pwc-archive/papers-with-abstracts huggingface dataset:
Dynamics of a Binary Option Market with Exogenous Information and Price Sensitivity
Authors: Hannah Gampe, Christopher Griffin
Date: 2022-05-18 00:00:00
Tasks: Sensitivity

In this paper, we derive and analyze a continuous of a binary option market with exogenous information. The resulting non-linear system has a discontinuous right hand side, which can be analyzed using zero-dimensional Filippov surfaces. Under general assumptions on purchasing rules, we show that when exogenous information is constant in the binary asset market, the price always converges. We then investigate market prices in the case of changing information, showing empirically that price sensitivity has a strong effect on price lag vs. information. We conclude with open questions on general $n$-ary option markets. As a by-product of the analysis, we show that these markets are equivalent to a simple recurrent neural network, helping to explain some of the predictive power associated with prediction markets, which are usually designed as $n$-ary option markets.
Multi-scale Analysis of Nitrogen Loss Mitigation in the US Corn Belt
Authors: Jing Liu, Laura Bowling, Christopher Kucharik, Sadia Jame, Uris Baldos, Larissa Jarvis, Navin Ramankutty, Thomas Hertel
Date: 2022-06-15 00:00:00
Tasks: Management

Reducing the size of the hypoxic zone in the Gulf of Mexico has proven to be a challenging task. A variety of mitigation options have been proposed, each likely to produce markedly different patterns of mitigation with widely varying consequences for the economy. The general consensus is that no single measure alone is sufficient to achieve the EPA Task Force goal for reducing the Gulf hypoxic zone and it appears that a combination of management practices must be employed. However, absent a highly resolved, multi-scale framework for assessing these policy combinations, it has been unclear what pattern of mitigation is likely to emerge from different policies and what the consequences would be for local, regional and national land use, food prices and farm returns. We address this research gap by utilizing a novel multi-scale framework for evaluating alternative N loss management policies in the Mississippi River basin. This combines fine-scale agro-ecosystem responses with an economic model capturing domestic and international market and price linkages. We find that wetland restoration combined with improved N use efficiency, along with a leaching tax could reduce the Mississippi River N load by 30-53\% while only modestly increasing corn prices. This study underscores the value of fine-resolution analysis and the potential of combined economic and ecological instruments in tackling nonpoint source nitrate pollution.
The trickle down from environmental innovation to productive complexity
Authors: Francesco de Cunzo, Alberto Petri, Andrea Zaccaria, Angelica Sbardella
Date: 2022-06-15 00:00:00

We study the empirical relationship between green technologies and industrial production at very fine-grained levels by employing Economic Complexity techniques. Firstly, we use patent data on green technology domains as a proxy for competitive green innovation and data on exported products as a proxy for competitive industrial production. Secondly, with the aim of observing how green technological development trickles down into industrial production, we build a bipartite directed network linking single green technologies at time $t_1$ to single products at time $t_2 \ge t_1$ on the basis of their time-lagged co-occurrences in the technological and industrial specialization profiles of countries. Thirdly we filter the links in the network by employing a maximum entropy null-model. In particular, we find that the industrial sectors most connected to green technologies are related to the processing of raw materials, which we know to be crucial for the development of clean energy innovations. Furthermore, by looking at the evolution of the network over time, we observe that more complex green technological know-how requires more time to be transmitted to industrial production, and is also linked to more complex products.
Learnable Frequency Filters for Speech Feature Extraction in Speaker Verification
Authors: Jingyu Li, Yusheng Tian, Tan Lee
Date: 2022-06-15 00:00:00
Tasks: Speaker Verification

