pwc-archive/papers-with-abstracts huggingface dataset:
Robust Estimation of Realized Correlation: New Insight about Intraday Fluctuations in Market Betas
Authors: Peter Reinhard Hansen, Yiyao Luo
Date: 2023-10-30 00:00:00
Tasks: Time Series

Time-varying volatility is an inherent feature of most economic time-series, which causes standard correlation estimators to be inconsistent. The quadrant correlation estimator is consistent but very inefficient. We propose a novel subsampled quadrant estimator that improves efficiency while preserving consistency and robustness. This estimator is particularly well-suited for high-frequency financial data and we apply it to a large panel of US stocks. Our empirical analysis sheds new light on intra-day fluctuations in market betas by decomposing them into time-varying correlations and relative volatility changes. Our results show that intraday variation in betas is primarily driven by intraday variation in correlations.
Spectral identification and estimation of mixed causal-noncausal invertible-noninvertible models
Authors: Alain Hecq, Daniel Velasquez-Gaviria
Date: 2023-10-30 00:00:00

This paper introduces new techniques for estimating, identifying and simulating mixed causal-noncausal invertible-noninvertible models. We propose a framework that integrates high-order cumulants, merging both the spectrum and bispectrum into a single estimation function. The model that most adequately represents the data under the assumption that the error term is i.i.d. is selected. Our Monte Carlo study reveals unbiased parameter estimates and a high frequency with which correct models are identified. We illustrate our strategy through an empirical analysis of returns from 24 Fama-French emerging market stock portfolios. The findings suggest that each portfolio displays noncausal dynamics, producing white noise residuals devoid of conditional heteroscedastic effects.
Green ammonia supply chain and associated market structure: an analysis based on transaction cost economics
Authors: Hanxin Zhao
Date: 2023-10-30 00:00:00
Tasks: Specificity

Green ammonia is poised to be a key part in the hydrogen economy. This paper discusses green ammonia supply chains from a higher-level industry perspective with a focus on market structures. The architecture of upstream and downstream supply chains are explored. Potential ways to accelerate market emergence are discussed. Market structure is explored based on transaction cost economics and lessons from the oil and gas industry. Three market structure prototypes are developed for different phases. In the infancy, a highly vertically integrated structure is proposed to reduce risks and ensure capital recovery. A restructuring towards a disintegrated structure is necessary in the next stage to improve the efficiency. In the late stage, a competitive structure characterized by a separation between asset ownership and production activities and further development of short-term and spot markets is proposed towards a market-driven industry. Finally, a multi-linear regression model is developed to evaluate the developed structures using a case in the gas industry. Results indicate that high asset specificity and uncertainty and low frequency lead to a more disintegrated market structure, and vice versa, thus supporting the structures designed. We assume the findings and results contribute to developing green ammonia supply chains and the hydrogen economy.
Multilateral matching with scale economies
Authors: Chao Huang
Date: 2023-10-30 00:00:00

This paper studies multilateral matching in which agents may negotiate contracts within any coalition. We assume scale economies such that an agent substitutes some existing contracts with new ones only if the latter involve a set of partners that is weakly larger than the original. A weakly setwise stable (or setwise stable) outcome exists and can be found by a Constrained Serial Dictatorship algorithm in markets with scale economies (resp. ordinal scale economies). The scale economies condition applies to an environment in which agents cooperate to achieve targets, such as markets in which countries sign bilateral agreements.
Harmonization-enriched domain adaptation with light fine-tuning for multiple sclerosis lesion segmentation
Authors: Jinwei Zhang, Lianrui Zuo, Blake E. Dewey, Samuel W. Remedios, Savannah P. Hays, Dzung L. Pham, Jerry L. Prince, Aaron Carass
Date: 2023-10-31 00:00:00
Tasks: Domain Adaptation, Domain Generalization, Lesion Segmentation

