pwc-archive/papers-with-abstracts huggingface dataset:
The economics of malnutrition: Dietary transition and food system transformation
Authors: William A. Masters, Amelia B. Finaret, Steven A. Block
Date: 2022-02-05 00:00:00

Rapid increases in food supplies have reduced global hunger, while rising burdens of diet-related disease have made poor diet quality the leading cause of death and disability around the world. Today's "double burden" of undernourishment in utero and early childhood then undesired weight gain and obesity later in life is accompanied by a third less visible burden of micronutrient imbalances. The triple burden of undernutrition, obesity, and unbalanced micronutrients that underlies many diet-related diseases such as diabetes, hypertension and other cardiometabolic disorders often coexist in the same person, household and community. All kinds of deprivation are closely linked to food insecurity and poverty, but income growth does not always improve diet quality in part because consumers cannot directly or immediately observe the health consequences of their food options, especially for newly introduced or reformulated items. Even after direct experience and epidemiological evidence reveals relative risks of dietary patterns and nutritional exposures, many consumers may not consume a healthy diet because food choice is driven by other factors. This chapter reviews the evidence on dietary transition and food system transformation during economic development, drawing implications for how research and practice in agricultural economics can improve nutritional outcomes.
The end of 'set it and forget it' pricing? Opportunities for market-based freight contracts
Authors: Angela Acocella, Chris Caplice, Yossi Sheffi
Date: 2022-02-04 00:00:00

In the for-hire truckload market, firms often experience unexpected transportation cost increases due to contracted transportation service provider (carrier) load rejections. The dominant procurement strategy results in long-term, fixed-price contracts that become obsolete as transportation providers' networks change and freight markets fluctuate between times of over and under supply. We build behavioral models of the contracted carrier's load acceptance decision under two distinct freight market conditions based on empirical load transaction data. With the results, we quantify carriers' likelihood of sticking to the contract as their best known alternative priced load options increase and become more attractive; in other words, carriers' contract price stickiness. Finally, we explore carriers' contract price stickiness for different lane, freight, and carrier segments and offer insights for shippers to identify where they can expect to see substantial improvement in contracted carrier load acceptance as they consider alternative, market-based pricing strategies.
The Marginal Labor Supply Disincentives of Welfare: Evidence from Administrative Barriers to Participation
Authors: Robert A. Moffitt, Matthew V. Zahn
Date: 2022-02-04 00:00:00
Tasks: counterfactual

Existing research on the static effects of the manipulation of welfare program benefit parameters on labor supply has allowed only restrictive forms of heterogeneity in preferences. Yet preference heterogeneity implies that the marginal effects on labor supply of welfare expansions and contractions may differ in different time periods with different populations and which sweep out different portions of the distribution of preferences. A new examination of the heavily studied AFDC program uses variation in state-level administrative barriers to entering the program in the late 1980s and early 1990s to estimate the marginal labor supply effects of changes in program participation induced by that variation. The estimates are obtained from a theory-consistent reduced form model which allows for a nonparametric specification of how changes in welfare program participation affect labor supply on the margin. Estimates using a form of local instrumental variables show that the marginal treatment effects are quadratic, rising and then falling as participation rates rise (i.e., becoming more negative then less negative on hours of work). The average work disincentive is not large but that masks some margins where effects are close to zero and some which are sizable. Traditional IV which estimates a weighted average of marginal effects gives a misleading picture of marginal responses. A counterfactual exercise which applies the estimates to three historical reform periods in 1967, 1981, and 1996 when the program tax rate was significantly altered shows that marginal labor supply responses differed in each period because of differences in the level of participation in the period and the composition of who was on the program.
Sequential Veto Bargaining with Incomplete Information
Authors: S. Nageeb Ali, Navin Kartik, Andreas Kleiner
Date: 2022-02-05 00:00:00

We study sequential bargaining between a proposer and a veto player. Both have single-peaked preferences, but the proposer is uncertain about the veto player's ideal point. The proposer cannot commit to future proposals. When players are patient, there can be equilibria with Coasian dynamics: the veto player's private information can largely nullify proposer's bargaining power. Our main result, however, is that under some conditions there are also equilibria in which the proposer obtains the high payoff that he would with commitment power. The driving force is that the veto player's single-peaked preferences give the proposer an option to "leapfrog", i.e., to secure agreement from only low-surplus types early on to credibly extract surplus from high types later. Methodologically, we exploit the connection between sequential bargaining and static mechanism design.
Neural Network based Inter bi-prediction Blending
Authors: Franck Galpin, Philippe Bordes, Thierry Dumas, Pavel Nikitin, Fabrice Le Leannec
Date: 2022-01-26 00:00:00
Tasks: CPU, Motion Compensation, Prediction, Quantization

