pwc-archive/papers-with-abstracts huggingface dataset:
Probabilistic volumetric speckle suppression in OCT using deep learning
Authors: Bhaskara Rao Chintada, Sebastián Ruiz-Lopera, René Restrepo, Brett E. Bouma, Martin Villiger, Néstor Uribe-Patarroyo
Date: 2023-12-07 00:00:00
Tasks: Deep Learning, Generative Adversarial Network

We present a deep learning framework for volumetric speckle reduction in optical coherence tomography (OCT) based on a conditional generative adversarial network (cGAN) that leverages the volumetric nature of OCT data. In order to utilize the volumetric nature of OCT data, our network takes partial OCT volumes as input, resulting in artifact-free despeckled volumes that exhibit excellent speckle reduction and resolution preservation in all three dimensions. Furthermore, we address the ongoing challenge of generating ground truth data for supervised speckle suppression deep learning frameworks by using volumetric non-local means despeckling-TNode to generate training data. We show that, while TNode processing is computationally demanding, it serves as a convenient, accessible gold-standard source for training data; our cGAN replicates efficient suppression of speckle while preserving tissue structures with dimensions approaching the system resolution of non-local means despeckling while being two orders of magnitude faster than TNode. We demonstrate fast, effective, and high-quality despeckling of the proposed network in different tissue types acquired with three different OCT systems compared to existing deep learning methods. The open-source nature of our work facilitates re-training and deployment in any OCT system with an all-software implementation, working around the challenge of generating high-quality, speckle-free training data.
Probabilistic Scenario-Based Assessment of National Food Security Risks with Application to Egypt and Ethiopia
Authors: Phoebe Koundouri, Georgios I. Papayiannis, Achilleas Vassilopoulos, Athanasios N. Yannacopoulos
Date: 2023-12-07 00:00:00

This study presents a novel approach to assessing food security risks at the national level, employing a probabilistic scenario-based framework that integrates both Shared Socioeconomic Pathways (SSP) and Representative Concentration Pathways (RCP). This innovative method allows each scenario, encompassing socio-economic and climate factors, to be treated as a model capable of generating diverse trajectories. This approach offers a more dynamic understanding of food security risks under varying future conditions. The paper details the methodologies employed, showcasing their applicability through a focused analysis of food security challenges in Egypt and Ethiopia, and underscores the importance of considering a spectrum of socio-economic and climatic factors in national food security assessments.
Alternative models for FX, arbitrage opportunities and efficient pricing of double barrier options in Lévy models
Authors: Svetlana Boyarchenko, Sergei Levendorskii
Date: 2023-12-06 00:00:00

We analyze the qualitative differences between prices of double barrier no-touch options in the Heston model and pure jump KoBoL model calibrated to the same set of the empirical data, and discuss the potential for arbitrage opportunities if the correct model is a pure jump model. We explain and demonstrate with numerical examples that accurate and fast calculations of prices of double barrier options in jump models are extremely difficult using the numerical methods available in the literature. We develop a new efficient method (GWR-SINH method) based of the Gaver-Wynn-Rho acceleration applied to the Bromwich integral; the SINH-acceleration and simplified trapezoid rule are used to evaluate perpetual double barrier options for each value of the spectral parameter in GWR-algorithm. The program in Matlab running on a Mac with moderate characteristics achieves the precision of the order of E-5 and better in several several dozen of milliseconds; the precision E-07 is achievable in about 0.1 sec. We outline the extension of GWR-SINH method to regime-switching models and models with stochastic parameters and stochastic interest rates.
Family Structure, Gender and Subjective Well-being: Effect of Child ren before and after COVID 19 in Japan
Authors: Eiji Yamamura, Fumio Ohtake
Date: 2023-12-07 00:00:00

