pwc-archive/papers-with-abstracts huggingface dataset:
Distributed Machine-Learning for Early HARQ Feedback Prediction in Cloud RANs
Authors: Barış Göktepe, Cornelius Hellge, Thomas Schierl, Slawomir Stanczak
Date: 2022-02-17 00:00:00
Tasks: BIG-bench Machine Learning, Denoising

In this work, we propose novel HARQ prediction schemes for Cloud RANs (C-RANs) that use feedback over a rate-limited feedback channel (2 - 6 bits) from the Remote Radio Heads (RRHs) to predict at the User Equipment (UE) the decoding outcome at the BaseBand Unit (BBU) ahead of actual decoding. In particular, we propose a Dual Autoencoding 2-Stage Gaussian Mixture Model (DA2SGMM) that is trained in an end-to-end fashion over the whole C-RAN setup. Using realistic link-level simulations in the sub-THz band at 100 GHz, we show that the novel DA2SGMM HARQ prediction scheme clearly outperforms all other adapted and state-of-the-art schemes. The DA2SGMM shows a superior performance in terms of blockage detection as well as HARQ prediction in the no-blockage and single-blockage cases. In particular, the DA2SGMM with 4~bit feedback achieves a more than 200 % higher throughput in average compared to its best alternative. Compared to regular HARQ, the DA2SGMM reduces the maximum transmission latency by more than 72.4 %, while maintaining more than 75 % of the throughput in the no-blockage scenario. In the single-blockage scenario, DA2SGMM significantly increases the throughput for most of the evaluated Signal-to-Noise-Ratios (SNRs) compared to regular HARQ.
FM Band Channel Measurements and Modeling
Authors: Omar Ahmadien, Nann Win Moe Thet, Mehmet Kemal Ozdemir
Date: 2022-02-17 00:00:00

As FM coverage is so ubiquitous around the world, several applications can be considered to better exploit this useful band. Thus, it is of significant interest to investigate and characterize channel properties of the FM Band for the potential two-way digital wireless systems. In this paper, we present the results of field measurements at 86 MHz conducted at Gebze, Kocaeli, Turkey. Through the measurements, some of the FM channel characteristics are identified. Measurements are performed for urban, hilly terrain, and rural areas. Our results show that the FM channel expectedly has a large coverage area but at the same time, it possesses large channel excess delays. While most of COST-207 channel power delay profile models are also applicable for the FM Band, for urban environments and hilly terrain environments, channel clusters and excess delays are higher than those of COST-207 models. As 5G systems aim to utilize lower frequency bands for supplementary links, FM Band can be considered as one of the potential bands and the channel models proposed in this study can then be exploited for performance analysis.
Energy Efficiency Tradeoffs for Sub-THz Multi-User MIMO Base Station Receivers
Authors: Benjamin W. Domae, Christopher Chen, Danijela Cabric
Date: 2022-02-16 00:00:00

Sub-terahertz (sub-THz) antenna array architectures significantly impact power usage and communications capacity in multi-user multiple-input multiple-output (MU-MIMO) systems. In this work, we compare the energy efficiency and spectral efficiency of three MU-MIMO capable array architectures for base station receivers. We provide a sub-THz circuits power analysis, based on our review of state-of-the-art D-band and G-band components, and compare communications capabilities through wideband simulations. Our analysis reveals that digital arrays can provide the highest spectral efficiency and energy efficiency, due to the high power consumption of sub-THz active phase shifters or when SNR and system spectral efficiency requirements are high.
Mapping molecular complexes with Super-Resolution Microscopy and Single-Particle Analysis
Authors: Afonso Mendes, Hannah S. Heil, Simao Coelho, Christophe Leterrier, Ricardo Henriques
Date: 2022-02-17 00:00:00
Tasks: Single Particle Analysis, Specificity, Super-Resolution

