pwc-archive/papers-with-abstracts huggingface dataset:
Automatic Identification and Normalisation of Physical Measurements in Scientific Literature
Authors: Luca Foppiano, Laurent Romary, Masashi Ishii, Mikiko Tanifuji
Date: 2019-09-23 00:00:00
Proceeding: Document Engineering 2019 9
Tasks: NER

We present Grobid-quantities, an open-source application for extracting and normalising measurements from scientific and patent literature. Tools of this kind, aiming to understand and make unstructured information accessible, represent the building blocks for large-scale Text and Data Mining (TDM) systems. Grobid-quantities is a module built on top of Grobid [6] [13], a machine learning framework for parsing and structuring PDF documents. Designed to process large quantities of data, it provides a robust implementation accessible in batch mode or via a REST API. The machine learning engine architecture follows the cascade approach, where each model is specialised in the resolution of a specific task. The models are trained using CRF (Conditional Random Field) algorithm [12] for extracting quantities (atomic values, intervals and lists), units (such as length, weight) and different value representations (numeric, alphabetic or scientific notation). Identified measurements are normalised according to the International System of Units (SI). Thanks to its stable recall and reliable precision, Grobid-quantities has been integrated as the measurement-extraction engine in various TDM projects, such as Marve (Measurement Context Extraction from Text), for extracting semantic measurements and meaning in Earth Science [10]. At the National Institute for Materials Science in Japan (NIMS), it is used in an ongoing project to discover new superconducting materials. Normalised materials characteristics (such as critical temperature, pressure) extracted from scientific literature are a key resource for materials informatics (MI) [9].
EchoBERT: A Transformer-Based Approach for Behavior Detection in Echograms
Authors: Håkon Måløy
Date: 2020-12-04 00:00:00
Proceeding: IEEE Access 2020 12

Monitoring fish welfare has become increasingly important for salmon farmers. Current approaches require manual labor and physical inspection or interpretation of video. Echo sounders make real-time monitoring of the entire fish population over time possible. However, current approaches for automatic interpretation of echograms mainly focus on species classification and therefore fail to appropriately encode the spatiotemporal properties contained within the data. Other approaches are primarily aimed at the feeding process and require a human-in-the-loop. Transformer-based approaches have been shown to better handle long sequences than Long Short-Term Memory networks in recent Natural Language Processing research. We therefore introduce EchoBERT - Echo Bidirectional Encoder Representation Transformer, a transformer-based approach for behavior detection in farmed Atlantic salmon (Salmo salar, Salmonidae), using the spatiotemporal properties contained in echograms. The model interprets the spatiotemporal dynamics of echograms through attention mechanisms to classify fish behavior. We compare EchoBERT to a traditional sequence modeling approach on the task of detecting behavior indicative of pancreas disease in a six-fold cross-validation study using data from 6 distinct farming cages. We show that EchoBERT shows a strong correlation between model predictions and true labels, indicated by a Matthew's Correlation Coefficient score of 0.694 ± 0.178 using an ensemble approach, compared to 0.626 ± 0.084 for traditional sequence models. We also find that EchoBERT is capable of detecting disease indicators over a month prior to detection using standard procedures. Our results show that EchoBERT has high potential for automatic behavior detection through unintrusive methods suitable for applications in aquaculture. The source code is available at: https://gitlab.com/hakonma/echobert.
Multimodal Performers for Genomic Selection and Crop Yield Prediction
Authors: Håkon Måløy, Susanne Windju, Stein Bergersen, Muath Alsheikh, Keith L. Downing
Date: 2021-10-20 00:00:00
Proceeding: Smart Agricultural Technology 2021 10
Tasks: Crop Yield Prediction

