pwc-archive/papers-with-abstracts huggingface dataset:
Comprehensive and user-analytics-friendly cancer patient database for physicians and researchers
Authors: Ali Firooz, Avery T. Funkhouser, Julie C. Martin, W. Jeffery Edenfield, Homayoun Valafar, Anna V. Blenda
Date: 2023-02-01 00:00:00
Tasks: Diagnostic

Nuanced cancer patient care is needed, as the development and clinical course of cancer is multifactorial with influences from the general health status of the patient, germline and neoplastic mutations, co-morbidities, and environment. To effectively tailor an individualized treatment to each patient, such multifactorial data must be presented to providers in an easy-to-access and easy-to-analyze fashion. To address the need, a relational database has been developed integrating status of cancer-critical gene mutations, serum galectin profiles, serum and tumor glycomic profiles, with clinical, demographic, and lifestyle data points of individual cancer patients. The database, as a backend, provides physicians and researchers with a single, easily accessible repository of cancer profiling data to aid-in and enhance individualized treatment. Our interactive database allows care providers to amalgamate cohorts from these groups to find correlations between different data types with the possibility of finding "molecular signatures" based upon a combination of genetic mutations, galectin serum levels, glycan compositions, and patient clinical data and lifestyle choices. Our project provides a framework for an integrated, interactive, and growing database to analyze molecular and clinical patterns across cancer stages and subtypes and provides opportunities for increased diagnostic and prognostic power.
Decentralised and Cooperative Control of Multi-Robot Systems through Distributed Optimisation
Authors: Yi Dong, Zhongguo Li, Xingyu Zhao, Zhengtao Ding, Xiaowei Huang
Date: 2023-02-03 00:00:00

Multi-robot cooperative control has gained extensive research interest due to its wide applications in civil, security, and military domains. This paper proposes a cooperative control algorithm for multi-robot systems with general linear dynamics. The algorithm is based on distributed cooperative optimisation and output regulation, and it achieves global optimum by utilising only information shared among neighbouring robots. Technically, a high-level distributed optimisation algorithm for multi-robot systems is presented, which will serve as an optimal reference generator for each individual agent. Then, based on the distributed optimisation algorithm, an output regulation method is utilised to solve the optimal coordination problem for general linear dynamic systems. The convergence of the proposed algorithm is theoretically proved. Both numerical simulations and real-time physical robot experiments are conducted to validate the effectiveness of the proposed cooperative control algorithms.
NBA-OMP: Near-field Beam-Split-Aware Orthogonal Matching Pursuit for Wideband THz Channel Estimation
Authors: Ahmet M. Elbir, Kumar Vijay Mishra, Symeon Chatzinotas
Date: 2023-02-03 00:00:00

The sixth-generation networks envision the terahertz (THz) band as one of the key enabling technologies because of its ultrawide bandwidth. To combat the severe attenuation, the THz wireless systems employ large arrays, wherein the near-field beam-split (NB) severely degrades the accuracy of channel acquisition. Contrary to prior works that examine only either narrowband beamforming or far-field models, we estimate the wideband THz channel via an NB-aware orthogonal matching pursuit (NBA-OMP) approach. We design an NBA dictionary of near-field steering vectors by exploiting the corresponding angular and range deviation. Our OMP algorithm accounts for this deviation thereby ipso facto mitigating the effect of NB. Numerical experiments demonstrate the effectiveness of the proposed channel estimation technique for wideband THz systems.
The Role of Game Networking in the Fusion of Physical and Digital Worlds through 6G Wireless Networks
Authors: Van-Phuc Bui, Shashi Raj Pandey, Andreas Casparsen, Federico Chiariotti, Petar Popovski
Date: 2023-02-03 00:00:00

