pwc-archive/papers-with-abstracts huggingface dataset:
Using Adapters to Overcome Catastrophic Forgetting in End-to-End Automatic Speech Recognition
Authors: Steven Vander Eeckt, Hugo Van hamme
Date: 2022-03-30 00:00:00
Tasks: All, Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

Learning a set of tasks in sequence remains a challenge for artificial neural networks, which, in such scenarios, tend to suffer from Catastrophic Forgetting (CF). The same applies to End-to-End (E2E) Automatic Speech Recognition (ASR) models, even for monolingual tasks. In this paper, we aim to overcome CF for E2E ASR by inserting adapters, small architectures of few parameters which allow a general model to be fine-tuned to a specific task, into our model. We make these adapters task-specific, while regularizing the parameters of the model shared by all tasks, thus stimulating the model to fully exploit the adapters while keeping the shared parameters to work well for all tasks. Our method outperforms all baselines on two monolingual experiments while being more storage efficient and without requiring the storage of data from previous tasks.
Multiple Narrow-band signals Direction Finding with TMLA by Nonuniform Period Modulation
Authors: Kebin Liu, Lening Zhang, Qingkui Zhan, Chong He
Date: 2022-03-30 00:00:00

A new array signal reconstruction and signal-channel DOA estimation method based on TMLA by nonuniform period modulation are proposed. By using non-uniform period modulation, the harmonic component produced by different elements could be separated. Therefore, the conventional snapshot could be reconstructed by analyzing the spectrum of the combined signal. Then spatial spectrum estimation method is used to implement DOA estimation. Numerical simulations are provided to verify the feasibility and accuracy of the proposed method. Since the duration of the signal in the frequency domain analysis processed in a single time is very short, this method is also applicable to narrowband signals. Another highlight is that this method can simultaneously measure the number of the elements-1 angle of incident signals.
The amplitude modulation pattern of Gaussian noise is a fingerprint of Gaussianity
Authors: Víctor Manuel Hidalgo, Juan-Carlos Letelier, Javier Díaz
Date: 2022-03-30 00:00:00
Tasks: Time Series, Time Series Analysis

We introduce a new approach for Gaussianity testing using the envelope of a signal and its coefficient of variation. The envelope of a Gaussian signal follows the Rayleigh distribution, and given that the coefficient of variation of the Rayleigh distribution is invariant, the envelope of Gaussian noise's coefficient of variation is a universal constant and can be exploited as a discriminating statistic. The main application of this approach is to detect the Gaussianity of time series. However, the coefficient of variation of the envelope is also a measure of amplitude modulation patterns that captures the structure of the Fourier phase profile, making it a useful parameter to differentiate types of non-Gaussianity and for signal classification.
Compressed Channel Estimation for IRS-Assisted Millimeter Wave OFDM Systems: A Low-Rank Tensor Decomposition-Based Approach
Authors: Xi Zheng, Peilan Wang, Jun Fang, Hongbin Li
Date: 2022-03-30 00:00:00
Tasks: Tensor Decomposition

We consider the problem of downlink channel estimation for intelligent reflecting surface (IRS)-assisted millimeter Wave (mmWave) orthogonal frequency division multiplexing (OFDM) systems. By exploring the inherent sparse scattering characteristics of mmWave channels, we show that the received signals can be expressed as a low-rank third-order tensor that admits a tensor rank decomposition, also known as canonical polyadic decomposition (CPD). A structured CPD-based method is then developed to estimate the channel parameters. Our analysis reveals that the training overhead required by our proposed method is as low as O(U^2), where U denotes the sparsity of the cascade channel. Simulation results are provided to illustrate the efficiency of the proposed method.
Time Series Fault Classification for Wave Propagation Systems with Sparse Fault Data
Authors: Erik Jakobsson, Erik Frisk, Mattias Krysander, Robert Pettersson
Date: 2022-03-30 00:00:00
Tasks: Classification, Dynamic Time Warping, Time Series, Time Series Analysis, Time Series Classification