Working towards optimal crop yields is a crucial step towards securing a stable food supply for the world. To this end, approaches to model and predict crop yields can help speed up research and reduce costs. However, crop yield prediction is very challenging due to the dependencies on factors such as genotype and environmental factors. In this paper we introduce a performer-based deep learning framework for crop yield prediction using single nucleotide polymorphisms and weather data. We compare the proposed models with traditional Bayesian-based methods and traditional neural network architectures on the task of predicting barley yields across 8 different locations in Norway for the years 2017 and 2018. We show that the performer-based models significantly outperform the traditional approaches, achieving an R score of 0.820 and a root mean squared error of 69.05, compared to 0.807 and 71.63, and 0.076 and 149.78 for the best traditional neural network and traditional Bayesian approach respectively. Furthermore, we show that visualizing the self-attention maps of a Multimodal Performer network indicates that the model makes meaningful connections between genotype and weather data that can be used by the breeder to inform breeding decisions and shorten breeding cycle length. The performer-based models can also be applied to other types of genomic selection such as salmon breeding for increased Omega-3 fatty acid production or similar animal husbandry applications. The code is available at: https://github.com/haakom/pay-attention-to-genomic-selection.
Optimal Scheduling of Flexible Power-to-X Technologies in the Day-ahead Electricity Market
Authors: Neeraj Dhanraj Bokde, Tim T Pedersen, Gorm Bruun Andresen
Date: 2021-10-19 00:00:00
Tasks: Scheduling

The ambitious CO2 emission targets of the Paris agreements are achievable only with renewable energy, CO2-free power generation, new policies, and planning. The main motivation of this paper is that future green fuels from power-to-X assets should be produced from power with the lowest possible emissions while still keeping the cost of electricity low. To this end we propose a power-to-X scheduling framework that is capable of co-optimizing CO2 emission intensity and electricity prices in the day-ahead electricity market scheduling. Three realistic models for local production units are developed for flexible dispatch and the impact on electricity market scheduling is examined. Furthermore, the possible benefits of using CO2 emission intensity and electricity prices trade-off in scheduling are discussed. We find that there is a non-linear trade-off between CO2 emission intensity and cost, favoring a weighted optimization between the two objectives.
Food Odor Recognition via Multi-step Classification
Authors: Ang Xu, Tianzhang Cai, Dinghao Shen, Asher Wang
Date: 2021-10-13 00:00:00
Tasks: Classification

Predicting food labels and freshness from its odor remains a decades-old task that requires a complicated algorithm combined with high sensitivity sensors. In this paper, we initiate a multi-step classifier, which firstly clusters food into four categories, then classifies the food label concerning the predicted category, and finally identifies the freshness. We use BME688 gas sensors packed with BME AI studio for data collection and feature extraction. The normalized dataset was preprocessed with PCA and LDA. We evaluated the effectiveness of algorithms such as tree methods, MLP, and CNN through assessment indexes at each stage. We also carried out an ablation experiment to show the necessity and feasibility of the multi-step classifier. The results demonstrated the robustness and adaptability of the multi-step classifier.
Bayesian Persuasion in Sequential Trials
Authors: Shih-Tang Su, Vijay G. Subramanian, Grant Schoenebeck
Date: 2021-10-18 00:00:00
Tasks: Persuasion Strategies

We consider a Bayesian persuasion or information design problem where the sender tries to persuade the receiver to take a particular action via a sequence of signals. This we model by considering multi-phase trials with different experiments conducted based on the outcomes of prior experiments. In contrast to most of the literature, we consider the problem with constraints on signals imposed on the sender. This we achieve by fixing some of the experiments in an exogenous manner; these are called determined experiments. This modeling helps us understand real-world situations where this occurs: e.g., multi-phase drug trials where the FDA determines some of the experiments, funding of a startup by a venture capital firm, start-up acquisition by big firms where late-stage assessments are determined by the potential acquirer, multi-round job interviews where the candidates signal initially by presenting their qualifications but the rest of the screening procedures are determined by the interviewer. The non-determined experiments (signals) in the multi-phase trial are to be chosen by the sender in order to persuade the receiver best. With a binary state of the world, we start by deriving the optimal signaling policy in the only non-trivial configuration of a two-phase trial with binary-outcome experiments. We then generalize to multi-phase trials with binary-outcome experiments where the determined experiments can be placed at any chosen node in the trial tree. Here we present a dynamic programming algorithm to derive the optimal signaling policy that uses the two-phase trial solution's structural insights. We also contrast the optimal signaling policy structure with classical Bayesian persuasion strategies to highlight the impact of the signaling constraints on the sender.
Can Dynamic TDD Enabled Half-Duplex Cell-Free Massive MIMO Outperform Full-Duplex Cellular Massive MIMO?
Authors: Anubhab Chowdhury, Ribhu Chopra, Chandra R. Murthy
Date: 2021-10-19 00:00:00
Tasks: Scheduling

