pwc-archive/papers-with-abstracts huggingface dataset:
Inference for Low-rank Completion without Sample Splitting with Application to Treatment Effect Estimation
Authors: Jungjun Choi, Hyukjun Kwon, Yuan Liao
Date: 2023-07-31 00:00:00

This paper studies the inferential theory for estimating low-rank matrices. It also provides an inference method for the average treatment effect as an application. We show that the least square estimation of eigenvectors following the nuclear norm penalization attains the asymptotic normality. The key contribution of our method is that it does not require sample splitting. In addition, this paper allows dependent observation patterns and heterogeneous observation probabilities. Empirically, we apply the proposed procedure to estimating the impact of the presidential vote on allocating the U.S. federal budget to the states.
The fiscal implications of stringent climate policy
Authors: Richard S. J. Tol
Date: 2023-07-31 00:00:00

Stringent climate policy compatible with the targets of the 2015 Paris Agreement would pose a substantial fiscal challenge. Reducing carbon dioxide emissions by 95% or more by 2050 would raise 7% (1-17%) of GDP in carbon tax revenue, half of current, global tax revenue. Revenues are relatively larger in poorer regions. Subsidies for carbon dioxide sequestration would amount to 6.6% (0.3-7.1%) of GDP. These numbers are conservative as they were estimated using models that assume first-best climate policy implementation and ignore the costs of raising revenue. The fiscal challenge rapidly shrinks if emission targets are relaxed.
Inequality in Educational Attainment: Urban-Rural Comparison in the Indian Context
Authors: Sangita Das
Date: 2023-07-30 00:00:00

The article tries to compare urban and rural literacy of fifteen selected Indian states during 1981 - 2011 and explores the instruments which can reduce the disparity in urban and rural educational attainment. The study constructs the Sopher urban-rural differential literacy index to analyze the trends of literacy disparity across fifteen states in India over time. Although literacy disparity has decreased over time, Sopher index shows that the states of Andhra Pradesh, Madhya Pradesh, Gujarat, Odisha, Maharashtra and even Karnataka faced high inequality in education between urban and rural India in 2011. Additionally, the Fixed Effect panel data regression technique has been applied in the study to identify the factors which influence urban-rural inequality in education. The model shows that the following factors can reduce literacy disparity between urban and rural areas of India: low fertility rate in rural women, higher percentages of rural females marrying after the age of 21 years, educational attainment of mothers and their labour force participation rate in rural areas.
An enhanced system for the detection and active cancellation of snoring signals
Authors: Valeria Bruschi, Michela Cantarini, Luca Serafini, Stefano Nobili, Stefania Cecchi, Stefano Squartini
Date: 2023-07-31 00:00:00
Tasks: Action Detection, Activity Detection

Snoring is a common disorder that affects people's social and marital lives. The annoyance caused by snoring can be partially solved with active noise control systems. In this context, the present work aims at introducing an enhanced system based on the use of a convolutional recurrent neural network for snoring activity detection and a delayless subband approach for active snoring cancellation. Thanks to several experiments conducted using real snoring signals, this work shows that the active snoring cancellation system achieves better performance when the snoring activity detection stage is turned on, demonstrating the beneficial effect of a preliminary snoring detection stage in the perspective of snoring cancellation.
All-In-One Metrical And Functional Structure Analysis With Neighborhood Attentions on Demixed Audio
Authors: Taejun Kim, Juhan Nam
Date: 2023-07-31 00:00:00
Tasks: All, Downbeat Tracking, Information Retrieval, Music Information Retrieval, Retrieval

Music is characterized by complex hierarchical structures. Developing a comprehensive model to capture these structures has been a significant challenge in the field of Music Information Retrieval (MIR). Prior research has mainly focused on addressing individual tasks for specific hierarchical levels, rather than providing a unified approach. In this paper, we introduce a versatile, all-in-one model that jointly performs beat and downbeat tracking as well as functional structure segmentation and labeling. The model leverages source-separated spectrograms as inputs and employs dilated neighborhood attentions to capture temporal long-term dependencies, along with non-dilated attentions for local instrumental dependencies. Consequently, the proposed model achieves state-of-the-art performance in all four tasks on the Harmonix Set while maintaining a relatively lower number of parameters compared to recent state-of-the-art models. Furthermore, our ablation study demonstrates that the concurrent learning of beats, downbeats, and segments can lead to enhanced performance, with each task mutually benefiting from the others.
Robust Self Supervised Speech Embeddings for Child-Adult Classification in Interactions involving Children with Autism
Authors: Rimita Lahiri, Tiantian Feng, Rajat Hebbar, Catherine Lord, So Hyun Kim, Shrikanth Narayanan
Date: 2023-07-31 00:00:00
Tasks: Classification

