pwc-archive/papers-with-abstracts huggingface dataset:
Endemic Oscillations for SARS-CoV-2 Omicron -- A SIRS model analysis
Authors: Florian Nill
Date: 2022-11-16 00:00:00

The SIRS model with constant vaccination and immunity waning rates is well known to show a transition from a disease-free to an endemic equilibrium as the basic reproduction number $r_0$ is raised above threshold. It is shown that this model maps to Hethcote's classic endemic model originally published in 1973. In this way one obtains unifying formulas for a whole class of models showing endemic bifurcation. In particular, if the vaccination rate is smaller than the recovery rate and $r_- < r_0 < r_+$ for certain upper and lower bounds $r_\pm$, then trajectories spiral into the endemic equilibrium via damped infection waves. Latest data of the SARS-CoV-2 Omicron variant suggest that according to this simplified model continuous vaccination programs will not be capable to escape the oscillating endemic phase. However, in view of the strong damping factors predicted by the model, in reality these oscillations will certainly be overruled by time-dependent contact behaviors.
Assessing potential insights of an imperfect testing strategy: Parameter estimation and practical identifiability using early COVID-19 data in India
Authors: Sarita Bugalia, Jai Prakash Tripathi
Date: 2022-11-16 00:00:00
Tasks: parameter estimation

A deterministic model with testing of infected individuals has been proposed to investigate the potential consequences of the impact of testing strategy. The model exhibits global dynamics concerning the disease-free and a unique endemic equilibrium depending on the basic reproduction number when the recruitment of infected individuals is zero; otherwise, the model does not have a disease-free equilibrium, and disease never dies out in the community. Model parameters have been estimated using the maximum likelihood method with respect to the data of early COVID-19 outbreak in India. The practical identifiability analysis shows that the model parameters are estimated uniquely. The consequences of the testing rate for the weekly new cases of early COVID-19 data in India tell that if the testing rate is increased by 20% and 30% from its baseline value, the weekly new cases at the peak are decreased by 37.63% and 52.90%; and it also delayed the peak time by four and fourteen weeks, respectively. Similar findings are obtained for the testing efficacy that if it is increased by 12.67% from its baseline value, the weekly new cases at the peak are decreased by 59.05% and delayed the peak by 15 weeks. Therefore, a higher testing rate and efficacy reduce the disease burden by tumbling the new cases, representing a real scenario. It is also obtained that the testing rate and efficacy reduce the epidemic's severity by increasing the final size of the susceptible population. The testing rate is found more significant if testing efficacy is high. Global sensitivity analysis using partial rank correlation coefficients (PRCCs) and Latin hypercube sampling (LHS) determine the key parameters that must be targeted to worsen/contain the epidemic.
A generative grammar of cooking
Authors: Ganesh Bagler
Date: 2022-10-12 00:00:00
Tasks: Nutrition, Recipe Generation

Cooking is a uniquely human endeavor for transforming raw ingredients into delicious dishes. Over centuries, cultures worldwide have evolved diverse cooking practices ingrained in their culinary traditions. Recipes, thus, are cultural capsules that capture culinary knowledge in elaborate cooking protocols. While simple quantitative models have probed the patterns in recipe composition and the process of cuisine evolution, unlike other cultural quirks such as language, the principles of cooking remain hitherto unexplored. The fundamental rules that drive the act of cooking, shaping recipe composition and cuisine architecture, are unclear. Here we present a generative grammar of cooking that captures the underlying culinary logic. By studying an extensive repository of structured recipes, we identify core concepts and rules that together forge a combinatorial system for culinary synthesis. Building on the body of work done in the context of language, the demonstration of a logically consistent generative framework offers profound insights into the act of cooking. Given the central role of food in nutrition and lifestyle disorders, culinary grammar provides leverage to improve public health through dietary interventions beyond applications for creative pursuits such as novel recipe generation.
Estimating Dynamic Spillover Effects along Multiple Networks in a Linear Panel Model
Authors: Clemens Possnig, Andreea Rotărescu, Kyungchul Song
Date: 2022-11-16 00:00:00

