pwc-archive/papers-with-abstracts huggingface dataset:
Context-Dependent Domain Adversarial Neural Network for Multimodal Emotion Recognition
Authors: Zheng Lian, JianHua Tao, Bin Liu, Jian Huang, Zhanlei Yang, Rongjun Li
Date: 2020-10-28 00:00:00
Proceeding: Interspeech 2020 10
Tasks: Emotion Recognition, Multimodal Emotion Recognition, Speech Emotion Recognition

Emotion recognition remains a complex task due to speaker variations and low-resource training samples. To address these difficulties, we focus on the domain adversarial neural networks (DANN) for emotion recognition. The primary task is to predict emotion labels. The secondary task is to learn a common representation where speaker identities can not be distinguished. By using this approach, we bring the representations of different speakers closer. Meanwhile, through using the unlabeled data in the training process, we alleviate the impact of low-resource training samples. In the meantime, prior work found that contextual information and multimodal features are important for emotion recognition. However, previous DANN-based approaches ignore this information, thus limiting their performance. In this paper, we propose the context-dependent domain adversarial neural network for multimodal emotion recognition. To verify the effectiveness of our proposed method, we conduct experiments on the benchmark dataset IEMOCAP. Experimental results demonstrate that the proposed method shows an absolute improvement of 3.48% over state-of-the-art strategies.
Speech Emotion Recognition Using Speech Feature and Word Embedding
Authors: Bagus Tris Atmaja, Kiyoaki Shirai, and Masato Akagi
Date: 2019-11-18 00:00:00
Proceeding: APSIPA ASC 2019 11
Tasks: Emotion Recognition, Speech Emotion Recognition

—Emotion recognition can be performed automatically from many modalities. This paper presents a categorical speech emotion recognition using speech features and word embedding. Text features can be combined with speech features to improve emotion recognition accuracy, and both features can be obtained from speech. Here, we use speech segments, by removing silences in an utterance, where the acoustic feature is extracted for speech-based emotion recognition. Word embedding is used as an input feature for text emotion recognition and a combination of both features is proposed for performance improvement purposes. Two unidirectional LSTM layers are used for text and fully connected layers are applied for acoustic emotion recognition. Both networks then are merged by fully connected networks in an early fusion way to produce one of four predicted emotion categories. The result shows the combination of speech and text achieve higher accuracy i.e. 75.49% compared to speech only with 58.29% or text only emotion recognition with 68.01%. This result also outperforms the previously proposed methods by others using the same dataset on the same modalities
Training on Polar Image Transformations Improves Biomedical Image Segmentation
Authors: Marin Benčević, Irena Galić, Marija Habijan, Danilo Babin
Date: 2021-09-29 00:00:00
Proceeding: IEEE Access 2021 9
Tasks: Image Segmentation, Lesion Segmentation, Liver Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation, Skin Cancer Segmentation

A key step in medical image-based diagnosis is image segmentation. A common use case for medical image segmentation is the identification of single structures of an elliptical shape. Most organs like the heart and kidneys fall into this category, as well as skin lesions, polyps, and other types of abnormalities. Neural networks have dramatically improved medical image segmentation results, but still require large amounts of training data and long training times to converge. In this paper, we propose a general way to improve neural network segmentation performance and data efficiency on medical imaging segmentation tasks where the goal is to segment a single roughly elliptically distributed object. We propose training a neural network on polar transformations of the original dataset, such that the polar origin for the transformation is the center point of the object. This results in a reduction of dimensionality as well as a separation of segmentation and localization tasks, allowing the network to more easily converge. Additionally, we propose two different approaches to obtaining an optimal polar origin: (1) estimation via a segmentation trained on non-polar images and (2) estimation via a model trained to predict the optimal origin. We evaluate our method on the tasks of liver, polyp, skin lesion, and epicardial adipose tissue segmentation. We show that our method produces state-of-the-art results for lesion, liver, and polyp segmentation and performs better than most common neural network architectures for biomedical image segmentation. Additionally, when used as a pre-processing step, our method generally improves data efficiency across datasets and neural network architectures.
Findings of the 2020 Conference on Machine Translation (WMT20)
Authors: Loïc Barrault, Magdalena Biesialska, Ondřej Bojar, Marta R. Costa-jussà, Christian Federmann, Yvette Graham, Roman Grundkiewicz, Barry Haddow, Matthias Huck, Eric Joanis, Tom Kocmi, Philipp Koehn, Chi-kiu Lo, Nikola Ljubešić, Christof Monz, Makoto Morishita, Masaaki Nagata, Toshiaki Nakazawa, Santanu Pal, Matt Post, Marcos Zampieri
Date: 2020-11-19 00:00:00
Proceeding: EMNLP 2020 11
Tasks: Machine Translation, Translation