The sixth generation (6G) of wireless technology is seen as one of the enablers of real-time fusion of the physical and digital realms, as in Digital Twin, eXtended reality, or the Metaverse. This would allow people to interact, work, and entertain themselves in an immersive social network of online 3D virtual environments. From the viewpoint of communication and networking, this will represent an evolution of the game networking technology, designed to interconnect massive users in real-time online gaming environments. This article presents the basic principles of game networking and discusses their evolution towards meeting the requirements of the Metaverse and similar applications. Several open research challenges are discussed, along with possible solutions through experimental case studies.
Relating EEG to continuous speech using deep neural networks: a review
Authors: Corentin Puffay, Bernd Accou, Lies Bollens, Mohammad Jalilpour Monesi, Jonas Vanthornhout, Hugo Van hamme, Tom Francart
Date: 2023-02-03 00:00:00
Tasks: Deep Learning, EEG, Electroencephalogram (EEG)

Objective. When a person listens to continuous speech, a corresponding response is elicited in the brain and can be recorded using electroencephalography (EEG). Linear models are presently used to relate the EEG recording to the corresponding speech signal. The ability of linear models to find a mapping between these two signals is used as a measure of neural tracking of speech. Such models are limited as they assume linearity in the EEG-speech relationship, which omits the nonlinear dynamics of the brain. As an alternative, deep learning models have recently been used to relate EEG to continuous speech. Approach. This paper reviews and comments on deep-learning-based studies that relate EEG to continuous speech in single- or multiple-speakers paradigms. We point out recurrent methodological pitfalls and the need for a standard benchmark of model analysis. Main results. We gathered 29 studies. The main methodological issues we found are biased cross-validations, data leakage leading to over-fitted models, or disproportionate data size compared to the model's complexity. In addition, we address requirements for a standard benchmark model analysis, such as public datasets, common evaluation metrics, and good practices for the match-mismatch task. Significance. We present a review paper summarizing the main deep-learning-based studies that relate EEG to speech while addressing methodological pitfalls and important considerations for this newly expanding field. Our study is particularly relevant given the growing application of deep learning in EEG-speech decoding.
Chance Constrained Stochastic Optimal Control for Linear Systems with a Time Varying Random Control Matrix
Authors: Shawn Priore, Meeko Oishi
Date: 2023-02-03 00:00:00

This work proposes an open-loop methodology to solve chance constrained stochastic optimal control problems for linear systems with a stochastic control matrix. We consider a joint chance constraint for polytopic time-varying target sets under moment and unimodality assumptions. We reformulate the chance constraint into individual biconvex constraints using the one-sided Vysochanskij-Petunin inequality. We demonstrate our methodology on two spacecraft rendezvous problems. We compare the proposed method with the scenario approach and moment-based methods based on Cantelli's inequality.
A Robust Time-Delay Approach to Extremum Seeking via ISS Analysis of the Averaged System
Authors: Xuefei Yang, Emilia Fridman
Date: 2023-02-03 00:00:00

For N-dimensional (ND) static quadratic map, we present a time-delay approach to gradient-based extremum seeking (ES) both, in the continuous and, for the first time, the discrete domains. As in the recently introduced (for 2D maps in the continuous domain), we transform the system to the time-delay one (neutral type system in the form of Hale in the continuous case). This system is O($\varepsilon$)-perturbation of the averaged linear ODE system, where $\varepsilon$ is a period of averaging. We further explicitly present the neutral system as the linear ODE, where O($\varepsilon$)-terms are considered as disturbances with distributed delays of the length of the small parameter $\varepsilon$. Regional input-to-state stability (ISS) analysis is provided by employing a variation of constants formula that greatly simplifies the previously used analysis via Lyapunov-Krasovskii (L-K) method, simplifies the conditions and improves the results. Examples from the literature illustrate the efficiency of the new approach, allowing essentially large uncertainty of the Hessian matrix with bounds on $\varepsilon$ that are not too small.
Frequency Quality in Low-Inertia Power Systems
Authors: Taulant Kerci, Manuel Hurtado, Mariglen Gjergji, Simon Tweed, Eoin Kennedy, Federico Milano
Date: 2023-02-03 00:00:00

