pwc-archive/papers-with-abstracts huggingface dataset:
Structure-Preserving Spectral Reflectance Estimation using Guided Filtering
Authors: Frank Sippel, Jürgen Seiler, Nils Genser, André Kaup
Date: 2022-09-16 00:00:00

Light spectra are a very important source of information for diverse classification problems, e.g., for discrimination of materials. To lower the cost for acquiring this information, multispectral cameras are used. Several techniques exist for estimating light spectra out of multispectral images by exploiting properties about the spectrum. Unfortunately, especially when capturing multispectral videos, the images are heavily affected by noise due to the nature of limited exposure times in videos. Therefore, models that explicitly try to lower the influence of noise on the reconstructed spectrum are highly desirable. Hence, a novel reconstruction algorithm is presented. This novel estimation method is based on the guided filtering technique which preserves basic structures, while using spatial information to reduce the influence of noise. The evaluation based on spectra of natural images reveals that this new technique yields better quantitative and subjective results in noisy scenarios than other state-of-the-art spatial reconstruction methods. Specifically, the proposed algorithm lowers the mean squared error and the spectral angle up to 46% and 35% in noisy scenarios, respectively. Furthermore, it is shown that the proposed reconstruction technique works out-of-the-box and does not need any calibration or training by reconstructing spectra from a real-world multispectral camera with nine channels.
Multi-Pose Fusion for Sparse-View CT Reconstruction Using Consensus Equilibrium
Authors: Diyu Yang, Craig A. J. Kemp, Gregery T. Buzzard, Charles A. Bouman
Date: 2022-09-15 00:00:00
Tasks: CT Reconstruction

CT imaging works by reconstructing an object of interest from a collection of projections. Traditional methods such as filtered-back projection (FBP) work on projection images acquired around a fixed rotation axis. However, for some CT problems, it is desirable to perform a joint reconstruction from projection data acquired from multiple rotation axes. In this paper, we present Multi-Pose Fusion, a novel algorithm that performs a joint tomographic reconstruction from CT scans acquired from multiple poses of a single object, where each pose has a distinct rotation axis. Our approach uses multi-agent consensus equilibrium (MACE), an extension of plug-and-play, as a framework for integrating projection data from different poses. We apply our method on simulated data and demonstrate that Multi-Pose Fusion can achieve a better reconstruction result than single pose reconstruction.
Real-Time Machine Learning Enabled Low-Cost Magnetometer System
Authors: Talha Siddique, Md. Shaad Mahmud
Date: 2022-09-16 00:00:00

Geomagnetically Induced Currents (GICs) are one of the most hazardous effects of space weather. The rate of change in ground horizontal magnetic component dBH/dt is used as a proxy measure for GIC. In order to monitor and predict dBH/dt, ground-based fluxgate magnetometers are used. However, baseline correction is crucial before such magnetometer data can be utilized. In this paper, a low-cost Machine Learning (ML) enabled magnetometer system has been implemented to perform realtime baseline correction of magnetometer data. The predicted geomagnetic components are then used to derive a forecast for dBH/dt. Two different ML models were deployed, and their real-time and offline prediction accuracy were examined. The localized peaks of the predicted dBH/dt are further validated using binary event analysis.
The APC Algorithm of Solving Large-Scale Linear Systems: A Generalized Analysis
Authors: Jiyan Zhang, Yue Xue, Yuan Qi, Jiale Wang
Date: 2022-09-16 00:00:00

A new algorithm called accelerated projection-based consensus (APC) has recently emerged as a promising approach to solve large-scale systems of linear equations in a distributed fashion. The algorithm adopts the federated architecture, and attracts increasing research interest; however, it's performance analysis is still incomplete, e.g., the error performance under noisy condition has not yet been investigated. In this paper, we focus on providing a generalized analysis by the use of the linear system theory, such that the error performance of the APC algorithm for solving linear systems in presence of additive noise can be clarified. We specifically provide a closed-form expression of the error of solution attained by the APC algorithm. Numerical results demonstrate the error performance of the APC algorithm, validating the presented analysis.
Toward 6G with Terahertz Communications: Understanding the Propagation Channels
Authors: Xuesong Cai, Xiang Cheng, Fredrik Tufvesson
Date: 2022-09-16 00:00:00