Understanding the structure of supramolecular complexes provides insight into their functional capabilities and how they can be modulated in the context of disease. Super-resolution microscopy (SRM) excels in performing this task by resolving ultrastructural details at the nanoscale with molecular specificity. However, technical limitations, such as underlabelling, preclude its ability to provide complete structures. Single-particle analysis (SPA) overcomes this limitation by combining information from multiple images of identical structures and producing an averaged model, effectively enhancing the resolution and coverage of image reconstructions. This review highlights important studies using SRM-SPA, demonstrating how it broadens our knowledge by elucidating features of key biological structures with unprecedented detail.
On the ideas of the origin of eukaryotes: a critical review
Authors: Parsifal Fidelio Islas-Morales, Luis Felipe Jimenez-Garcia
Date: 2022-02-17 00:00:00
Tasks: Diversity

The origin and early evolution of eukaryotes are one of the major transitions in the evolution of life on earth. One of its most interesting aspects is the emergence of cellular organelles, their dynamics, their functions, and their divergence. Cell compartmentalization and architecture in prokaryotes is a less understood complex property. In eukaryotes it is related to cell size, specific genomic architecture, evolution of cell cycles, biogenesis of membranes and endosymbiotic processes. Explaining cell evolution through form and function demands an interdisciplinary approach focused on microbial diversity, phylogenetic and functional cell biology. Two centuries of views on eukaryotic origin have completed the disciplinary tools necessarily to answer these questions. We have moved from Haeckel SCALA NATURAE to the un-rooted tree of life. However, the major relations among cell domains are still elusive and keep the nature of eukaryotic ancestor enigmatic. Here we present a review on state of art views of eukaryogenesis; the background and perspectives of different disciplines involved in this topic
Total consensus under high reproductive-variance conditions
Authors: Hiro-Sato Niwa
Date: 2022-02-16 00:00:00
Tasks: Diversity, Vocal Bursts Intensity Prediction

Star-shaped branching patterns of genealogies are common in marine species. High-fecundity marine populations are characterized by low ratios of effective to actual population size, which reflect high variance in reproductive success among parents in mass spawns. When extreme reproduction events occur, offspring from very few parents dominate the population (whereby multiple mergers, or subsets of lineages with star-like trees, arise) and thus, the loss of genetic diversity is significant. Under high reproductive-variance conditions (assuming that reproduction occurs by sampling from the Pareto distribution), this paper explores the distribution of heterozygosity across generations. The result shows that zero heterozygosity is not achieved, implying that the populations may decline without evident loss of genetic variation. It is also found that there are singularities in the heterozygosity distributions. However, in the case of high reproductive variance, the locations of the singular points subtly deviate from those of the case where reproduction occurs by Wright-Fisher sampling.
The IWSLT 2019 Evaluation Campaign
Authors: Jan Niehues, Rolando Cattoni, Sebastian Stüker, Matteo Negri, Marco Turchi, Thanh-Le Ha, Elizabeth Salesky, Ramon Sanabria, Loic Barrault, Lucia Specia, Marcello Federico
Date: 2019-11-01 00:00:00
Proceeding: EMNLP (IWSLT) 2019 11
Tasks: Speech-to-Text, Translation

The IWSLT 2019 evaluation campaign featured three tasks: speech translation of (i) TED talks and (ii) How2 instructional videos from English into German and Portuguese, and (iii) text translation of TED talks from English into Czech. For the first two tasks we encouraged submissions of end- to-end speech-to-text systems, and for the second task participants could also use the video as additional input. We received submissions by 12 research teams. This overview provides detailed descriptions of the data and evaluation conditions of each task and reports results of the participating systems.
OPPO NMT System for IWSLT 2019
Authors: Xiaopu Li, Zhengshan Xue, Jie Hao
Date: 2019-11-01 00:00:00
Proceeding: EMNLP (IWSLT) 2019 11
Tasks: NMT, Translation

This paper illustrates the OPPO's submission for IWSLT2019 text translation task Our system is based on Transformer architecture. Besides, we also study the effect of model ensembling. On the devsets of IWSLT 2019, the BLEU of our system reaches 19.94.
The IWSLT 2019 KIT Speech Translation System
Authors: Ngoc-Quan Pham, Thai-Son Nguyen, Thanh-Le Ha, Juan Hussain, Felix Schneider, Jan Niehues, Sebastian Stüker, Alexander Waibel
Date: 2019-11-01 00:00:00
Proceeding: EMNLP (IWSLT) 2019 11
Tasks: speech-recognition, Speech Recognition, Translation