Deep learning algorithms utilizing magnetic resonance (MR) images have demonstrated cutting-edge proficiency in autonomously segmenting multiple sclerosis (MS) lesions. Despite their achievements, these algorithms may struggle to extend their performance across various sites or scanners, leading to domain generalization errors. While few-shot or one-shot domain adaptation emerges as a potential solution to mitigate generalization errors, its efficacy might be hindered by the scarcity of labeled data in the target domain. This paper seeks to tackle this challenge by integrating one-shot adaptation data with harmonized training data that incorporates labels. Our approach involves synthesizing new training data with a contrast akin to that of the test domain, a process we refer to as "contrast harmonization" in MRI. Our experiments illustrate that the amalgamation of one-shot adaptation data with harmonized training data surpasses the performance of utilizing either data source in isolation. Notably, domain adaptation using exclusively harmonized training data achieved comparable or even superior performance compared to one-shot adaptation. Moreover, all adaptations required only minimal fine-tuning, ranging from 2 to 5 epochs for convergence.
Structured Two-Stage True-Time-Delay Array Codebook Design for Multi-User Data Communication
Authors: Aditya Wadaskar, Ding Zhao, Ibrahim Pehlivan, Danijela Cabric
Date: 2023-10-31 00:00:00

Wideband millimeter-wave and terahertz (THz) systems can facilitate simultaneous data communication with multiple spatially separated users. It is desirable to orthogonalize users across sub-bands by deploying frequency-dependent beams with a sub-band-specific spatial response. True-Time-Delay (TTD) antenna arrays are a promising wideband architecture to implement sub-band-specific dispersion of beams across space using a single radio frequency (RF) chain. This paper proposes a structured design of analog TTD codebooks to generate beams that exhibit quantized sub-band-to-angle mapping. We introduce a structured Staircase TTD codebook and analyze the frequency-spatial behaviour of the resulting beam patterns. We develop the closed-form two-stage design of the proposed codebook to achieve the desired sub-band-specific beams and evaluate their performance in multi-user communication networks.
SWIPT in Mixed Near- and Far-Field Channels: Joint Beam Scheduling and Power Allocation
Authors: Yunpu Zhang, Changsheng You
Date: 2023-10-31 00:00:00
Tasks: Scheduling

Extremely large-scale array (XL-array) has emerged as a promising technology to enhance the spectrum efficiency and spatial resolution in future wireless networks by exploiting massive number of antennas for generating pencil-like beamforming. This also leads to a fundamental paradigm shift from conventional far-field communications towards the new near-field communications. In contrast to the existing works that mostly considered simultaneous wireless information and power transfer (SWIPT) in the far field, we consider in this paper a new and practical scenario, called mixed near- and far-field SWIPT, where energy harvesting (EH) and information decoding (ID) receivers are located in the near- and far-field regions of the XL-array base station (BS), respectively. Specifically, we formulate an optimization problem to maximize the weighted sum-power harvested at all EH receivers by jointly designing the BS beam scheduling and power allocation, under the constraints on the maximum sum-rate and BS transmit power. First, for the general case with multiple EH and ID receivers, we propose an efficient algorithm to obtain a suboptimal solution by utilizing the binary variable elimination and successive convex approximation methods. To obtain useful insights, we then study the joint design for special cases. In particular, we show that when there are multiple EH receivers and one ID receiver, in most cases, the optimal design is allocating a portion of power to the ID receiver for satisfying the rate constraint, while the remaining power is allocated to one EH receiver with the highest EH capability. This is in sharp contrast to the conventional far-field SWIPT case, for which all powers should be allocated to ID receivers. Numerical results show that our proposed joint design significantly outperforms other benchmark schemes without the optimization of beam scheduling and/or power allocation.
Deep Learning-Enabled Text Semantic Communication under Interference: An Empirical Study
Authors: Tilahun M. Getu, Georges Kaddoum, Mehdi Bennis
Date: 2023-10-30 00:00:00
Tasks: Semantic Communication