We address the problem of detecting who spoke when in child-inclusive spoken interactions i.e., automatic child-adult speaker classification. Interactions involving children are richly heterogeneous due to developmental differences. The presence of neurodiversity e.g., due to Autism, contributes additional variability. We investigate the impact of additional pre-training with more unlabelled child speech on the child-adult classification performance. We pre-train our model with child-inclusive interactions, following two recent self-supervision algorithms, Wav2vec 2.0 and WavLM, with a contrastive loss objective. We report 9 - 13% relative improvement over the state-of-the-art baseline with regards to classification F1 scores on two clinical interaction datasets involving children with Autism. We also analyze the impact of pre-training under different conditions by evaluating our model on interactions involving different subgroups of children based on various demographic factors.
Pre-training End-to-end ASR Models with Augmented Speech Samples Queried by Text
Authors: Eric Sun, Jinyu Li, Jian Xue, Yifan Gong
Date: 2023-07-30 00:00:00
Tasks: Automatic Speech Recognition, Data Augmentation, speech-recognition, Speech Recognition

In end-to-end automatic speech recognition system, one of the difficulties for language expansion is the limited paired speech and text training data. In this paper, we propose a novel method to generate augmented samples with unpaired speech feature segments and text data for model pre-training, which has the advantage of low cost without using additional speech data. When mixing 20,000 hours augmented speech data generated by our method with 12,500 hours original transcribed speech data for Italian Transformer transducer model pre-training, we achieve 8.7% relative word error rate reduction. The pre-trained model achieves similar performance as the model pre-trained with multilingual transcribed 75,000 hours raw speech data. When merging the augmented speech data with the multilingual data to pre-train a new model, we achieve even more relative word error rate reduction of 12.2% over the baseline, which further verifies the effectiveness of our method for speech data augmentation.
METTS: Multilingual Emotional Text-to-Speech by Cross-speaker and Cross-lingual Emotion Transfer
Authors: Xinfa Zhu, Yi Lei, Tao Li, Yongmao Zhang, Hongbin Zhou, Heng Lu, Lei Xie
Date: 2023-07-29 00:00:00
Tasks: Disentanglement, Diversity, Quantization, Speech Synthesis, text-to-speech, Text to Speech

Previous multilingual text-to-speech (TTS) approaches have considered leveraging monolingual speaker data to enable cross-lingual speech synthesis. However, such data-efficient approaches have ignored synthesizing emotional aspects of speech due to the challenges of cross-speaker cross-lingual emotion transfer - the heavy entanglement of speaker timbre, emotion, and language factors in the speech signal will make a system produce cross-lingual synthetic speech with an undesired foreign accent and weak emotion expressiveness. This paper proposes the Multilingual Emotional TTS (METTS) model to mitigate these problems, realizing both cross-speaker and cross-lingual emotion transfer. Specifically, METTS takes DelightfulTTS as the backbone model and proposes the following designs. First, to alleviate the foreign accent problem, METTS introduces multi-scale emotion modeling to disentangle speech prosody into coarse-grained and fine-grained scales, producing language-agnostic and language-specific emotion representations, respectively. Second, as a pre-processing step, formant shift-based information perturbation is applied to the reference signal for better disentanglement of speaker timbre in the speech. Third, a vector quantization-based emotion matcher is designed for reference selection, leading to decent naturalness and emotion diversity in cross-lingual synthetic speech. Experiments demonstrate the good design of METTS.
RIS-Enhanced Semantic Communications Adaptive to User Requirements
Authors: Peiwen Jiang, Chao-Kai Wen, Shi Jin, Geoffrey Ye Li
Date: 2023-07-30 00:00:00
Tasks: Semantic Communication

