pwc-archive/papers-with-abstracts huggingface dataset:
Topological Signal Processing over Cell Complexes
Authors: Stefania Sardellitti, Sergio Barbarossa, Lucia Testa
Date: 2021-12-13 00:00:00

The Topological Signal Processing (TSP) framework has been recently developed to analyze signals defined over simplicial complexes, i.e. topological spaces represented by finite sets of elements that are closed under inclusion of subsets [1]. However, the same inclusion property represents sometimes a too rigid assumption that prevents the application of simplicial complexes to many cases of interest. The goal of this paper is to extend TSP to the analysis of signals defined over cell complexes, which represent a generalization of simplicial complexes, as they are not restricted to satisfy the inclusion property. In particular, the richer topological structure of cell complexes enables them to reveal cycles of any order, as representative of data features. We propose an efficient method to infer the topology of cell complexes from data by showing how their use enables sparser edge signal representations than simplicial-based methods. Furthermore, we show how to design optimal finite impulse response (FIR) filters operating on solenoidal and irrotational signals in order to minimize the approximation error with respect to the desired spectral masks.
Statistics of the Effective Massive MIMO Channel in Correlated Rician Fading
Authors: Jens Abraham, Pablo Ramirez-Espinosa, Torbjörn Ekman
Date: 2021-12-13 00:00:00
Tasks: Diversity

Massive MIMO base stations use multiple spatial diversity branches, which are often assumed to be uncorrelated in theoretical work. Correlated branches are considered seldom since they are mathematically less tractable. For correlated Rician fading, only the first- and second-order moments have been explored. To describe propagation environments more accurately, full distribution functions are needed. This manuscript provides these functions for the maximum ratio combining effective channel, a quadratic form of a random complex normal channel vector. Its mean vector and covariance matrix are based on a plane wave model incorporating array geometry, antenna element pattern, power angular spectra and power delay profiles. Closed-form approximations of the distribution functions are presented, to allow the fast evaluation of many real-world scenarios. The statistical framework is used to show that low-directivity antenna elements provide better performance in angular constricted Rician fading with off-axis incidence than high-directivity elements. Moreover, two base station array layouts are compared, showing that a half-circle array illuminates a cell more evenly than a uniform linear array. With the full distribution functions available, performance can be compared over the full range of received powers and not only based on the average SNR.
MIMO Radar Transmit Beampattern Shaping for Spectrally Dense Environments
Authors: Ehsan Raei, Saeid Sedighi, Mohammad Alaee-Kerahroodi, M. R. Bhavani Shankar
Date: 2021-12-13 00:00:00
Tasks: Radar waveform design

Designing unimodular waveforms with a desired beampattern, spectral occupancy and orthogonality level is of vital importance in the next generation Multiple-Input Multiple-Output (MIMO) radar systems. Motivated by this fact, in this paper, we propose a framework for shaping the beampattern in MIMO radar systems under the constraints simultaneously ensuring unimodularity, desired spectral occupancy and orthogonality of the designed waveform. In this manner, the proposed framework is the most comprehensive approach for MIMO radar waveform design focusing on beampattern shaping. The problem formulation leads to a non-convex quadratic fractional programming. We propose an effective iterative to solve the problem, where each iteration is composed of a Semi-Definite Programming (SDP) followed by eigenvalue decomposition. Some numerical simulations are provided to illustrate the superior performance of our proposed over the state-of-the-art.
Reconfigurable Holographic Surfaces for Future Wireless Communications
Authors: Ruoqi Deng, Boya Di, Hongliang Zhang, Dusit Niyato, Zhu Han, H. Vincent Poor, Lingyang Song
Date: 2021-12-13 00:00:00