At the confluence of 6G, deep learning (DL), and natural language processing (NLP), DL-enabled text semantic communication (SemCom) has emerged as a 6G enabler since it minimizes bandwidth consumption, transmission delay, and power usage. Among existing text SemCom techniques, a popular text SemCom scheme -- that can reliably transmit semantic information in the low signal-to-noise ratio (SNR) regimes -- is DeepSC, whose fundamental asymptotic performance limits under radio frequency interference (RFI) were accurately predicted by our recently developed theory [1]. Although our theory was corroborated by simulations, trained deep networks can defy classical statistical wisdom, calling for extensive computer experiments. This empirical work thus follows using the training, validation, and testing sets tokenized and vectorized from the Proceedings of the European Parliament (Europarl) dataset. Specifically, we train the DeepSC architecture in Keras 2.9 with TensorFlow 2.9 as a backend and test it under Gaussian multi-interferer RFI received over Rayleigh fading channels. Our testing results corroborate that DeepSC produces semantically irrelevant sentences under huge Gaussian RFI emitters, validating our theory. Therefore, a fundamental 6G design paradigm for interference-resistant and robust SemCom (IR$^2$ SemCom) is needed.
Transmission line condition prediction based on semi-supervised learning
Authors: Sizhe Li, Xun Ma, Nan Liu, Yi Jin
Date: 2023-10-30 00:00:00
Tasks: Prediction, Representation Learning

Transmission line state assessment and prediction are of great significance for the rational formulation of operation and maintenance strategy and improvement of operation and maintenance level. Aiming at the problem that existing models cannot take into account the robustness and data demand, this paper proposes a state prediction method based on semi-supervised learning. Firstly, for the expanded feature vector, the regular matrix is used to fill in the missing data, and the sparse coding problem is solved by representation learning. Then, with the help of a small number of labelled samples to initially determine the category centers of line segments in different defective states. Finally, the estimated parameters of the model are corrected using unlabeled samples. Example analysis shows that this method can improve the recognition accuracy and use data more efficiently than the existing models.
A Low-Complexity Machine Learning Design for mmWave Beam Prediction
Authors: Muhammad Qurratulain Khan, Abdo Gaber, Mohammad Parvini, Philipp Schulz, Gerhard Fettweis
Date: 2023-10-30 00:00:00
Tasks: Beam Prediction, Prediction

The 3rd Generation Partnership Project (3GPP) is currently studying machine learning (ML) for the fifth generation (5G)-Advanced New Radio (NR) air interface, where spatial and temporal-domain beam prediction are important use cases. With this background, this letter presents a low-complexity ML design that expedites the spatial-domain beam prediction to reduce the power consumption and the reference signaling overhead, which are currently imperative for frequent beam measurements. Complexity analysis and evaluation results showcase that the proposed model achieves state-of-the-art accuracy with lower computational complexity, resulting in reduced power consumption and faster beam prediction. Furthermore, important observations on the generalization of the proposed model are presented in this letter.
Persistence diagrams as morphological signatures of cells: A method to measure and compare cells within a population
Authors: Yossi Bokor Bleile, Patrice Koehl, Florian Rehfeldt
Date: 2023-10-31 00:00:00
Tasks: Topological Data Analysis

Cell biologists study in parallel the morphology of cells with the regulation mechanisms that modify this morphology. Such studies are complicated by the inherent heterogeneity present in the cell population. It remains difficult to define the morphology of a cell with parameters that can quantify this heterogeneity, leaving the cell biologist to rely on manual inspection of cell images. We propose an alternative to this manual inspection that is based on topological data analysis. We characterise the shape of a cell by its contour and nucleus. We build a filtering of the edges defining the contour using a radial distance function initiated from the nucleus. This filtering is then used to construct a persistence diagram that serves as a signature of the cell shape. Two cells can then be compared by computing the Wasserstein distance between their persistence diagrams. Given a cell population, we then compute a distance matrix that includes all pairwise distances between its members. We analyse this distance matrix using hierarchical clustering with different linkage schemes and define a purity score that quantifies consistency between those different schemes, which can then be used to assess homogeneity within the cell population. We illustrate and validate our approach to identify sub-populations in human mesenchymal stem cell populations.
Using systemic modeling and Bayesian calibration to investigate the role of the tumor microenvironment on chemoresistance
Authors: Sabrina Schönfeld, Laura Scarabosio, Alican Ozkan, Marissa Nichole Rylander, Christina Kuttler
Date: 2023-10-30 00:00:00