Spillover of economic outcomes often arises over multiple networks, and distinguishing their separate roles is important in empirical research. For example, the direction of spillover between two groups (such as banks and industrial sectors linked in a bipartite graph) has important economic implications, and a researcher may want to learn which direction is supported in the data. For this, we need to have an empirical methodology that allows for both directions of spillover simultaneously. In this paper, we develop a dynamic linear panel model and asymptotic inference with large $n$ and small $T$, where both directions of spillover are accommodated through multiple networks. Using the methodology developed here, we perform an empirical study of spillovers between bank weakness and zombie-firm congestion in industrial sectors, using firm-bank matched data from Spain between 2005 and 2012. Overall, we find that there is positive spillover in both directions between banks and sectors.
Causal Bandits: Online Decision-Making in Endogenous Settings
Authors: Jingwen Zhang, Yifang Chen, Amandeep Singh
Date: 2022-11-16 00:00:00
Tasks: Decision Making, Multi-Armed Bandits

The deployment of Multi-Armed Bandits (MAB) has become commonplace in many economic applications. However, regret guarantees for even state-of-the-art linear bandit algorithms (such as Optimism in the Face of Uncertainty Linear bandit (OFUL)) make strong exogeneity assumptions w.r.t. arm covariates. This assumption is very often violated in many economic contexts and using such algorithms can lead to sub-optimal decisions. Further, in social science analysis, it is also important to understand the asymptotic distribution of estimated parameters. To this end, in this paper, we consider the problem of online learning in linear stochastic contextual bandit problems with endogenous covariates. We propose an algorithm we term $\epsilon$-BanditIV, that uses instrumental variables to correct for this bias, and prove an $\tilde{\mathcal{O}}(k\sqrt{T})$ upper bound for the expected regret of the algorithm. Further, we demonstrate the asymptotic consistency and normality of the $\epsilon$-BanditIV estimator. We carry out extensive Monte Carlo simulations to demonstrate the performance of our algorithms compared to other methods. We show that $\epsilon$-BanditIV significantly outperforms other existing methods in endogenous settings. Finally, we use data from real-time bidding (RTB) system to demonstrate how $\epsilon$-BanditIV can be used to estimate the causal impact of advertising in such settings and compare its performance with other existing methods.
The impact of access to credit on energy efficiency
Authors: Jun Zhou, Zhichao Yin, Pengpeng Yue
Date: 2022-11-16 00:00:00

This paper proposes a brand-new measure of energy efficiency at household level and explores how it is affected by access to credit. We calculate the energy and carbon intensity of the related sectors, which experience a substantial decline from 2005 to 2019. Although there is still high inequality in energy use and carbon emissions among Chinese households, the energy efficiency appears to be improved in long run. Our research further maps the relationship between financial market and energy. The results suggest that broadened access to credit encourages households to improve energy efficiency, with higher energy use and carbon emission.
A multi-asset, agent-based approach applied to DeFi lending protocol modelling
Authors: Amit Chaudhary, Daniele Pinna
Date: 2022-11-16 00:00:00

We assess the market risk of the DeFi lending protocols using a multi-asset agent-based model to simulate ensembles of users subject to price-driven liquidation risk. Our multi-asset methodology shows that the protocol's systemic risk is small under stress and that enough collateral is always present to underwrite active loans. Our simulations use a wide variety of historical data to model market volatility and run the agent-based simulation to show that even if all the assets like ETH, BTC and MATIC increase their hourly volatility by more than ten times, the protocol carries less than 0.1\% default risk given suggested protocol parameter values for liquidation loan-to-value ratio and liquidation incentives.
On using the UA-Speech and TORGO databases to validate automatic dysarthric speech classification approaches
Authors: Guilherme Schu, Parvaneh Janbakhshi, Ina Kodrasi
Date: 2022-11-16 00:00:00
Tasks: Action Detection, Activity Detection, Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