Future wireless communications look forward to constructing a ubiquitous intelligent information network with high data rates through cost-efficient devices. Benefiting from the tunability and programmability of metamaterials, the reconfigurable holographic surface (RHS) composed of numerous metamaterial radiation elements is developed as a promising solution to fulfill such challenging visions. The RHS is more likely to serve as an ultra-thin and lightweight surface antenna integrated with the transceiver to generate beams with desirable directions by leveraging the holographic principle. This is different from reconfigurable intelligent surfaces (RISs) widely used as passive relays due to the reflection characteristic. In this article, we investigate RHS-aided wireless communications. Starting with a basic introduction of the RHS including its hardware structure, holographic principle, and fabrication methodologies, we propose a hybrid beamforming scheme for RHS-aided multi-user communication systems. A joint sum-rate maximization algorithm is then developed where the digital beamforming performed at the base station and the holographic beamforming performed at the RHS are optimized iteratively. Furthermore, key challenges in RHS-aided wireless communications are also discussed.
Continuous Human Action Detection Based on Wearable Inertial Data
Authors: Xia Gong, Yan Lu, Haoran Wei
Date: 2021-12-11 00:00:00
Tasks: Action Detection, Gesture Recognition

Human action detection is a hot topic, which is widely used in video surveillance, human machine interface, healthcare monitoring, gaming, dancing training and musical instrument teaching. As inertial sensors are low cost, portable, and having no operating space, it is suitable to detect human action. In real-world applications, actions that are of interest appear among actions of non interest without pauses in between. Recognizing and detecting actions of interests from continuous action streams is more challenging and useful for real applications. Based on inertial sensor and C-MHAD smart TV gesture recognition dataset, this paper utilized different inertial sensor feature formats, then compared the performance with different deep neural network structures according to these feature formats. Experiment results show the best performance was achieved by image based inertial feature with convolution neural network, which got 51.1% F1 score.
An Overview of Signal Processing Techniques for RIS/IRS-aided Wireless Systems
Authors: Cunhua Pan, Gui Zhou, Kangda Zhi, Sheng Hong, Tuo Wu, Yijin Pan, Hong Ren, Marco Di Renzo, A. Lee Swindlehurst, Rui Zhang, Angela Yingjun Zhang
Date: 2021-12-11 00:00:00

In the past as well as present wireless communication systems, the wireless propagation environment is regarded as an uncontrollable black box that impairs the received signal quality, and its negative impacts are compensated for by relying on the design of various sophisticated transmission/reception schemes. However, the improvements through applying such schemes operating at two endpoints (i.e., transmitter and receiver) only are limited even after five generations of wireless systems. Reconfigurable intelligent surface (RIS) or intelligent reflecting surface (IRS) have emerged as a new and revolutionary technology that can configure the wireless environment in a favorable manner by properly tuning the phase shifts of a large number of quasi passive and low-cost reflecting elements, thus standing out as a promising candidate technology for the next-/sixth-generation (6G) wireless system. However, to reap the performance benefits promised by RIS/IRS, efficient signal processing techniques are crucial, for a variety of purposes such as channel estimation, transmission design, radio localization, and so on. In this paper, we provide a comprehensive overview of recent advances on RIS/IRS-aided wireless systems from the signal processing perspective. We also highlight promising research directions that are worthy of investigation in the future.
Circuit Characterization of IRS to Control Beamforming Design for Efficient Wireless Communication
Authors: Bhupendra Sharma, Anirudh Agarwal, Deepak Mishra, Soumitra Debnath
Date: 2021-12-11 00:00:00

Intelligent reflecting surface (IRS) has emerged as a transforming solution to enrich wireless communications by efficiently reconfiguring the propagation environment. In this paper, a novel IRS circuit characterization model is proposed for practical beamforming design incorporating various electrical parameters of the meta-surface unit cell. Specifically, we have modelled the IRS control parameters, phase shift (PS) and reflection amplitude (RA) at the communication receiver, in addition to the circuit level parameter, variable effective capacitance $C$ of IRS unit cell. We have obtained closed-form expressions of PS, RA and $C$ in terms of transmission frequency of signal incident to IRS and various electrical parameters of IRS circuit, with a novel touch towards an accurate analytical model for a better beamforming design perspective. Numerical results demonstrate the efficacy of the proposed characterization thereby providing key design insights.
mREAL-GAN: Generating Multiple Residential Electrical Appliance Load Profiles with Inter-Dependencies using a Generative Adversarial Network
Authors: Edward Sanderson, Aikaterini Fragaki, Jules Simo, Bogdan J. Matuszewski
Date: 2021-12-08 00:00:00
Tasks: Generative Adversarial Network