This article aims at providing insights for a comprehensive understanding of terahertz (THz) propagation channels. Specifically, we discuss essential THz channel characteristics to be well understood for the success of THz communications. The methodology of establishing realistic and 6G-compliant THz channel models based on measurements is then elaborated on, followed by a discussion on existing THz channel measurements in the literature. Finally, future research directions, challenges and measures to enrich the understanding of THz channels are discussed.
A Unified Multi-Task Semantic Communication System for Multimodal Data
Authors: Guangyi Zhang, Qiyu Hu, Zhijin Qin, Yunlong Cai, Guanding Yu, Xiaoming Tao
Date: 2022-09-16 00:00:00
Tasks: feature selection, Semantic Communication

Task-oriented semantic communications have achieved significant performance gains. However, the employed deep neural networks in semantic communications have to be updated when the task is changed or multiple models need to be stored for performing different tasks. To address this issue, we develop a unified deep learning-enabled semantic communication system (U-DeepSC), where a unified end-to-end framework can serve many different tasks with multiple modalities of data. As the number of required features varies from task to task, we propose a vector-wise dynamic scheme that can adjust the number of transmitted symbols for different tasks. Moreover, our dynamic scheme can also adaptively adjust the number of transmitted features under different channel conditions to optimize the transmission efficiency. Particularly, we devise a lightweight feature selection module (FSM) to evaluate the importance of feature vectors, which can hierarchically drop redundant feature vectors and significantly accelerate the inference. To reduce the transmission overhead, we then design a unified codebook for feature representation to serve multiple tasks, where only the indices of these task-specific features in the codebook are transmitted. According to the simulation results, the proposed U-DeepSC achieves comparable performance to the task-oriented semantic communication system designed for a specific task but with significant reduction in both transmission overhead and model size.
Free-Space Optical Communications for 6G Wireless Networks: Challenges, Opportunities, and Prototype Validation
Authors: Hong-Bae Jeon, Soo-Min Kim, Hyung-Joo Moon, Do-Hoon Kwon, Joon-Woo Lee, Jong-Moon Chung, Sang-Kook Han, Chan-Byoung Chae, Mohamed-Slim Alouini
Date: 2022-09-16 00:00:00

Numerous researchers have studied innovations in future sixth-generation (6G) wireless communications. Indeed, a critical issue that has emerged is to contend with society's insatiable demand for high data rates and massive 6G connectivity. Some scholars consider one innovation to be a breakthrough--the application of free-space optical (FSO) communication. Owing to its exceedingly high carrier frequency/bandwidth and the potential of the unlicensed spectrum domain, FSO communication provides an excellent opportunity to develop ultrafast data links that can be applied in a variety of 6G applications, including heterogeneous networks with enormous connectivity and wireless backhauls for cellular systems. In this study, we perform video signal transmissions via an FPGA-based FSO communication prototype to investigate the feasibility of an FSO link with a distance of up to 20~km. We use a channel emulator to reliably model turbulence, scintillation, and power attenuation of the long-range FSO channel. We use the FPGA-based real-time SDR prototype to process the transmitted and received video signals. Our study also presents the channel-generation process of a given long-distance FSO link. To enhance the link quality, we apply spatial selective filtering to suppress the background noise generated by sunlight. To measure the misalignment of the transceiver, we use sampling-based pointing, acquisition, and tracking to compensate for it by improving the signal-to-noise ratio. For the main video signal transmission testbed, we consider various environments by changing the amount of turbulence and wind speed. We demonstrate that the testbed even permits the successful transmission of ultra-high-definition (UHD: 3840 x 2160 resolution) 60 fps videos under severe turbulence and high wind speeds.
Inductive Matrix Completion and Root-MUSIC-Based Channel Estimation for Intelligent Reflecting Surface (IRS)-Aided Hybrid MIMO Systems
Authors: K. F. Masood, J. Tong, J. Xi, J. Yuan, Y. Yu
Date: 2022-09-15 00:00:00
Tasks: Matrix Completion, parameter estimation, Super-Resolution