Grandparents were anticipated to participated in grand-rearing. The COVID-19 pandemic had detached grandparents from rearing grandchildren. The research questions of this study were as follows: How does the change in family relations impact the well-being (SWB) of grandparents and parents? We examined how family structure influenced subjective SWB before and after COVID-19. We focused on the effects of children, grandchildren, and their gender on grandparents and parents. We found that compared with the happiness level before COVID-19, (1) granddaughters increased their grandmothers SWB after COVID-19, (2) both daughters and sons reduced their fathers SWB after COVID-19, whereas neither daughters nor sons changed their mothers SWB, and (3) the negative effect of sons reduced substantially if their fathers had younger brothers. Learning from interactions with younger brothers in childhood, fathers could avoid the deterioration of relationships with their sons, even when unexpected events possibly changed the lifestyle of the family and their relationship.
Multi-level emission impacts of electrification and coal pathways in China's netzero transition
Authors: Chen Chris Gong, Falko Ueckerdt, Christoph Bertram, Yuxin Yin, David Bantje, Robert Pietzcker, Johanna Hoppe, Robin Hasse, Michaja Pehl, Simón Moreno-Leiva, Jakob Duerrwaechter, Jarusch Muessel, Gunnar Luderer
Date: 2023-12-07 00:00:00

Decarbonizing China's energy system requires both greening the power supply and electrifying end-use sectors. However, concerns exist that electrification may increase emissions while coal power dominates. Using a global climate model, we explore electrification scenarios with varying coal phase-out timelines and assess their climate impact on China's sectors. A ten-year delay in coal phase-out could increase global peak temperature by about 0.02{\deg}C. However, on a sectoral level, there is no evidence of significant additional emissions from electrification, even with a slower coal phase-out. This challenges the sequential ``order of abatement'' view, showing electrification can start before the power sector is fully decarbonized. As long as power emission intensity drops below 150 gCO2/kWh by 2040, electrification can substantially reduce the carbon footprint of buildings, steel, and transport services, and along with energy efficiency measures, it can avoid approximately 0.035{\deg}C of additional global warming by 2060.
The Cost Function of a Two-Level Inventory System with identical retailers benefitting from Information Sharing
Authors: Amir Hosein Afshar Sedigh, Rasoul Haji, Seyed Mehdi Sajadifar
Date: 2023-12-06 00:00:00

This paper investigates a two-echelon inventory system with a central warehouse and N (N > 2) retailers managed by a centralized information-sharing mechanism. In particular, the paper mathematically models an easy-to-implement inventory control system that facilitates making use of information. Some assumptions of the paper include: a) constant delivery time for retailers and the central warehouse and b) Poisson demand with identical rates for retailers. The inventory policy comprises continuous review (R,Q)-policy on the part of retailers and triggering the system with m batches (of a given size Q) at the central warehouse. Besides, the central warehouse monitors retailers' inventory and may order batches sooner than retailers' reorder point, when their inventory position reaches R + s. An earlier study proposed the policy and its cost function approximation. This paper derives an exact mathematical model of the cost function for the aforementioned problem.
An Unsupervised Machine Learning Scheme for Index-Based CSI Feedback in Wi-Fi
Authors: Mrugen Deshmukh, Zinan Lin, Hanqing Lou, Mahmoud Kamel, Rui Yang, Ismail Guvenc
Date: 2023-12-07 00:00:00

With the ever-increasing demand for high-speed wireless data transmission, beamforming techniques have been proven to be crucial in improving the data rate and the signal-to-noise ratio (SNR) at the receiver. However, they require feedback mechanisms that need an overhead of information and increase the system complexity, potentially challenging the efficiency and capacity of modern wireless networks. This paper investigates novel index-based feedback mechanisms that aim at reducing the beamforming feedback overhead in Wi-Fi links. The proposed methods mitigate the overhead by generating a set of candidate beamforming vectors using an unsupervised learning-based framework. The amount of feedback information required is thus reduced by using the index of the candidate as feedback instead of transmitting the entire beamforming matrix. We explore several methods that consider different representations of the data in the candidate set. In particular, we propose five different ways to generate and represent the candidate sets that consider the covariance matrices of the channel, serialize the feedback matrix, and account for the effective distance, among others. Additionally, we also discuss the implications of using partial information in the compressed beamforming feedback on the link performance and compare it with the newly proposed index-based methods. Extensive IEEE 802.11 standard-compliant simulation results show that the proposed methods effectively minimize the feedback overhead, enhancing the throughput while maintaining an adequate link performance.
Enhanced Index-Based Feedback Overhead Reduction for WLANs
Authors: Mrugen Deshmukh, Zinan Lin, Hanqing Lou, Mahmoud Kamel, Rui Yang, Ismail Guvenc
Date: 2023-12-07 00:00:00
Tasks: Clustering

