pwc-archive/papers-with-abstracts huggingface dataset:
Network-Controlled Repeaters vs. Reconfigurable Intelligent Surfaces for 6G mmW Coverage Extension
Authors: Reza Aghazadeh Ayoubi, Marouan Mizmizi, Dario Tagliaferri, Danilo De Donno, Umberto Spagnolini
Date: 2022-11-15 00:00:00
Tasks: valid

Network-controlled repeaters (NCR) and reconfigurable intelligent surfaces (RIS) are being considered by the third generation partnership project (3GPP) as valid candidates for range extension in millimeter-wave (mmW, 30-300 GHz frequency) 5G and 6G networks, to counteract large path and penetration losses. Nowadays, there is no definite answer on which of the two technologies is better for coverage extension, since existing comparative studies focused either on communication-related metrics only or on very large-scale analysis, without physical layer details or environmental considerations. This paper aims at comparing NCR and RIS solutions in terms of coverage in an urban scenario. Numerical results suggest a possible usage of both technologies, showing that preferring RIS or NCR depends not only on their physical capabilities and cost (e.g., the number of antenna elements, the angular separation between panels for NCR, etc.) but also on environment geometry.
Low-Cost Beamforming and DOA Estimation Based on One-Bit Reconfigurable Intelligent Surface
Authors: Zihan Yang, Peng Chen, Ziyu Guo, Dahai Ni
Date: 2022-11-15 00:00:00

In this work, we consider the Direction-of-Arrival (DOA) estimation problem in a low-cost architecture where only one antenna as the receiver is aided by a reconfigurable intelligent surface (RIS). We introduce the one-bit RIS as a signal reflector to enhance signal transmission in non-line-of-sight (NLOS) situations and substantially simplify the physical hardware for DOA estimation. We optimize the beamforming scheme called measurement matrix to focus the echo power on the receiver with the coarse localization information of the targets as the prior. A beamforming scheme based on the modified genetic algorithm is proposed to optimize the measurement matrix, guaranteeing restricted isometry property (RIP) and meeting single beamforming requirements. The DOA results are finely estimated by solving an atomic-norm based sparse reconstruction problem. Simulation results show that the proposed method outperforms the existing methods in the DOA estimation performance.
A Framework for Mutual Information-based MIMO Integrated Sensing and Communication Beamforming Design
Authors: Jin Li, Gui Zhou, Tantao Gong, Nan Liu
Date: 2022-11-15 00:00:00
Tasks: Integrated sensing and communication, ISAC

Integrated sensing and communication (ISAC) unifies sensing and communication, and improves the efficiency of the spectrum, energy, and hardware. In this work, we investigate the ISAC beamforming design to maximize the mutual information between the target response matrix of a point radar target and the echo signals, while ensuring the data rate requirements of the communication users. In order to study the impact of the echo interference caused by communication users on sensing performance, we study two scenarios: a single communication user and multiple communication users. For the case of a single communication user, we consider three types of echo interference, no interference, a point interference, and an extended interference. For the case of multiple communication users, the interference is also an extended one, and furthermore, each user's communication rate requirement needs to be satisfied. To find the optimal beamforming design in these problems, we provide a closed-form solution with low complexiy, a semidefinite relaxation (SDR) method, a low-complexity algorithm based on the Majorization-Minimization (MM) method and the successive convex approximation (SCA) method, and an algorithm based on MM method and SCA method, respectively. Numerical results demonstrate that, compared to the ISAC beamforming schemes based on the Cram\'er-Rao bound (CRB) metric and the beampattern metric, the proposed mutual information metric can bring better beampattern and root mean square error (RMSE) of angle estimation. Furthermore, our proposed schemes designed based on the mutual information metric can suppress the echo interference from the communication users effectively.
A CNN based Multifaceted Signal Processing Framework for Heart Rate Proctoring Using Millimeter Wave Radar Ballistocardiography
Authors: Rafid Umayer Murshed, Md. Abrar Istiak, Md. Toufiqur Rahman, Zulqarnain B Ashraf, Md Saheed Ullah, Mohammad Saquib
Date: 2022-11-14 00:00:00
Tasks: Diagnostic