Although the UA-Speech and TORGO databases of control and dysarthric speech are invaluable resources made available to the research community with the objective of developing robust automatic speech recognition systems, they have also been used to validate a considerable number of automatic dysarthric speech classification approaches. Such approaches typically rely on the underlying assumption that recordings from control and dysarthric speakers are collected in the same noiseless environment using the same recording setup. In this paper, we show that this assumption is violated for the UA-Speech and TORGO databases. Using voice activity detection to extract speech and non-speech segments, we show that the majority of state-of-the-art dysarthria classification approaches achieve the same or a considerably better performance when using the non-speech segments of these databases than when using the speech segments. These results demonstrate that such approaches trained and validated on the UA-Speech and TORGO databases are potentially learning characteristics of the recording environment or setup rather than dysarthric speech characteristics. We hope that these results raise awareness in the research community about the importance of the quality of recordings when developing and evaluating automatic dysarthria classification approaches.
Classification of the Cervical Vertebrae Maturation (CVM) stages Using the Tripod Network
Authors: Salih Atici, Hongyi Pan, Mohammed H. Elnagar, Veerasathpurush Allareddy, Omar Suhaym, Rashid Ansari, Ahmet Enis Cetin
Date: 2022-11-15 00:00:00
Tasks: Data Augmentation

We present a novel deep learning method for fully automated detection and classification of the Cervical Vertebrae Maturation (CVM) stages. The deep convolutional neural network consists of three parallel networks (TriPodNet) independently trained with different initialization parameters. They also have a built-in set of novel directional filters that highlight the Cervical Verte edges in X-ray images. Outputs of the three parallel networks are combined using a fully connected layer. 1018 cephalometric radiographs were labeled, divided by gender, and classified according to the CVM stages. Resulting images, using different training techniques and patches, were used to train TripodNet together with a set of tunable directional edge enhancers. Data augmentation is implemented to avoid overfitting. TripodNet achieves the state-of-the-art accuracy of 81.18\% in female patients and 75.32\% in male patients. The proposed TripodNet achieves a higher accuracy in our dataset than the Swin Transformers and the previous network models that we investigated for CVM stage estimation.
Definition and Quantification of Shock/Impact/Transient Vibrations
Authors: Pontus Johannisson, Hans Lindell
Date: 2022-11-16 00:00:00

Vibration injury in the hand-arm system from hand-held machines is one of the most common occupational health injuries and causes severe and often chronic nerve and vascular injury to the operator. Machines emitting shock vibrations, e.g., impact wrenches have since long been identified as a special risk factor. In legislative and standard texts the terms shock, impact, and transient vibration are frequently used to underline the special risk associated with these kinds of vibrations. In spite of this, there is no mathematically stringent definition what a shock vibration is or how the amplitude of the shock is defined. This lack of definitions is the subject of this article. This document discusses a number of candidate definitions for a vibration shock index (VSI) that quantifies different vibration signals in terms of how localized they are in the time domain. The VSI is intended to be used to classify and compare different vibration sources. The VSI is independent of the vibration level, i.e., it is unchanged if the vibration signal is rescaled. The traditional root mean square method to determine the vibration level will not produce a value representative for the shocks occurring in a signal with high VSI. Thus, there is a need for a complementing quantification method for the localized signal parts. Possible definitions for such a vibration shock level (VSL) are suggested. A problem formulation is first stated together with a description of the approach used for designing the VSI and the VSL. After this, model signals are defined, which are used to discuss and evaluate the different candidate definitions. Then, a number of candidate definitions are discussed, leading up to a conclusion on which candidate definitions that are promising for experimental evaluation.
OLIA: an open-source digital lock-in amplifier
Authors: Andrew J. Harvie, John C. de Mello
Date: 2022-11-16 00:00:00
Tasks: Noise Estimation

