pwc-archive/papers-with-abstracts huggingface dataset:
Deep Learning based Skin-layer Segmentation for Characterizing Cutaneous Wounds from Optical Coherence Tomography Images
Authors: Prashant Kumar, Swatantra Dhara, Ayan Gope, Jyotirmoy Chatterjee, Subhamoy Mandal
Date: 2023-06-02 00:00:00

Optical coherence tomography (OCT) is a medical imaging modality that allows us to probe deeper substructures of skin. The state-of-the-art wound care prediction and monitoring methods are based on visual evaluation and focus on surface information. However, research studies have shown that sub-surface information of the wound is critical for understanding the wound healing progression. This work demonstrated the use of OCT as an effective imaging tool for objective and non-invasive assessments of wound severity, the potential for healing, and healing progress by measuring the optical characteristics of skin components. We have demonstrated the efficacy of OCT in studying wound healing progress in vivo small animal models. Automated analysis of OCT datasets poses multiple challenges, such as limitations in the training dataset size, variation in data distribution induced by uncertainties in sample quality and experiment conditions. We have employed a U-Net-based model for the segmentation of skin layers based on OCT images and to study epithelial and regenerated tissue thickness wound closure dynamics and thus quantify the progression of wound healing. In the experimental evaluation of the OCT skin image datasets, we achieved the objective of skin layer segmentation with an average intersection over union (IOU) of 0.9234. The results have been corroborated using gold-standard histology images and co-validated using inputs from pathologists. Clinical Relevance: To monitor wound healing progression without disrupting the healing procedure by superficial, noninvasive means via the identification of pixel characteristics of individual layers.
Introduction of Medical Imaging Modalities
Authors: S. K. M Shadekul Islam, MD Abdullah Al Nasim, Ismail Hossain, Dr. Md Azim Ullah, Dr. Kishor Datta Gupta, Md Monjur Hossain Bhuiyan
Date: 2023-06-01 00:00:00
Tasks: Computed Tomography (CT), Diagnostic

The diagnosis and treatment of various diseases had been expedited with the help of medical imaging. Different medical imaging modalities, including X-ray, Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Nuclear Imaging, Ultrasound, Electrical Impedance Tomography (EIT), and Emerging Technologies for in vivo imaging modalities is presented in this chapter, in addition to these modalities, some advanced techniques such as contrast-enhanced MRI, MR approaches for osteoarthritis, Cardiovascular Imaging, and Medical Imaging data mining and search. Despite its important role and potential effectiveness as a diagnostic tool, reading and interpreting medical images by radiologists is often tedious and difficult due to the large heterogeneity of diseases and the limitation of image quality or resolution. Besides the introduction and discussion of the basic principles, typical clinical applications, advantages, and limitations of each modality used in current clinical practice, this chapter also highlights the importance of emerging technologies in medical imaging and the role of data mining and search aiming to support translational clinical research, improve patient care, and increase the efficiency of the healthcare system.
Social Interactions in Endogenous Groups
Authors: Shuyang Sheng, Xiaoting Sun
Date: 2023-06-02 00:00:00
Tasks: Selection bias

This paper investigates social interactions in endogenous groups. We specify a two-sided many-to-one matching model, where individuals select groups based on preferences, while groups admit individuals based on qualifications until reaching capacities. Endogenous formation of groups leads to selection bias in peer effect estimation, which is complicated by equilibrium effects and alternative groups. We propose novel methods to simplify selection bias and develop a sieve OLS estimator for peer effects that is \sqrt{n}-consistent and asymptotically normal. Using Chilean data, we find that ignoring selection into high schools leads to overestimated peer influence and distorts the estimation of school effectiveness.
The shape of business cycles: a cross-country analysis of Friedman s plucking theory
Authors: Emanuel Kohlscheen, Richhild Moessner, Daniel Rees
Date: 2023-06-02 00:00:00