In this work Time Series Classification techniques are investigated, and especially their applicability in applications where there are significant differences between the individuals where data is collected, and the individuals where the classification is evaluated. Classification methods are applied to a fault classification case, where a key assumption is that data from a fault free reference case for each specific individual is available. For the investigated application, wave propagation cause almost chaotic changes of a measured pressure signal, and physical modeling is difficult. Direct application of One-Nearest-Neighbor Dynamic Time Warping, a common technique for this kind of problem, and other machine learning techniques are shown to fail for this case and new methods to improve the situation are presented. By using relative features describing the difference from the reference case rather than the absolute time series, improvements are made compared to state-of-the-art time series classification algorithms.
Energy-Efficient IRS-Aided NOMA Beamforming for 6G Wireless Communications
Authors: Asim Ihsan, Wen Chen, Muhammad Asif, Wali Ullah Khan, Jun Li
Date: 2022-03-30 00:00:00

This manuscript presents an energy-efficient alternating optimization framework based on intelligent reflective surfaces (IRS) aided non-orthogonal multiple access beamforming (NOMA-BF) system for 6G wireless communications. Specifically, this work proposes a centralized IRS-enabled design for the NOMA-BF system to optimize the active beamforming and power allocation coefficient (PAC) of users at the transmitter in the first stage and passive beamforming at IRS in the 2nd stage to maximize the energy efficiency (EE) of the network. However, an increment in the number of supportable users with the NOMA-BF system will lead to NOMA user interference and inter-cluster interference (ICI). To mitigate the effect of ICI, first zero-forcing beamforming along with efficient user clustering algorithm is exploited and then NOMA user interference is tackled efficiently through a proposed iterative algorithm that computes PAC of NOMA user through simplified closed-form expression under the required system constraints. In the 2nd stage, the problem of passive beamforming is solved through a technique based on difference-of-convex (DC) programming and successive convex approximation (SCA). Simulation results demonstrate that the proposed alternating framework for energy-efficient IRS-assisted NOMA-BF system can achieve convergence within a few iterations and provide efficient performance in terms of EE of the system with low complexity.
Integration of Radar Sensing into Communications with Asynchronous Transceivers
Authors: J. Andrew Zhang, Kai Wu, Xiaojing Huang, Y. Jay Guo, Daqing Zhang, Robert W. Heath Jr
Date: 2022-03-30 00:00:00

Clock asynchronism is a critical issue in integrating radar sensing into communication networks. It can cause ranging ambiguity and prevent coherent processing of dis-continuous measurements in integration with asynchronous transceivers. Should it be resolved, sensing can be efficiently realized in communication networks, requiring little network infrastructure and hardware changes. This article provides a systematic overview of existing and potential new techniques for tackling this fundamental problem. We first review existing solutions, including using a fine-tuned global reference clock, and single-node-based and network-based techniques. We then examine open problems and research opportunities, offering insights into what may be better realized in each of the three solution areas.
Adaptive Multi-User Clustering and Power Allocation for NOMA Systems with Imperfect SIC
Authors: Nemalidinne Siva Mouni, Pavan Reddy M., Abhinav Kumar, Prabhat K. Upadhyay
Date: 2022-03-29 00:00:00
Tasks: Clustering

Non-orthogonal multiple access (NOMA) is recognized as a promising radio access technique for the next generation wireless systems. We consider a practical downlink NOMA system with imperfect successive interference cancellation and derive bounds on the power allocation factors for a given number of users in each cluster. We propose a minimum signal-to-interference-plus-noise ratio difference criterion between two successive NOMA users in a cluster of users to achieve higher rates than an equivalent orthogonal multiple access (OMA) system. We then propose adaptive multi-user clustering and power allocation algorithms for downlink NOMA systems. Through extensive simulations, we show that the proposed algorithms achieve higher rates than the state-of-the-art algorithms.
A thermoelectric generation system using waste heat recovery from petrochemical pipeline to power wireless sensor
Authors: Bo Li, Xiao-Liang Guo, Yu-Tao Li
Date: 2022-03-29 00:00:00