The recent pandemic has refocused the medical world's attention on the diagnostic techniques associated with cardiovascular disease. Heart rate provides a real-time snapshot of cardiovascular health. A more precise heart rate reading provides a better understanding of cardiac muscle activity. Although many existing diagnostic techniques are approaching the limits of perfection, there remains potential for further development. In this paper, we propose MIBINET, a convolutional neural network for real-time proctoring of heart rate via inter-beat-interval (IBI) from millimeter wave (mm-wave) radar ballistocardiography signals. This network can be used in hospitals, homes, and passenger vehicles due to its lightweight and contactless properties. It employs classical signal processing prior to fitting the data into the network. Although MIBINET is primarily designed to work on mm-wave signals, it is found equally effective on signals of various modalities such as PCG, ECG, and PPG. Extensive experimental results and a thorough comparison with the current state-of-the-art on mm-wave signals demonstrate the viability and versatility of the proposed methodology. Keywords: Cardiovascular disease, contactless measurement, heart rate, IBI, mm-wave radar, neural network
Reconfigurable Intelligent Surface Aided Spatial Media-Based Modulation
Authors: Burak Ahmet Ozden, Erdogan Aydin, Fatih Cogen
Date: 2022-11-14 00:00:00

The demands for high data rate, reliability, high energy efficiency, high spectral efficiency, and low latency communication have been increasing rapidly. For this reason, communication models that use limited resources in the best way, allow fast data transmission, and increase performance has become very important. In this work, a novel high energy and spectral efficient reconfigurable intelligent surface aided spatial media-based modulation system, called RIS-SMBM, is proposed for Rayleigh fading channels. In addition to the bits carried in the M-QAM symbol, while media-based modulation (MBM) provides data bits to be carried in the indices of different channels according to the radio frequency (RF) mirrors are on or off, spatial modulation (SM) provides data bits to be carried in the indices of the transmit antennas. By combining these two modulation schemes, the spectral efficiency increases considerably since the amount of information transmitted in the same time interval is substantially increased. The optimal maximum-likelihood (ML) detector and the enhanced low-complexity (ELC) detector for the RIS-SMBM system are proposed. The ELC detector achieves near ML performance while reducing the complexity of the optimal ML detector for the proposed RIS-SMBM system. We analyze the average bit error rate (ABER), throughput, complexity, and energy efficiency for the RIS-SMBM scheme and verify the analytical results with Monte Carlo simulations. It has been observed that the proposed system provides better error performance as well as providing higher spectral and energy efficiency than benchmark systems.
Sensitivity to control signals in triphasic rhythmic neural systems: a comparative mechanistic analysis via infinitesimal local timing response curves
Authors: Zhuojun Yu, Jonathan E. Rubin, Peter J. Thomas
Date: 2022-11-15 00:00:00
Tasks: Model Selection, Sensitivity

Similar activity patterns may arise from model neural networks with distinct coupling properties and individual unit dynamics. These similar patterns may, however, respond differently to parameter variations and, specifically, to tuning of inputs that represent control signals. In this work, we analyze the responses resulting from modulation of a localized input in each of three classes of model neural networks that have been recognized in the literature for their capacity to produce robust three-phase rhythms: coupled fast-slow oscillators, near-heteroclinic oscillators, and threshold-linear networks. Triphasic rhythms, in which each phase consists of a prolonged activation of a corresponding subgroup of neurons followed by a fast transition to another phase, represent a fundamental activity pattern observed across a range of central pattern generators underlying behaviors critical to survival, including respiration, locomotion, and feeding. To perform our analysis, we extend the recently developed local timing response curve (lTRC), which allows us to characterize the timing effects due to perturbations, and we complement our lTRC approach with model-specific dynamical systems analysis. Interestingly, we observe disparate effects of similar perturbations across distinct model classes. Thus, this work provides an analytical framework for studying control of oscillations in nonlinear dynamical systems, and may help guide model selection in future efforts to study systems exhibiting triphasic rhythmic activity.
Incorporating Pre-training Paradigm for Antibody Sequence-Structure Co-design
Authors: Kaiyuan Gao, Lijun Wu, Jinhua Zhu, Tianbo Peng, Yingce Xia, Liang He, Shufang Xie, Tao Qin, Haiguang Liu, Kun He, Tie-Yan Liu
Date: 2022-10-26 00:00:00
Tasks: Language Modeling, Language Modelling, Specificity