Semantic communication significantly reduces required bandwidth by understanding semantic meaning of the transmitted. However, current deep learning-based semantic communication methods rely on joint source-channel coding design and end-to-end training, which limits their adaptability to new physical channels and user requirements. Reconfigurable intelligent surfaces (RIS) offer a solution by customizing channels in different environments. In this study, we propose the RIS-SC framework, which allocates semantic contents with varying levels of RIS assistance to satisfy the changing user requirements. It takes into account user movement and line-of-sight obstructions, enabling the RIS resource to protect important semantics in challenging channel conditions. The simulation results indicate reasonable task performance, but some semantic parts that have no effect on task performances are abandoned under severe channel conditions. To address this issue, a reconstruction method is also introduced to improve visual acceptance by inferring those missing semantic parts. Furthermore, the framework can adjust RIS resources in friendly channel conditions to save and allocate them efficiently among multiple users. Simulation results demonstrate the adaptability and efficiency of the RIS-SC framework across diverse channel conditions and user requirements.
A New Multi-Level Hazy Image and Video Dataset for Benchmark of Dehazing Methods
Authors: Bedrettin Cetinkaya, Yucel Cimtay, Fatma Nazli Gunay, Gokce Nur Yilmaz
Date: 2023-07-29 00:00:00
Tasks: SSIM

The changing level of haze is one of the main factors which affects the success of the proposed dehazing methods. However, there is a lack of controlled multi-level hazy dataset in the literature. Therefore, in this study, a new multi-level hazy color image dataset is presented. Color video data is captured for two real scenes with a controlled level of haze. The distance of the scene objects from the camera, haze level, and ground truth (clear image) are available so that different dehazing methods and models can be benchmarked. In this study, the dehazing performance of five different dehazing methods/models is compared on the dataset based on SSIM, PSNR, VSI and DISTS image quality metrics. Results show that traditional methods can generalize the dehazing problem better than many deep learning based methods. The performance of deep models depends mostly on the scene and is generally poor on cross-dataset dehazing.
Grid Hopping: Accelerating Direct Estimation Algorithms for Multistatic FMCW Radar
Authors: Gilles Monnoyer, Thomas Feuillen, Maxime Drouguet, Laurent Jacques, Luc Vandendorpe
Date: 2023-07-31 00:00:00

This paper presents a novel signal processing technique, coined grid hopping, as well as an active multistatic Frequency-Modulated Continuous Wave (FMCW) radar system designed to evaluate its performance. The design of grid hopping is motivated by two existing estimation algorithms. The first one is the indirect algorithm estimating ranges and speeds separately for each received signal, before combining them to obtain location and velocity estimates. The second one is the direct method jointly processing the received signals to directly estimate target location and velocity. While the direct method is known to provide better performance, it is seldom used because of its high computation time. Our grid hopping approach, which relies on interpolation strategies, offers a reduced computation time while its performance stays on par with the direct method. We validate the efficiency of this technique on actual FMCW radar measurements and compare it with other methods.
Sensor Selection using the Two-Target Cramér-Rao Bound for Angle of Arrival Estimation
Authors: Costas A. Kokke, Mario Coutiño, Laura Anitori, Richard Heusdens, Geert Leus
Date: 2023-07-31 00:00:00

Sensor selection is a useful method to help reduce data throughput, as well as computational, power, and hardware requirements, while still maintaining acceptable performance. Although minimizing the Cram\'er-Rao bound has been adopted previously for sparse sensing, it did not consider multiple targets and unknown source models. We propose to tackle the sensor selection problem for angle of arrival estimation using the worst-case Cram\'er-Rao bound of two uncorrelated sources. We cast the problem as a convex semi-definite program and retrieve the binary selection by randomized rounding. Through numerical examples related to a linear array, we illustrate the proposed method and show that it leads to the selection of elements at the edges plus the center of the linear array.
Trajectory Optimization for Cellular-Enabled UAV with Connectivity and Battery Constraints
Authors: Hyeon-Seong Im, Kyu-Yeong Kim, Si-Hyeon Lee
Date: 2023-07-30 00:00:00