Using a novel modeling approach based on the so-called environmental stress level (ESL), we develop a mathematical model to describe systematically the collective influence of oxygen concentration and stiffness of the extracellular matrix on the response of tumor cells to a combined chemotherapeutic treatment. We perform Bayesian calibrations of the resulting model using particle filters, with in vitro experimental data for different hepatocellular carcinoma cell lines. The calibration results support the validity of our mathematical model. Furthermore, they shed light on individual as well as synergistic effects of hypoxia and tissue stiffness on tumor cell dynamics under chemotherapy.
Effective growth rates in a periodically changing environment: From mutation to invasion
Authors: Manuel Esser, Anna Kraut
Date: 2023-10-31 00:00:00

We consider a stochastic individual-based model of adaptive dynamics for an asexually reproducing population with mutation, with linear birth and death rates, as well as a density-dependent competition. To depict repeating changes of the environment, all of these parameters vary over time as piecewise constant and periodic functions, on an intermediate time-scale between those of stabilization of the resident population (fast) and exponential growth of mutants (slow). Studying the growth of emergent mutants and their invasion of the resident population in the limit of small mutation rates for a simultaneously diverging population size, we are able to determine their effective growth rates. We describe this growth as a mesoscopic scaling-limit of the orders of population sizes, where we observe an averaging effect of the invasion fitness. Moreover, we prove a limit result for the sequence of consecutive macroscopic resident traits that is similar to the so-called trait-substitution-sequence.
Law-Invariant Return and Star-Shaped Risk Measures
Authors: Roger J. A. Laeven, Emanuela Rosazza Gianin, Marco Zullino
Date: 2023-10-30 00:00:00

This paper presents novel characterization results for classes of law-invariant star-shaped functionals. We begin by establishing characterizations for positively homogeneous and star-shaped functionals that exhibit second- or convex-order stochastic dominance consistency. Building on these characterizations, we proceed to derive Kusuoka-type representations for these functionals, shedding light on their mathematical structure and intimate connections to Value-at-Risk and Expected Shortfall. Furthermore, we offer representations of general law-invariant star-shaped functionals as robustifications of Value-at-Risk. Notably, our results are versatile, accommodating settings that may, or may not, involve monotonicity and/or cash-additivity. All of these characterizations are developed within a general locally convex topological space of random variables, ensuring the broad applicability of our results in various financial, insurance and probabilistic contexts.
Exploring the Role of Molecular Dynamics Simulations in Most Recent Cancer Research: Insights into Treatment Strategies
Authors: Reza Bozorgpour
Date: 2023-10-30 00:00:00

Cancer is a complex disease that is characterized by uncontrolled growth and division of cells. It involves a complex interplay between genetic and environmental factors that lead to the initiation and progression of tumors. Recent advances in molecular dynamics simulations have revolutionized our understanding of the molecular mechanisms underlying cancer initiation and progression. Molecular dynamics simulations enable researchers to study the behavior of biomolecules at an atomic level, providing insights into the dynamics and interactions of proteins, nucleic acids, and other molecules involved in cancer development. In this review paper, we provide an overview of the latest advances in molecular dynamics simulations of cancer cells. We will discuss the principles of molecular dynamics simulations and their applications in cancer research. We also explore the role of molecular dynamics simulations in understanding the interactions between cancer cells and their microenvironment, including signaling pathways, proteinprotein interactions, and other molecular processes involved in tumor initiation and progression. In addition, we highlight the current challenges and opportunities in this field and discuss the potential for developing more accurate and personalized simulations. Overall, this review paper aims to provide a comprehensive overview of the current state of molecular dynamics simulations in cancer research, with a focus on the molecular mechanisms underlying cancer initiation and progression.
A Lipid Rafts Theory of Alzheimer's Disease
Authors: Ari Rappoport
Date: 2023-10-31 00:00:00