We consider a dynamic time division duplex (DTDD) enabled cell-free massive multiple-input multiple-output (CF-mMIMO) system, where each half-duplex (HD) access point (AP) is scheduled to operate in the uplink (UL) or downlink (DL) mode based on the data demands of the user equipments (UEs), with the goal of maximizing the sum UL-DL spectral efficiency (SE). We develop a new, low complexity, greedy algorithm for the combinatorial AP scheduling problem, with an optimality guarantee theoretically established via showing that a lower bound of the sum UL-DL SE is sub-modular. We also consider pilot sequence reuse among the UEs to limit the channel estimation overhead. In CF systems, all the APs estimate the channel from every UE, making pilot allocation problem different from the cellular case. We develop a novel algorithm that iteratively minimizes the maximum pilot contamination across the UEs. We compare the performance of our solutions, both theoretically and via simulations, against a full duplex (FD) multi-cell mMIMO system. Our results show that, due to the joint processing of the signals at the central processing unit, CF-mMIMO with dynamic HD AP-scheduling significantly outperforms cellular FD-mMIMO in terms of the sum SE and 90% likely SE. Thus, DTDD enabled HD CF-mMIMO is a promising alternative to cellular FD-mMIMO, without the cost of hardware for self-interference suppression.
Coordinated Beamforming in Quantized Massive MIMO Systems with Per-Antenna Constraints
Authors: Yunseong Cho, Jinseok Choi, Brian L. Evans
Date: 2021-10-19 00:00:00

In this work, we present a solution for coordinated beamforming for large-scale downlink (DL) communication systems with low-resolution data converters when employing a per-antenna power constraint that limits the maximum antenna power to alleviate hardware cost. To this end, we formulate and solve the antenna power minimax problem for the coarsely quantized DL system with target signal-to-interference-plus-noise ratio requirements. We show that the associated Lagrangian dual with uncertain noise covariance matrices achieves zero duality gap and that the dual solution can be used to obtain the primal DL solution. Using strong duality, we propose an iterative algorithm to determine the optimal dual solution, which is used to compute the optimal DL beamformer. We further update the noise covariance matrices using the optimal DL solution with an associated subgradient and perform projection onto the feasible domain. Through simulation, we evaluate the proposed method in maximum antenna power consumption and peak-to-average power ratio which are directly related to hardware efficiency.
Model Order Estimation for A Sum of Complex Exponentials
Authors: Raymundo Albert, Cecilia G. Galarza
Date: 2021-10-18 00:00:00

In this paper, we present a new method for estimating the number of terms in a sum of exponentially damped sinusoids embedded in noise. In particular, we propose to combine the shift-invariance property of the Hankel matrix associated with the signal with a constraint over its singular values to penalize small order estimations. With this new methodology, the algebraic and statistical structures of the Hankel matrix are considered. The new order estimation technique shows significant improvements over subspace-based methods. In particular, when a good separation between the noise and the signal subspaces is not possible, the new methodology outperforms known techniques. We evaluate the performance of our method using numerical experiments and comparing its performance with previous results found in the literature.
Spatial and color hallucinations in a mathematical model of primary visual cortex
Authors: Olivier D. Faugeras, Anna Song, Romain Veltz
Date: 2021-10-19 00:00:00
Tasks: LEMMA

