pwc-archive/papers-with-abstracts huggingface dataset:
Deep Learning Based DOA Estimation for Hybrid Massive MIMO Receive Array with Overlapped Subarrays
Authors: YiFan Li, Baihua Shi, Feng Shu, Yaoliang Song, Jiangzhou Wang
Date: 2022-09-11 00:00:00

To improve the accuracy of direction-of-arrival (DOA) estimation, a deep learning (DL)-based method called CDAE-DNN is proposed for hybrid analog and digital (HAD) massive MIMO receive array with overlapped subarray (OSA) architecture in this paper. In the proposed method, the sample covariance matrix (SCM) is first input to a convolution denoise autoencoder (CDAE) to remove the approximation error, then the output of CDAE is imported to a fully-connected (FC) network to get the estimation result. Based on the simulation results, the proposed CDAE-DNN has great performance advantages over traditional MUSIC algorithm and CNN-based method, especially in the situations with low signal to noise ratio (SNR) and low snapshot numbers. And the OSA architecture has also been shown to significantly improve the estimation accuracy compared to non-overlapped subarray (NOSA) architecture. In addition, the Cramer-Rao lower bound (CRLB) for the HAD-OSA architecture is presented.
Hybrid Beamforming in mmWave Dual-Function Radar-Communication Systems: Models, Technologies, and Challenges
Authors: Ziyang Cheng, Linlong Wu, Bowen Wang, Bhavani Shankar, Bin Liao, Björn Ottersten
Date: 2022-09-10 00:00:00

As a promising technology in beyond-5G (B5G) and 6G, dual-function radar-communication (DFRC) aims to ensure both radar sensing and communication on a single integrated platform with unified signaling schemes. To achieve accurate sensing and reliable communication, large-scale arrays are anticipated to be implemented in such systems, which brings out the prominent issues on hardware cost and power consumption. To address these issues, hybrid beamforming (HBF), beyond its successful deployment in communication-only systems, could be a promising approach in the emerging DFRC ones. In this article, we investigate the development of the HBF techniques on the DFRC system in a self-contained manner. Specifically, we first introduce the basics of the HBF based DFRC system, where the system model and different receive modes are discussed with focus. Then we illustrate the corresponding design principles, which span from the performance metrics and optimization formulations to the design approaches and our preliminary results. Finally, potential extension and key research opportunities, such as the combination with the reconfigurable intelligent surface, are discussed concisely.
Synthesis of Sparse Linear Arrays via Low-Rank Hankel Matrix Completion
Authors: Xuejing Zhang
Date: 2022-09-10 00:00:00
Tasks: Matrix Completion

In the process of realizing the synthesis of the antenna array synthesis, it is of practical significance to arrange the nonuniform array and reduce the number of elements. According to the Matrix Pencil Method (MPM), we proposed an improved nonuniform array algorithm for reducing the number of array elements. We design the Hankel matrix by low-rank completion via log-det heuristic, and impose relatively loose constraints on the pattern, which may increase the degree of freedom of the radar array, so as to achieve the purpose of antenna array synthesis and non-uniform array.
Exemplar-Based Radio Map Reconstruction of Missing Areas Using Propagation Priority
Authors: Songyang Zhang, Tianhang Yu, Jonathan Tivald, Brian Choi, Feng Ouyang, Zhi Ding
Date: 2022-09-10 00:00:00

Radio map describes network coverage and is a practically important tool for network planning in modern wireless systems. Generally, radio strength measurements are collected to construct fine-resolution radio maps for analysis. However, certain protected areas are not accessible for measurement due to physical constraints and security considerations, leading to blanked spaces on a radio map. Non-uniformly spaced measurement and uneven observation resolution make it more difficult for radio map estimation and spectrum planning in protected areas. This work explores the distribution of radio spectrum strengths and proposes an exemplar-based approach to reconstruct missing areas on a radio map. Instead of taking generic image processing approaches, we leverage radio propagation models to determine directions of region filling and develop two different schemes to estimate the missing radio signal power. Our test results based on high-fidelity simulation demonstrate efficacy of the proposed methods for radio map reconstruction.
A semi-Markovian approach to model the tick-by-tick dynamics of stock price
Authors: Garima Agrawal, Anindya Goswami
Date: 2022-09-10 00:00:00