Mel-scale spectrum features are used in various recognition and classification tasks on speech signals. There is no reason to expect that these features are optimal for all different tasks, including speaker verification (SV). This paper describes a learnable front-end feature extraction model. The model comprises a group of filters to transform the Fourier spectrum. Model parameters that define these filters are trained end-to-end and optimized specifically for the task of speaker verification. Compared to the standard Mel-scale filter-bank, the filters' bandwidths and center frequencies are adjustable. Experimental results show that applying the learnable acoustic front-end improves speaker verification performance over conventional Mel-scale spectrum features. Analysis on the learned filter parameters suggests that narrow-band information benefits the SV system performance. The proposed model achieves a good balance between performance and computation cost. In resource-constrained computation settings, the model significantly outperforms CNN-based learnable front-ends. The generalization ability of the proposed model is also demonstrated on different embedding extraction models and datasets.
EDITnet: A Lightweight Network for Unsupervised Domain Adaptation in Speaker Verification
Authors: Jingyu Li, Wei Liu, Tan Lee
Date: 2022-06-15 00:00:00
Tasks: Domain Adaptation, Self-Supervised Learning, Speaker Verification, Unsupervised Domain Adaptation

Performance degradation caused by language mismatch is a common problem when applying a speaker verification system on speech data in different languages. This paper proposes a domain transfer network, named EDITnet, to alleviate the language-mismatch problem on speaker embeddings without requiring speaker labels. The network leverages a conditional variational auto-encoder to transfer embeddings from the target domain into the source domain. A self-supervised learning strategy is imposed on the transferred embeddings so as to increase the cosine distance between embeddings from different speakers. In the training process of the EDITnet, the embedding extraction model is fixed without fine-tuning, which renders the training efficient and low-cost. Experiments on Voxceleb and CN-Celeb show that the embeddings transferred by EDITnet outperform the un-transferred ones by around 30% with the ECAPA-TDNN512. Performance improvement can also be achieved with other embedding extraction models, e.g., TDNN, SE-ResNet34.
The ZevoMOS entry to VoiceMOS Challenge 2022
Authors: Adriana Stan
Date: 2022-06-15 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Self-Supervised Learning, speech-recognition, Speech Recognition

This paper introduces the ZevoMOS entry to the main track of the VoiceMOS Challenge 2022. The ZevoMOS submission is based on a two-step finetuning of pretrained self-supervised learning (SSL) speech models. The first step uses a task of classifying natural versus synthetic speech, while the second step's task is to predict the MOS scores associated with each training sample. The results of the finetuning process are then combined with the confidence scores extracted from an automatic speech recognition model, as well as the raw embeddings of the training samples obtained from a wav2vec SSL speech model. The team id assigned to the ZevoMOS system within the VoiceMOS Challenge is T01. The submission was placed on the 14th place with respect to the system-level SRCC, and on the 9th place with respect to the utterance-level MSE. The paper also introduces additional evaluations of the intermediate results.
Deep-based Film Grain Removal and Synthesis
Authors: Zoubida Ameur, Wassim Hamidouche, Edouard François, Miloš Radosavljević, Daniel Menard, Claire-Hélène Demarty
Date: 2022-06-15 00:00:00

In this paper, deep learning-based techniques for film grain removal and synthesis that can be applied in video coding are proposed. Film grain is inherent in analog film content because of the physical process of capturing images and video on film. It can also be present in digital content where it is purposely added to reflect the era of analog film and to evoke certain emotions in the viewer or enhance the perceived quality. In the context of video coding, the random nature of film grain makes it both difficult to preserve and very expensive to compress. To better preserve it while compressing the content efficiently, film grain is removed and modeled before video encoding and then restored after video decoding. In this paper, a film grain removal model based on an encoder-decoder architecture and a film grain synthesis model based on a \ac{cgan} are proposed. Both models are trained on a large dataset of pairs of clean (grain-free) and grainy images. Quantitative and qualitative evaluations of the developed solutions were conducted and showed that the proposed film grain removal model is effective in filtering film grain at different intensity levels using two configurations: 1) a non-blind configuration where the film grain level of the grainy input is known and provided as input, 2) a blind configuration where the film grain level is unknown. As for the film grain synthesis task, the experimental results show that the proposed model is able to reproduce realistic film grain with a controllable intensity level specified as input.
Variance Reduction for Inverse Trace Estimation via Random Spanning Forests
Authors: Yusuf Yigit Pilavci, Pierre-Olivier Amblard, Simon Barthelme, Nicolas Tremblay
Date: 2022-06-15 00:00:00