We address the path planning problem for a cellular-enabled unmanned aerial vehicle (UAV) considering both connectivity and battery constraints. The UAV's mission is to expeditiously transport a payload from an initial point to a final point, while persistently keeping the connection with a base station and complying with its battery limit. At a charging station, the UAV's depleted battery can be swapped with a completely charged one. Our primary contribution lies in proposing an algorithm that outputs an optimal UAV trajectory with polynomial computational complexity, by converting the problem into an equivalent two-level graph-theoretic shortest path search problem. We compare our algorithm with several existing algorithms with respect to performance and computational complexity, and show that only our algorithm outputs an optimal UAV trajectory in polynomial time. Furthermore, we consider other objectives of minimizing the UAV energy consumption and of maximizing the deliverable payload weight, and propose algorithms that output an optimal UAV trajectory in polynomial time.
Reconfigurable Intelligent Surfaces Enhanced NOMA D2D Communications Underlaying UAV Networks
Authors: Wali Ullah Khan, Eva Lagunas, Asad Mahmood, Zain Ali, Symeon Chatzinotas, Björn Ottersten
Date: 2023-07-29 00:00:00

Device-to-device (D2D) communications offers high spectral efficiency, low energy consumption and transmission latency. However, one of the main limitations of D2D communications is co-channel interference from underlaying wireless system. Reconfigurable intelligent surfaces (RIS) is a promising technology because it can manipulate the electromagnetic waves in their environment to overcome interference and enhance wireless communications. This paper considers RIS enhanced D2D communications underlaying unmanned aerial vehicle (UAV) networks with non-orthogonal multiple access (NOMA). The objective is to maximize the sum rate of NOMA D2D communications by simultaneously optimizing the power budget of D2D transmitter, NOMA power allocation coefficients of D2D receivers and passive beamforming of RIS while guaranteeing the quality of services of UAV user. Due to non-convexity, the optimization problem is intractable and challenging to handle. Therefore, it is solved in two parts using alternating optimization. Simulation results unviel the performance of the proposed RIS enhanced D2D communications scheme. Results demonstrate that the proposed scheme achieves 15\% and 27\% higher sum rates compared to the fixed power D2D and orthogonal D2D schemes.
Angle-of-Arrival Estimation of Narrow Gaussian Beams for Mobile FSO Platforms
Authors: Ming-Cheng Tsai, Muhammad Salman Bashir, Mohamed-Slim Alouini
Date: 2023-07-29 00:00:00

Due to the narrow beamwidths of laser Gaussian beams, accurate tracking of laser beam's angle-of-arrival is an important problem in mobile free-space optical communications. In most optical receivers today, fine tracking of angle-of-arrival involves estimating the location of the focused beam spot projected onto a focal plane array. However, for very thin Gaussian beams, both the location as well as the energy of the spot varies considerably with the variation of angle-of-arrival. In this study, we have analyzed the relationship between the angle-of-arrival and the energy of laser spot on the focal plane. We then exploited this relationship to enhance the angle-of-arrival estimation performance of our proposed receiver that takes into account both the location as well as the energy of the laser spot while estimating the angle-of-arrival. The derived Cramer-Rao bounds indicate that the system performance can be enhanced significantly for narrow Gaussian beams when both the spot location and energy are exploited for angle-of-arrival estimation.
Learning a Common Dictionary for CSI Feedback in FDD Massive MU-MIMO-OFDM Systems
Authors: Pavan Kumar Gadamsetty, K. V. S. Hari, Lajos Hanzo
Date: 2023-07-29 00:00:00
Tasks: Compressive Sensing, Dictionary Learning