This paper presents the results of the news translation task and the similar language translation task, both organised alongside the Conference on Machine Translation (WMT) 2020. In the news task, participants were asked to build machine translation systems for any of 11 language pairs, to be evaluated on test sets consisting mainly of news stories. The task was also opened up to additional test suites to probe specific aspects of translation. In the similar language translation task, participants built machine translation systems for translating between closely related pairs of languages.
Derivative Extrapolation Using Least Squares
Authors: Nick Butler, Ian Faber, Boris Shapoval, Armen Davis, Peter Borrell, Jake Mcgrath
Date: 2021-09-27 00:00:00

Here, we present three methods for differentiating discrete sets from streaming processes, e.g. WIFI. One approach is based on optimization of the well-known Savitzky-Golay algorithm. These methods are tested on synthetic data sets and will be implemented on subsets of a real university campus WIFI data set. The applicability of all methods are discussed, where we provide insights on both some of their benefits and pitfalls. This article ends with our conclusion on which method is better for our WIFI data.
Manifold learning-supported estimation of relative transfer functions for spatial filtering
Authors: Andreas Brendel, Johannes Zeitler, Walter Kellermann
Date: 2021-10-05 00:00:00
Tasks: Audio Signal Processing

Many spatial filtering algorithms used for voice capture in, e.g., teleconferencing applications, can benefit from or even rely on knowledge of Relative Transfer Functions (RTFs). Accordingly, many RTF estimators have been proposed which, however, suffer from performance degradation under acoustically adverse conditions or need prior knowledge on the properties of the interfering sources. While state-of-the-art RTF estimators ignore prior knowledge about the acoustic enclosure, audio signal processing algorithms for teleconferencing equipment are often operating in the same or at least a similar acoustic enclosure, e.g., a car or an office, such that training data can be collected. In this contribution, we use such data to train Variational Autoencoders (VAEs) in an unsupervised manner and apply the trained VAEs to enhance imprecise RTF estimates. Furthermore, a hybrid between classic RTF estimation and the trained VAE is investigated. Comprehensive experiments with real-world data confirm the efficacy for the proposed method.
Detection of blue whale vocalisations using a temporal-domain convolutional neural network
Authors: Bryan Sagredo, Sonia Español-Jiménez, Felipe Tobar
Date: 2021-10-05 00:00:00
Tasks: Denoising

We present a framework for detecting blue whale vocalisations from acoustic submarine recordings. The proposed methodology comprises three stages: i) a preprocessing step where the audio recordings are conditioned through normalisation, filtering, and denoising; ii) a label-propagation mechanism to ensure the consistency of the annotations of the whale vocalisations, and iii) a convolutional neural network that receives audio samples. Based on 34 real-world submarine recordings (28 for training and 6 for testing) we obtained promising performance indicators including an Accuracy of 85.4\% and a Recall of 93.5\%. Furthermore, even for the cases where our detector did not match the ground-truth labels, a visual inspection validates the ability of our approach to detect possible parts of whale calls unlabelled as such due to not being complete calls.
Late reverberation suppression using U-nets
Authors: Diego León, Felipe Tobar
Date: 2021-10-05 00:00:00
Tasks: Decoder, Speech Dereverberation, speech-recognition, Speech Recognition, Speech-to-Text