The trace $\tr(q(\ma{L} + q\ma{I})^{-1})$, where $\ma{L}$ is a symmetric diagonally dominant matrix, is the quantity of interest in some machine learning problems. However, its direct computation is impractical if the matrix size is large. State-of-the-art methods include Hutchinson's estimator combined with iterative solvers, as well as the estimator based on random spanning forests (a random process on graphs). In this work, we show two ways of improving the forest-based estimator via well-known variance reduction techniques, namely control variates and stratified sampling. Implementing these techniques is easy, and provides substantial variance reduction, yielding comparable or better performance relative to state-of-the-art algorithms.
A Neural Network-Prepended GLRT Framework for Signal Detection Under Nonlinear Distortions
Authors: Rajeev Sahay, Swaroop Appadwedula, David J. Love, Christopher G. Brinton
Date: 2022-06-15 00:00:00

Many communications and sensing applications hinge on the detection of a signal in a noisy, interference-heavy environment. Signal processing theory yields techniques such as the generalized likelihood ratio test (GLRT) to perform detection when the received samples correspond to a linear observation model. Numerous practical applications exist, however, where the received signal has passed through a nonlinearity, causing significant performance degradation of the GLRT. In this work, we propose prepending the GLRT detector with a neural network classifier capable of identifying the particular nonlinear time samples in a received signal. We show that pre-processing received nonlinear signals using our trained classifier to eliminate excessively nonlinear samples (i) improves the detection performance of the GLRT on nonlinear signals and (ii) retains the theoretical guarantees provided by the GLRT on linear observation models for accurate signal detection.
Broadband Beamforming via Linear Embedding
Authors: Coleman DeLude, Santhosh Karnik, Mark Davenport, Justin Romberg
Date: 2022-06-14 00:00:00
Tasks: Dimensionality Reduction

In modern applications multi-sensor arrays are subject to an ever-present demand to accommodate signals with higher bandwidths. Standard methods for broadband beamforming, namely digital beamforming and true-time delay, are difficult and expensive to implement at scale. In this work, we explore an alternative method of broadband beamforming that uses a set of linear measurements and a robust low-dimensional signal subspace model. The linear measurements, taken directly from the sensors, serve as a method for dimensionality reduction and serve to limit the array readout. From these embedded samples, we show how the original samples can be recovered to within a provably small residual error using a Slepian subspace model. Previous work in multi-sensor array subspace models have largely analyzed performance from a qualitative or asymptotic perspective. In contrast, we give quantitative estimates of how well different dimensionality reduction strategies preserve the array gain. We also show how spatial and temporal correlations can be used to relax the standard Nyquist sampling criterion, how recovery can be achieved through fast algorithms, and how "hardware friendly" linear measurements can be designed.
Experimental Comparison of PAM-8 Probabilistic Shaping with Different Gaussian Orders at 200 Gb/s Net Rate in IM/DD System with O-Band TOSA
Authors: Md Sabbir-Bin Hossain, Georg Böcherer, Youxi Lin, Shuangxu Li, Stefano Calabrò, Andrei Nedelcu, Talha Rahman, Tom Wettlin, Jinlong Wei, Nebojša Stojanović, Changsong Xie, Maxim Kuschnerov, Stephan Pachnicke
Date: 2022-06-14 00:00:00
Tasks: Sensitivity

For 200Gb/s net rates, cap probabilistic shaped PAM-8 with different Gaussian orders are experimentally compared against uniform PAM-8. In back-to-back and 5km measurements, cap-shaped 85-GBd PAM-8 with Gaussian order of 5 outperforms 71-GBd uniform PAM-8 by up to 2.90dB and 3.80dB in receiver sensitivity, respectively.
Flexible Raman Amplifier Optimization Based on Machine Learning-aided Physical Stimulated Raman Scattering Model
Authors: Metodi Plamenov Yankov, Francesco Da Ros, Uiara Celine de Moura, Andrea Carena, Darko Zibar
Date: 2022-06-13 00:00:00