Antibodies are versatile proteins that can bind to pathogens and provide effective protection for human body. Recently, deep learning-based computational antibody design has attracted popular attention since it automatically mines the antibody patterns from data that could be complementary to human experiences. However, the computational methods heavily rely on high-quality antibody structure data, which is quite limited. Besides, the complementarity-determining region (CDR), which is the key component of an antibody that determines the specificity and binding affinity, is highly variable and hard to predict. Therefore, the data limitation issue further raises the difficulty of CDR generation for antibodies. Fortunately, there exists a large amount of sequence data of antibodies that can help model the CDR and alleviate the reliance on structure data. By witnessing the success of pre-training models for protein modeling, in this paper, we develop the antibody pre-training language model and incorporate it into the (antigen-specific) antibody design model in a systemic way. Specifically, we first pre-train an antibody language model based on the sequence data, then propose a one-shot way for sequence and structure generation of CDR to avoid the heavy cost and error propagation from an autoregressive manner, and finally leverage the pre-trained antibody model for the antigen-specific antibody generation model with some carefully designed modules. Through various experiments, we show that our method achieves superior performances over previous baselines on different tasks, such as sequence and structure generation and antigen-binding CDR-H3 design.
Rhythmogenesis in the mean field model of the neuron-glial network
Authors: Nikita Barabash, Tatiana Levanova, Sergey Stasenko
Date: 2022-11-15 00:00:00

Despite the fact that the phenomenon of bursting activity is important for functioning of living neural networks, the mechanisms of its origin are still not clear. In this paper, we propose a new phenomenological model that can explain the mechanisms of the formation of bursting activity based on short-term synaptic plasticity, recurrent connections, and neuron-glial interactions. We show that neuron-glial interactions can induce bursting activity. The bifurcation scenarios of emergence of bursting activity are in the focus of the paper. Proposed study is important for understanding of the complex dynamics in neural networks.
Variations of the SIR model for COVID-19 evolution
Authors: Nana Cabo Bizet, Jonanthan Hidalgo Núñez, Gil Estefano Rodrígez Rivera
Date: 2022-11-15 00:00:00

In this work, we discuss the SIR epidemiological model and different variations of it applied to the propagation of the COVID-19 pandemia; we employ the data of the state of Guanajuato and of Mexico. We present some considerations that can improve the predictions made by those models. We consider a time-dependent infection rate, which we adjust to the data. Starting from a linear regime where the populations are much smaller that the country or state population and the population of susceptible (S) can be approximated in convenient units to S approximately 1, we make fits of the parameters. We also consider the case when the susceptible starts departing from 1, for this case we adjust an effective contagion rate. We also explore the ratio of detected populations and the real ones, obtaining that -for the analyzed case it is of approximately 10%. We estimate the number of deaths by making a fit versus the recovered cases, this fit is in first approximation linear, but other powers can give a good agreement. By predictions to past data, we conclude that adaptations of the SIR model can be of great use in describing pandemia's propagation, specially in limited time periods.
Association of vaccine-induced or hybrid immunity with COVID-19-related mortality during the Omicron wave -- a retrospective observational study in elderly Bavarians
Authors: Maximilian Weigert, Andreas Beyerlein, Katharina Katz, Rickmer Schulte, Wolfgang Hartl, Helmut Küchenhoff
Date: 2022-11-15 00:00:00