This paper studies the estimation of cascaded channels in passive intelligent reflective surface (IRS)- aided multiple-input multiple-output (MIMO) systems employing hybrid precoders and combiners. We propose a low-complexity solution that estimates the channel parameters progressively. The angles of departure (AoDs) and angles of arrival (AoAs) at the transmitter and receiver, respectively, are first estimated using inductive matrix completion (IMC) followed by root-MUSIC based super-resolution spectrum estimation. Forward-backward spatial smoothing (FBSS) is applied to address the coherence issue. Using the estimated AoAs and AoDs, the training precoders and combiners are then optimized and the angle differences between the AoAs and AoDs at the IRS are estimated using the least squares (LS) method followed by FBSS and the root-MUSIC algorithm. Finally, the composite path gains of the cascaded channel are estimated using on-grid sparse recovery with a small-size dictionary. The simulation results suggest that the proposed estimator can achieve improved channel parameter estimation performance with lower complexity as compared to several recently reported alternatives, thanks to the exploitation of the knowledge of the array responses and low-rankness of the channel using low-complexity algorithms at all the stages.
Dynamic fibronectin assembly and remodeling by leader neural crest cells prevents jamming in collective cell migration
Authors: W. Duncan Martinson, Rebecca McLennan, Jessica M. Teddy, Mary C. McKinney, Lance A. Davidson, Ruth E. Baker, Helen M. Byrne, Paul M. Kulesa, Philip K. Maini
Date: 2022-09-16 00:00:00

Collective cell migration plays an essential role in vertebrate development, yet the extent to which dynamically changing microenvironments influence this phenomenon remains unclear. Observations of the distribution of the extracellular matrix (ECM) component fibronectin during the migration of loosely connected neural crest cells (NCCs) lead us to hypothesize that NCC remodeling of an initially punctate ECM creates a scaffold for trailing cells, enabling them to form robust and coherent stream patterns. We evaluate this idea in a theoretical setting by developing an individual-based computational model that incorporates reciprocal interactions between NCCs and their ECM. ECM remodeling, haptotaxis, contact guidance, and cell-cell repulsion are sufficient for cells to establish streams in silico, however additional mechanisms, such as chemotaxis, are required to consistently guide cells along the correct target corridor. Further model investigations imply that contact guidance and differential cell-cell repulsion between leader and follower cells are key contributors to robust collective cell migration by preventing stream breakage. Global sensitivity analysis and simulated gain- and loss-of-function experiments suggest that long-distance migration without jamming is most likely to occur when leading cells specialize in creating ECM fibers, and trailing cells specialize in responding to environmental cues by upregulating mechanisms such as contact guidance.
Prediction of Cross-Fitness for Adaptive Evolution to Different Environmental Conditions: Consequence of Phenotypic Dimensional Reduction
Authors: Takuya U. Sato, Chikara Furusawa, Kunihiko Kaneko
Date: 2022-09-16 00:00:00

How adaptive evolution to one environmental stress improves or suppresses adaptation to another is an important problem in evolutionary biology. For instance, in microbiology, the evolution of bacteria to be resistant to different antibiotics is a critical issue that has been investigated as cross-resistance. In fact, recent experiments on bacteria have suggested that the cross-resistance of their evolution to various stressful environments can be predicted by the changes to their transcriptome upon application of stress. However, there are no studies so far that explain a possible theoretical relationship between cross-resistance and changes in the transcriptome, which causes high-dimensional changes to cell phenotype. Here, we show that a correlation exists between fitness change in stress tolerance evolution and response to the environment, using a cellular model with a high-dimensional phenotype and establishing the relationship theoretically. The present results allow for the prediction of evolution from transcriptome information in response to different stresses before evolution. The relevance of this to microbiological evolution experiments is discussed.
Neuro-evolutionary evidence for a universal fractal primate brain shape
Authors: Yujiang Wang, Karoline Leiberg, Nathan Kindred, Christopher R. Madan, Colline Poirier, Christopher I. Petkov, Peter N. Taylor, Bruno C. C. Mota
Date: 2022-09-16 00:00:00
Tasks: Diversity, Morphological Analysis