The Open Lock-In Amplifier (OLIA) is a microcontroller-based digital lock-in amplifier built from a small number of inexpensive and easily sourced electronic components. Despite its small credit card-sized form-factor and low build-cost of around US$35, OLIA is a capable instrument that offers many features associated with far costlier commercial devices. Key features include dual-phase lock-in detection at multiple harmonic frequencies up to 50 kHz, internal and external reference modes, adjustable levels of input gain, a choice between low-pass filtering and synchronous filtering, noise estimation, and a comprehensive programming interface for remote software control. OLIA comes with an optional optical breakout board that allows noise-tolerant optical detection down to the 40 pW level. OLIA and its breakout board are released here as open hardware, with technical diagrams, full parts-lists, and source-code for the firmware provided as Supporting Information.
Temporal-spatial Representation Learning Transformer for EEG-based Emotion Recognition
Authors: Zhe Wang, Yongxiong Wang, Chuanfei Hu, Zhong Yin, Yu Song
Date: 2022-11-16 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Emotion Recognition, Representation Learning

Both the temporal dynamics and spatial correlations of Electroencephalogram (EEG), which contain discriminative emotion information, are essential for the emotion recognition. However, some redundant information within the EEG signals would degrade the performance. Specifically,the subjects reach prospective intense emotions for only a fraction of the stimulus duration. Besides, it is a challenge to extract discriminative features from the complex spatial correlations among a number of electrodes. To deal with the problems, we propose a transformer-based model to robustly capture temporal dynamics and spatial correlations of EEG. Especially, temporal feature extractors which share the weight among all the EEG channels are designed to adaptively extract dynamic context information from raw signals. Furthermore, multi-head self-attention mechanism within the transformers could adaptively localize the vital EEG fragments and emphasize the essential brain regions which contribute to the performance. To verify the effectiveness of the proposed method, we conduct the experiments on two public datasets, DEAP and MAHNOBHCI. The results demonstrate that the proposed method achieves outstanding performance on arousal and valence classification.
Codebook-Based User Tracking in IRS-Assisted mmWave Communication Networks
Authors: Moritz Garkisch, Vahid Jamali, Robert Schober
Date: 2022-11-16 00:00:00

In this paper, we present a novel mobile user tracking (UT) scheme for codebook-based intelligent reflecting surface (IRS) aided millimeter wave (mmWave) systems. The proposed UT scheme exploits the temporal correlation of the direction from the IRS to the mobile user for selecting IRS phase shifts that provide reflection towards the user. To this end, the user's direction is periodically estimated based on a generalized likelihood ratio test (GLRT) and the user's movement trajectory is extrapolated from several past direction estimates. The efficiency of the proposed UT scheme is evaluated in terms of the average effective rate, which accounts for both the required signaling overhead and the achieved signal to noise ratio (SNR). Our results show that for medium to high SNR, the proposed codebook based UT scheme achieves a higher effective rate than two reference approaches based on full codebook search and optimization of the individual IRS unit cells, respectively.
A SVD-based Dynamic Harmonic Phasor Estimator with Improved Suppression of Out-of-Band Interference
Authors: Dongfang Zhao, Shisong Li, Fuping Wang, Wei Zhao, Songling Huang, Qing Wang
Date: 2022-11-16 00:00:00

The diffusion of nonlinear loads and power electronic devices in power systems deteriorates the signal environment and increases the difficulty of measuring harmonic phasors. Considering accurate harmonic phasor information is necessary to deal with harmonic-related issues, this paper focuses on realizing accurate dynamic harmonic phasor estimation when the signal is contaminated by certain interharmonic tones, i.e. the out-of-band interference (OBI). Specifically, this work introduces the singular value decomposition into the Taylor-Fourier transform algorithm, deriving a general decomposition form for harmonic phasor filters, and yielding a set of adjustable parameters. Then these parameters are configured to minimize the negative influence of OBI on the dynamic harmonic phasor filters. Based on the recommended parameter values, the optimized harmonic estimator is obtained, and then tested under signals distorted by OBI, harmonic, noise, and in worse cases, frequency deviation, and some dynamic conditions. Test results show the proposal achieves good harmonic phasor estimation even under multiple interferences and dynamic scenarios, and has a much weaker dependence on OBI tones compared to conventional approaches.
Adaptive Task Offloading for Space Missions: A State-Graph-Based Approach
Authors: Jiaqi Cao, Shengli Zhang, Mingzhe Wang, Qingxia Chen, Houtian Wang, Naijin Liu
Date: 2022-11-16 00:00:00