We study a simplified model of the representation of colors in the primate primary cortical visual area V1. The model is described by an initial value problem related to a Hammerstein equation. The solutions to this problem represent the variation of the activity of populations of neurons in V1 as a function of space and color. The two space variables describe the spatial extent of the cortex while the two color variables describe the hue and the saturation represented at every location in the cortex. We prove the well-posedness of the initial value problem. We focus on its stationary, i.e. independent of time, and periodic in space solutions. We show that the model equation is equivariant with respect to the direct product G of the group of the Euclidean transformations of the planar lattice determined by the spatial periodicity and the group of color transformations, isomorphic to O(2), and study the equivariant bifurcations of its stationary solutions when some parameters in the model vary. Their variations may be caused by the consumption of drugs and the bifurcated solutions may represent visual hallucinations in space and color. Some of the bifurcated solutions can be determined by applying the Equivariant Branching Lemma (EBL) by determining the axial subgroups of G . These define bifurcated solutions which are invariant under the action of the corresponding axial subgroup. We compute analytically these solutions and illustrate them as color images. Using advanced methods of numerical bifurcation analysis we then explore the persistence and stability of these solutions when varying some parameters in the model. We conjecture that we can rely on the EBL to predict the existence of patterns that survive in large parameter domains but not to predict their stability. On our way we discover the existence of spatially localized stable patterns through the phenomenon of "snaking".
Set-based State Estimation with Probabilistic Consistency Guarantee under Epistemic Uncertainty
Authors: Shen Li, Theodoros Stouraitis, Michael Gienger, Sethu Vijayakumar, Julie A. Shah
Date: 2021-10-18 00:00:00
Tasks: State Estimation

Consistent state estimation is challenging, especially under the epistemic uncertainties arising from learned (nonlinear) dynamic and observation models. In this work, we propose a set-based estimation algorithm, named Gaussian Process-Zonotopic Kalman Filter (GP-ZKF), that produces zonotopic state estimates while respecting both the epistemic uncertainties in the learned models and aleatoric uncertainties. Our method guarantees probabilistic consistency, in the sense that the true states are bounded by sets (zonotopes) across all time steps, with high probability. We formally relate GP-ZKF with the corresponding stochastic approach, GP-EKF, in the case of learned (nonlinear) models. In particular, when linearization errors and aleatoric uncertainties are omitted and epistemic uncertainties are simplified, GP-ZKF reduces to GP-EKF. We empirically demonstrate our method's efficacy in both a simulated pendulum domain and a real-world robot-assisted dressing domain, where GP-ZKF produced more consistent and less conservative set-based estimates than all baseline stochastic methods.
Chunked Autoregressive GAN for Conditional Waveform Synthesis
Authors: Max Morrison, Rithesh Kumar, Kundan Kumar, Prem Seetharaman, Aaron Courville, Yoshua Bengio
Date: 2021-10-19 00:00:00
Proceeding: ICLR 2022 4
Tasks: Inductive Bias

Conditional waveform synthesis models learn a distribution of audio waveforms given conditioning such as text, mel-spectrograms, or MIDI. These systems employ deep generative models that model the waveform via either sequential (autoregressive) or parallel (non-autoregressive) sampling. Generative adversarial networks (GANs) have become a common choice for non-autoregressive waveform synthesis. However, state-of-the-art GAN-based models produce artifacts when performing mel-spectrogram inversion. In this paper, we demonstrate that these artifacts correspond with an inability for the generator to learn accurate pitch and periodicity. We show that simple pitch and periodicity conditioning is insufficient for reducing this error relative to using autoregression. We discuss the inductive bias that autoregression provides for learning the relationship between instantaneous frequency and phase, and show that this inductive bias holds even when autoregressively sampling large chunks of the waveform during each forward pass. Relative to prior state-of-the-art GAN-based models, our proposed model, Chunked Autoregressive GAN (CARGAN) reduces pitch error by 40-60%, reduces training time by 58%, maintains a fast generation speed suitable for real-time or interactive applications, and maintains or improves subjective quality.
The Cocktail Fork Problem: Three-Stem Audio Separation for Real-World Soundtracks
Authors: Darius Petermann, Gordon Wichern, Zhong-Qiu Wang, Jonathan Le Roux
Date: 2021-10-19 00:00:00
Tasks: Audio Source Separation