This paper analyses the issue of frequency quality in low-inertia power systems. The analysis is based on a real-world large-scale low-inertia power system namely, the All-Island transmission system (AITS) of Ireland and Northern Ireland currently accommodating up to 75% of non-synchronous generation. The paper is motivated by a recent trend of some frequency quality parameters such as the standard frequency deviation and the slow frequency restoration. The paper first discusses the frequency control services currently in place to ensure frequency quality in the AITS. An analysis of the frequency quality parameters of the AITS is then presented based on actual data. The paper also discusses, through an illustrative example, the effectiveness of automatic generation control as a potential approach to keep frequency within the operational range.
Design of generalized fuzzy multiple deferred state (GFMDS) sampling plan for attributes
Authors: Julia Thampy Thomas, Mahesh Kumar
Date: 2023-02-03 00:00:00

. A sampling plan is a pilot tool for a supply and demand chain quality check strategy. These plans proved to be economically viable for the quality inspection processes but the uncertainty in the plan parameters challenged the reliability of the application of traditional acceptance sampling plans. This study proposes a generalized fuzzy multiple deferred state (GFMDS) sampling plan for attributes that consider the ambiguity in determining the exact value of the percentage of defectives in a batch. The performance measures have been derived and the plan is designed in terms of a minimum average sample number. A comparison study is done over the existing fuzzy acceptance sampling plans for attributes and a pertinent observation is made regarding the efficiency of the GFMDS scheme. The effect of inspection errors on the sampling procedure is analyzed and the drop in the acceptance criteria of the plan is observed corresponding to the intensified inspection errors. Several numerical examples are presented to validate the results
Jointly-Optimized Searching and Tracking with Random Finite Sets
Authors: Savvas Papaioannou, Panayiotis Kolios, Theocharis Theocharides, Christos G. Panayiotou, Marios M. Polycarpou
Date: 2023-02-01 00:00:00

In this paper, we investigate the problem of joint searching and tracking of multiple mobile targets by a group of mobile agents. The targets appear and disappear at random times inside a surveillance region and their positions are random and unknown. The agents have limited sensing range and receive noisy measurements from the targets. A decision and control problem arises, where the mode of operation (i.e., search or track) as well as the mobility control action for each agent, at each time instance, must be determined so that the collective goal of searching and tracking is achieved. We build our approach upon the theory of random finite sets (RFS) and we use Bayesian multi-object stochastic filtering to simultaneously estimate the time-varying number of targets and their states from a sequence of noisy measurements. We formulate the above problem as a non-linear binary program (NLBP) and show that it can be approximated by a genetic algorithm. Finally, to study the effectiveness and performance of the proposed approach we have conducted extensive simulation experiments.
Using bayesmixedlogit and bayesmixedlogitwtp in Stata
Authors: Matthew J. Baker
Date: 2023-02-03 00:00:00

This document presents an overview of the bayesmixedlogit and bayesmixedlogitwtp Stata packages. It mirrors closely the helpfile obtainable in Stata (i.e., through help bayesmixedlogit or help bayesmixedlogitwtp). Further background for the packages can be found in Baker(2014).
Agreed and Disagreed Uncertainty
Authors: Luca Gambetti, Dimitris Korobilis, John Tsoukalas, Francesco Zanetti
Date: 2023-02-03 00:00:00

When agents' information is imperfect and dispersed, existing measures of macroeconomic uncertainty based on the forecast error variance have two distinct drivers: the variance of the economic shock and the variance of the information dispersion. The former driver increases uncertainty and reduces agents' disagreement (agreed uncertainty). The latter increases both uncertainty and disagreement (disagreed uncertainty). We use these implications to identify empirically the effects of agreed and disagreed uncertainty shocks, based on a novel measure of consumer disagreement derived from survey expectations. Disagreed uncertainty has no discernible economic effects and is benign for economic activity, but agreed uncertainty exerts significant depressing effects on a broad spectrum of macroeconomic indicators.
Inference in Non-stationary High-Dimensional VARs
Authors: Alain Hecq, Luca Margaritella, Stephan Smeekes
Date: 2023-02-02 00:00:00
Tasks: Time Series, Time Series Analysis, Vocal Bursts Intensity Prediction