I present a theory of Alzheimer's Disease (AD) that explains its symptoms, pathology, and risk factors. To do this, I introduce a new theory of brain plasticity that elucidates the physiological roles of AD-related agents. New events generate synaptic and branching candidates competing for long-term enhancement. Competition resolution crucially depends on the formation of membrane lipid rafts, which requires astrocyte-produced cholesterol. Sporadic AD is caused by impaired formation of plasma membrane lipid rafts, which prevents the conversion of short- to long-term memory, and yields excessive tau phosphorylation, intracellular cholesterol accumulation, synaptic dysfunction, and neurodegeneration. Amyloid beta (Abeta) production is promoted by cholesterol during the switch to competition resolution, and cholesterol accumulation stimulates chronic Abeta production, secretion, and aggregation. The theory addresses all of the major established facts known about the disease, and is supported by strong evidence.
Effective connectivity signatures in major depressive disorder: fMRI study using a multi-site dataset
Authors: Peishan Dai, Yun Shi, Tong Xiong, Xiaoyan Zhou, Shenghui Liao, Zhongchao Huang, Xiaoping Yi, Bihong T. Chen
Date: 2023-10-31 00:00:00
Tasks: Classification, feature selection, Sensitivity, Specificity

Diagnosis of major depressive disorder (MDD) primarily relies on the patient's self-reported symptoms and a clinical evaluation. Effective connectivity (EC) from resting-state functional magnetic resonance imaging (rs-fMRI) analysis can reflect the directionality of connections between brain regions, making it a candidate method to classify MDD. This study used Granger causality analysis to extract EC features from a large multi-site MDD dataset. The ComBat algorithm and multivariate linear regression were used to harmonize site difference and to remove age and sex covariates, respectively. Two-sample t-tests and model-based feature selection methods were used to screen for highly discriminative EC features for MDD, and LightGBM was used to classify MDD. In this large-scale multi-site rs-fMRI dataset, 97 EC features deemed highly discriminative for MDD were screened. In the nested five-fold cross-validation, the best classification model with the 97 EC features achieved accuracy, sensitivity, and specificity of 94.35%, 93.52%, and 95.25%, respectively. In another independent large dataset, which tested the generalization performance of the 97 EC features, the best classification models achieved 94.74%, 90.59%, and 96.75% for accuracy, sensitivity, and specificity, respectively. This work demonstrated that EC had a reasonable discriminative ability and supported the notion for using EC to potentially assist clinical diagnosis of MDD.
Brain decoding: toward real-time reconstruction of visual perception
Authors: Yohann Benchetrit, Hubert Banville, Jean-Rémi King
Date: 2023-10-18 00:00:00
Tasks: Brain Decoding, Decoder, Image Retrieval

In the past five years, the use of generative and foundational AI systems has greatly improved the decoding of brain activity. Visual perception, in particular, can now be decoded from functional Magnetic Resonance Imaging (fMRI) with remarkable fidelity. This neuroimaging technique, however, suffers from a limited temporal resolution ($\approx$0.5 Hz) and thus fundamentally constrains its real-time usage. Here, we propose an alternative approach based on magnetoencephalography (MEG), a neuroimaging device capable of measuring brain activity with high temporal resolution ($\approx$5,000 Hz). For this, we develop an MEG decoding model trained with both contrastive and regression objectives and consisting of three modules: i) pretrained embeddings obtained from the image, ii) an MEG module trained end-to-end and iii) a pretrained image generator. Our results are threefold: Firstly, our MEG decoder shows a 7X improvement of image-retrieval over classic linear decoders. Second, late brain responses to images are best decoded with DINOv2, a recent foundational image model. Third, image retrievals and generations both suggest that high-level visual features can be decoded from MEG signals, although the same approach applied to 7T fMRI also recovers better low-level features. Overall, these results, while preliminary, provide an important step towards the decoding -- in real-time -- of the visual processes continuously unfolding within the human brain.