The cerebral cortex displays a bewildering diversity of shapes and sizes across and within species. Despite this diversity, we present a universal multi-scale description of primate cortices. We show that all cortical shapes can be described as a set of nested folds of different sizes. As neighbouring folds are gradually merged, the cortices of 11 primate species follow a common scale-free morphometric trajectory, that also overlaps with over 70 other mammalian species. Our results indicate that all cerebral cortices are approximations of the same archetypal fractal shape with a fractal dimension of $d_f=2.5$. Importantly, this new understanding enables a more precise quantification of brain morphology as a function of scale. To demonstrate the importance of this new understanding, we show a scale-dependent effect of ageing on brain morphology. We observe a more than four-fold increase in effect size (from 2 standard deviations to 8 standard deviations) at a spatial scale of approximately 2 mm compared to standard morphological analyses. Our new understanding may therefore generate superior biomarkers for a range of conditions in the future.
Combinatorial geometry of neural codes, neural data analysis, and neural networks
Authors: Caitlin Lienkaemper
Date: 2022-09-15 00:00:00

This dissertation explores applications of discrete geometry in mathematical neuroscience. We begin with convex neural codes, which model the activity of hippocampal place cells and other neurons with convex receptive fields. In Chapter 4, we introduce order-forcing, a tool for constraining convex realizations of codes, and use it to construct new examples of non-convex codes with no local obstructions. In Chapter 5, we relate oriented matroids to convex neural codes, showing that a code has a realization with convex polytopes iff it is the image of a representable oriented matroid under a neural code morphism. We also show that determining whether a code is convex is at least as difficult as determining whether an oriented matroid is representable, implying that the problem of determining whether a code is convex is NP-hard. Next, we turn to the problem of the underlying rank of a matrix. This problem is motivated by the problem of determining the dimensionality of (neural) data which has been corrupted by an unknown monotone transformation. In Chapter 6, we introduce two tools for computing underlying rank, the minimal nodes and the Radon rank. We apply these to analyze calcium imaging data from a larval zebrafish. In Chapter 7, we explore the underlying rank in more detail, establish connections to oriented matroid theory, and show that computing underlying rank is also NP-hard. Finally, we study the dynamics of threshold-linear networks (TLNs), a simple model of the activity of neural circuits. In Chapter 9, we describe the nullcline arrangement of a threshold linear network, and show that a subset of its chambers are an attracting set. In Chapter 10, we focus on combinatorial threshold linear networks (CTLNs), which are TLNs defined from a directed graph. We prove that if the graph of a CTLN is a directed acyclic graph, then all trajectories of the CTLN approach a fixed point.
Periodic and non-periodic brainwaves emerging via random syncronization of closed loops of firing neurons
Authors: P. Mazzetti, A. Carbone
Date: 2022-08-30 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

Periodic and nonperiodic components of electrophysiological signals are modelled in terms of syncronized sequences of closed loops of firing neurons correlated in Markov chains. Single closed loops of firing neurons reproduce fundamental and harmonic components, appearing as lines in the power spectra at frequencies ranging about from $0.5 Hz$ to $ 100 Hz$. Further interesting features of the brainwave signals emerge by considering multiple syncronized sequences of closed loops. In particular, we show that the fluctuations of the number of syncronized loops leads to the onset of broadband power spectral components. By effect of the fluctuations of the number of synchronized loops and the emergence of the related broadband component, highly distorted waveform and nonstationarity of the signal are observed, consistently with empirical EEG and MEG signals. The analytical relationships of the periodic and aperiodic components are evaluated by using typical firing neuron pulse amplitudes and durations.
Model-based gym environments for limit order book trading
Authors: Joseph Jerome, Leandro Sanchez-Betancourt, Rahul Savani, Martin Herdegen
Date: 2022-09-16 00:00:00
Tasks: Algorithmic Trading, Reinforcement Learning (RL)