We model the stock price dynamics through a semi-Markov process obtained using a Poisson random measure. We establish the existence and uniqueness of the classical solution of a non-homogeneous terminal value problem and we show that the expected value of stock price at horizon can be obtained as a classical solution of a linear partial differential equation that is a special case of the terminal value problem studied in this paper. We further analyze the market making problem using the point of view of an agent who posts the limit orders at the best price available. We use the dynamic programming principle to obtain a HJB equation. In no-risk aversion case, we obtain the value function as a classical solution of a linear pde and derive the expressions for optimal controls by solving the HJB equation.
Folding Molecular Dynamics Simulations of the Transmembrane Peptides of Influenza A, B M2, and MERS-, SARS-CoV E Viral Proteins
Authors: Antonios Kolocouris, Isaiah Arkin, Nicholas M. Glykos
Date: 2022-09-12 00:00:00

Viroporins are small viral proteins that oligomerize in the membrane of host cells and induce the formation of hydrophilic pores in these membranes, thus altering the physiological properties of the host cells. Due to their significance for viral pathogenicity, they have become targets for pharmaceutical intervention, especially through compounds that block their pore-forming activity. Here we add to the growing literature concerning the structure and function of viroporins by studying and comparing -- through molecular dynamics simulations -- the folding of the transmembrane domain peptides of viroporins derived from four viruses : influenza A, influenza B, and the coronaviruses MERS-Cov-2 and SARS-CoV-2. Through a total of more than 50 {\mu}s of simulation time in explicit solvent (TFE) and with full electrostatics, we characterize the folding behavior, helical stability and helical propensity of these transmembrane peptides in their monomeric state and we identify common motifs that may reflect their quaternary organization and/or biological function. We show that the two influenza-derived peptides are significantly different in peptide sequence and secondary structure from the two coronavirus-derived peptides, and that they are organized in two structurally distinct parts : a significantly more stable N-terminal half, and a fast converting C-terminal half that continuously folds and unfolds between $\alpha$-helical structures and non-canonical structures which are mostly turns. In contrast, the two coronavirus-derived transmembrane peptides are much more stable and fast helix formers when compared with the influenza ones. We discuss possible interpretations of these findings and their putative connection to the structural characteristics of the respective viroporins.
The molecular memory code and synaptic plasticity: a synthesis
Authors: Samuel J. Gershman
Date: 2022-09-11 00:00:00

The most widely accepted view of memory in the brain holds that synapses are the storage sites of memory, and that memories are formed through associative modification of synapses. This view has been challenged on conceptual and empirical grounds. As an alternative, it has been proposed that molecules within the cell body are the storage sites of memory, and that memories are formed through biochemical operations on these molecules. This paper proposes a synthesis of these two views, grounded in a computational theory of memory. Synapses are conceived as storage sites for the parameters of an approximate posterior probability distribution over latent causes. Intracellular molecules are conceived as storage sites for the parameters of a generative model. The theory stipulates how these two components work together as part of an integrated algorithm for learning and inference.
Quantifying the attractor landscape and transition path of distributed working memory from large-scale brain network
Authors: Leijun Ye, Chunhe Li
Date: 2022-09-12 00:00:00