This paper describes KIT’s submission to the IWSLT 2019 Speech Translation task on two sub-tasks corresponding to two different datasets. We investigate different end-to-end architectures for the speech recognition module, including our new transformer-based architectures. Overall, our modules in the pipe-line are based on the transformer architecture which has recently achieved great results in various fields. In our systems, using transformer is also advantageous compared to traditional hybrid systems in term of simplicity while still having competent results.
ESPnet How2 Speech Translation System for IWSLT 2019: Pre-training, Knowledge Distillation, and Going Deeper
Authors: Hirofumi Inaguma, Shun Kiyono, Nelson Enrique Yalta Soplin, Jun Suzuki, Kevin Duh, Shinji Watanabe
Date: 2019-11-01 00:00:00
Proceeding: EMNLP (IWSLT) 2019 11
Tasks: All, Knowledge Distillation, NMT, Translation

This paper describes the ESPnet submissions to the How2 Speech Translation task at IWSLT2019. In this year, we mainly build our systems based on Transformer architectures in all tasks and focus on the end-to-end speech translation (E2E-ST). We first compare RNN-based models and Transformer, and then confirm Transformer models significantly and consistently outperform RNN models in all tasks and corpora. Next, we investigate pre-training of E2E-ST models with the ASR and MT tasks. On top of the pre-training, we further explore knowledge distillation from the NMT model and the deeper speech encoder, and confirm drastic improvements over the baseline model. All of our codes are publicly available in ESPnet.
End-to-end Speech Translation System Description of LIT for IWSLT 2019
Authors: Mei Tu, Wei Liu, Lijie Wang, Xiao Chen, Xue Wen
Date: 2019-11-01 00:00:00
Proceeding: EMNLP (IWSLT) 2019 11
Tasks: Data Augmentation, Decoder, Translation

This paper describes our end-to-end speech translation system for the speech translation task of lectures and TED talks from English to German for IWSLT Evaluation 2019. We propose layer-tied self-attention for end-to-end speech translation. Our method takes advantage of sharing weights of speech encoder and text decoder. The representation of source speech and the representation of target text are coordinated layer by layer, so that the speech and text can learn a better alignment during the training procedure. We also adopt data augmentation to enhance the parallel speech-text corpus. The En-De experimental results show that our best model achieves 17.68 on tst2015. Our ASR achieves WER of 6.6% on TED-LIUM test set. The En-Pt model can achieve about 11.83 on the MuST-C dev set.
Domain Adaptation of Document-Level NMT in IWSLT19
Authors: Martin Popel, Christian Federmann
Date: 2019-11-01 00:00:00
Proceeding: EMNLP (IWSLT) 2019 11
Tasks: Domain Adaptation, NMT, Sentence, Translation

We describe our four NMT systems submitted to the IWSLT19 shared task in English→Czech text-to-text translation of TED talks. The goal of this study is to understand the interactions between document-level NMT and domain adaptation. All our systems are based on the Transformer model implemented in the Tensor2Tensor framework. Two of the systems serve as baselines, which are not adapted to the TED talks domain: SENTBASE is trained on single sen- tences, DOCBASE on multi-sentence (document-level) sequences. The other two submitted systems are adapted to TED talks: SENTFINE is fine-tuned on single sentences, DOCFINE is fine-tuned on multi-sentence sequences. We present both automatic-metrics evaluation and manual analysis of the translation quality, focusing on the differences between the four systems.
Samsung and University of Edinburgh’s System for the IWSLT 2019
Authors: Joanna Wetesko, Marcin Chochowski, Pawel Przybysz, Philip Williams, Roman Grundkiewicz, Rico Sennrich, Barry Haddow, None Barone, Valerio Miceli, Alexandra Birch
Date: 2019-11-01 00:00:00
Proceeding: EMNLP (IWSLT) 2019 11
Tasks: Reranking

This paper describes the joint submission to the IWSLT 2019 English to Czech task by Samsung RD Institute, Poland, and the University of Edinburgh. Our submission was ultimately produced by combining four Transformer systems through a mixture of ensembling and reranking.
CMU’s Machine Translation System for IWSLT 2019
Authors: Tejas Srinivasan, Ramon Sanabria, Florian Metze
Date: 2019-11-01 00:00:00
Proceeding: EMNLP (IWSLT) 2019 11
Tasks: Decoder, Machine Translation, NMT, Translation