Compressed beamforming algorithm is used in the current Wi-Fi standard to reduce the beamforming feedback overhead (BFO). However, with each new amendment of the standard the number of supported antennas in Wi-Fi devices increases, leading to increased BFO and hampering the throughput despite using compressed beamforming. In this paper, a novel index-based method is presented to reduce the BFO in Wi-Fi links. In particular, a k-means clustering-based approach is presented to generate candidate beamforming feedback matrices, thereby reducing the BFO to only the index of the said candidate matrices. With extensive simulation results, we compare the newly proposed method with the IEEE 802.11be baseline and our previously published index-based method. We show approximately 54% gain in throughput at high signal-to-noise (SNR) against the IEEE 802.11be baseline. Our comparison also shows approximately 4 dB gain compared to our previously published method at the packet-error-rate (PER) of 0.01 using MCS index 11. Additionally, we also discuss the impact of the distance metric chosen for clustering as well as candidate selection on the link performance.
RIS-Aided Interference Cancellation for Joint Device-to-Device and Cellular Communications
Authors: Ly V. Nguyen, A. Lee Swindlehurst
Date: 2023-12-07 00:00:00

Joint device-to-device (D2D) and cellular communication is a promising technology for enhancing the spectral efficiency of future wireless networks. However, the interference management problem is challenging since the operating devices and the cellular users share the same spectrum. The emerging reconfigurable intelligent surfaces (RIS) technology is a potentially ideal solution for this interference problem since RISs can shape the wireless channel in desired ways. This paper considers an RIS-aided joint D2D and cellular communication system where the RIS is exploited to cancel interference to the D2D links and maximize the minimum signal-to-interference plus noise (SINR) of the device pairs and cellular users. First, we adopt a popular alternating optimization (AO) approach to solve the minimum SINR maximization problem. Then, we propose an interference cancellation (IC)-based approach whose complexity is much lower than that of the AO algorithm. We derive a representation for the RIS phase shift vector which cancels the interference to the D2D links. Based on this representation, the RIS phase shift optimization problem is transformed into an effective D2D channel optimization. We show that the AO approach can converge faster and can even give better performance when it is initialized by the proposed IC solution. We also show that for the case of a single D2D pair, the proposed IC approach can be implemented with limited feedback from the single receive device.
Slepian Beamforming: Broadband Beamforming using Streaming Least Squares
Authors: Coleman DeLude, Mark A. Davenport, Justin Romberg
Date: 2023-12-06 00:00:00

In this paper we revisit the classical problem of estimating a signal as it impinges on a multi-sensor array. We focus on the case where the impinging signal's bandwidth is appreciable and is operating in a broadband regime. Estimating broadband signals, often termed broadband (or wideband) beamforming, is traditionally done through filter and summation, true time delay, or a coupling of the two. Our proposed method deviates substantially from these paradigms in that it requires no notion of filtering or true time delay. We use blocks of samples taken directly from the sensor outputs to fit a robust Slepian subspace model using a least squares approach. We then leverage this model to estimate uniformly spaced samples of the impinging signal. Alongside a careful discussion of this model and how to choose its parameters we show how to fit the model to new blocks of samples as they are received, producing a streaming output. We then go on to show how this method naturally extends to adaptive beamforming scenarios, where we leverage signal statistics to attenuate interfering sources. Finally, we discuss how to use our model to estimate from dimensionality reducing measurements. Accompanying these discussions are extensive numerical experiments establishing that our method outperforms existing filter based approaches while being comparable in terms of computational complexity.
Community Detection in High-Dimensional Graph Ensembles
Authors: Robert Malinas, Dogyoon Song, Alfred O. Hero III
Date: 2023-12-06 00:00:00
Tasks: Community Detection, Stochastic Block Model