In this paper we construct an inferential procedure for Granger causality in high-dimensional non-stationary vector autoregressive (VAR) models. Our method does not require knowledge of the order of integration of the time series under consideration. We augment the VAR with at least as many lags as the suspected maximum order of integration, an approach which has been proven to be robust against the presence of unit roots in low dimensions. We prove that we can restrict the augmentation to only the variables of interest for the testing, thereby making the approach suitable for high dimensions. We combine this lag augmentation with a post-double-selection procedure in which a set of initial penalized regressions is performed to select the relevant variables for both the Granger causing and caused variables. We then establish uniform asymptotic normality of a second-stage regression involving only the selected variables. Finite sample simulations show good performance, an application to investigate the (predictive) causes and effects of economic uncertainty illustrates the need to allow for unknown orders of integration.
An Insurance Paradigm for Improving Power System Resilience via Distributed Investment
Authors: Farhad Billimoria, Filiberto Fele, Iacopo Savelli, Thomas Morstyn, Malcolm McCulloch
Date: 2023-02-02 00:00:00

Extreme events, exacerbated by climate change, pose significant risks to the energy system and its consumers. However there are natural limits to the degree of protection that can be delivered from a centralised market architecture. Distributed energy resources provide resilience to the energy system, but their value remains inadequately recognized by regulatory frameworks. We propose an insurance framework to align residual outage risk exposure with locational incentives for distributed investment. We demonstrate that leveraging this framework in large-scale electricity systems could improve consumer welfare outcomes in the face of growing risks from extreme events via investment in distributed energy.
Analysis of mesh-based motion compensation in wavelet lifting of dynamical 3-D+t CT data
Authors: Wolfgang Schnurrer, Thomas Richter, Jürgen Seiler, André Kaup
Date: 2023-02-03 00:00:00
Tasks: Motion Compensation

Factorized in the lifting structure, the wavelet transform can easily be extended by arbitrary compensation methods. Thereby, the transform can be adapted to displacements in the signal without losing the ability of perfect reconstruction. This leads to an improvement of scalability. In temporal direction of dynamic medical 3-D+t volumes from Computed Tomography, displacement is mainly given by expansion and compression of tissue. We show that these smooth movements can be well compensated with a mesh-based method. We compare the properties of triangle and quadrilateral meshes. We also show that with a mesh-based compensation approach coding results are comparable to the common slice wise coding with JPEG 2000 while a scalable representation in temporal direction can be achieved.
Graph-Based Compensated Wavelet Lifting for Scalable Lossless Coding of Dynamic Medical Data
Authors: Daniela Lanz, André Kaup
Date: 2023-02-03 00:00:00
Tasks: Data Compression, Motion Compensation

Lossless compression of dynamic 2D+t and 3D+t medical data is challenging regarding the huge amount of data, the characteristics of the inherent noise, and the high bit depth. Beyond that, a scalable representation is often required in telemedicine applications. Motion Compensated Temporal Filtering works well for lossless compression of medical volume data and additionally provides temporal, spatial, and quality scalability features. To achieve a high quality lowpass subband, which shall be used as a downscaled representative of the original data, graph-based motion compensation was recently introduced to this framework. However, encoding the motion information, which is stored in adjacency matrices, is not well investigated so far. This work focuses on coding these adjacency matrices to make the graph-based motion compensation feasible for data compression. We propose a novel coding scheme based on constructing so-called motion maps. This allows for the first time to compare the performance of graph-based motion compensation to traditional block- and mesh-based approaches. For high quality lowpass subbands our method is able to outperform the block- and mesh-based approaches by increasing the visual quality in terms of PSNR by 0.53dB and 0.28dB for CT data, as well as 1.04dB and 1.90dB for MR data, respectively, while the bit rate is reduced at the same time.
Scalable Lossless Coding of Dynamic Medical CT Data Using Motion Compensated Wavelet Lifting with Denoised Prediction and Update
Authors: Daniela Lanz, Franz Schilling, André Kaup
Date: 2023-02-03 00:00:00
Tasks: Denoising, Motion Compensation