We test the international applicability of Friedman s famous plucking theory of the business cycle in 12 advanced economies between 1970 and 2021. We find that in countries where labour markets are flexible (Australia, Canada, United Kingdom and United States), unemployment rates typically return to pre-recession levels, in line with Friedman s theory. Elsewhere, unemployment rates are less cyclical. Output recoveries differ less across countries, but more across episodes: on average, half of the decline in GDP during a recession persists. In terms of sectors, declines in manufacturing are typically fully reversed. In contrast, construction-driven recessions, which are often associated with bursting property price bubbles, tend to be persistent.
The Dynamic Persistence of Economic Shocks
Authors: Jozef Barunik, Lukas Vacha
Date: 2023-06-02 00:00:00
Tasks: Time Series

We propose a novel framework for modeling time-varying persistence in economic time series, allowing for smoothly evolving heterogeneity in shock dynamics. We leverage localized regression techniques to flexibly identify changes in persistence over time, offering a data-driven alternative to traditional parametric models. We applied this methodology to U.S. inflation and stock market volatility data and found substantial persistence variations that align with key macroeconomic events and market conditions. The results reveal previously undetected pockets of predictability and provide significant increases in out-of-sample forecast accuracy. These findings have important implications for economic modeling, forecasting, and policy analysis.
Post-COVID Inflation & the Monetary Policy Dilemma: An Agent-Based Scenario Analysis
Authors: Max Sina Knicker, Karl Naumann-Woleske, Jean-Philippe Bouchaud, Francesco Zamponi
Date: 2023-06-02 00:00:00
Tasks: Decision Making

The economic shocks that followed the COVID-19 pandemic have brought to light the difficulty, both for academics and policy makers, of describing and predicting the dynamics of inflation. This paper offers an alternative modelling approach. We study the 2020-2023 period within the well-studied Mark-0 Agent-Based Model, in which economic agents act and react according to plausible behavioural rules. We include a mechanism through which trust of economic agents in the Central Bank can de-anchor. We investigate the influence of regulatory policies on inflationary dynamics resulting from three exogenous shocks, calibrated on those that followed the COVID-19 pandemic: a production/consumption shock due to COVID-related lockdowns, a supply-chain shock, and an energy price shock exacerbated by the Russian invasion of Ukraine. By exploring the impact of these shocks under different assumptions about monetary policy efficacy and transmission channels, we review various explanations for the resurgence of inflation in the United States, including demand-pull, cost-push, and profit-driven factors. Our main results are four-fold: (i) without appropriate fiscal policy, the shocked economy can take years to recover, or even tip over into a deep recession; {(ii) the success of monetary policy in curbing inflation is primarily due to expectation anchoring, rather than to the direct economic impact of interest rate hikes; (iii) however, strong inflation anchoring is detrimental to consumption and unemployment, leading to a narrow window of ``optimal'' policy responses due to the trade-off between inflation and unemployment;} (iv) the two most sensitive model parameters are those describing wage and price indexation. The results of our study have implications for Central Bank decision-making, and offers an easy-to-use tool that may help anticipate the consequences of different monetary and fiscal policies.
On Crowdsourcing-design with Comparison Category Rating for Evaluating Speech Enhancement Algorithms
Authors: Angélica S. Z. Suárez, Clément Laroche, Line H. Clemmensen, Sneha Das
Date: 2023-06-02 00:00:00
Tasks: Speech Enhancement, speech-recognition, Speech Recognition