Many cognitive processes, including working memory, recruit multiple distributed interacting brain regions to encode information. How to understand the underlying cognition function mechanism of working memory is a challenging problem, which involves neural circuit configuration from multiple brain regions as well as stochastic transition dynamics between brain states. The energy landscape idea provides a tool to study the global stability and stochastic transition dynamics in the distributed cognitive function system. However, how to quantify the energy landscape in a realistic large-scale brain network remains unclear. Here, based on an anatomically constrained computational model of large-scale macaque cortex, we quantified the underlying multistable attractor landscape of distributed working memory. In the absence of external stimulation, the landscape exhibits three stable attractors, a spontaneous state, and two memory states. In the attractor landscape framework, the working memory function is governed by the change of landscape topography and the switch of system state according to the task requirement. The barrier height inferred from landscape topography quantifies the global stability of memory state and robustness to non-selective random fluctuations and distractor stimuli. The kinetic transition path identified by the minimum action path approach reveals that the spontaneous state serves as an intermediate state during the switch between the two memory states, the memory stored in the cortical area with higher hierarchy is more stable, and information flow follows the direction of hierarchical structure. These results provide new insights into the underlying mechanism of distributed working memory function, and the landscape and kinetic path approach can be applied to other cognitive function-related problems in brain networks.
A Theory of Visibility Measures in the Dissociation Paradigm
Authors: Thomas Schmidt, Melanie Biafora
Date: 2022-08-29 00:00:00

Research on perception without awareness primarily relies on the dissociation paradigm, which compares a measure of awareness of a critical stimulus (direct measures) with a measure indicating that the stimulus has been processed at all (indirect measure). We argue that dissociations between direct and indirect measures can only be demonstrated with respect to the critical stimulus feature that generates the indirect effect, and the observer's awareness of that feature, the critical cue. We expand Kahneman's (1968) concept of criterion content to comprise the set of all cues than an observer actually uses to perform the direct task. Different direct measures can then be compared by studying the overlap of their criterion contents and their containment of the critical cue. Because objective and subjective measures may integrate different sets of cues, one measure generally cannot replace the other without sacrificing important information. Using a simple mathematical formalization, we redefine and clarify the concepts of validity, exclusiveness, and exhaustiveness in the dissociation paradigm, show how dissociations among different awareness measures falsify simple theories of consciousness, and formulate the demand that theories of visual awareness should be sufficiently specific to explain dissociations among different facets of awareness.
Meta-CTA Trading Strategies and Rational Market Failures
Authors: Bernhard K Meister
Date: 2022-09-12 00:00:00

Investors trade shifting prices, portfolio values, and in turn their ability to borrow. Concentrated ownership, high price impact and low collateral requirements are propitious for arbitrage.
Systemic Risk of Optioned Portfolios: Controllability and Optimization
Authors: Xiaochuan Pang, Shushang Zhu, Xueting Cui, Jiali Ma
Date: 2022-09-10 00:00:00
Tasks: Portfolio Optimization

We investigate the portfolio selection problem against the systemic risk which is measured by CoVaR. We first demonstrate that the systemic risk of pure stock portfolios is essentially uncontrollable due to the contagion effect and the seesaw effect. Next, we prove that it is necessary and sufficient to introduce options to make the systemic risk controllable by the correlation hedging and the extreme loss hedging. In addition to systemic risk control, we show that using options can also enhance return-risk performance. Then, with a reasonable approximation of the conditional distribution of optioned portfolios, we show that the portfolio optimization problem can be formulated as a second-order cone program (SOCP) that allows for efficient computation. Finally, we carry out comprehensive simulations and empirical tests to illustrate the theoretical findings and the performance of our method.
Convex Risk Measures for the Aggregation of Multiple Information Sources and Applications in Insurance
Authors: Georgios I. Papayiannis, Athanasios N. Yannacopoulos
Date: 2022-09-12 00:00:00
Tasks: Management

We propose a novel class of convex risk measures, based on the concept of the Fr\'echet mean, designed in order to handle uncertainty which arises from multiple information sources regarding the risk factors of interest. The proposed risk measures robustly characterize the exposure of the firm, by filtering out appropriately the partial information available in individual sources into an aggregate model for the risk factors of interest. Importantly, the proposed risks can be expressed in closed analytic forms allowing for interesting qualitative interpretations as well as comparative statics and thus facilitate their use in the everyday risk management process of the insurance firms. The potential use of the proposed risk measures in insurance is illustrated by two concrete applications, capital risk allocation and premia calculation under uncertainty.
Building Machine Translation System for Software Product Descriptions Using Domain-specific Sub-corpora Extraction
Authors: Pintu Lohar, Sinead Madden, Edmond O’Connor, Maja Popovic, Tanya Habruseva
Date: 2022-09-01 00:00:00
Proceeding: AMTA 2022 9
Tasks: Machine Translation, Sentence, Sentence Embedding, Sentence-Embedding, Translation

