Miracle-dz/UniFAR-FTUs huggingface dataset:
Far-Field Automatic Speech Recognition

The machine recognition of speech spoken at a distance from the microphones, known as far-field automatic speech recognition (ASR), has received a significant increase in attention in science and industry, which caused or was caused by an equally significant improvement in recognition accuracy. Meanwhile, it has entered the consumer market with digital home assistants with a spoken language interface being its most prominent application. Speech recorded at a distance is affected by various acoustic distortions, and consequently, quite different processing pipelines have emerged compared with ASR for close-talk speech. A signal enhancement front end for dereverberation, source separation, and acoustic beamforming is employed to clean up the speech, and the back-end ASR engine is robustified by multicondition training and adaptation. We will also describe the so-called end-to-end approach to ASR, which is a new promising architecture that has recently been extended to the far-field scenario. This tutorial article gives an account of the algorithms used to enable accurate speech recognition from a distance, and it will be seen that, although deep learning has a significant share in the technological breakthroughs, a clever combination with traditional signal processing can lead to surprisingly effective solutions.
Imported anthropogenic bacteria may survive the Antarctic winter and introduce new genes into local bacterial communities

We studied dynamic changes in anthropogenic bacterial communities at a summer-operated Czech research base (the Mendel Research Station) in the Antarctic during 2012 and 2013. We observed an increase in total numbers of detected bacteria between the beginning and the end of each stay in the Antarctic. In the first series of samples, bacteria of Bacillus sp. predominated. Surprisingly, high numbers of Gram-positive cocci and coliforms were found (including opportunistic human pathogens), although the conditions for bacterial life were unfavourable (Antarctic winter). In the second series of samples, coliforms and Gram-positive cocci predominated. Dangerous human pathogens were also detected. Yersinia enterocolitica was identified as serotype O:9. Antibiotic susceptibility testing showed medium-to-high resistance rates to ampicillin, cefalotin, cefuroxime, amoxicillin-clavulanate and gentamicin in Enterobacteriaceae. 16S rRNA sequencing showed high rates of accordance between nucleotide sequences among the tested strains. Three conclusions were drawn: (1) Number of anthropogenic bacteria were able to survive the harsh conditions of the Antarctic winter (inside and outside the polar station). Under certain circumstances (e.g. impaired immunity), the surviving bacteria might pose a health risk to the participants of future expeditions or to other visitors to the base. (2) The bacteria released into the outer environment might have impacts on local ecosystems. (3) New characteristics (e.g. resistance to antibiotics) may be introduced into local bacterial communities.
$pi_t$- Enhancing the Precision of Eye Tracking using Iris Feature Motion Vectors

A new high-precision eye-tracking method has been demonstrated recently by tracking the motion of iris features rather than by exploiting pupil edges. While the method provides high precision, it suffers from temporal drift, an inability to track across blinks, and loss of texture matches in the presence of motion blur. In this work, we present a new methodology $pi_t$ to address these issues by optimally combining the information from both iris textures and pupil edges. With this method, we show an improvement in precision (S2S-RMS&STD) of at least 48% and 10% respectively while fixating a series of small targets and following a smoothly moving target. Further, we demonstrate the capability in the identification of microsaccades between targets separated by 0.2-degree.
Deep learning achieves radiologist-level performance of tumor segmentation in breast MRI

Purpose: The goal of this research was to develop a deep network architecture that achieves fully-automated radiologist-level segmentation of breast tumors in MRI. Materials and Methods: We leveraged 38,229 clinical MRI breast exams collected retrospectively from women aged 12-94 (mean age 54) who presented between 2002 and 2014 at a single clinical site. The training set for the network consisted of 2,555 malignant breasts that were segmented in 2D by experienced radiologists, as well as 60,108 benign breasts that served as negative controls. The test set consisted of 250 exams with tumors segmented independently by four radiologists. We selected among several 3D deep convolutional neural network architectures, input modalities and harmonization methods. The outcome measure was the Dice score for 2D segmentation, and was compared between the network and radiologists using the Wilcoxon signed-rank test and the TOST procedure. Results: The best-performing network on the training set was a volumetric U-Net with contrast enhancement dynamic as input and with intensity normalized for each exam. In the test set the median Dice score of this network was 0.77. The performance of the network was equivalent to that of the radiologists (TOST procedure with radiologist performance of 0.69-0.84 as equivalence bounds: p = 5e-10 and p = 2e-5, respectively; N = 250) and compares favorably with published state of the art (0.6-0.77). Conclusion: When trained on a dataset of over 60 thousand breasts, a volumetric U-Net performs as well as expert radiologists at segmenting malignant breast lesions in MRI.
Learning to Attack: Towards Textual Adversarial Attacking in Real-world Situations