The problem of Raman amplifier optimization is studied. A differentiable interpolation function is obtained for the Raman gain coefficient using machine learning (ML), which allows for the gradient descent optimization of forward-propagating Raman pumps. Both the frequency and power of an arbitrary number of pumps in a forward pumping configuration are then optimized for an arbitrary data channel load and span length. The forward propagation model is combined with an experimentally-trained ML model of a backward-pumping Raman amplifier to jointly optimize the frequency and power of the forward amplifier's pumps and the powers of the backward amplifier's pumps. The joint forward and backward amplifier optimization is demonstrated for an unrepeatered transmission of 250 km. A gain flatness of $<$ 1~dB over 4 THz is achieved. The optimized amplifiers are validated using a numerical simulator.
Explainable AI for Suicide Risk Assessment Using Eye Activities and Head Gestures
Authors: Siyu Liu, Catherine Lu, Sharifa Alghowinem, Lea Gotoh, Cynthia Breazeal, Hae Won Park
Date: 2022-06-10 00:00:00
Tasks: feature selection

The prevalence of suicide has been on the rise since the 20th century, causing severe emotional damage to individuals, families, and communities alike. Despite the severity of this suicide epidemic, there is so far no reliable and systematic way to assess suicide intent of a given individual. Through efforts to automate and systematize diagnosis of mental illnesses over the past few years, verbal and acoustic behaviors have received increasing attention as biomarkers, but little has been done to study eyelids, gaze, and head pose in evaluating suicide risk. This study explores statistical analysis, feature selection, and machine learning classification as means of suicide risk evaluation and nonverbal behavioral interpretation. Applying these methods to the eye and head signals extracted from our unique dataset, this study finds that high-risk suicidal individuals experience psycho-motor retardation and symptoms of anxiety and depression, characterized by eye contact avoidance, slower blinks and a downward eye gaze. By comparing results from different methods of classification, we determined that these features are highly capable of automatically classifying different levels of suicide risk consistently and with high accuracy, above 98%. Our conclusion corroborates psychological studies, and shows great potential of a systematic approach in suicide risk evaluation that is adoptable by both healthcare providers and naive observers.
Fault Diagnosis of Inter-turn Short Circuit in Permanent Magnet Synchronous Motors with Current Signal Imaging and Unsupervised Learning
Authors: W. Jung, S. H. Yun, Y. S. Lim, S. Cheong, J. Bae, Y. H. Park
Date: 2022-06-09 00:00:00
Tasks: Fault Diagnosis, Feature Engineering, Time Series, Time Series Analysis

This paper proposes machine-independent feature engineering for winding inter-turn short circuit fault that uses electrical current signals. Electrical current signal collected from permanent magnet synchronous motor (PMSM) is subjected to different environmental and operational conditions. To solve these problems, robust current signal imaging method and deep learning-based feature extraction method are developed. The overall procedure includes the following three key steps: (1) transformation of a time-series current signal to two-dimensional image, (2) extracting features using convolutional neural networks, and (3) calculating a health indicator using Mahalanobis distance. Transformation of the time-series signal is based on recurrence plots (RP). The proposed RP method develops from feature engineering that provides the dominant fault feature representations in a robust way. The proposed RP is designed that maximizes the features of inter-turn short fault and minimizes the effect of noise from systems with various capacities. To demonstrate the validity of the proposed method, two case studies are conducted using an artificial fault seeded testbed with two different capacities of motor. By calculating the feature using only the electrical current signal of the motor without the parameters related to the capacity of the motor, the proposed feature can be applied to motors with different capacities while maintaining the same performance.
Two-stage Human Activity Recognition on Microcontrollers with Decision Trees and CNNs
Authors: Francesco Daghero, Daniele Jahier Pagliari, Massimo Poncino
Date: 2022-06-07 00:00:00
Tasks: Activity Recognition, Human Activity Recognition