In this paper, we introduce mREAL-GAN, a generative adversarial network (GAN) for the parallel generation of multiple residential electrical appliance load (mREAL) profiles. mREAL-GAN is intended for use in community-scale low-voltage network analysis, and represents a departure from previous methods for this purpose, which break the generation of appliance load profiles into several steps and largely model each appliance independently. Instead, mREAL-GAN models appliance load profiles in an end-to-end manner, and generates multiple appliance load profiles in parallel in a way that captures inter-dependencies. We show that mREAL-GAN generates load profiles for individual appliance-types with greater fidelity than a popular example of previous methods, and demonstrate its ability to capture inter-dependencies between appliances.
DriPP: Driven Point Processes to Model Stimuli Induced Patterns in M/EEG Signals
Authors: Cédric Allain, Alexandre Gramfort, Thomas Moreau
Date: 2021-12-08 00:00:00
Proceeding: ICLR 2022 4
Tasks: Dictionary Learning, EEG, Electroencephalogram (EEG), Point Processes

The quantitative analysis of non-invasive electrophysiology signals from electroencephalography (EEG) and magnetoencephalography (MEG) boils down to the identification of temporal patterns such as evoked responses, transient bursts of neural oscillations but also blinks or heartbeats for data cleaning. Several works have shown that these patterns can be extracted efficiently in an unsupervised way, e.g., using Convolutional Dictionary Learning. This leads to an event-based description of the data. Given these events, a natural question is to estimate how their occurrences are modulated by certain cognitive tasks and experimental manipulations. To address it, we propose a point process approach. While point processes have been used in neuroscience in the past, in particular for single cell recordings (spike trains), techniques such as Convolutional Dictionary Learning make them amenable to human studies based on EEG/MEG signals. We develop a novel statistical point process model-called driven temporal point processes (DriPP)-where the intensity function of the point process model is linked to a set of point processes corresponding to stimulation events. We derive a fast and principled expectation-maximization (EM) algorithm to estimate the parameters of this model. Simulations reveal that model parameters can be identified from long enough signals. Results on standard MEG datasets demonstrate that our methodology reveals event-related neural responses-both evoked and induced-and isolates non-task specific temporal patterns.
Deep learning in automated ultrasonic NDE -- developments, axioms and opportunities
Authors: Sergio Cantero-Chinchilla, Paul D. Wilcox, Anthony J. Croxford
Date: 2021-12-07 00:00:00
Tasks: Deep Learning, Defect Detection

The analysis of ultrasonic NDE data has traditionally been addressed by a trained operator manually interpreting data with the support of rudimentary automation tools. Recently, many demonstrations of deep learning (DL) techniques that address individual NDE tasks (data pre-processing, defect detection, defect characterisation, and property measurement) have started to emerge in the research community. These methods have the potential to offer high flexibility, efficiency, and accuracy subject to the availability of sufficient training data. Moreover, they enable the automation of complex processes that span one or more NDE steps (e.g. detection, characterisation, and sizing). There is, however, a lack of consensus on the direction and requirements that these new methods should follow. These elements are critical to help achieve automation of ultrasonic NDE driven by artificial intelligence such that the research community, industry, and regulatory bodies embrace it. This paper reviews the state-of-the-art of autonomous ultrasonic NDE enabled by DL methodologies. The review is organised by the NDE tasks that are addressed by means of DL approaches. Key remaining challenges for each task are noted. Basic axiomatic principles for DL methods in NDE are identified based on the literature review, relevant international regulations, and current industrial needs. By placing DL methods in the context of general NDE automation levels, this paper aims to provide a roadmap for future research and development in the area.
Laotian Bat Sarbecovirus BANAL-236 Uses ACE2 to Infect Cells by an Unknown Mechanism
Authors: Steven Carl Quay
Date: 2021-12-12 00:00:00