Adversarial attacking aims to fool deep neural networks with adversarial examples. In the field of natural language processing, various textual adversarial attack models have been proposed, varying in the accessibility to the victim model. Among them, the attack models that only require the output of the victim model are more fit for real-world situations of adversarial attacking. However, to achieve high attack performance, these models usually need to query the victim model too many times, which is neither efficient nor viable in practice. To tackle this problem, we propose a reinforcement learning based attack model, which can learn from attack history and launch attacks more efficiently. In experiments, we evaluate our model by attacking several state-of-the-art models on the benchmark datasets of multiple tasks including sentiment analysis, text classification and natural language inference. Experimental results demonstrate that our model consistently achieves both better attack performance and higher efficiency than recently proposed baseline methods. We also find our attack model can bring more robustness improvement to the victim model by adversarial training. All the code and data of this paper will be made public.
Dynamic Horizon Value Estimation for Model-based Reinforcement Learning

Existing model-based value expansion methods typically leverage a world model for value estimation with a fixed rollout horizon to assist policy learning. However, the fixed rollout with an inaccurate model has a potential to harm the learning process. In this paper, we investigate the idea of using the model knowledge for value expansion adaptively. We propose a novel method called Dynamic-horizon Model-based Value Expansion (DMVE) to adjust the world model usage with different rollout horizons. Inspired by reconstruction-based techniques that can be applied for visual data novelty detection, we utilize a world model with a reconstruction module for image feature extraction, in order to acquire more precise value estimation. The raw and the reconstructed images are both used to determine the appropriate horizon for adaptive value expansion. On several benchmark visual control tasks, experimental results show that DMVE outperforms all baselines in sample efficiency and final performance, indicating that DMVE can achieve more effective and accurate value estimation than state-of-the-art model-based methods.
Vector Projection Network for Few-shot Slot Tagging in Natural Language Understanding

Few-shot slot tagging becomes appealing for rapid domain transfer and adaptation, motivated by the tremendous development of conversational dialogue systems. In this paper, we propose a vector projection network for few-shot slot tagging, which exploits projections of contextual word embeddings on each target label vector as word-label similarities. Essentially, this approach is equivalent to a normalized linear model with an adaptive bias. The contrastive experiment demonstrates that our proposed vector projection based similarity metric can significantly surpass other variants. Specifically, in the five-shot setting on benchmarks SNIPS and NER, our method outperforms the strongest few-shot learning baseline by $6.30$ and $13.79$ points on F$_1$ score, respectively. Our code will be released at this https URL.
Variability and parsec-scale radio structure of candidate compact symmetric objects

We report results on multi-epoch Very Large Array (VLA) and pc-scale Very Long Baseline Array (VLBA) observations of candidate compact symmetric objects (CSOs) from the faint sample of high frequency peakers. New VLBA observations could resolve the radio structure in about 42 per cent of the observed sources, showing double components that may be either mini-lobes or core-jet structures. Almost all the sources monitored by the VLA show some variability on time scale of a decade, and only 1 source does not show any significant variation. In 17 sources the flux density changes randomly as it is expected in blazars, and in 4 sources the spectrum becomes flat in the last observing epoch, confirming that samples selected in the GHz regime are highly contaminated by beamed objects. In 16 objects, the pc-scale and variability properties are consistent with a young radio source in adiabatic expansion, with a steady decrease of the flux density in the optically-thin part of the spectrum, and a flux density increase in the optically-thick part. For these sources we estimate dynamical ages between a few tens to a few hundreds years. The corresponding expansion velocity is generally between 0.1c and 0.7c, similar to values found in CSOs with different approaches. The fast evolution that we observe in some CSO candidates suggests that not all the objects would become classical Fanaroff-Riley radio sources.
Distributed optimization with tunable learned priors for robust ptycho-tomography

Joint ptycho-tomography is a powerful computational imaging framework to recover the refractive properties of a 3D object while relaxing the requirements for probe overlap that is common in conventional phase retrieval. We use an augmented Lagrangian scheme for formulating the constrained optimization problem and employ an alternating direction method of multipliers (ADMM) for the joint solution. ADMM allows the problem to be split into smaller and computationally more manageable subproblems: ptychographic phase retrieval, tomographic reconstruction, and regularization of the solution. We extend our ADMM framework with plug-and-play (PnP) denoisers by replacing the regularization subproblem with a general denoising operator based on machine learning. While the PnP framework enables integrating such learned priors as denoising operators, tuning of the denoiser prior remains challenging. To overcome this challenge, we propose a tuning parameter to control the effect of the denoiser and to accelerate the solution. In our simulations, we demonstrate that our proposed framework with parameter tuning and learned priors generates high-quality reconstructions under limited and noisy measurement data.
Line Flow based SLAM