Wireless monitoring sensor gradually replaces wired equipment for data support in petrochemical industry production. And wireless monitoring sensor with continuous energy supply is necessary and still faces great challenges. Thermoelectric generator makes it possible to power wireless monitoring sensor by continuously generating thermal energy from hot fluids in a collection petrochemical pipeline in any environment. The inability to obtain a larger temperature difference under TEG's high-temperature condition results in low energy efficiency, limiting the scheme's application. This work describes a thermoelectric generation system with an optimized heat sink for wireless monitoring sensor power supply at high temperature. The system is simple to install and use and is based on a high-performance passive heat sink with no power consumption and micro-vibration. We establish a practical prototype system to demonstrate performance and theoretical verification after analyzing the system's power loss to achieve high power and high efficiency. The experimental results show that the system proposed in this work can generate enough power to power the wireless monitoring sensor for 60 seconds. The minimum output power is 24.7"mW" at the hot-side of thermoelectric generation (TEG) temperature of 100 {\deg}C- 190 {\deg}C. The maximum power conversion efficiency in the considered input voltage range can reach an ideal value.
Cusp Bifurcation in Metastatic Breast Cancer Cells
Authors: Brenda Delamonica, Gabor Balazsi, Michael Shub
Date: 2022-03-29 00:00:00

Ordinary differential equations (ODEs) can model the transition of cell states over time. Bifurcation theory is a branch of dynamical systems which studies changes in the behavior of an ODE system while one or more parameters are varied. We have found that concepts in bifurcation theory may be applied to model metastatic cell behavior. Our results show how a specific phenomenon called a cusp bifurcation describes metastatic cell state transitions, separating two qualitatively different transition modalities. Moreover, we show how the cusp bifurcation models other genetic networks, and we relate the dynamics after the bifurcation to observed phenomena in commitment to enter the cell cycle.
Seasonality, density dependence and spatial population synchrony
Authors: Pedro G Nicolau, Sigrunn H Sørbye, Nigel G Yoccoz, Rolf A Ims
Date: 2022-03-30 00:00:00
Tasks: Time Series, Time Series Analysis

Spatial population synchrony has been the focus of theoretical and empirical studies for decades, in the hopes of understanding mechanisms and interactions driving ecological dynamics. In many systems, it is well-known that seasonality plays a critical role in the density dependence structure of the populations, yet this has hardly received any attention in synchrony studies. Here, we propose a protocol that allows to elucidate deterministic and stochastic sources of spatial synchrony, while accounting for geographic- and season-specific density dependence. We apply our protocol to seasonally-sampled time series of sub-arctic gray-sided voles, known for marked spatial synchrony. Dissociating seasonal density-dependence contributions to the total observed synchrony reveals differential strength and shape of synchrony patterns by season. Mild winter weather reveals to be an important driver of vole spatial synchrony, with lagged effects in the fall. This has direct implications to the future population dynamics of such species when facing climate change.
Cointegration of SARS-CoV-2 Transmission with Weather Conditions and Mobility during the First Year of the COVID-19 Pandemic in the United States
Authors: Hong Qin, Syed Tareq, William Torres, Megan Doman, Cleo Falvey, Jamaree Moore, Meng Hsiu Tsai, Yingfeng Wang, Azad Hossain, Mengjun Xie, Li Yang
Date: 2022-03-29 00:00:00
Tasks: Time Series, Time Series Analysis

Correlation between weather and the transmission of SARS-CoV-2 may suggest its seasonality. Cointegration analysis can avoid spurious correlation among time series data. We examined the cointegration of virus transmission with daily temperature, dewpoint, and confounding factors of mobility measurements during the first year of the pandemic in the United States. We examined the cointegration of the effective reproductive rate, Rt, of the virus with the dewpoint at two meters, the temperature at two meters, Apple driving mobility, and Google workplace mobility measurements. We found that dewpoint and Apple driving mobility are the best factors to cointegrate with Rt, although temperature and Google workplace mobility also cointegrate with Rt at substantial levels. We found that the optimal lag is two days for cointegration between Rt and weather variables, and three days for Rt and mobility. We observed clusters of states that share similar cointegration results of Rt, weather, and mobility, suggesting regional patterns. Our results support the correlation of weather with the spread of SARS-CoV-2 and its potential seasonality.
Sample Recycling for Nested Simulation with Application in Portfolio Risk Measurement
Authors: Kun Zhang, Ben Mingbin Feng, Guangwu Liu, Shiyu Wang
Date: 2022-03-29 00:00:00
Tasks: valid