Advances in space exploration have led to an explosion of tasks. Conventionally, these tasks are offloaded to ground servers for enhanced computing capability, or to adjacent low-earth-orbit satellites for reduced transmission delay. However, the overall delay is determined by both computation and transmission costs. The existing offloading schemes, while being highly-optimized for either costs, can be abysmal for the overall performance. The computation-transmission cost dilemma is yet to be solved. In this paper, we propose an adaptive offloading scheme to reduce the overall delay. The core idea is to jointly model and optimize the transmission-computation process over the entire network. Specifically, to represent the computation state migrations, we generalize graph nodes with multiple states. In this way, the joint optimization problem is transformed into a shortest path problem over the state graph. We further provide an extended Dijkstra's algorithm for efficient path finding. Simulation results show that the proposed scheme outperforms the ground and one-hop offloading schemes by up to 37.56% and 39.35% respectively on SpaceCube v2.0.
Joint Linear Precoding and DFT Beamforming Design for Massive MIMO Satellite Communication
Authors: Vu Nguyen Ha, Zaid Abdullah, Geoffrey Eappen, Juan Carlos Merlano Duncan, Rakesh Palisetty, Jorge Luis Gonzalez Rios, Wallace Alves Martins, Hong-Fu Chou, Juan Andres Vasquez, Luis Manuel Garces-Socarras, Haythem Chaker, Symeon Chatzinotas
Date: 2022-11-16 00:00:00
Tasks: Benchmarking

This paper jointly designs linear precoding (LP) and codebook-based beamforming implemented in a satellite with massive multiple-input multiple-output (mMIMO) antenna technology. The codebook of beamforming weights is built using the columns of the discrete Fourier transform (DFT) matrix, and the resulting joint design maximizes the achievable throughput under limited transmission power. The corresponding optimization problem is first formulated as a mixed integer non-linear programming (MINP). To adequately address this challenging problem, an efficient LP and DFT-based beamforming algorithm are developed by utilizing several optimization tools, such as the weighted minimum mean square error transformation, duality method, and Hungarian algorithm. In addition, a greedy algorithm is proposed for benchmarking. A complexity analysis of these solutions is provided along with a comprehensive set of Monte Carlo simulations demonstrating the efficiency of our proposed algorithms.
Holographic Integrated Sensing and Communications: Principles, Technology, and Implementation
Authors: Haobo Zhang, Hongliang Zhang, Boya Di, Lingyang Song
Date: 2022-11-16 00:00:00
Tasks: Diversity, Integrated sensing and communication, ISAC

Integrated sensing and communication (ISAC) has attracted much attention as a promising approach to alleviate spectrum congestion. However, traditional ISAC systems rely on phased arrays to provide high spatial diversity, where enormous power-consuming components such as phase shifters are used, leading to the high power consumption of the system. In this article, we introduce holographic ISAC, a new paradigm to enable high spatial diversity with low power consumption by using reconfigurable holographic surfaces (RHSs), which is an innovative type of planar antenna with densely deployed metamaterial elements. We first introduce the hardware structure and working principle of the RHS and then propose a novel holographic beamforming scheme for ISAC. Moreover, we build an RHS-enabled hardware prototype for ISAC and evaluate the system performance in the built prototype. Simulation and experimental results verify the feasibility of holographic ISAC and reveal the great potential of the RHS for reducing power consumption. Furthermore, future research directions and key challenges related to holographic ISAC are discussed.
Single-channel EEG completion using Cascade Transformer
Authors: Chao Zhang, Siqi Han, Milin Zhang
Date: 2022-11-16 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

It is easy for the electroencephalogram (EEG) signal to be incomplete due to packet loss, electrode falling off, etc. This paper proposed a Cascade Transformer architecture and a loss weighting method for the single-channel EEG completion, which reduced the Normalized Root Mean Square Error (NRMSE) by 2.8% and 8.5%, respectively. With the percentage of the missing points ranging from 1% to 50%, the proposed method achieved a NRMSE from 0.026 to 0.063, which aligned with the state-of-the-art multi-channel completion solution. The proposed work shows it's feasible to perform the EEG completion with only single-channel EEG.