In real-world settings, speech signals are almost always affected by reverberation produced by the working environment; these corrupted signals need to be \emph{dereverberated} prior to performing, e.g., speech recognition, speech-to-text conversion, compression, or general audio enhancement. In this paper, we propose a supervised dereverberation technique using \emph{U-nets with skip connections}, which are fully-convolutional encoder-decoder networks with layers arranged in the form of an "U" and connections that "skip" some layers. Building on this architecture, we address speech dereverberation through the lens of Late Reverberation Suppression (LS). Via experiments on synthetic and real-world data with different noise levels and reverberation settings, we show that our proposed method termed "LS U-net" improves quality, intelligibility and other performance metrics compared to the original U-net method and it is on par with the state-of-the-art GAN-based approaches.
Deep Optimization of Parametric IIR Filters for Audio Equalization
Authors: Giovanni Pepe, Leonardo Gabrielli, Stefano Squartini, Carlo Tripodi, Nicolò Strozzi
Date: 2021-10-05 00:00:00

This paper describes a novel Deep Learning method for the design of IIR parametric filters for automatic audio equalization. A simple and effective neural architecture, named BiasNet, is proposed to determine the IIR equalizer parameters. An output denormalization technique is used to obtain accurate tuning of the IIR filters center frequency, quality factor and gain. All layers involved in the proposed method are shown to be differentiable, allowing backpropagation to optimize the network weights and achieve, after a number of training iterations, the optimal output. The parameters are optimized with respect to a loss function based on a spectral distance between the measured and desired magnitude response, and a regularization term used to achieve a spatialization of the acoustc scene. Two scenarios with different characteristics were considered for the experimental evaluation: a room and a car cabin. The performance of the proposed method improves over the baseline techniques and achieves an almost flat band. Moreover IIR filters provide a consistently lower computational cost during runtime with respect to FIR filters.
DNSMOS P.835: A Non-Intrusive Perceptual Objective Speech Quality Metric to Evaluate Noise Suppressors
Authors: Chandan K A Reddy, Vishak Gopal, Ross Cutler
Date: 2021-10-05 00:00:00

Human subjective evaluation is the gold standard to evaluate speech quality optimized for human perception. Perceptual objective metrics serve as a proxy for subjective scores. We have recently developed a non-intrusive speech quality metric called Deep Noise Suppression Mean Opinion Score (DNSMOS) using the scores from ITU-T Rec. P.808 subjective evaluation. The P.808 scores reflect the overall quality of the audio clip. ITU-T Rec. P.835 subjective evaluation framework gives the standalone quality scores of speech and background noise in addition to the overall quality. In this work, we train an objective metric based on P.835 human ratings that outputs 3 scores: i) speech quality (SIG), ii) background noise quality (BAK), and iii) the overall quality (OVRL) of the audio. The developed metric is highly correlated with human ratings, with a Pearson's Correlation Coefficient (PCC)=0.94 for SIG and PCC=0.98 for BAK and OVRL. This is the first non-intrusive P.835 predictor we are aware of. DNSMOS P.835 is made publicly available as an Azure service.
Operation-Adversarial Scenario Generation
Authors: Zhirui Liang, Robert Mieth, Yury Dvorkin
Date: 2021-10-05 00:00:00
Tasks: Generative Adversarial Network

This paper proposes a modified conditional generative adversarial network (cGAN) model to generate net load scenarios for power systems that are statistically credible, conditioned by given labels (e.g., seasons), and, at the same time, "stressful" to the system operations and dispatch decisions. The measure of stress used in this paper is based on the operating cost increases due to net load changes. The proposed operation-adversarial cGAN (OA-cGAN) internalizes a DC optimal power flow model and seeks to maximize the operating cost and achieve a worst-case data generation. The training and testing stages employed in the proposed OA-cGAN use historical day-ahead net load forecast errors and has been implemented for the realistic NYISO 11-zone system. Our numerical experiments demonstrate that the generated operation-adversarial forecast errors lead to more cost-effective and reliable dispatch decisions.
Connect Everywhere: Wireless Connectivity in Protected Areas
Authors: Thembelihle Dlamini, Mengistu Abera Mulatu, Sifiso Vilakati
Date: 2021-10-05 00:00:00
Tasks: Management