This paper presents a learning-based method to improve bi-prediction in video coding. In conventional video coding solutions, the motion compensation of blocks from already decoded reference pictures stands out as the principal tool used to predict the current frame. Especially, the bi-prediction, in which a block is obtained by averaging two different motion-compensated prediction blocks, significantly improves the final temporal prediction accuracy. In this context, we introduce a simple neural network that further improves the blending operation. A complexity balance, both in terms of network size and encoder mode selection, is carried out. Extensive tests on top of the recently standardized VVC codec are performed and show a BD-rate improvement of -1.4% in random access configuration for a network size of fewer than 10k parameters. We also propose a simple CPU-based implementation and direct network quantization to assess the complexity/gains tradeoff in a conventional codec framework.
AirNN: Neural Networks with Over-the-Air Convolution via Reconfigurable Intelligent Surfaces
Authors: Sara Garcia Sanchez, Guillem Reus Muns, Carlos Bocanegra, Yanyu Li, Ufuk Muncuk, Yousof Naderi, Yanzhi Wang, Stratis Ioannidis, Kaushik R. Chowdhury
Date: 2022-02-07 00:00:00

Over-the-air analog computation allows offloading computation to the wireless environment through carefully constructed transmitted signals. In this paper, we design and implement the first-of-its-kind over-the-air convolution and demonstrate it for inference tasks in a convolutional neural network (CNN). We engineer the ambient wireless propagation environment through reconfigurable intelligent surfaces (RIS) to design such an architecture, which we call 'AirNN'. AirNN leverages the physics of wave reflection to represent a digital convolution, an essential part of a CNN architecture, in the analog domain. In contrast to classical communication, where the receiver must react to the channel-induced transformation, generally represented as finite impulse response (FIR) filter, AirNN proactively creates the signal reflections to emulate specific FIR filters through RIS. AirNN involves two steps: first, the weights of the neurons in the CNN are drawn from a finite set of channel impulse responses (CIR) that correspond to realizable FIR filters. Second, each CIR is engineered through RIS, and reflected signals combine at the receiver to determine the output of the convolution. This paper presents a proof-of-concept of AirNN by experimentally demonstrating over-the-air convolutions. We then validate the entire resulting CNN model accuracy via simulations for an example task of modulation classification.
Efficient coverage planning for full-area C-ITS communications based on radio propagation simulation and measurement tools
Authors: Hagen Ußler, Christian Setzefand, Daniel Kanis, Paul Schwarzbach, Oliver Michler
Date: 2022-02-07 00:00:00

Intelligent infrastructure, currently often consisting of C-ITS stations and prospectively supplemented by 5G, is a key-enabler for application-oriented and area-wide realization of highly automated and connected driving. For this, radio coverage along the routes must be ensured, leading to high demands on location- and radio-specific planning and parameterization of roadside units (RSU). Hence, this paper presents efficient planning, measurement and evaluation methods for RSU coverage outlining, allowing economically efficient and technically secured planning of intelligent infrastructure. Necessary scientific technical steps are showcased along a 3.5 km testbed for automated and connected driving in rural environments. First, a radio propagation simulation based on a 3D environment model and its electro-magnetic properties is performed, allowing the examination and optimization of RSU quantity as well as site and antenna selection. Additionally, the necessary calibration of simulation results based on continuous wave (CW) and C-ITS service measurements in both lab-based and real-world scenarios is presented.
Weakly Supervised Indoor Localization via Manifold Matching
Authors: Erez Peterfreund, Ioannis G. Kevrekidis, Ariel Jaffe
Date: 2022-02-07 00:00:00
Tasks: Indoor Localization

Inferring the location of a mobile device in an indoor setting is an open problem of utmost significance. A leading approach that does not require the deployment of expensive infrastructure is fingerprinting, where a classifier is trained to predict the location of a device based on its captured signal. The main caveat of this approach is that acquiring a sufficiently large and accurate training set may be prohibitively expensive. Here, we propose a weakly supervised method that only requires the location of a small number of devices. The localization is done by matching a low-dimensional spectral representation of the signals to a given sketch of the indoor environment. We test our approach on simulated and real data and show that it yields an accuracy of a few meters, which is on par with fully supervised approaches. The simplicity of our method and its accuracy with minimal supervision makes it ideal for implementation in indoor localization systems.
Quasi-Monostatic Antenna Displacement in Radar Target Simulation
Authors: Axel Diewald, Benjamin Nuss, Thomas Zwick
Date: 2022-02-07 00:00:00