Speech enhancement techniques improve the quality or the intelligibility of an audio signal by removing unwanted noise. It is used as preprocessing in numerous applications such as speech recognition, hearing aids, broadcasting and telephony. The evaluation of such algorithms often relies on reference-based objective metrics that are shown to correlate poorly with human perception. In order to evaluate audio quality as perceived by human observers it is thus fundamental to resort to subjective quality assessment. In this paper, a user evaluation based on crowdsourcing (subjective) and the Comparison Category Rating (CCR) method is compared against the DNSMOS, ViSQOL and 3QUEST (objective) metrics. The overall quality scores of three speech enhancement algorithms from real time communications (RTC) are used in the comparison using the P.808 toolkit. Results indicate that while the CCR scale allows participants to identify differences between processed and unprocessed audio samples, two groups of preferences emerge: some users rate positively by focusing on noise suppression processing, while others rate negatively by focusing mainly on speech quality. We further present results on the parameters, size considerations and speaker variations that are critical and should be considered when designing the CCR-based crowdsourcing evaluation.
Tensor decomposition for minimization of E2E SLU model toward on-device processing
Authors: Yosuke Kashiwagi, Siddhant Arora, Hayato Futami, Jessica Huynh, Shih-Lun Wu, Yifan Peng, Brian Yan, Emiru Tsunoo, Shinji Watanabe
Date: 2023-06-02 00:00:00
Tasks: speech-recognition, Speech Recognition, Spoken Language Understanding, Tensor Decomposition

Spoken Language Understanding (SLU) is a critical speech recognition application and is often deployed on edge devices. Consequently, on-device processing plays a significant role in the practical implementation of SLU. This paper focuses on the end-to-end (E2E) SLU model due to its small latency property, unlike a cascade system, and aims to minimize the computational cost. We reduce the model size by applying tensor decomposition to the Conformer and E-Branchformer architectures used in our E2E SLU models. We propose to apply singular value decomposition to linear layers and the Tucker decomposition to convolution layers, respectively. We also compare COMP/PARFAC decomposition and Tensor-Train decomposition to the Tucker decomposition. Since the E2E model is represented by a single neural network, our tensor decomposition can flexibly control the number of parameters without changing feature dimensions. On the STOP dataset, we achieved 70.9% exact match accuracy under the tight constraint of only 15 million parameters.
Model-based demosaicking for acquisitions by a RGBW color filter array
Authors: Matthieu Muller, Daniele Picone, Mauro Dalla Mura, Magnus O Ulfarsson
Date: 2023-06-02 00:00:00
Tasks: Demosaicking

Microsatellites and drones are often equipped with digital cameras whose sensing system is based on color filter arrays (CFAs), which define a pattern of color filter overlaid over the focal plane. Recent commercial cameras have started implementing RGBW patterns, which include some filters with a wideband spectral response together with the more classical RGB ones. This allows for additional light energy to be captured by the relevant pixels and increases the overall SNR of the acquisition. Demosaicking defines reconstructing a multi-spectral image from the raw image and recovering the full color components for all pixels. However, this operation is often tailored for the most widespread patterns, such as the Bayer pattern. Consequently, less common patterns that are still employed in commercial cameras are often neglected. In this work, we present a generalized framework to represent the image formation model of such cameras. This model is then exploited by our proposed demosaicking algorithm to reconstruct the datacube of interest with a Bayesian approach, using a total variation regularizer as prior. Some preliminary experimental results are also presented, which apply to the reconstruction of acquisitions of various RGBW cameras.
Training Terahertz Wireless Systems to Battle I/Q Imbalance
Authors: Alexandros-Apostolos A. Boulogeorgos, Angeliki Alexiou
Date: 2023-06-02 00:00:00

Due to the non-ideality of analog components, transceivers experience high levels of hardware imperfections, like in-phase and quadrature imbalance (IQI), which manifests itself as the mismatches of amplitude and phase between the I and Q branches. Unless proper mitigated, IQI has an important and negative impact on the reliability and efficiency of high-frequency and high-data-rate systems, such as terahertz wireless networks. Recognizing this, the current paper presents an intelligent transmitter (TX) and an intelligent receiver (RX) architecture that by employing machine learning (ML) methodologies is capable to fully-mitigate the impact of IQI without performing IQI coefficients estimation. They key idea lies on co-training the TX mapper's and RX demapper in order to respectively design a constellation and detection scheme that takes accounts for IQI. Two training approaches are implemented, namely: i) conventional that requires a considerable amount of data for training, and ii) a reinforcement learning based one, which demands a shorter dataset in comparison to the former. The feasibility and efficiency of the proposed architecture and training approaches are validated through respective Monte Carlo simulations.
Detecting Low Pass Graph Signals via Spectral Pattern: Sampling Complexity and Applications
Authors: Chenyue Zhang, Yiran He, Hoi-To Wai
Date: 2023-06-02 00:00:00
Tasks: Graph Learning