In a transmit preprocessing aided frequency division duplex (FDD) massive multi-user (MU) multiple-input multiple-output (MIMO) scheme assisted orthogonal frequency-division multiplexing (OFDM) system, it is required to feed back the frequency domain channel transfer function (FDCHTF) of each subcarrier at the user equipment (UE) to the base station (BS). The amount of channel state information (CSI) to be fed back to the BS increases linearly with the number of antennas and subcarriers, which may become excessive. Hence we propose a novel CSI feedback compression algorithm based on compressive sensing (CS) by designing a common dictionary (CD) to reduce the CSI feedback of existing algorithms. Most of the prior work on CSI feedback compression considered single-UE systems. Explicitly, we propose a common dictionary learning (CDL) framework for practical frequency-selective channels and design a CD suitable for both single-UE and multi-UE systems. A set of two methods is proposed. Specifically, the first one is the CDL-K singular value decomposition (KSVD) method, which uses the K-SVD algorithm. The second one is the CDL-orthogonal Procrustes (OP) method, which relies on solving the orthogonal Procrustes problem. The CD conceived for exploiting the spatial correlation of channels across all the subcarriers and UEs compresses the CSI at each UE, and {upon reception} reconstructs it at the BS. Our simulation results show that the proposed dictionary's estimated channel vectors have lower normalized mean-squared error (NMSE) than the traditional fixed Discrete Fourier Transform (DFT) based dictionary. The CSI feedback is reduced by 50%, and the memory reduction at both the UE and BS starts from 50% and increases with the number of subcarriers.
Fusing Sparsity with Deep Learning for Rotating Scatter Mask Gamma Imaging
Authors: YIlun Zhu, Clayton Scott, Darren Holland, George Landon, Aaron Fjeldsted, Azaree Lintereur
Date: 2023-07-29 00:00:00
Tasks: Deep Learning, Denoising, Image Reconstruction

Many nuclear safety applications need fast, portable, and accurate imagers to better locate radiation sources. The Rotating Scatter Mask (RSM) system is an emerging device with the potential to meet these needs. The main challenge is the under-determined nature of the data acquisition process: the dimension of the measured signal is far less than the dimension of the image to be reconstructed. To address this challenge, this work aims to fuse model-based sparsity-promoting regularization and a data-driven deep neural network denoising image prior to perform image reconstruction. An efficient algorithm is developed and produces superior reconstructions relative to current approaches.
Persistent homology analysis of type 2 diabetes genome-wide association studies in protein-protein interaction networks
Authors: Euijun Song
Date: 2023-07-31 00:00:00

Genome-wide association studies (GWAS) involving increasing sample sizes have identified hundreds of genetic variants associated with complex diseases, such as type 2 diabetes (T2D); however, it is unclear how GWAS hits form unique topological structures in protein-protein interaction (PPI) networks. Using persistent homology, this study explores the evolution and persistence of the topological features of T2D GWAS hits in the PPI network with increasing P-value thresholds. We define an $n$-dimensional persistent disease module as a higher-order generalization of the largest connected component (LCC). The 0-dimensional persistent T2D disease module is the LCC of the T2D GWAS hits, which is significantly detected in the PPI network (196 nodes and 235 edges, P$<$0.05). In the 1-dimensional homology group analysis, all 18 1-dimensional holes (loops) of the T2D GWAS hits persist over all P-value thresholds. The 1-dimensional persistent T2D disease module comprising these 18 persistent 1-dimensional holes is significantly larger than that expected by chance (59 nodes and 83 edges, P$<$0.001), indicating a significant topological structure in the PPI network. Our computational topology framework potentially possesses broad applicability to other complex phenotypes in identifying topological features that play an important role in disease pathobiology.
Parameter identifiability in PDE models of fluorescence recovery after photobleaching
Authors: Maria-Veronica Ciocanel, Lee Ding, Lucas Mastromatteo, Sarah Reichheld, Sarah Cabral, Kimberly Mowry, Bjorn Sandstede
Date: 2023-07-29 00:00:00

Identifying unique parameters for mathematical models describing biological data can be challenging and often impossible. Parameter identifiability for partial differential equations models in cell biology is especially difficult given that many established \textit{in vivo} measurements of protein dynamics average out the spatial dimensions. Here, we are motivated by recent experiments on the binding dynamics of the RNA-binding protein PTBP3 in RNP granules of frog oocytes based on fluorescence recovery after photobleaching (FRAP) measurements. FRAP is a widely-used experimental technique for probing protein dynamics in living cells, and is often modeled using simple reaction-diffusion models of the protein dynamics. We show that current methods of structural and practical parameter identifiability provide limited insights into identifiability of kinetic parameters for these PDE models and spatially-averaged FRAP data. We thus propose a pipeline for assessing parameter identifiability and for learning parameter combinations based on re-parametrization and profile likelihoods analysis. We show that this method is able to recover parameter combinations for synthetic FRAP datasets and investigate its application to real experimental data.