Radar target simulators (RTS) have recently drawn much attention in research and commercial development, as they are capable of performing over-the-air validation tests under laboratory conditions by generating virtual radar echoes that are perceived as targets by a radar under test (RuT). The estimated angle of arrival (AoA) of such a virtual target is determined by the physical position of the particular RTS channel that creates it, which must therefore be considered when planning the setup. A single channel employs two antennas, one for the reception and the other for the re-transmission of the incoming radar signal. The antennas are positioned close together, but still spatially separated, thus an RTS channel can be considered quasi-monostatic, which causes non-negligible inaccuracies in the angle simulation. In this paper, the authors examine the analytical implications of this systemic deficiency on the angle estimation, which provides support for the design and setup of angle-simulating RTS systems. The mathematical derivations developed are verified by measurement.
Two-Dimensional Arbitrary Angle of Arrival in Radar Target Simulation
Authors: Axel Diewald, Benjamin Nuss, Johannes Galinsky, Thomas Zwick
Date: 2022-02-07 00:00:00
Tasks: Autonomous Vehicles, Vocal Bursts Valence Prediction

Automotive radar sensors play a key role in the current development of advanced driver assistance systems (ADAS). Their ability to detect objects even under adverse weather conditions makes them indispensable for environment-sensing tasks in autonomous vehicles. Since an operational failure presents a potential risk to human life, thorough and practical validation testing must be performed, requiring an integrative test solution. Radar target simulators (RTS) are capable of performing over-the-air validation tests by generating virtual radar echoes that are perceived as targets by the radar under test (RuT). Since the authenticity and credibility of these targets is based on the accuracy with which they are created, their simulated position must be arbitrarily adjustable. In this work, an existing approach to synthesize virtual radar targets at an arbitrary angle of arrival (AoA) is extended to cover both, the azimuth and elevation domain. The concept is based on the superposition of the returning signals from four neighboring RTS channels. A theoretical model describing the basic principle and its constraints is developed. In addition, a measurement campaign is conducted to verify the practical functionality of the proposed approach.
An evaluation of the data space dimension in phase retrieval: results in Fresnel zone
Authors: Rocco Pierri, Raffaele Moretta
Date: 2022-02-06 00:00:00
Tasks: Retrieval

In this paper, we address the problem of computing the dimension of data space in phase retrieval problem. Starting from the quadratic formulation of the phase retrieval,the analysis is performed in two steps. First, we exploit the lifting technique to obtain a linear representation of the data. Later, we evaluate the dimension of data space by computing analytically the number of relevant singular values of the linear operator that represents the data. The study is done with reference to a 2D scalar geometry consisting of an electric current strip whose square amplitude of the electric radiated field is observed on a twodimensional extended domain in Fresnel zone.
Blind source separation of baseband RF communication signals using mixed-signal matrix multiplication circuit
Authors: Bindu Madhavan, Edward Lee, Joshua Zusman, Anthony F. J. Levi
Date: 2022-02-06 00:00:00
Tasks: blind source separation

An 8 x 8 mixed-signal matrix multiplier architecture based on 64 hybrid capacitor-resistor multiplying digital to analogue converters implemented in a 65 nm CMOS technology was developed for the application of blind source separation of baseband RF signals. The integrated circuit has 13-bit resolution for each matrix weight and achieves a measured dynamic range of > 62 dB with a bandwidth of > 15 MHz and typical power dissipation of < 30 mW per matrix row. Separation of single-tone signal is measured to be better than 57 dBc.
Flexible fast-convolution processing for cellular radio evolution
Authors: Juha Yli-Kaakinen, Toni Levanen, Arto Palin, Markku Renfors, Mikko Valkama
Date: 2022-02-05 00:00:00

Orthogonal frequency-division multiplexing (OFDM) has been selected as a baseline waveform for long-term evolution (LTE) and fifth-generation new radio (5G NR). Fast-convolution (FC)-based frequency-domain signal processing has been considered recently as an effective tool for spectrum enhancement of OFDM-based waveforms. FC-based filtering approximates linear convolution by effective fast Fourier transform (FFT)-based circular convolutions using partly overlapping processing blocks. In earlier work, we have shown that FC-based filtering is a very flexible and efficient tool for filtered-OFDM signal generation and receiver side subband filtering. In this paper, we present a symbol-synchronous FC-processing scheme flexibly allowing filter re-configuration time resolution equal to one OFDM symbol while supporting tight carrier-wise filtering for 5G NR in mixed-numerology scenarios with adjustable subcarrier spacings, center frequencies, and subband bandwidths as well as providing co-exitence with LTE. The proposed scheme is demonstrated to support envisioned use cases of 5G NR and provide flexible starting point for sixth generation development.
An Olfactory EEG Signal Classification Network Based on Frequency Band Feature Extraction
Authors: Biao Sun, Zhigang Wei, Pei Liang, Huirang Hou
Date: 2022-02-05 00:00:00
Tasks: Classification, EEG, EEG Signal Classification, Electroencephalogram (EEG)