This paper proposes a blind detection problem for low pass graph signals. Without assuming knowledge of the exact graph topology, we aim to detect if a set of graph signal observations are generated from a low pass graph filter. Our problem is motivated by the widely adopted assumption of low pass (a.k.a.~smooth) signals required by many existing works in graph signal processing (GSP), as well as the longstanding problem of network dynamics identification. Focusing on detecting low pass graph signals on modular graphs whose cutoff frequency coincides with the number of clusters in the graph, we propose to leverage the unique spectral pattern exhibited by such low pass graph signals. We analyze the sample complexity of these detectors considering the effects of graph filter's properties, random delays, and other parameters. We show novel applications of the blind detector on robustifying graph learning, identifying antagonistic ties in opinion dynamics, and detecting anomalies in power systems. Numerical experiments validate our findings.
Joint User Association and UAV Location Optimization for Two-Tired Visible Light Communication Networks
Authors: Alireza Qazavi, Foroogh S Tabataba, Mehdi Naderi Soorki
Date: 2023-06-02 00:00:00

In this paper, an unmanned aerial vehicle (UAVs)-assisted visible light communication (VLC) has been considered which has two tiers: UAV-to-centroid and device-to-device (D2D). In the UAV-to-centroid tier, each UAV can simultaneously provide communications and illumination for the centroids of the ground users over VLC links. In the D2D tier, the centroids retransmit received data from UAV over D2D links to the cluster members. For network, the optimization problem of joint user association and deployment location of UAVs is formulated to maximize the received data, satisfy illumination constraint, and the user cluster size. An iterative algorithm is first proposed to transform the optimization problem into a series of two interdependent sub problems. Following the smallest enclosing disk theorem, a random incremental construction method is designed to find the optimal UAV locations. Then, inspired by unsupervised learning method, a clustering algorithm to find a suboptimal user association is proposed. Our simulation results show that the proposed scheme on average guarantees the users brightness 0.77 lux more than their threshold requirements. Moreover, the received bitrate plus number of D2D connected users under our proposed method is 50.69% more than the scenario in which we have RF Link instead of VLC link and do not optimize UAV location.
Synchro-Transient-Extracting Transform for the Analysis of Signals with Both Harmonic and Impulsive Components
Authors: Yunlong Ma, Gang Yu, Tianran Lin, Qingtang Jiang
Date: 2023-06-02 00:00:00
Tasks: Fault Diagnosis

Time-frequency analysis (TFA) techniques play an important role in the field of machine fault diagnosis attributing to their superiority in dealing with nonstationary signals. Synchroextracting transform (SET) and transient-extracting transform (TET) are two newly emerging techniques that can produce energy concentrated representation for nonstationary signals. However, SET and TET are only suitable for processing harmonic signals and impulsive signals, respectively. This poses a challenge for each of these two techniques when a signal contains both harmonic and impulsive components. In this paper, we propose a new TFA technique to solve this problem. The technique first divides time-frequency (TF) coefficients into two types according to the chirp rate of the signal, and then TF ridges are respectively extracted to prevent the limitation for the independent use of SET and TET. This allows the technique to generate an energy-concentrated TF representation for both harmonics and impulses in the signal. Furthermore, we theoretically demonstrate that the proposed technique retains the signal reconstruction capability. The effectiveness of the proposed technique is verified using numerical and real-world signals.
Brezinski Inverse and Geometric Product-Based Steffensen's Methods for Image Reverse Filtering
Authors: Guang Deng
Date: 2023-06-02 00:00:00