Building Machine Translation systems for a specific domain requires a sufficiently large and good quality parallel corpus in that domain. However, this is a bit challenging task due to the lack of parallel data in many domains such as economics, science and technology, sports etc. In this work, we build English-to-French translation systems for software product descriptions scraped from LinkedIn website. Moreover, we developed a first-ever test parallel data set of product descriptions. We conduct experiments by building a baseline translation system trained on general domain and then domain-adapted systems using sentence-embedding based corpus filtering and domain-specific sub-corpora extraction. All the systems are tested on our newly developed data set mentioned earlier. Our experimental evaluation reveals that the domain-adapted model based on our proposed approaches outperforms the baseline.
Strategies for Adapting Multilingual Pre-training for Domain-Specific Machine Translation
Authors: Neha Verma, Kenton Murray, Kevin Duh
Date: 2022-09-01 00:00:00
Proceeding: AMTA 2022 9
Tasks: Domain Adaptation, Machine Translation, Translation

Pretrained multilingual sequence-to-sequence models have been successful in improving translation performance for mid- and lower-resourced languages. However, it is unclear if these models are helpful in the domain adaptation setting, and if so, how to best adapt them to both the domain and translation language pair. Therefore, in this work, we propose two major fine-tuning strategies: our language-first approach first learns the translation language pair via general bitext, followed by the domain via in-domain bitext, and our domain-first approach first learns the domain via multilingual in-domain bitext, followed by the language pair via language pair-specific in-domain bitext. We test our approach on 3 domains at different levels of data availability, and 5 language pairs. We find that models using an mBART initialization generally outperform those using a random Transformer initialization. This holds for languages even outside of mBART’s pretraining set, and can result in improvements of over +10 BLEU. Additionally, we find that via our domain-first approach, fine-tuning across multilingual in-domain corpora can lead to stark improvements in domain adaptation without sourcing additional out-of-domain bitext. In larger domain availability settings, our domain-first approach can be competitive with our language-first approach, even when using over 50X less data.
Prefix Embeddings for In-context Machine Translation
Authors: Suzanna Sia, Kevin Duh
Date: 2022-09-01 00:00:00
Proceeding: AMTA 2022 9
Tasks: Language Modeling, Language Modelling, Large Language Model, Machine Translation, Sentence, Translation

Very large language models have been shown to translate with few-shot in-context examples. However, they have not achieved state-of-art results for translating out of English. In this work, we investigate an extremely lightweight fixed-parameter method for conditioning a large language model to better translate into the target language. Our method introduces additional embeddings, known as prefix embeddings which do not interfere with the existing weights of the model. Using unsupervised and weakly semi-supervised methods that train only 0.0001% of the model parameters, the simple method improves ~0.2-1.3 BLEU points across 3 domains and 3 languages. We analyze the resulting embeddings’ training dynamics, and where they lie in the embedding space, and show that our trained embeddings can be used for both in-context translation, and diverse generation of the target sentence.
Language Tokens: Simply Improving Zero-Shot Multi-Aligned Translation in Encoder-Decoder Models
Authors: Muhammad N ElNokrashy, Amr Hendy, Mohamed Maher, Mohamed Afify, Hany Hassan
Date: 2022-09-01 00:00:00
Proceeding: AMTA 2022 9
Tasks: Decoder, Translation

