-sequence model, where the encoder generates the phonetic embeddings and phone-aware acoustic representations, and the decoder controls the fusion of the two embedding streams to produce the target token sequence. In addition to investigating different fusion strategies, we explore the specific usage of byte pair encoding (BPE), which compresses a phone sequence into a syllable-like segmented sequence. Due to the conversion of symbols, a segmented sequence represents not only pronunciation but also language-dependent information lacking in phones. Experiments conducted on the Fisher Spanish-English and Taigi-Mandarin drama corpora show that our method outperforms the conformer-based baseline, and the performance is close to that of the existing best method using source transcription.
Computing Optimal Persistent Cycles for Levelset Zigzag on Manifold-like Complexes

In standard persistent homology, a persistent cycle born and dying with a persistence interval (bar) associates the bar with a concrete topological representative, which provides means to effectively navigate back from the barcode to the topological space. Among the possibly many, optimal persistent cycles bring forth further information due to having guaranteed quality. However, topological features usually go through variations in the lifecycle of a bar which a single persistent cycle may not capture. Hence, for persistent homology induced from PL functions, we propose levelset persistent cycles consisting of a sequence of cycles that depict the evolution of homological features from birth to death. Our definition is based on levelset zigzag persistence which involves four types of persistence intervals as opposed to the two types in standard persistence. For each of the four types, we present a polynomial-time algorithm computing an optimal sequence of levelset persistent $p$-cycles for the so-called weak $(p+1)$-pseudomanifolds. Given that optimal cycle problems for homology are NP-hard in general, our results are useful in practice because weak pseudomanifolds do appear in applications. Our algorithms draw upon an idea of relating optimal cycles to min-cuts in a graph that we exploited earlier for standard persistent cycles. Note that levelset zigzag poses non-trivial challenges for the approach because a sequence of optimal cycles instead of a single one needs to be computed in this case.
Photon Conversion and Interaction on Chip

The conversion and interaction between quantum signals at a single-photon level are essential for scalable quantum photonic information technology. Using a fully-optimized, periodically-poled lithium niobate microring, we demonstrate ultra-efficient sum-frequency generation on chip. The external quantum efficiency reaches (65±3)% with only (104±4) uW pump power, improving the state-of-the-art by over one order of magnitude. At the peak conversion, 3×10-5 noise photon is created during the cavity lifetime, which meets the requirement of quantum applications using single-photon pulses. Using pump and signal in single-photon coherent states, we directly measure the conversion probability produced by a single pump photon to be 10-5---breaking the record by 100 times---and the photon-photon coupling strength to be 9.1 MHz. Our results mark a new milestone toward quantum nonlinear optics at the ultimate single photon limit, creating new background in highly integrated photonics and quantum optical computing.
Graph Vulnerability and Robustness: A Survey

The study of network robustness is a critical tool in the characterization and sense making of complex interconnected systems such as infrastructure, communication and social networks. While significant research has been conducted in these areas, gaps in the surveying literature still exist. Answers to key questions are currently scattered across multiple scientific fields and numerous papers. In this survey, we distill key findings across numerous domains and provide researchers crucial access to important information by— (1) summarizing and comparing recent and classical graph robustness measures; (2) exploring which robustness measures are most applicable to different categories of networks (e.g., social, infrastructure); (3) reviewing common network attack strategies, and summarizing which attacks are most effective across different network topologies; and (4) extensive discussion on selecting defense techniques to mitigate attacks across a variety of networks. This survey guides researchers and practitioners in navigating the expansive field of network robustness, while summarizing answers to key questions. We conclude by highlighting current research directions and open problems.
NuSPAN: A Proximal Average Network for Nonuniform Sparse Model - Application to Seismic Reflectivity Inversion

We solve the problem of sparse signal deconvolution in the context of seismic reflectivity inversion, which pertains to high-resolution recovery of the subsurface reflection coefficients. Our formulation employs a nonuniform, non-convex synthesis sparse model comprising a combination of convex and non-convex regularizers, which results in accurate approximations of the l0 pseudo-norm. The resulting iterative algorithm requires the proximal average strategy. When unfolded, the iterations give rise to a learnable proximal average network architecture that can be optimized in a data-driven fashion. We demonstrate the efficacy of the proposed approach through numerical experiments on synthetic 1-D seismic traces and 2-D wedge models in comparison with the benchmark techniques. We also present validations considering the simulated Marmousi2 model as well as real 3-D seismic volume data acquired from the Penobscot 3D survey off the coast of Nova Scotia, Canada.
Switching Contexts: Transportability Measures for NLP