This work develops extensions of Steffensen's method to provide new tools for solving the semi-blind image reverse filtering problem. Two extensions are presented: a parametric Steffensen's method for accelerating the Mann iteration, and a family of 12 Steffensen's methods for vector variables. The development is based on Brezinski inverse and geometric product vector inverse. Variants of these methods are presented with adaptive parameter setting and first-order method acceleration. Implementation details, complexity, and convergence are discussed, and the proposed methods are shown to generalize existing algorithms. A comprehensive study of 108 variants of the vector Steffensen's methods is presented in the Supplementary Material. Representative results and comparison with current state-of-the-art methods demonstrate that the vector Steffensen's methods are efficient and effective tools in reversing the effects of commonly used filters in image processing.
Cell jamming and unjamming in development: physical aspects
Authors: Ivana Pajic-Lijakovic, Milan Milivojevic
Date: 2023-06-02 00:00:00

Collective cell migration is essential for a wide range of biological processes such as: morphogenesis, wound healing, and cancer spreading. However, it is well known that migrating epithelial collectives frequently undergo jamming, stay trapped some period of time, and then start migration again. Consequently, only a part of epithelial cells actively contributes to the tissue development. In contrast to epithelial cells, migrating mesenchymal collectives successfully avoid the jamming. It has been confirmed that the epithelial unjamming cannot be treated as the epithelial-to-mesenchymal transition. Some other mechanism is responsible for the epithelial jamming/unjamming. Despite extensive research devoted to study the cell jamming/unjamming, we still do not understand the origin of this phenomenon. The origin is connected to physical factors such as: the cell compressive residual stress accumulation and surface characteristics of migrating (unjamming) and resting (jamming) epithelial clusters which depend primarily on the strength of cell-cell adhesion contacts and cell contractility. The main goal of this theoretical consideration is to clarify these cause-consequence relations.
A FAIR platform for reproducing mutational signature detection on tumor sequencing data
Authors: Aaron Ge, Tongwu Zhang, Clara Bodelon, Montserrat Garcia-Closas, Jonas Almeida, Jeya Balasubramanian
Date: 2023-06-02 00:00:00
Tasks: Privacy Preserving

This paper presents a portable, privacy-preserving, in-browser platform for the reproducible assessment of mutational signature detection methods from sparse sequencing data generated by targeted gene panels. The platform aims to address the reproducibility challenges in mutational signature research by adhering to the FAIR principles, making it findable, accessible, interoperable, and reusable. Our approach focuses on the detection of specific mutational signatures, such as SBS3, which have been linked to specific mutagenic processes. The platform relies on publicly available data, simulation, downsampling techniques, and machine learning algorithms to generate training data and labels and to train and evaluate models. The key achievement of our platform is its transparency, reusability, and privacy preservation, enabling researchers and clinicians to analyze mutational signatures with the guarantee that no data circulates outside the client machine.
Segment Anything Meets Semantic Communication
Authors: Shehbaz Tariq, Brian Estadimas Arfeto, Chaoning Zhang, Hyundong Shin
Date: 2023-06-03 00:00:00
Tasks: Image Reconstruction, Image Segmentation, Segmentation, Semantic Communication, Semantic Segmentation, Zero Shot Segmentation

In light of the diminishing returns of traditional methods for enhancing transmission rates, the domain of semantic communication presents promising new frontiers. Focusing on image transmission, this paper explores the application of foundation models, particularly the Segment Anything Model (SAM) developed by Meta AI Research, to improve semantic communication. SAM is a promptable image segmentation model that has gained attention for its ability to perform zero-shot segmentation tasks without explicit training or domain-specific knowledge. By employing SAM's segmentation capability and lightweight neural network architecture for semantic coding, we propose a practical approach to semantic communication. We demonstrate that this approach retains critical semantic features, achieving higher image reconstruction quality and reducing communication overhead. This practical solution eliminates the resource-intensive stage of training a segmentation model and can be applied to any semantic coding architecture, paving the way for real-world applications.