Nested simulation is a natural approach to tackle nested estimation problems in operations research and financial engineering. The outer-level simulation generates outer scenarios and the inner-level simulations are run in each outer scenario to estimate the corresponding conditional expectation. The resulting sample of conditional expectations is then used to estimate different risk measures of interest. Despite its flexibility, nested simulation is notorious for its heavy computational burden. We introduce a novel simulation procedure that reuses inner simulation outputs to improve efficiency and accuracy in solving nested estimation problems. We analyze the convergence rates of the bias, variance, and MSE of the resulting estimator. In addition, central limit theorems and variance estimators are presented, which lead to asymptotically valid confidence intervals for the nested risk measure of interest. We conduct numerical studies on two financial risk measurement problems. Our numerical studies show consistent results with the asymptotic analysis and show that the proposed approach outperforms the standard nested simulation and a state-of-art regression approach for nested estimation problems.
Economic state classification and portfolio optimisation with application to stagflationary environments
Authors: Nick James, Max Menzies, Kevin Chin
Date: 2022-03-29 00:00:00
Tasks: Portfolio Optimization, Time Series, Time Series Analysis

Motivated by the current fears of a potentially stagflationary global economic environment, this paper uses new and recently introduced mathematical techniques to study multivariate time series pertaining to country inflation (CPI), economic growth (GDP) and equity index behaviours. We begin by assessing the temporal evolution among various economic phenomena, and complement this analysis with `economic driver analysis,' where we decouple country economic trajectories and determine what is most important in their association. Next, we study the temporal self-similarity of global inflation, growth and equity index returns to identify the most anomalous historic periods, and windows in the past that are most similar to current market dynamics. We then introduce a new algorithm to construct economic state classifications and compute an economic state integral, where countries are determined to belong in one of four candidate states based on their inflation and growth behaviours. Finally, we implement a decade-by-decade portfolio optimisation to determine which equity indices and portfolio assets have been most beneficial in maximising portfolio risk-adjusted returns in various market conditions. This could be of great interest to those looking for asset allocation guidance in the current period of high economic uncertainty.
Shift-Robust Node Classification via Graph Adversarial Clustering
Authors: Qi Zhu, Chao Zhang, Chanyoung Park, Carl Yang, Jiawei Han
Date: 2022-03-07 00:00:00
Tasks: Classification, Clustering, Domain Adaptation, Node Classification

Graph Neural Networks (GNNs) are de facto node classification models in graph structured data. However, during testing-time, these algorithms assume no data shift, i.e., $\Pr_\text{train}(X,Y) = \Pr_\text{test}(X,Y)$. Domain adaption methods can be adopted for data shift, yet most of them are designed to only encourage similar feature distribution between source and target data. Conditional shift on classes can still affect such adaption. Fortunately, graph yields graph homophily across different data distributions. In response, we propose Shift-Robust Node Classification (SRNC) to address these limitations. We introduce an unsupervised cluster GNN on target graph to group the similar nodes by graph homophily. An adversarial loss with label information on source graph is used upon clustering objective. Then a shift-robust classifier is optimized on training graph and adversarial samples on target graph, which are generated by cluster GNN. We conduct experiments on both open-set shift and representation-shift, which demonstrates the superior accuracy of SRNC on generalizing to test graph with data shift. SRNC is consistently better than previous SoTA domain adaption algorithm on graph that progressively use model predictions on target graph for training.
AI Gone Astray: Technical Supplement
Authors: Janice Yang, Ludvig Karstens, Casey Ross, Adam Yala
Date: 2022-03-01 00:00:00

This study is a technical supplement to "AI gone astray: How subtle shifts in patient data send popular algorithms reeling, undermining patient safety." from STAT News, which investigates the effect of time drift on clinically deployed machine learning models. We use MIMIC-IV, a publicly available dataset, to train models that replicate commercial approaches by Dascena and Epic to predict the onset of sepsis, a deadly and yet treatable condition. We observe some of these models degrade overtime; most notably an RNN built on Epic features degrades from a 0.729 AUC to a 0.525 AUC over a decade, leading us to investigate technical and clinical drift as root causes of this performance drop.
Explicitising The Implicit Intrepretability of Deep Neural Networks Via Duality
Authors: Chandrashekar Lakshminarayanan, Amit Vikram Singh, Arun Rajkumar
Date: 2022-03-01 00:00:00