This paper explores the topic of transportability, as a sub-area of generalisability. By proposing the utilisation of metrics based on well-established statistics, we are able to estimate the change in performance of NLP models in new contexts. Defining a new measure for transportability may allow for better estimation of NLP system performance in new domains, and is crucial when assessing the performance of NLP systems in new tasks and domains. Through several instances of increasing complexity, we demonstrate how lightweight domain similarity measures can be used as estimators for the transportability in NLP applications. The proposed transportability measures are evaluated in the context of Named Entity Recognition and Natural Language Inference tasks.
Credibility in second-price auctions: an experimental test

We provide the first direct test of how the credibility of an auction format affects bidding behavior and final outcomes. To do so, we conduct a series of laboratory experiments where the role of the seller is played by a human subject who receives the revenue from the auction and who (depending on the treatment) has agency to determine the outcome of the auction. Contrary to theoretical predictions, we find that the non-credible second-price auction fails to converge to the first-price auction. We provide a behavioral explanation for our results based on sellers’ aversion to rule-breaking, which is confirmed by an additional experiment.
A Deep Learning Framework for Lifelong Machine Learning

Humans can learn a variety of concepts and skills incrementally over the course of their lives while exhibiting many desirable properties, such as continual learning without forgetting, forward transfer and backward transfer of knowledge, and learning a new concept or task with only a few examples. Several lines of machine learning research, such as lifelong machine learning, few-shot learning, and transfer learning attempt to capture these properties. However, most previous approaches can only demonstrate subsets of these properties, often by different complex mechanisms. In this work, we propose a simple yet powerful unified deep learning framework that supports almost all of these properties and approaches through one central mechanism. Experiments on toy examples support our claims. We also draw connections between many peculiarities of human learning (such as memory loss and"rain man") and our framework. As academics, we often lack resources required to build and train, deep neural networks with billions of parameters on hundreds of TPUs. Thus, while our framework is still conceptual, and our experiment results are surely not SOTA, we hope that this unified lifelong learning framework inspires new work towards large-scale experiments and understanding human learning in general. This paper is summarized in two short YouTube videos: https://youtu.be/gCuUyGETbTU (part 1) and https://youtu.be/XsaGI01b-1o (part 2).
Enhancing Fine-Grained Classification for Low Resolution Images

Low resolution fine-grained classification has widespread applicability for applications where data is captured at a distance such as surveillance and mobile photography. While fine-grained classification with high resolution images has received significant attention, limited attention has been given to low resolution images. These images suffer from the inherent challenge of limited information content and the absence of fine details useful for sub-category classification. This results in low inter-class variations across samples of visually similar classes. In order to address these challenges, this research proposes a novel attribute-assisted loss, which utilizes ancillary information to learn discriminative features for classification. The proposed loss function enables a model to learn class-specific discriminative features, while incorporating attribute-level separability. Evaluation is performed on multiple datasets with different models, for four resolutions varying from $32\times 32$ to $224\times 224$. Different experiments demonstrate the efficacy of the proposed attribute-assisted loss for low resolution fine-grained classification.
Bag of Baselines for Multi-objective Joint Neural Architecture Search and Hyperparameter Optimization

Neural architecture search (NAS) and hyperparameter optimization (HPO) make deep learning accessible to non-experts by automatically finding the architecture of the deep neural network to use and tuning the hyperparameters of the used training pipeline. While both NAS and HPO have been studied extensively in recent years, NAS methods typically assume fixed hyperparameters and vice versa - there exists little work on joint NAS + HPO. Furthermore, NAS has recently often been framed as a multi-objective optimization problem, in order to take, e.g., resource requirements into account. In this paper, we propose a set of methods that extend current approaches to jointly optimize neural architectures and hyperparameters with respect to multiple objectives. We hope that these methods will serve as simple baselines for future research on multi-objective joint NAS + HPO. To facilitate this, all our code is available at https://github.com/automl/multi-obj-baselines.
A pr 2 02 1 Parameter mapping between the microscopic nonlocal Nambu – Jona-Lasinio and constant-sound-speed models