In Neural Machine Translation (NMT) the usage of sub-words and characters as source and target units offers a simple and flexible solution for translation of rare and unseen words. However, selecting the optimal subword segmentation involves a trade-off between expressiveness and flexibility, and is language and dataset-dependent. We present Block Multitask Learning (BMTL), a novel NMT architecture that predicts multiple targets of different granularities simulta- neously, removing the need to search for the optimal seg- mentation strategy. Our multi-task model exhibits improvements of up to 1.7 BLEU points on each decoder over single-task baseline models with the same number of parameters on datasets from two language pairs of IWSLT15 and one from IWSLT19. The multiple hypotheses generated at different granularities can also be combined as a post-processing step to give better translations.
Samsung’s System for the IWSLT 2019 End-to-End Speech Translation Task
Authors: Tomasz Potapczyk, Pawel Przybysz, Marcin Chochowski, Artur Szumaczuk
Date: 2019-11-01 00:00:00
Proceeding: EMNLP (IWSLT) 2019 11
Tasks: Translation

This paper describes the submission to IWSLT 2019 End- to-End speech translation task by Samsung R&D Institute, Poland. We decided to focus on end-to-end English to German TED lectures translation and did not provide any submission for other speech tasks. We used a slightly altered Transformer architecture with standard convolutional layer preparing the audio input to Transformer en- coder. Additionally, we propose an audio segmentation al- gorithm maximizing BLEU score on tst2015 test set.
KIT’s Submission to the IWSLT 2019 Shared Task on Text Translation
Authors: Felix Schneider, Alex Waibel
Date: 2019-11-01 00:00:00
Proceeding: EMNLP (IWSLT) 2019 11
Tasks: Translation

In this paper, we describe KIT’s submission for the IWSLT 2019 shared task on text translation. Our system is based on the transformer model [1] using our in-house implementation. We augment the available training data using back-translation and employ fine-tuning for the final model. For our best results, we used a 12-layer transformer-big config- uration, achieving state-of-the-art results on the WMT2018 test set. We also experiment with student-teacher models to improve performance of smaller models.
Data Augmentation for End-to-End Speech Translation: FBK@IWSLT ‘19
Authors: Mattia A. Di Gangi, Matteo Negri, Viet Nhat Nguyen, Amirhossein Tebbifakhr, Marco Turchi
Date: 2019-11-01 00:00:00
Proceeding: EMNLP (IWSLT) 2019 11
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Data Augmentation, Machine Translation, speech-recognition, Speech Recognition, Transfer Learning, Translation

This paper describes FBK’s submission to the end-to-end speech translation (ST) task at IWSLT 2019. The task consists in the “direct” translation (i.e. without intermediate discrete representation) of English speech data derived from TED Talks or lectures into German texts. Our participation had a twofold goal: i) testing our latest models, and ii) eval- uating the contribution to model training of different data augmentation techniques. On the model side, we deployed our recently proposed S-Transformer with logarithmic distance penalty, an ST-oriented adaptation of the Transformer architecture widely used in machine translation (MT). On the training side, we focused on data augmentation techniques recently proposed for ST and automatic speech recognition (ASR). In particular, we exploited augmented data in different ways and at different stages of the process. We first trained an end-to-end ASR system and used the weights of its encoder to initialize the decoder of our ST model (transfer learning). Then, we used an English-German MT system trained on large data to translate the English side of the English-French training set into German, and used this newly-created data as additional training material. Finally, we trained our models using SpecAugment, an augmentation technique that randomly masks portions of the spectrograms in order to make them different at every training epoch. Our synthetic corpus and SpecAugment resulted in an improvement of 5 BLEU points over our baseline model on the test set of MuST-C En-De, reaching the score of 22.3 with a single end-to-end system.
Analysis of Positional Encodings for Neural Machine Translation
Authors: Jan Rosendahl, Viet Anh Khoa Tran, Weiyue Wang, Hermann Ney
Date: 2019-11-01 00:00:00
Proceeding: EMNLP (IWSLT) 2019 11
Tasks: Machine Translation, Sentence, Translation