A manuscript identified bat sarbecoviruses with high sequence homology to SARS-CoV-2 found in caves in Laos that can directly infect human cells via the human ACE2 receptor (Coronaviruses with a SARS-CoV-2-like receptor binding domain allowing ACE2-mediated entry into human cells isolated from bats of Indochinese peninsula, Temmam S., et al.). Here, I examine the genomic sequence of one of these viruses, BANAL-236, and show it has 5-UTR and 3-UTR secondary structures that are non-canonical and, in fact, have never been seen in an infective coronavirus. Specifically, the 5-UTR has a 177 nt copy-back extension which forms an extended, highly stable duplex RNA structure. Because of this copy-back, the four obligate Stem Loops (SL) -1, -2, -3, and -4 cis-acting elements found in all currently known replicating coronaviruses are buried in the extended duplex. The 3-UTR has a similar fold-back duplex of 144 nts and is missing the obligate poly-A tail. Taken together, these findings demonstrate BANAL-236 is missing eight obligate UTR cis-acting elements; each one of which has previously been lethal to replication when modified individually. Neither duplex copyback has ever been observed in an infective sarbecovirus, although some of the features have been seen in defective interfering particles, which can be found in co-infections with non-defective, replicating viruses. They are also a common error seen during synthetic genome assembly in a laboratory. BANAL-236 must have evolved an entirely unique mechanism for replication, RNA translation, and RNA packaging never seen in a coronavirus and because it is a bat sarbecovirus closely related to SARS-CoV-2, it is imperative that we understand its unique mode of infectivity by a collaborative, international research effort.
Seeking the Building Blocks of Visual Imagery and Creativity in a Cognitively Inspired Neural Network
Authors: Shekoofeh Hedayati, Roger Beaty, Brad Wyble
Date: 2021-12-13 00:00:00
Proceeding: NeurIPS Workshop SVRHM 2021 12

How do we imagine visual objects and combine them to create new forms? To answer this question, we need to explore the cognitive, computational and neural mechanisms underlying imagery and creativity. The body of research on deep learning models with creative behaviors is growing. However, in this paper we suggest that the complexity of such models and their training sets is an impediment to using them as tools to understand human aspects of creativity. We propose using simpler models, inspired by neural and cognitive mechanisms, that are trained with smaller data sets. We show that a standard deep learning architecture can demonstrate imagery by generating shape/color combinations using only symbolic codes as input. However, generating a new combination that was not experienced by the model was not possible. We discuss the limitations of such models, and explain how creativity could be embedded by incorporating memory mechanisms to combine the output of the network into new combinations and use that as new training data.
Impact of Weight Loss on Brain Age: Improved Brain Health Following Bariatric Surgery
Authors: Yashar Zeighami, Mahsa Dadar, Justine Daoust, Melissa Pelletier, Laurent Biertho, Leonie Bouvet-Bouchard, Stephanie Fulton, Andre Tchernof, Alain Dagher, Denis Richard, Alan Evans, Andreanne Michaud
Date: 2021-12-12 00:00:00

Overweight and obese individuals tend to have increased brain age, reflecting poorer brain health likely due to grey and white matter atrophy related to obesity. However, it is unclear if older brain age associated with obesity can be reversed following weight loss and cardiometabolic health improvement. The aim of this study was to assess the impact of weight loss and cardiometabolic improvement following bariatric surgery on brain health, as measured by change in brain age estimated based on voxel-based morphometry (VBM). We used three datasets: 1) CamCAN to train the brain age prediction model, 2) Human Connectome Project to investigate whether individuals with obesity have greater brain age than individuals with normal weight, and 3) pre-surgery, as well as 4, 12, and 24 month post-surgery data from participants (n=87) who underwent a bariatric surgery to investigate whether weight loss and cardiometabolic improvement as a result of bariatric surgery lowers the brain age. Our results from the HCP dataset showed a higher brain age for individuals with obesity compared to individuals with normal weight (p<0.0001). We also found significant improvement in brain health, indicated by a decrease of 2.9 and 5.6 years in adjusted delta age at 12 and 24 months following bariatric surgery compared to baseline (p<0.0005). While the overall effect seemed to be driven by a global change across all brain regions and not from a specific region, our exploratory analysis showed lower delta age in certain brain regions at 24 months. This reduced age was also associated with post-surgery improvements in BMI, systolic/diastolic blood pressure, and HOMA-IR (p<0.05). In conclusion, these results suggest that obesity-related brain health abnormalities might be reversed by bariatric surgery-induced weight loss and widespread improvements in cardiometabolic alterations.
Proper solutions for Epstein-Zin Stochastic Differential Utility
Authors: Martin Herdegen, David Hobson, Joseph Jerome
Date: 2021-12-13 00:00:00