Background: There is a lack of population-based studies on the effectiveness and durability of the SARS-CoV-2-induced immune protection during the Omicron wave. Methods: This retrospective study included 470 159 cases aged 60 years or older, who tested positive for SARS-CoV-2 between January 1 and June 30, 2022 in Bavaria, Germany. We examined time to death, measured from the earliest date of reporting/symptom onset until day 60. Cox models were used to estimate adjusted hazard ratios (HR) for sex, age, earliest calendar date of documented infection, and level of vaccine-induced or hybrid immunity (LI). Results: We observed 3836 deaths (case fatality rate 0.82%). Risk of death was significantly lower in all LIs than in unvaccinated cases (adjusted HR for a full primary LI achieved within the last six months: 0.30, 95% confidence interval (CI) 0.23-0.39; after six months: 0.46, 95% CI 0.35-0.60). Boosting further decreased the risk of death (within the last three months: HR 0.17, 95% CI 0.15-0.20; after three months: HR 0.25, 95% CI 0.21-0.29). Conclusion: In elderly Bavarians, an increasing LI was associated with an increasing protection against death during the Omicron wave. Protection, however, may decrease to some extent after several months.
Efficient evaluation of double-barrier options and joint cpdf of a Lévy process and its two extrema
Authors: Svetlana Boyarchenko, Sergei Levendorskiĭ
Date: 2022-10-30 00:00:00
Tasks: CPU

In the paper, we develop a very fast and accurate method for pricing double barrier options with continuous monitoring in wide classes of L\'evy models; the calculations are in the dual space, and the Wiener-Hopf factorization is used. For wide regions in the parameter space, the precision of the order of $10^{-15}$ is achievable in seconds, and of the order of $10^{-9}-10^{-8}$ - in fractions of a second. The Wiener-Hopf factors and repeated integrals in the pricing formulas are calculated using sinh-deformations of the lines of integration, the corresponding changes of variables and the simplified trapezoid rule. If the Bromwich integral is calculated using the Gaver-Wynn Rho acceleration instead of the sinh-acceleration, the CPU time is typically smaller but the precision is of the order of $10^{-9}-10^{-6}$, at best. Explicit pricing algorithms and numerical examples are for no-touch options, digitals (equivalently, for the joint distribution function of a L\'evy process and its supremum and infimum processes), and call options. Several graphs are produced to explain fundamental difficulties for accurate pricing of barrier options using time discretization and interpolation-based calculations in the state space.
Multimodal Generative Models for Bankruptcy Prediction Using Textual Data
Authors: Rogelio A. Mancisidor, Kjersti Aas
Date: 2022-10-26 00:00:00
Tasks: All, Prediction

Textual data from financial filings, e.g., the Management's Discussion & Analysis (MDA) section in Form 10-K, has been used to improve the prediction accuracy of bankruptcy models. In practice, however, we cannot obtain the MDA section for all public companies, which limits the use of MDA data in traditional bankruptcy models, as they need complete data to make predictions. The two main reasons for the lack of MDA are: (i) not all companies are obliged to submit the MDA and (ii) technical problems arise when crawling and scrapping the MDA section. To solve this limitation, this research introduces the Conditional Multimodal Discriminative (CMMD) model that learns multimodal representations that embed information from accounting, market, and textual data modalities. The CMMD model needs a sample with all data modalities for model training. At test time, the CMMD model only needs access to accounting and market modalities to generate multimodal representations, which are further used to make bankruptcy predictions and to generate words from the missing MDA modality. With this novel methodology, it is realistic to use textual data in bankruptcy prediction models, since accounting and market data are available for all companies, unlike textual data. The empirical results of this research show that if financial regulators, or investors, were to use traditional models using MDA data, they would only be able to make predictions for 60% of the companies. Furthermore, the classification performance of our proposed methodology is superior to that of a large number of traditional classifier models, taking into account all the companies in our sample.
Forecasting Bitcoin volatility spikes from whale transactions and CryptoQuant data using Synthesizer Transformer models
Authors: Dorien Herremans, Kah Wee Low
Date: 2022-10-06 00:00:00
Tasks: Explainable Artificial Intelligence (XAI), Management