In this work we analyze and compare the behavior of the Transformer architecture when using different positional encoding methods. While absolute and relative positional encoding perform equally strong overall, we show that relative positional encoding is vastly superior (4.4% to 11.9% BLEU) when translating a sentence that is longer than any observed training sentence. We further propose and analyze variations of relative positional encoding and observe that the number of trainable parameters can be reduced without a performance loss, by using fixed encoding vectors or by removing some of the positional encoding vectors.
Generic and Specialized Word Embeddings for Multi-Domain Machine Translation
Authors: MinhQuang Pham, Josep Crego, François Yvon, Jean Senellart
Date: 2019-11-01 00:00:00
Proceeding: EMNLP (IWSLT) 2019 11
Tasks: Domain Adaptation, Machine Translation, Translation, Word Embeddings

Supervised machine translation works well when the train and test data are sampled from the same distribution. When this is not the case, adaptation techniques help ensure that the knowledge learned from out-of-domain texts generalises to in-domain sentences. We study here a related setting, multi-domain adaptation, where the number of domains is potentially large and adapting separately to each domain would waste training resources. Our proposal transposes to neural machine translation the feature expansion technique of (Daumé III, 2007): it isolates domain-agnostic from domain-specific lexical representations, while sharing the most of the network across domains. Our experiments use two architectures and two language pairs: they show that our approach, while simple and computationally inexpensive, outperforms several strong baselines and delivers a multi-domain system that successfully translates texts from diverse sources.
Lexical Micro-adaptation for Neural Machine Translation
Authors: Jitao Xu, Josep Crego, Jean Senellart
Date: 2019-11-01 00:00:00
Proceeding: EMNLP (IWSLT) 2019 11
Tasks: Machine Translation, NMT, Translation

This work is inspired by a typical machine translation industry scenario in which translators make use of in-domain data for facilitating translation of similar or repeating sentences. We introduce a generic framework applied at inference in which a subset of segment pairs are first extracted from training data according to their similarity to the input sentences. These segments are then used to dynamically update the parameters of a generic NMT network, thus performing a lexical micro-adaptation. Our approach demonstrates strong adaptation performance to new and existing datasets including pseudo in-domain data. We evaluate our approach on a heterogeneous English-French training dataset showing accuracy gains on all evaluated domains when compared to strong adaptation baselines.
Efficient Bilingual Generalization from Neural Transduction Grammar Induction
Authors: Yuchen Yan, Dekai Wu, Serkan Kumyol
Date: 2019-11-01 00:00:00
Proceeding: EMNLP (IWSLT) 2019 11
Tasks: Machine Translation, NMT, Sentence, Translation

We introduce (1) a novel neural network structure for bilingual modeling of sentence pairs that allows efficient capturing of bilingual relationship via biconstituent composition, (2) the concept of neural network biparsing, which applies to not only machine translation (MT) but also to a variety of other bilingual research areas, and (3) the concept of a biparsing-backpropagation training loop, which we hypothesize that can efficiently learn complex biparse tree patterns. Our work distinguishes from sequential attention-based models, which are more traditionally found in neural machine translation (NMT) in three aspects. First, our model enforces compositional constraints. Second, our model has a smaller search space in terms of discovering bilingual relationships from bilingual sentence pairs. Third, our model produces explicit biparse trees, which enable transparent error analysis during evaluation and external tree constraints during training.
Multi-Task Modeling of Phonographic Languages: Translating Middle Egyptian Hieroglyphs
Authors: Philipp Wiesenbach, Stefan Riezler
Date: 2019-11-01 00:00:00
Proceeding: EMNLP (IWSLT) 2019 11
Tasks: Machine Translation, Multi-Task Learning, POS, POS Tagging, speech-recognition, Speech Recognition, Translation

Machine translation of ancient languages faces a low-resource problem, caused by the limited amount of available textual source data and their translations. We present a multi-task modeling approach to translating Middle Egyptian that is inspired by recent successful approaches to multi-task learning in end-to-end speech translation. We leverage the phonographic aspect of the hieroglyphic writing system, and show that similar to multi-task learning of speech recognition and translation, joint learning and sharing of structural information between hieroglyph transcriptions, translations, and POS tagging can improve direct translation of hieroglyphs by several BLEU points, using a minimal amount of manual transcriptions.