Within the mathematical finance literature there is a rich catalogue of mathematical models for studying algorithmic trading problems -- such as market-making and optimal execution -- in limit order books. This paper introduces \mbtgym, a Python module that provides a suite of gym environments for training reinforcement learning (RL) agents to solve such model-based trading problems. The module is set up in an extensible way to allow the combination of different aspects of different models. It supports highly efficient implementations of vectorized environments to allow faster training of RL agents. In this paper, we motivate the challenge of using RL to solve such model-based limit order book problems in mathematical finance, we explain the design of our gym environment, and then demonstrate its use in solving standard and non-standard problems from the literature. Finally, we lay out a roadmap for further development of our module, which we provide as an open source repository on GitHub so that it can serve as a focal point for RL research in model-based algorithmic trading.
Multivariate Hawkes-based Models in LOB: European, Spread and Basket Option Pricing
Authors: Qi Guo, Anatoliy Swishchuk, Bruno Rémillard
Date: 2022-09-15 00:00:00

In this paper, we consider pricing of European options and spread options for Hawkes-based model for the limit order book. We introduce multivariate Hawkes process and the multivariable general compound Hawkes process. Exponential multivariate general compound Hawkes processes and limit theorems for them, namely, LLN and FCLT, are considered then. We also consider a special case of one-dimensional EMGCHP and its limit theorems. Option pricing with $1D$ EGCHP in LOB, hedging strategies, and numerical example are presented. We also introduce greeks calculations for those models. Margrabe's spread options valuations with Hawkes-based models for two assets and numerical example are presented. Also, Margrabe's spread option pricing with two $2D$ EMGCHP and numerical example are included. Basket options valuations with numerical example are included. We finally discuss the implied volatility and implied order flow. It reveals the relationship between stock volatility and the order flow in the limit order book system. In this way, the Hawkes-based model can provide more market forecast information than the classical Black-Scholes model.
Towards a Better Microcredit Decision
Authors: Mengnan Song, Jiasong Wang, Suisui Su
Date: 2022-08-23 00:00:00

Reject inference comprises techniques to infer the possible repayment behavior of rejected cases. In this paper, we model credit in a brand new view by capturing the sequential pattern of interactions among multiple stages of loan business to make better use of the underlying causal relationship. Specifically, we first define 3 stages with sequential dependence throughout the loan process including credit granting(AR), withdrawal application(WS) and repayment commitment(GB) and integrate them into a multi-task architecture. Inside stages, an intra-stage multi-task classification is built to meet different business goals. Then we design an Information Corridor to express sequential dependence, leveraging the interaction information between customer and platform from former stages via a hierarchical attention module controlling the content and size of the information channel. In addition, semi-supervised loss is introduced to deal with the unobserved instances. The proposed multi-stage interaction sequence(MSIS) method is simple yet effective and experimental results on a real data set from a top loan platform in China show the ability to remedy the population bias and improve model generalization ability.
Prediction of $\textrm{CO}_2$ Adsorption in Nano-Pores with Graph Neural Networks
Authors: Guojing Cong, Anshul Gupta, Rodrigo Neumann, Maira de Bayser, Mathias Steiner, Breanndán Ó Conchúir
Date: 2022-08-22 00:00:00

We investigate the graph-based convolutional neural network approach for predicting and ranking gas adsorption properties of crystalline Metal-Organic Framework (MOF) adsorbents for application in post-combustion capture of $\textrm{CO}_2$. Our model is based solely on standard structural input files containing atomistic descriptions of the adsorbent material candidates. We construct novel methodological extensions to match the prediction accuracy of classical machine learning models that were built with hundreds of features at much higher computational cost. Our approach can be more broadly applied to optimize gas capture processes at industrial scale.