Recent work by Lakshminarayanan and Singh [2020] provided a dual view for fully connected deep neural networks (DNNs) with rectified linear units (ReLU). It was shown that (i) the information in the gates is analytically characterised by a kernel called the neural path kernel (NPK) and (ii) most critical information is learnt in the gates, in that, given the learnt gates, the weights can be retrained from scratch without significant loss in performance. Using the dual view, in this paper, we rethink the conventional interpretations of DNNs thereby explicitsing the implicit interpretability of DNNs. Towards this, we first show new theoretical properties namely rotational invariance and ensemble structure of the NPK in the presence of convolutional layers and skip connections respectively. Our theory leads to two surprising empirical results that challenge conventional wisdom: (i) the weights can be trained even with a constant 1 input, (ii) the gating masks can be shuffled, without any significant loss in performance. These results motivate a novel class of networks which we call deep linearly gated networks (DLGNs). DLGNs using the phenomenon of dual lifting pave way to more direct and simpler interpretation of DNNs as opposed to conventional interpretations. We show via extensive experiments on CIFAR-10 and CIFAR-100 that these DLGNs lead to much better interpretability-accuracy tradeoff.
Intelligent Blockage Prediction and Proactive Handover for Seamless Connectivity in Vision-Aided 5G/6G UDNs
Authors: Mohammad Al-Quraan, Ahsan Khan, Lina Mohjazi, Anthony Centeno, Ahmed Zoha, Muhammad Ali Imran
Date: 2022-02-21 00:00:00
Tasks: Blocking, Management

The upsurge in wireless devices and real-time service demands force the move to a higher frequency spectrum. Millimetre-wave (mmWave) and terahertz (THz) bands combined with the beamforming technology offer significant performance enhancements for ultra-dense networks (UDNs). Unfortunately, shrinking cell coverage and severe penetration loss experienced at higher spectrum render mobility management a critical issue in UDNs, especially optimizing beam blockages and frequent handover (HO). Mobility management challenges have become prevalent in city centres and urban areas. To address this, we propose a novel mechanism driven by exploiting wireless signals and on-road surveillance systems to intelligently predict possible blockages in advance and perform timely HO. This paper employs computer vision (CV) to determine obstacles and users' location and speed. In addition, this study introduces a new HO event, called block event {BLK}, defined by the presence of a blocking object and a user moving towards the blocked area. Moreover, the multivariate regression technique predicts the remaining time until the user reaches the blocked area, hence determining best HO decision. Compared to typical wireless networks without blockage prediction, simulation results show that our BLK detection and PHO algorithm achieves 40\% improvement in maintaining user connectivity and the required quality of experience (QoE).
Learning Local-Global Contextual Adaptation for Multi-Person Pose Estimation
Authors: Nan Xue, Tianfu Wu, Gui-Song Xia, Liangpei Zhang
Date: 2021-09-08 00:00:00
Proceeding: CVPR 2022 1
Tasks: Multi-Person Pose Estimation, Pose Estimation

This paper studies the problem of multi-person pose estimation in a bottom-up fashion. With a new and strong observation that the localization issue of the center-offset formulation can be remedied in a local-window search scheme in an ideal situation, we propose a multi-person pose estimation approach, dubbed as LOGO-CAP, by learning the LOcal-GlObal Contextual Adaptation for human Pose. Specifically, our approach learns the keypoint attraction maps (KAMs) from the local keypoints expansion maps (KEMs) in small local windows in the first step, which are subsequently treated as dynamic convolutional kernels on the keypoints-focused global heatmaps for contextual adaptation, achieving accurate multi-person pose estimation. Our method is end-to-end trainable with near real-time inference speed in a single forward pass, obtaining state-of-the-art performance on the COCO keypoint benchmark for bottom-up human pose estimation. With the COCO trained model, our method also outperforms prior arts by a large margin on the challenging OCHuman dataset.