Professional applications like telemedicine often require scalable lossless coding of sensitive data. 3-D subband coding has turned out to offer good compression results for dynamic CT data and additionally provides a scalable representation in terms of low- and highpass subbands. To improve the visual quality of the lowpass subband, motion compensation can be incorporated into the lifting structure, but leads to inferior compression results at the same time. Prior work has shown that a denoising filter in the update step can improve the compression ratio. In this paper, we present a new processing order of motion compensation and denoising in the update step and additionally introduce a second denoising filter in the prediction step. This allows for reducing the overall file size by up to 4.4%, while the visual quality of the lowpass subband is kept nearly constant.
On the Optimal Spoofing Attack and Countermeasure in Satellite Navigation Systems
Authors: Laura Crosara, Francesco Ardizzon, Stefano Tomasin, Nicola Laurenti
Date: 2023-02-03 00:00:00

The threat of signal spoofing attacks against GNSS has grown in recent years and has motivated the study of anti-spoofing techniques. However, defense methods have been designed only against specific attacks. This paper introduces a general model of the spoofing attack framework in GNSS, from which optimal attack and defense strategies are derived. We consider a scenario with a legitimate receiver (Bob) testing if the received signals come from multiple legitimate space vehicles (Alice) or from an attack device (Eve). We first derive the optimal attack strategy against a Gaussian transmission from Alice, by minimizing an outer bound on the achievable error probability region of the spoofing detection test. Then, framing the spoofing and its detection as an adversarial game, we show that the Gaussian transmission and the corresponding optimal attack constitute a Nash equilibrium. Lastly, we consider the case of practical modulation schemes for Alice and derive the generalized likelihood ratio test. Numerical results validate the analytical derivations and show that the bound on the achievable error region is representative of the actual performance.
Monitoring of Underwater Critical Infrastructures: the Nord Stream and Other Recent Case Studies
Authors: Giovanni Soldi, Domenico Gaglione, Simone Raponi, Nicola Forti, Enrica d'Afflisio, Paweł Kowalski, Leonardo M. Millefiori, Dimitris Zissis, Paolo Braca, Peter Willett, Alain Maguer, Sandro Carniel, Giovanni Sembenini, Catherine Warner
Date: 2023-02-03 00:00:00

The explosions on September 26th, 2022, which damaged the gas pipelines of Nord Stream 1 and Nord Stream 2, have highlighted the need and urgency of improving the resilience of Underwater Critical Infrastructures (UCIs). Comprising gas pipelines and power and communication cables, these connect countries worldwide and are critical for the global economy and stability. An attack targeting multiple of such infrastructures simultaneously could potentially cause significant damage and greatly affect various aspects of daily life. Due to the increasing number and continuous deployment of UCIs, existing underwater surveillance solutions, such as Autonomous Underwater Vehicles (AUVs) or Remotely Operated Vehicles (ROVs), are not adequate enough to ensure thorough monitoring. We show that the combination of information from both underwater and above-water surveillance sensors enables achieving Seabed-to-Space Situational Awareness (S3A), mainly thanks to Artificial Intelligence (AI) and Information Fusion (IF) methodologies. These are designed to process immense volumes of information, fused from a variety of sources and generated from monitoring a very large number of assets on a daily basis. The learned knowledge can be used to anticipate future behaviors, identify threats, and determine critical situations concerning UCIs. To illustrate the capabilities and importance of S3A, we consider three events that occurred in the second half of 2022: the aforementioned Nord Stream explosions, the cutoff of the underwater communication cable SHEFA-2 connecting the Shetland Islands and the UK mainland, and the suspicious activity of a large vessel in the Adriatic Sea. Specifically, we provide analyses of the available data, from Automatic Identification System (AIS) and satellite data, integrated with possible contextual information, e.g., bathymetry, weather conditions, and human intelligence.