In this article, we consider the optimal investment-consumption problem for an agent with preferences governed by Epstein--Zin stochastic differential utility (EZ-SDU) who invests in a constant-parameter Black-Scholes-Merton market over the infinite horizon. The parameter combinations that we consider in this paper are such that the risk aversion parameter $R$ and the elasticity of intertemporal complementarity $S$ satisfy $\theta=\frac{1-R}{1-S}>1$. In this sense, this paper is complementary to Herdegen, Hobson and Jerome [arXiv:2107.06593]. The main novelty of the case $\theta>1$ (as opposed to $\theta\in(0,1)$) is that there is an infinite family of utility processes associated to every nonzero consumption stream. To deal with this issue, we introduce the economically motivated notion of a proper utility process, where, roughly speaking, a utility process is proper if it is nonzero whenever future consumption is nonzero. We then proceed to show that for a very wide class of consumption streams $C$, there exists a proper utility process $V$ associated to $C$. Furthermore, for a wide class of consumption streams $C$, the proper utility process $V$ is unique. Finally, we solve the optimal investment-consumption problem in a constant parameter financial market, where we optimise over the right-continuous attainable consumption streams that have a unique proper utility process associated to them.
Optimal Expansion of Business Opportunity
Authors: Ling Wang, Kexin Chen, Mei Choi Chiu, Hoi Ying Wong
Date: 2021-12-13 00:00:00

Any firm whose business strategy has an exposure constraint that limits its potential gain naturally considers expansion, as this can increase its exposure. We model business expansion as an enlargement of the opportunity set for business policies. However, expansion is irreversible and has an opportunity cost attached. We use the expected optimization of utility to formulate this as a novel stochastic control problem combined with an optimal stopping time, and we derive an explicit solution for exponential utility. We apply the framework to an investment and a reinsurance scenario. In the investment problem, the cost and incentives to increase the trading exposure are analyzed, while the optimal timing for an insurer to launch its reinsurance business is investigated in the reinsurance problem. Our model predicts that the additional income gained through business expansion is the key incentive for a decision to expand. Interestingly, companies may have this incentive but are likely to wait for a period of time before expanding, although situations of zero opportunity cost or specific restrictive conditions on the model parameters are exceptions to waiting. The business policy remains on the boundary of the opportunity set before expansion during the waiting period. The length of the waiting period is related to the opportunity cost, return, and risk of the expanded business.
Scalar systemic risk measures and Aumann-Shapley allocations
Authors: Ludger Overbeck, Florian Schindler
Date: 2021-12-13 00:00:00

We study two different contributions to the theory of (scalar) systemic risk measures. Namely the first aggregate or axiomatic approach and the first inject capital approach. For this purpose we establish a general framework, which is rich enough to embed both approaches. It turns out that in most relevant situations systemic risk measures of the first inject capital approach have a representation in the more general axiomatic approach. Moreover, we study capital allocation rules (CARs). In both situations there exist canonical ways to answer the capital allocation problem. Additionally, a capital allocation rule (CAR) in the spirit of Aumann-Shapley is introduced, which gives us the opportunity to compute systemic capital allocations regardless of the risk measurement approach. This CAR also serves as an instrument to compare both approaches and to identify commonalities.