Human Activity Recognition (HAR) has become an increasingly popular task for embedded devices such as smartwatches. Most HAR systems for ultra-low power devices are based on classic Machine Learning (ML) models, whereas Deep Learning (DL), although reaching state-of-the-art accuracy, is less popular due to its high energy consumption, which poses a significant challenge for battery-operated and resource-constrained devices. In this work, we bridge the gap between on-device HAR and DL thanks to a hierarchical architecture composed of a decision tree (DT) and a one dimensional Convolutional Neural Network (1D CNN). The two classifiers operate in a cascaded fashion on two different sub-tasks: the DT classifies only the easiest activities, while the CNN deals with more complex ones. With experiments on a state-of-the-art dataset and targeting a single-core RISC-V MCU, we show that this approach allows to save up to 67.7% energy w.r.t. a "stand-alone" DL architecture at iso-accuracy. Additionally, the two-stage system either introduces a negligible memory overhead (up to 200 B) or on the contrary, reduces the total memory occupation.
Design of an electronic circuit for loudspeaker real-time digital signal processing
Authors: Oliver Munroe, Stephane Letourneur, Antonin Novak
Date: 2022-06-03 00:00:00

In modern audio systems, real-time digital signal processing algorithms are widely used for a variety of applications. The possibility of using a simple electronic circuit for variety of research projects has shown remarkable potential and is gradually attracting more and more attention from researchers and engineers. This contribution describes a design of such a board used in the framework of a PhD thesis whose subject is centred on the real-time correction of loudspeaker nonlinearities. The solution chosen in this work is based on a Teensy 3.6 microcontroller which is easy to program using the Arduino IDE and the libraries provided by Teensy. Two solutions are provided : one with an Audio board available on the market and another with a homemade board. Both solutions contain two inputs and at least one output (all 16 bits). This contribution does not detail the compensation algorithm related to the loudspeaker nonlinearities but focuses on the boards design, comparison of proposed solutions, and provides the basic codes to perform the real-time digital signal processing.
A Deep Learning Network for the Classification of Intracardiac Electrograms in Atrial Tachycardia
Authors: Zerui Chen, Sonia Xhyn Teo, Andrie Ochtman, Shier Nee Saw, Nicholas Cheng, Eric Tien Siang Lim, Murphy Lyu, Hwee Kuan Lee
Date: 2022-06-02 00:00:00

A key technology enabling the success of catheter ablation treatment for atrial tachycardia is activation mapping, which relies on manual local activation time (LAT) annotation of all acquired intracardiac electrogram (EGM) signals. This is a time-consuming and error-prone procedure, due to the difficulty in identifying the signal activation peaks for fractionated signals. This work presents a Deep Learning approach for the automated classification of EGM signals into three different types: normal, abnormal, and unclassified, which forms part of the LAT annotation pipeline, and contributes towards bypassing the need for manual annotations of the LAT. The Deep Learning network, the CNN-LSTM model, is a hybrid network architecture which combines convolutional neural network (CNN) layers with long short-term memory (LSTM) layers. 1452 EGM signals from a total of 9 patients undergoing clinically-indicated 3D cardiac mapping were used for the training, validation and testing of our models. From our findings, the CNN-LSTM model achieved an accuracy of 81% for the balanced dataset. For comparison, we separately developed a rule-based Decision Trees model which attained an accuracy of 67% for the same balanced dataset. Our work elucidates that analysing the EGM signals using a set of explicitly specified rules as proposed by the Decision Trees model is not suitable as EGM signals are complex. The CNN-LSTM model, on the other hand, has the ability to learn the complex, intrinsic features within the signals and identify useful features to differentiate the EGM signals.
A Survey of Detection Methods for Die Attachment and Wire Bonding Defects in Integrated Circuit Manufacturing
Authors: Lamia Alam, Nasser Kehtarnavaz
Date: 2022-06-02 00:00:00
Tasks: Defect Detection, Survey

Defect detection plays a vital role in the manufacturing process of integrated circuits (ICs). Die attachment and wire bonding are two steps of the manufacturing process that determine the power and signal transmission quality and dependability in an IC. This paper presents a survey or literature review of the methods used for detecting these defects based on different sensing modalities used including optical, radiological, acoustical, and infrared thermography. A discussion of the detection methods used is provided in this survey. Both conventional and deep learning approaches for detecting die attachment and wire bonding defects are considered along with challenges and future research directions.