Detecting communities in high-dimensional graphs can be achieved by applying random matrix theory where the adjacency matrix of the graph is modeled by a Stochastic Block Model (SBM). However, the SBM makes an unrealistic assumption that the edge probabilities are homogeneous within communities, i.e., the edges occur with the same probabilities. The Degree-Corrected SBM is a generalization of the SBM that allows these edge probabilities to be different, but existing results from random matrix theory are not directly applicable to this heterogeneous model. In this paper, we derive a transformation of the adjacency matrix that eliminates this heterogeneity and preserves the relevant eigenstructure for community detection. We propose a test based on the extreme eigenvalues of this transformed matrix and (1) provide a method for controlling the significance level, (2) formulate a conjecture that the test achieves power one for all positive significance levels in the limit as the number of nodes approaches infinity, and (3) provide empirical evidence and theory supporting these claims.
Partial Information in a Mean-Variance Portfolio Selection Game
Authors: Yu-Jui Huang, Li-Hsien Sun
Date: 2023-12-07 00:00:00

This paper considers finitely many investors who perform mean-variance portfolio selection under relative performance criteria. That is, each investor is concerned about not only her terminal wealth, but how it compares to the average terminal wealth of all investors. At the inter-personal level, each investor selects a trading strategy in response to others' strategies. This selected strategy additionally needs to yield an equilibrium intra-personally, so as to resolve time inconsistency among the investor's current and future selves (triggered by the mean-variance objective). A Nash equilibrium we look for is thus a tuple of trading strategies under which every investor achieves her intra-personal equilibrium simultaneously. We derive such a Nash equilibrium explicitly in the idealized case of full information (i.e., the dynamics of the underlying stock is perfectly known) and semi-explicitly in the realistic case of partial information (i.e., the stock evolution is observed, but the expected return of the stock is not precisely known). The formula under partial information consists of the myopic trading and intertemporal hedging terms, both of which depend on an additional state process that serves to filter the true expected return and whose influence on trading is captured by a degenerate Cauchy problem. Our results identify that relative performance criteria can induce downward self-reinforcement of investors' wealth--if every investor suffers a wealth decline simultaneously, then everyone's wealth tends to decline further. This phenomenon, as numerical examples show, is negligible under full information but pronounced under partial information.
Simulation of a Lévy process, its extremum, and hitting time of the extremum via characteristic functions
Authors: Svetlana Boyarchenko, Sergei Levendorskii
Date: 2023-12-06 00:00:00
Tasks: Triplet

We suggest a general framework for simulation of the triplet $(X_T,\bar X_ T,\tau_T)$ (L\'evy process, its extremum, and hitting time of the extremum), and, separately, $X_T,\bar X_ T$ and pairs $(X_T,\bar X_ T)$, $(\bar X_ T,\tau_T)$, $(\bar X_ T-X_T,\tau_T)$, via characteristic functions and conditional characteristic functions. The conformal deformations technique allows one to evaluate probability distributions, joint probability distributions and conditional probability distributions accurately and fast. For simulations in the far tails of the distribution, we precalculate and store the values of the (conditional) characteristic functions on multi-grids on appropriate surfaces in $C^n$, and use these values to calculate the quantiles in the tails. For simulation in the central part of a distribution, we precalculate the values of the cumulative distribution at points of a non-uniform (multi-)grid, and use interpolation to calculate quantiles.
Disentangling Fact from Grid Cell Fiction in Trained Deep Path Integrators
Authors: Rylan Schaeffer, Mikail Khona, Sanmi Koyejo, Ila Rani Fiete
Date: 2023-12-06 00:00:00