We propose a method of visual SLAM by predicting and updating line flows that represent sequential 2D projections of 3D line segments. While indirect SLAM methods using points and line segments have achieved excellent results, they still face problems in challenging scenarios such as occlusions, image blur, and repetitive textures. To deal with these problems, we leverage line flows which encode the coherence of 2D and 3D line segments in spatial and temporal domains as the sequence of all the 2D line segments corresponding to a specific 3D line segment. Thanks to the line flow representation, the corresponding 2D line segment in a new frame can be predicted based on 2D and 3D line segment motions. We create, update, merge, and discard line flows on-the-fly. We model our Line Flow-based SLAM (LF-SLAM) using a Bayesian network. We perform short-term optimization in front-end, and long-term optimization in back-end. The constraints introduced in line flows improve the performance of our LF-SLAM. Extensive experimental results demonstrate that our method achieves better performance than state-of-the-art direct and indirect SLAM approaches. Specifically, it obtains good localization and mapping results in challenging scenes with occlusions, image blur, and repetitive textures.
A Predictive Interference Management Algorithm for URLLC in Beyond 5G Networks

Interference mitigation is a major design challenge in wireless systems, especially in the context of ultra-reliable low-latency communication (URLLC) services. Conventional average-based interference management schemes are not suitable for URLLC as they do not accurately capture the tail information of the interference distribution. This letter proposes a novel interference prediction algorithm that considers the entire interference distribution instead of only the mean. The key idea is to model the interference variation as a discrete state space discrete-time Markov chain. The state transition probability matrix is used to estimate the state evolution in time, and allocate radio resources accordingly. The proposed scheme is found to meet the target reliability requirements in a low-latency single-shot transmission system considering realistic system assumptions, while requiring only ~25% more resources than the optimum case with perfect interference knowledge.
Humans learn too: Better Human-AI Interaction using Optimized Human Inputs

Humans rely more and more on systems with AI components. The AI community typically treats human inputs as a given and optimizes AI models only. This thinking is one-sided and it neglects the fact that humans can learn, too. In this work, human inputs are optimized for better interaction with an AI model while keeping the model fixed. The optimized inputs are accompanied by instructions on how to create them. They allow humans to save time and cut on errors, while keeping required changes to original inputs limited. We propose continuous and discrete optimization methods modifying samples in an iterative fashion. Our quantitative and qualitative evaluation including a human study on different hand-generated inputs shows that the generated proposals lead to lower error rates, require less effort to create and differ only modestly from the original samples.
Not Low-Resource Anymore: Aligner Ensembling, Batch Filtering, and New Datasets for Bengali-English Machine Translation

Despite being the seventh most widely spoken language in the world, Bengali has received much less attention in machine translation literature due to being low in resources. Most publicly available parallel corpora for Bengali are not large enough; and have rather poor quality, mostly because of incorrect sentence alignments resulting from erroneous sentence segmentation, and also because of a high volume of noise present in them. In this work, we build a customized sentence segmenter for Bengali and propose two novel methods for parallel corpus creation on low-resource setups: aligner ensembling and batch filtering. With the segmenter and the two methods combined, we compile a high-quality Bengali-English parallel corpus comprising of 2.75 million sentence pairs, more than 2 million of which were not available before. Training on neural models, we achieve an improvement of more than 9 BLEU score over previous approaches to Bengali-English machine translation. We also evaluate on a new test set of 1000 pairs made with extensive quality control. We release the segmenter, parallel corpus, and the evaluation set, thus elevating Bengali from its low-resource status. To the best of our knowledge, this is the first ever large scale study on Bengali-English machine translation. We believe our study will pave the way for future research on Bengali-English machine translation as well as other low-resource languages. Our data and code are available at this https URL.
Exploiting Diversity for Ultra-Reliable and Low-Latency Wireless Control