S. Antić, 2, ∗ M. Shahrbaf, D. Blaschke, 3, 4 and A. G. Grunfeld 6 Institute for Theoretical Physics, University of Wroclaw, Max Born Pl. 9, 50-204, Wroclaw, Poland 2∗ Physics Department, Faculty of Science, University of Zagreb, Bijenička cesta 32, 10000 Zagreb, Croatia Bogoliubov Laboratory of Theoretical Physics, Joint Institute for Nuclear research, Joliot-Curie str. 6, 141980 Dubna, Russia National Research Nuclear University (MEPhI), Kashirskoe Shosse 31, 115409 Moscow, Russia CONICET, Godoy Cruz 2290, Buenos Aires, Argentina Departamento de F́ısica, Comisión Nacional de Enerǵıa Atómica, Av. Libertador 8250, (1429) Buenos Aires, Argentina (Dated: May 4, 2021)
Data-driven discovery of physical laws with human-understandable deep learning

There is an opportunity for deep learning to revolutionize science and technology by revealing its findings in a human interpretable manner. We develop a novel data-driven approach for creating a human-machine partnership to accelerate scientific discovery. By collecting physical system responses, under carefully selected excitations, we train rational neural networks to learn Green’s functions of hidden partial differential equation. These solutions reveal human-understandable properties and features, such as linear conservation laws, and symmetries, along with shock and singularity locations, boundary effects, and dominant modes. We illustrate this technique on several examples and capture a range of physics, including advection-diffusion, viscous shocks, and Stokes flow in a lid-driven cavity.
Generative Adversarial Reward Learning for Generalized Behavior Tendency Inference

Recent advances in reinforcement learning have inspired increasing interest in learning user modeling adaptively through dynamic interactions, e.g., in reinforcement learning based recommender systems. In most reinforcement learning applications, reward functions provide the critical guideline for optimization. However, current reinforcement learning-based methods rely on manually-defined reward functions, which cannot adapt to dynamic, noisy environments. Moreover, they generally use task-specific reward functions that sacrifice generalization ability. We propose a generative inverse reinforcement learning for user behavioral preference modeling to address the above issues. Instead of using predefined reward functions, our model can automatically learn the rewards from user's actions based on discriminative actor-critic network and Wasserstein GAN. Our model provides a general approach to characterizing and explaining underlying behavioral tendencies. Our experiments show our method outperforms state-of-the-art methods in several scenarios, namely traffic signal control, online recommender systems, and scanpath prediction.
GoHammer Blockchain Performance Test Tool

Decentralized services are increasingly being developed and their Decentralized applications are increasingly developed but their performance metrics are not tested enough. The total number of transactions that can be supported by the blockchain network and the performance effects of selecting different consensus protocols, using different block intervals and block size should be tested. There are some blockchain performance tools but most are built for specific blockchain frameworks and require complex configuration. The GoHammer tool is developed to provide an easy to use, flexible test tool for the Ethereum/Quorum blockchain frameworks. Transaction per second (TPS) values and several performance metrics will be tested. This tool is also a part of the series of tools that can be integrated with Tubu-io. This tool will help in developing more efficient decentralized systems and will affect decreasing the costs of developing decentralized application projects.
LiveStyle - An Application to Transfer Artistic Styles

Art is a variety of human activities that include the production of visual, auditory, or performing objects that express the creativity, creative concepts, or technological abilities of the artist, intended primarily for their beauty or emotional power to be appreciated. The renaissance of historic and forgotten art has been made possible by modern developments in Artificial Intelligence. Techniques for Computer Vision have long been related to such arts. Style Transfer using Neural Networks refers to optimization techniques, where a content image and a style image are taken and blended such that it feels like the content image is reconstructed in the style image color palette. This paper implements the Style Transfer using three different Neural Networks in form of an application that is accessible to the general population thereby reviving interest in lost art styles.
Synthetic Data for Model Selection

Recent breakthroughs in synthetic data generation approaches made it possible to produce highly photorealistic images which are hardly distinguishable from real ones. Furthermore, synthetic generation pipelines have the potential to generate an unlimited number of images. The combination of high photorealism and scale turn synthetic data into a promising candidate for improving various machine learning (ML) pipelines. Thus far, a large body of research in this field has focused on using synthetic images for training, by augmenting and enlarging training data. In contrast to using synthetic data for training, in this work we explore whether synthetic data can be beneficial for model selection. Considering the task of image classification, we demonstrate that when data is scarce, synthetic data can be used to replace the held out validation set, thus allowing to train on a larger dataset. We also introduce a novel method to calibrate the synthetic error estimation to fit that of the real domain. We show that such calibration significantly improves the usefulness of synthetic data for model selection.
CMA-Net: A Cascaded Mutual Attention Network for Light Field Salient Object Detection