Mobile connectivity has become more important, especially for visitors to parks and protected areas. However, governments' policies prohibit cable wiring in these areas in order to preserve the beauty and protect the historical interest of the landscape. Through observed practices from other countries, mobile network operators and other licensed service providers can cooperate with the administrators of such protected areas in order to provide mobile connectivity without disturbing the environment. However, the most pervasive problem is the high energy consumption of the wireless systems and it becomes expensive to power them using the electricity grid. One attractive solution is to make use of green energy to power the communication systems and then share the base station (BS) infrastructure that is co-located with the edge server, an entity responsible for computing the offloaded delay-sensitive workloads. To alleviate this problem, this paper offers a resource management solution that seeks to minimize the energy consumption per communication site through (i) BS infrastructure and resource sharing, and (ii) assisted (peer-to-peer) computation offloading for the energy-constrained communication sites, within a protected area. Using this resource management strategy guarantees a Quality of Service (QoS). The performance evaluation conducted through simulations validates our analysis as the prediction variations observed shows greater accuracy between the harvested energy and the traffic load. Towards energy savings, the proposed algorithm achieves a 52 % energy savings when compared with the 46 % obtained by our benchmark algorithm. The energy savings that can be achieved decreases as the QoS is prioritized, within each communication site, and when the number of active computing resources increases.
BeamSurfer: Minimalist Beam Management of Mobile mm-Wave Devices
Authors: Santosh Ganji, Tzu-Hsiang Lin, Francisco A. Espinal, P. R. Kumar
Date: 2021-10-04 00:00:00
Tasks: Management

Management of narrow directional beams is critical for mm-wave communication systems. Translational or rotational motion of the user can cause misalignment of transmit and receive beams with the base station losing track of the mobile. Reacquiring the user can take about one second in 5G NewRadio systems and significantly impair performance of applications, besides being energy intensive. It is therefore important to manage beams to continually maintain high received signal strength and prevent outages. It is also important to be able to recover from sudden but transient blockage caused by a handor face interposed in the Line-of-Sight (LoS) path. This work presents a beam management protocol called BeamSurfer that is targeted to the use case of users walking indoors near a base station. It is designed to be minimalistic, employing only in-band information, and not requiring knowledge such as location or orientation of the mobile device or any additional sensors. Evaluations under pedestrian mobility show that96%of the time it employs beams that are within3dB of what an omniscient Oracle would have chosen as the pair of optimal transmit-receive LoS beams. It also recovers from transient LoS blockage by continuing to maintain control packet communication over a pre-selected reflected path that preserves time-synchronization with the base station, which allows it to rapidly recover to the LoS link as soon as it is unblocked
Feedback control of social distancing for COVID-19 via elementary formulae
Authors: Michel Fliess, Cédric Join, Alberto D'Onofrio
Date: 2021-10-04 00:00:00

Social distancing has been enacted in order to mitigate the spread of COVID-19. Like many authors, we adopt the classic epidemic SIR model, where the infection rate is the control variable. Its differential flatness property yields ele mentary closed-form formulae for open-loop social distancing scenarios, where, for instance, the increase of the number of uninfected people may be taken into account. Those formulae might therefore be useful to decision makers. A feedback loop stemming from model-free control leads to a remarkable robustness with respect to severe uncertainties and mismatches. Although an identification procedure is presented, a good knowledge of the recovery rate is not necessary for our control strategy.
Epidemiological parameter sensitivity in Covid-19 dynamics and estimation
Authors: Jyoti Bhadana, R. K. Brojen Singh
Date: 2021-10-01 00:00:00
Tasks: Sensitivity