This paper introduces a wireless communication protocol for industrial control systems that uses channel quality awareness to dynamically create network-device cooperation and assist the nodes in momentary poor channel conditions. To that point, channel state information is used to identify nodes with strong and weak channel conditions. We show that strong nodes in the network are best to be served in a single-hop transmission with transmission rate adapted to their instantaneous channel conditions. Meanwhile, the remainder of time-frequency resources is used to serve the nodes with weak channel condition using a two-hop transmission with cooperative communication among all the nodes to meet the target reliability in their communication with the controller. We formulate the achievable multi-user and multi-antenna diversity gain in the low-latency regime, and propose a new scheme for exploiting those on-demand, in favor of reliability and efficiency. The proposed transmission scheme is therefore dubbed adaptive network-device cooperation (ANDCoop), since it is able to adaptively allocate cooperation resources while enjoying the multi-user diversity gain of the network. We formulate the optimization problem of associating nodes to each group and dividing resources between the two groups. Numerical solutions show significant improvement in spectral efficiency and system reliability compared to the existing schemes in the literature. System design incorporating the proposed transmission strategy can thus reduce infrastructure cost for future private wireless networks.
City-Scale Visual Place Recognition with Deep Local Features Based on Multi-Scale Ordered VLAD Pooling

Visual place recognition is the task of recognizing a place depicted in an image based on its pure visual appearance without metadata. In visual place recognition, the challenges lie upon not only the changes in lighting conditions, camera viewpoint, and scale, but also the characteristic of scene level images and the distinct features of the area. To resolve these challenges, one must consider both the local discriminativeness and the global semantic context of images. On the other hand, the diversity of the datasets is also particularly important to develop more general models and advance the progress of the field. In this paper, we present a fully-automated system for place recognition at a city-scale based on content-based image retrieval. Our main contributions to the community lie in three aspects. Firstly, we take a comprehensive analysis of visual place recognition and sketch out the unique challenges of the task compared to general image retrieval tasks. Next, we propose yet a simple pooling approach on top of convolutional neural network activations to embed the spatial information into the image representation vector. Finally, we introduce new datasets for place recognition, which are particularly essential for application-based research. Furthermore, throughout extensive experiments, various issues in both image retrieval and place recognition are analyzed and discussed to give some insights for improving the performance of retrieval models in reality.
Computer Assisted Translation with Neural Quality Estimation and Auotmatic Post-Editing

With the advent of neural machine translation, there has been a marked shift towards leveraging and consuming the machine translation results. However, the gap between machine translation systems and human translators needs to be manually closed by post-editing. In this paper, we propose an end-to-end deep learning framework of the quality estimation and automatic post-editing of the machine translation output. Our goal is to provide error correction suggestions and to further relieve the burden of human translators through an interpretable model. To imitate the behavior of human translators, we design three efficient delegation modules – quality estimation, generative post-editing, and atomic operation post-editing and construct a hierarchical model based on them. We examine this approach with the English–German dataset from WMT 2017 APE shared task and our experimental results can achieve the state-of-the-art performance. We also verify that the certified translators can significantly expedite their post-editing processing with our model in human evaluation.
From Static to Dynamic Node Embeddings

We introduce a general framework for leveraging graph stream data for temporal prediction-based applications. Our proposed framework includes novel methods for learning an appropriate graph time-series representation, modeling and weighting the temporal dependencies, and generalizing existing embedding methods for such data. While previous work on dynamic modeling and embedding has focused on representing a stream of timestamped edges using a time-series of graphs based on a specific time-scale (e.g., 1 month), we propose the notion of an $\epsilon$-graph time-series that uses a fixed number of edges for each graph, and show its superiority over the time-scale representation used in previous work. In addition, we propose a number of new temporal models based on the notion of temporal reachability graphs and weighted temporal summary graphs. These temporal models are then used to generalize existing base (static) embedding methods by enabling them to incorporate and appropriately model temporal dependencies in the data. From the 6 temporal network models investigated (for each of the 7 base embedding methods), we find that the top-3 temporal models are always those that leverage the new $\epsilon$-graph time-series representation. Furthermore, the dynamic embedding methods from the framework almost always achieve better predictive performance than existing state-of-the-art dynamic node embedding methods that are developed specifically for such temporal prediction tasks. Finally, the findings of this work are useful for designing better dynamic embedding methods.
A P ] 2 7 A ug 2 02 0 STRONG CONVERGENCE OF THE VORTICITY FOR THE 2 D EULER EQUATIONS IN THE INVISCID LIMIT

In this paper we prove that, if (u, ω) is a renormalized/Lagrangian solution of the 2D Euler equations with ω in L uniformly in time obtained as inviscid limit of solutions (u , ω) of the 2D incompressible Navier-Stokes equations, then ω converge to ω strongly in L uniformly in time. We also prove that, in the class of solutions with bounded vorticity, it is possible to obtain a rate for the convergence of ω to ω in L. Finally, in the whole space R we prove that solutions of the Euler equations with L vorticity conserve the kinetic energy. The proofs are given by using both a (stochastic) Lagrangian approach and an Eulerian approach.