In the past few years, numerous deep learning methods have been proposed to address the task of segmenting salient objects from RGB images. However, these approaches depending on single modality fail to achieve the state-of-the-art performance on widely used light field salient object detection (SOD) datasets, which collect large-scale natural images and provide multiple modalities such as multi-view, micro-lens images and depth maps. Most recently proposed light field SOD methods have acquired improving detecting accuracy, yet still predict rough objects' structures and perform slow inference speed. To this end, we propose CMA-Net, which consists of two novel cascaded mutual attention modules aiming at fusing the high level features from the modalities of all-in-focus and depth. Our proposed CMA-Net outperforms 30 SOD methods (by a large margin) on two widely applied light field benchmark datasets. Besides, the proposed CMA-Net can run at a speed of 53 fps, thus being four times faster than the state-of-the-art multi-modal SOD methods. Extensive quantitative and qualitative experiments illustrate both the effectiveness and efficiency of our CMA-Net, inspiring future development of multi-modal learning for both the RGB-D and light field SOD.
Parameterized nonrelativistic limit of stellar structure equations in Ricci-based gravity theories

Gonzalo J. Olmo, 2, ∗ Diego Rubiera-Garcia, † and Aneta Wojnar ‡ Departamento de F́ısica Teórica and IFIC, Centro Mixto Universidad de Valencia CSIC. Universidad de Valencia, Burjassot-46100, Valencia, Spain Departamento de F́ısica, Universidade Federal da Paráıba, 58051-900 João Pessoa, Paráıba, Brazil Departamento de F́ısica Teórica and IPARCOS, Universidad Complutense de Madrid, E-28040 Madrid, Spain Laboratory of Theoretical Physics, Institute of Physics, University of Tartu, W. Ostwaldi 1, 50411 Tartu, Estonia (Dated: May 4, 2021)
A novel hybrid methodology of measuring sentence similarity

The problem of measuring sentence similarity is an essential issue in the natural language processing area. It is necessary to measure the similarity between sentences accurately. Sentence similarity measuring is the task of finding semantic symmetry between two sentences, regardless of word order and context of the words. There are many approaches to measuring sentence similarity. Deep learning methodology shows a state-of-the-art performance in many natural language processing fields and is used a lot in sentence similarity measurement methods. However, in the natural language processing field, considering the structure of the sentence or the word structure that makes up the sentence is also important. In this study, we propose a methodology combined with both deep learning methodology and a method considering lexical relationships. Our evaluation metric is the Pearson correlation coefficient and Spearman correlation coefficient. As a result, the proposed method outperforms the current approaches on a KorSTS standard benchmark Korean dataset. Moreover, it performs a maximum of a 65% increase than only using deep learning methodology. Experiments show that our proposed method generally results in better performance than those with only a deep learning model.
EntrapNet: A Blockchain-Based Verification Protocol for Trustless Computing

In this article, we propose a blockchain-based computing verification protocol, called EntrapNet, for distributed shared computing networks, an emerging underlying network for many Internet of Things (IoT) applications. EntrapNet borrows the idea from the practice of entrapment in criminal law to reduce the possibility of receiving incorrect computing results from trustless service providers who have offered the computing resources. Furthermore, we mathematically optimize EntrapNet to deal with the fundamental tradeoff of a network: security and efficiency. We present an asymptotic optimal solution to this optimization. It will be seen that EntrapNet can be performed as an independent and low-cost layer atop of any trustless network that requires outsourced computing, thus making secure computing affordable and practical.
BI-REC: Guided Data Analysis for Conversational Business Intelligence

Conversational interfaces to Business Intelligence (BI) applications enable data analysis using a natural language dialog in small incremental steps. To truly unleash the power of conversational BI to democratize access to data, a system needs to provide effective and continuous support for data analysis. In this paper, we propose BI-REC, a conversational recommendation system for BI applications to help users accomplish their data analysis tasks. We define the space of data analysis in terms of BI patterns, augmented with rich semantic information extracted from the OLAP cube definition, and use graph embeddings learned using GraphSAGE to create a compact representation of the analysis state. We propose a two-step approach to explore the search space for useful BI pattern recommendations. In the first step, we train a multi-class classifier using prior query logs to predict the next high-level actions in terms of a BI operation (e.g., {\em Drill-Down} or {\em Roll-up}) and a measure that the user is interested in. In the second step, the high-level actions are further refined into actual BI pattern recommendations using collaborative filtering. This two-step approach allows us to not only divide and conquer the huge search space, but also requires less training data. Our experimental evaluation shows that BI-REC achieves an accuracy of 83% for BI pattern recommendations and up to 2X speedup in latency of prediction compared to a state-of-the-art baseline. Our user study further shows that BI-REC provides recommendations with a precision@3 of 91.90% across several different analysis tasks.