Work on deep learning-based models of grid cells suggests that grid cells generically and robustly arise from optimizing networks to path integrate, i.e., track one's spatial position by integrating self-velocity signals. In previous work, we challenged this path integration hypothesis by showing that deep neural networks trained to path integrate almost always do so, but almost never learn grid-like tuning unless separately inserted by researchers via mechanisms unrelated to path integration. In this work, we restate the key evidence substantiating these insights, then address a response to by authors of one of the path integration hypothesis papers. First, we show that the response misinterprets our work, indirectly confirming our points. Second, we evaluate the response's preferred "unified theory for the origin of grid cells" in trained deep path integrators and show that it is at best "occasionally suggestive," not exact or comprehensive. We finish by considering why assessing model quality through prediction of biological neural activity by regression of activity in deep networks can lead to the wrong conclusions.
A Process for Topic Modelling Via Word Embeddings
Authors: Diego Saldaña Ulloa
Date: 2023-10-06 00:00:00
Tasks: Clustering, Dimensionality Reduction, Diversity, Word Embeddings

This work combines algorithms based on word embeddings, dimensionality reduction, and clustering. The objective is to obtain topics from a set of unclassified texts. The algorithm to obtain the word embeddings is the BERT model, a neural network architecture widely used in NLP tasks. Due to the high dimensionality, a dimensionality reduction technique called UMAP is used. This method manages to reduce the dimensions while preserving part of the local and global information of the original data. K-Means is used as the clustering algorithm to obtain the topics. Then, the topics are evaluated using the TF-IDF statistics, Topic Diversity, and Topic Coherence to get the meaning of the words on the clusters. The results of the process show good values, so the topic modeling of this process is a viable option for classifying or clustering texts without labels.
An Evaluation of State-of-the-Art Large Language Models for Sarcasm Detection
Authors: Juliann Zhou
Date: 2023-10-07 00:00:00
Tasks: Sarcasm Detection

Sarcasm, as defined by Merriam-Webster, is the use of words by someone who means the opposite of what he is trying to say. In the field of sentimental analysis of Natural Language Processing, the ability to correctly identify sarcasm is necessary for understanding people's true opinions. Because the use of sarcasm is often context-based, previous research has used language representation models, such as Support Vector Machine (SVM) and Long Short-Term Memory (LSTM), to identify sarcasm with contextual-based information. Recent innovations in NLP have provided more possibilities for detecting sarcasm. In BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding, Jacob Devlin et al. (2018) introduced a new language representation model and demonstrated higher precision in interpreting contextualized language. As proposed by Hazarika et al. (2018), CASCADE is a context-driven model that produces good results for detecting sarcasm. This study analyzes a Reddit corpus using these two state-of-the-art models and evaluates their performance against baseline models to find the ideal approach to sarcasm detection.
Multi-label Text Classification using GloVe and Neural Network Models
Authors: Hongren Wang
Date: 2023-10-25 00:00:00
Tasks: Multi Label Text Classification, Multi-Label Text Classification, text-classification, Text Classification

This study addresses the challenges of multi-label text classification. The difficulties arise from imbalanced data sets, varied text lengths, and numerous subjective feature labels. Existing solutions include traditional machine learning and deep neural networks for predictions. However, both approaches have their limitations. Traditional machine learning often overlooks the associations between words, while deep neural networks, despite their better classification performance, come with increased training complexity and time. This paper proposes a method utilizing the bag-of-words model approach based on the GloVe model and the CNN-BiLSTM network. The principle is to use the word vector matrix trained by the GloVe model as the input for the text embedding layer. Given that the GloVe model requires no further training, the neural network model can be trained more efficiently. The method achieves an accuracy rate of 87.26% on the test set and an F1 score of 0.8737, showcasing promising results.