Covid-19 is one of the most dreaded pandemics/epidemics in the world threatening the human population. The dynamics of this pandemic is quite complicated and prediction of pandemic states often fails. In this work, we study and correlate the SIR epidemiological model with the ongoing pandemic and found that pandemic dynamics and states are quite sensitively dependent on model parameters. The analysis of the exact parametric solution of the deterministic SIR model shows that the fixed points ($g^*$) depend on the SIR parameters, where, if $g^*\langle\frac{\gamma}{\beta x_0}$ then $g^*$ is stable and the pandemic can be controlled, whereas, if $g^*\rangle\frac{\gamma}{\beta x_0}$ then $g^*$ is unstable indicating active pandemic state, and $g^*=0$ corresponds to an endemic state. The dynamics show asymptotic stability bifurcation. The analytical solution of the stochastic SIR model allows to predict endemic time $t_E\propto\frac{1}{\gamma}$ and mean infected population becomes constant for small values of $\gamma$. The noise in the system even can modulate pandemic dynamics. Hence, we estimated the values of these parameters during lockdown periods using the ABC SSA algorithm and found strong dependence on control strategy, namely, lockdown and follows power-law behaviors. The numerical results show strong agreement with the real data and have the positive effect of social distancing or lockdown in the pandemic control.
Mining for Potent Inhibitors through Artificial Intelligence and Physics: A Unified Methodology for Ligand Based and Structure Based Drug Design
Authors: Jie Li, Oufan Zhang, Yingze Wang, Kunyang Sun, Xingyi Guan, Dorian Bagni, Mojtaba Haghighatlari, Fiona L. Kearns, Conor Parks, Rommie E. Amaro, Teresa Head-Gordon
Date: 2021-10-05 00:00:00
Tasks: Deep Reinforcement Learning, Drug Design, Drug Discovery, Molecular Docking, Reinforcement Learning (RL)

The viability of a new drug molecule is a time and resource intensive task that makes computer-aided assessments a vital approach to rapid drug discovery. Here we develop a machine learning algorithm, iMiner, that generates novel inhibitor molecules for target proteins by combining deep reinforcement learning with real-time 3D molecular docking using AutoDock Vina, thereby simultaneously creating chemical novelty while constraining molecules for shape and molecular compatibility with target active sites. Moreover, through the use of various types of reward functions, we can generate new molecules that are chemically similar to a target ligand, which can be grown from known protein bound fragments, as well as to create molecules that enforce interactions with target residues in the protein active site. The iMiner algorithm is embedded in a composite workflow that filters out Pan-assay interference compounds, Lipinski rule violations, and poor synthetic accessibility, with options for cross-validation against other docking scoring functions and automation of a molecular dynamics simulation to measure pose stability. Because our approach only relies on the structure of the target protein, iMiner can be easily adapted for future development of other inhibitors or small molecule therapeutics of any target protein.
A New Multivariate Predictive Model for Stock Returns
Authors: Jianying Xie
Date: 2021-10-05 00:00:00
Tasks: model

One of the most important studies in finance is to find out whether stock returns could be predicted. This research aims to create a new multivariate model, which includes dividend yield, earnings-to-price ratio, book-to-market ratio as well as consumption-wealth ratio as explanatory variables, for future stock returns predictions. The new multivariate model will be assessed for its forecasting performance using empirical analysis. The empirical analysis is performed on S&P500 quarterly data from Quarter 1, 1952 to Quarter 4, 2019 as well as S&P500 monthly data from Month 12, 1920 to Month 12, 2019. Results have shown this new multivariate model has predictability for future stock returns. When compared to other benchmark models, the new multivariate model performs the best in terms of the Root Mean Squared Error (RMSE) most of the time.
Bayesian autoregressive spectral estimation
Authors: Alejandro Cuevas, Sebastián López, Danilo Mandic, Felipe Tobar
Date: 2021-10-05 00:00:00
Tasks: Time Series, Time Series Analysis

Autoregressive (AR) time series models are widely used in parametric spectral estimation (SE), where the power spectral density (PSD) of the time series is approximated by that of the \emph{best-fit} AR model, which is available in closed form. Since AR parameters are usually found via maximum-likelihood, least squares or the method of moments, AR-based SE fails to account for the uncertainty of the approximate PSD, and thus only yields point estimates. We propose to handle the uncertainty related to the AR approximation by finding the full posterior distribution of the AR parameters to then propagate this uncertainty to the PSD approximation by \emph{integrating out the AR parameters}; we implement this concept by assuming two different priors over the model noise. Through practical experiments, we show that the proposed Bayesian autoregressive spectral estimation (BASE) provides point estimates that follow closely those of standard autoregressive spectral estimation (ASE), while also providing error bars. BASE is validated against ASE and the Periodogram on both synthetic and real-world signals.