The cocktail party problem aims at isolating any source of interest within a complex acoustic scene, and has long inspired audio source separation research. Recent efforts have mainly focused on separating speech from noise, speech from speech, musical instruments from each other, or sound events from each other. However, separating an audio mixture (e.g., movie soundtrack) into the three broad categories of speech, music, and sound effects (understood to include ambient noise and natural sound events) has been left largely unexplored, despite a wide range of potential applications. This paper formalizes this task as the cocktail fork problem, and presents the Divide and Remaster (DnR) dataset to foster research on this topic. DnR is built from three well-established audio datasets (LibriSpeech, FMA, FSD50k), taking care to reproduce conditions similar to professionally produced content in terms of source overlap and relative loudness, and made available at CD quality. We benchmark standard source separation algorithms on DnR, and further introduce a new multi-resolution model to better address the variety of acoustic characteristics of the three source types. Our best model produces SI-SDR improvements over the mixture of 11.0 dB for music, 11.2 dB for speech, and 10.8 dB for sound effects.
Speech Enhancement Based on Cyclegan with Noise-informed Training
Authors: Wen-Yuan Ting, Syu-Siang Wang, Hsin-Li Chang, Borching Su, Yu Tsao
Date: 2021-10-19 00:00:00
Tasks: Speech Enhancement

Cycle-consistent generative adversarial networks (CycleGAN) were successfully applied to speech enhancement (SE) tasks with unpaired noisy-clean training data. The CycleGAN SE system adopted two generators and two discriminators trained with losses from noisy-to-clean and clean-to-noisy conversions. CycleGAN showed promising results for numerous SE tasks. Herein, we investigate a potential limitation of the clean-to-noisy conversion part and propose a novel noise-informed training (NIT) approach to improve the performance of the original CycleGAN SE system. The main idea of the NIT approach is to incorporate target domain information for clean-to-noisy conversion to facilitate a better training procedure. The experimental results confirmed that the proposed NIT approach improved the generalization capability of the original CycleGAN SE system with a notable margin.
Speech Enhancement-assisted Voice Conversion in Noisy Environments
Authors: Yun-Ju Chan, Chiang-Jen Peng, Syu-Siang Wang, Hsin-Min Wang, Yu Tsao, Tai-Shih Chi
Date: 2021-10-19 00:00:00
Tasks: Speech Enhancement, Voice Conversion

Numerous voice conversion (VC) techniques have been proposed for the conversion of voices among different speakers. Although good quality of the converted speech can be observed when VC is applied in a clean environment, the quality degrades drastically when the system is run in noisy conditions. In order to address this issue, we propose a novel speech enhancement (SE)-assisted VC system that utilizes the SE techniques for signal pre-processing, where the VC and SE components are optimized in an joint training strategy with the aim to provide high-quality converted speech signals. We adopt a popular model, StarGAN, as the VC component and thus call the combined system as EStarGAN. We test the proposed EStarGAN system using a Mandarin speech corpus. The experimental results first verified the effectiveness of joint training strategy used in EStarGAN. Moreover, EStarGAN demonstrated performance robustness in various unseen noisy environments. The subjective listening test results further showed that EStarGAN can improve the sound quality of speech signals converted from noise-corrupted source utterances.
Multi-Modal Pre-Training for Automated Speech Recognition
Authors: David M. Chan, Shalini Ghosh, Debmalya Chakrabarty, Björn Hoffmeister
Date: 2021-10-12 00:00:00
Tasks: Language Modeling, Language Modelling, Masked Language Modeling, Self-Supervised Learning, speech-recognition, Speech Recognition

Traditionally, research in automated speech recognition has focused on local-first encoding of audio representations to predict the spoken phonemes in an utterance. Unfortunately, approaches relying on such hyper-local information tend to be vulnerable to both local-level corruption (such as audio-frame drops, or loud noises) and global-level noise (such as environmental noise, or background noise) that has not been seen during training. In this work, we introduce a novel approach which leverages a self-supervised learning technique based on masked language modeling to compute a global, multi-modal encoding of the environment in which the utterance occurs. We then use a new deep-fusion framework to integrate this global context into a traditional ASR method, and demonstrate that the resulting method can outperform baseline methods by up to 7% on Librispeech; gains on internal datasets range from 6% (on larger models) to 45% (on smaller models).