The cryptocurrency market is highly volatile compared to traditional financial markets. Hence, forecasting its volatility is crucial for risk management. In this paper, we investigate CryptoQuant data (e.g. on-chain analytics, exchange and miner data) and whale-alert tweets, and explore their relationship to Bitcoin's next-day volatility, with a focus on extreme volatility spikes. We propose a deep learning Synthesizer Transformer model for forecasting volatility. Our results show that the model outperforms existing state-of-the-art models when forecasting extreme volatility spikes for Bitcoin using CryptoQuant data as well as whale-alert tweets. We analysed our model with the Captum XAI library to investigate which features are most important. We also backtested our prediction results with different baseline trading strategies and the results show that we are able to minimize drawdown while keeping steady profits. Our findings underscore that the proposed method is a useful tool for forecasting extreme volatility movements in the Bitcoin market.
On counterfactual inference with unobserved confounding
Authors: Abhin Shah, Raaz Dwivedi, Devavrat Shah, Gregory W. Wornell
Date: 2022-11-14 00:00:00
Tasks: counterfactual, Counterfactual Inference, Imputation

Given an observational study with $n$ independent but heterogeneous units, our goal is to learn the counterfactual distribution for each unit using only one $p$-dimensional sample per unit containing covariates, interventions, and outcomes. Specifically, we allow for unobserved confounding that introduces statistical biases between interventions and outcomes as well as exacerbates the heterogeneity across units. Modeling the conditional distribution of the outcomes as an exponential family, we reduce learning the unit-level counterfactual distributions to learning $n$ exponential family distributions with heterogeneous parameters and only one sample per distribution. We introduce a convex objective that pools all $n$ samples to jointly learn all $n$ parameter vectors, and provide a unit-wise mean squared error bound that scales linearly with the metric entropy of the parameter space. For example, when the parameters are $s$-sparse linear combination of $k$ known vectors, the error is $O(s\log k/p)$. En route, we derive sufficient conditions for compactly supported distributions to satisfy the logarithmic Sobolev inequality. As an application of the framework, our results enable consistent imputation of sparsely missing covariates.
Low-Thrust Orbital Transfer using Dynamics-Agnostic Reinforcement Learning
Authors: Carlos M. Casas, Belen Carro, Antonio Sanchez-Esguevillas
Date: 2022-10-06 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Low-thrust trajectory design and in-flight control remain two of the most challenging topics for new-generation satellite operations. Most of the solutions currently implemented are based on reference trajectories and lead to sub-optimal fuel usage. Other solutions are based on simple guidance laws that need to be updated periodically, increasing the cost of operations. Whereas some optimization strategies leverage Artificial Intelligence methods, all of the approaches studied so far need either previously generated data or a strong a priori knowledge of the satellite dynamics. This study uses model-free Reinforcement Learning to train an agent on a constrained pericenter raising scenario for a low-thrust medium-Earth-orbit satellite. The agent does not have any prior knowledge of the environment dynamics, which makes it unbiased from classical trajectory optimization patterns. The trained agent is then used to design a trajectory and to autonomously control the satellite during the cruise. Simulations show that a dynamics-agnostic agent is able to learn a quasi-optimal guidance law and responds well to uncertainties in the environment dynamics. The results obtained open the door to the usage of Reinforcement Learning on more complex scenarios, multi-satellite problems, or to explore trajectories in environments where a reference solution is not known