This paper proposes a simple and effective method to improve direct translation for the zero-shot case and when direct data is available. We modify the input tokens at both the encoder and decoder to include signals for the source and target languages. We show a performance gain when training from scratch, or finetuning a pretrained model with the proposed setup. In in-house experiments, our method shows nearly a 10.0 BLEU points difference depending on the stoppage criteria. In a WMT-based setting, we see 1.3 and 0.4 BLEU points improvement for the zero-shot setting, and when using direct data for training, respectively, while from-English performance improves by 4.17 and 0.85 BLEU points. In the low-resource setting, we see a 1.5 ∼ 1.7 point improvement when finetuning on directly translated domain data.
Low Resource Chat Translation: A Benchmark for Hindi–English Language Pair
Authors: Baban Gain, Ramakrishna Appicharla, Asif Ekbal, Muthusamy Chelliah, Soumya Chennabasavraj, Nikesh Garera
Date: 2022-09-01 00:00:00
Proceeding: AMTA 2022 9
Tasks: Domain Adaptation, Machine Translation, Sentence, Transfer Learning, Translation

Chatbots are used in various sectors such as banking, healthcare, e-commerce, etc, and are mainly available in English. Machine Translation (MT) could be an effective way to develop multilingual chatbots. This paper provides a benchmark setup for Chat and QnA translation for English-Hindi, a relatively low-resource language pair. We create the English-Hindi parallel corpus consisting of both synthetic and gold standard parallel sentences from WMT20 Chat, MMD, and Flipkart QA corpora. We conduct experiments on Transfer Learning, Domain Adaptation techniques at sentence-level and context-level. We achieve BLEU scores of 57.6 and 55.5 on the En-Hi and Hi-En subsets of WMT20 Chat corpus, 51.5 and 80.4 BLEU scores on En-Hi and Hi-En subsets of MMD corpus, and 50.6 and 46.3 BLEU scores on question and answer subsets of QA corpus respectively. We also perform thorough quality tests by a very well-known e-commerce industry by deploying them to real users.
On the Effectiveness of Quasi Character-Level Models for Machine Translation
Authors: Salvador Carrión-Ponz, Francisco Casacuberta
Date: 2022-09-01 00:00:00
Proceeding: AMTA 2022 9
Tasks: Machine Translation, NMT, Translation

Neural Machine Translation (NMT) models often use subword-level vocabularies to deal with rare or unknown words. Although some studies have shown the effectiveness of purely character-based models, these approaches have resulted in highly expensive models in computational terms. In this work, we explore the benefits of quasi-character-level models for very low-resource languages and their ability to mitigate the effects of the catastrophic forgetting problem. First, we conduct an empirical study on the efficacy of these models, as a function of the vocabulary and training set size, for a range of languages, domains, and architectures. Next, we study the ability of these models to mitigate the effects of catastrophic forgetting in machine translation. Our work suggests that quasi-character-level models have practically the same generalization capabilities as character-based models but at lower computational costs. Furthermore, they appear to help achieve greater consistency between domains than standard subword-level models, although the catastrophic forgetting problem is not mitigated.
Improving Translation of Out Of Vocabulary Words using Bilingual Lexicon Induction in Low-Resource Machine Translation
Authors: Jonas Waldendorf, Alexandra Birch, Barry Hadow, Antonio Valerio Micele Barone
Date: 2022-09-01 00:00:00
Proceeding: AMTA 2022 9
Tasks: Bilingual Lexicon Induction, Data Augmentation, Domain Adaptation, Machine Translation, Translation, Word Embeddings, Word Translation

Dictionary-based data augmentation techniques have been used in the field of domain adaptation to learn words that do not appear in the parallel training data of a machine translation model. These techniques strive to learn correct translations of these words by generating a synthetic corpus from in-domain monolingual data utilising a dictionary obtained from bilingual lexicon induction. This paper applies these techniques to low resource machine translation, where there is often a shift in distribution of content between the parallel data and any monolingual data. English-Pashto machine learning systems are trained using a novel approach that introduces monolingual data to existing joint learning techniques for bilingual word embeddings, combined with word-for-word back-translation to improve the translation of words that do not or rarely appear in the parallel training data. Improvements are made both in terms of BLEU, chrF and word translation accuracy for an En->Ps model, compared to a baseline and when combined with back-translation.