Classification of olfactory-induced electroencephalogram (EEG) signals has shown great potential in many fields. Since different frequency bands within the EEG signals contain different information, extracting specific frequency bands for classification performance is important. Moreover, due to the large inter-subject variability of the EEG signals, extracting frequency bands with subject-specific information rather than general information is crucial. Considering these, the focus of this letter is to classify the olfactory EEG signals by exploiting the spectral-domain information of specific frequency bands. In this letter, we present an olfactory EEG signal classification network based on frequency band feature extraction. A frequency band generator is first designed to extract frequency bands via the sliding window technique. Then, a frequency band attention mechanism is proposed to optimize frequency bands for a specific subject adaptively. Last, a convolutional neural network (CNN) is constructed to extract the spatio-spectral information and predict the EEG category. Comparison experiment results reveal that the proposed method outperforms a series of baseline methods in terms of both classification quality and inter-subject robustness. Ablation experiment results demonstrate the effectiveness of each component of the proposed method.
Detecting Soil Moisture Levels Using Battery-Free Wi-Fi Tag
Authors: Wenli Jiao, Ju Wang, Yelu He, Xiangdong Xi, Xiaojiang Chen
Date: 2022-02-04 00:00:00
Tasks: TAG

Soil sensing plays an important role in increasing agricultural output and protecting soil sites. Existing soil sensing methods failed to achieve both high accuracy and low cost. In this paper, we design and implement a high-accuracy and low cost chipless soil moisture sensing system called SoilTAG. We propose a general chipless sensor design methodology which can allow us to customize the signal feature for sensing soil moisture, instead of blindly capturing the disturbance law of the soil. Based on this principle, we design a battery-free passive tag which can respond to different soil-moisture. Further, we optimize hardware and algorithm design of SoilTAG to locate the passive tag and extract its reflection signal feature to identify soil-moisture using WiFi signals. Extensive experimental results reveal that it can identify 2% absolute soil water content with a sensing distance up to 3m in open field. When the sensing distance is up to 13 m, it can also achieve 5% absolute soil-moisture sensing resolution.
Spectro Temporal EEG Biomarkers For Binary Emotion Classification
Authors: Upasana Tiwari, Rupayan Chakraborty, Sunil Kumar Kopparapu
Date: 2022-02-02 00:00:00
Tasks: Classification, EEG, Electroencephalogram (EEG), Emotion Classification

Electroencephalogram (EEG) is one of the most reliable physiological signal for emotion detection. Being non-stationary in nature, EEGs are better analysed by spectro temporal representations. Standard features like Discrete Wavelet Transformation (DWT) can represent temporal changes in spectral dynamics of an EEG, but is insufficient to extract information other way around, i.e. spectral changes in temporal dynamics. On the other hand, Empirical mode decomposition (EMD) based features can be useful to bridge the above mentioned gap. Towards this direction, we extract two novel features on top of EMD, namely, (a) marginal hilbert spectrum (MHS) and (b) Holo-Hilbert spectral analysis (HHSA) based on EMD, to better represent emotions in 2D arousal-valence (A-V) space. The usefulness of these features for EEG emotion classification is investigated through extensive experiments using state-of-the-art classifiers. In addition, experiments conducted on DEAP dataset for binary emotion classification in both A-V space, reveal the efficacy of the proposed features over the standard set of temporal and spectral features.
Radio Map Estimation: A Data-Driven Approach to Spectrum Cartography
Authors: Daniel Romero, Seung-Jun Kim
Date: 2022-02-01 00:00:00
Tasks: regression, Spectrum Cartography

Radio maps characterize quantities of interest in radio communication environments, such as the received signal strength and channel attenuation, at every point of a geographical region. Radio map estimation typically entails interpolative inference based on spatially distributed measurements. In this tutorial article, after presenting some representative applications of radio maps, the most prominent radio map estimation methods are discussed. Starting from simple regression, the exposition gradually delves into more sophisticated algorithms, eventually touching upon state-of-the-art techniques. To gain insight into this versatile toolkit, illustrative toy examples will also be presented.
Experimental Investigation of Variational Mode Decomposition and Deep Learning for Short-Term Multi-horizon Residential Electric Load Forecasting
Authors: Mohamed Aymane Ahajjam, Daniel Bonilla Licea, Mounir Ghogho, Abdellatif Kobbane
Date: 2022-02-01 00:00:00
Tasks: Deep Learning, Load Forecasting, Time Series, Time Series Analysis

With the booming growth of advanced digital technologies, it has become possible for users as well as distributors of energy to obtain detailed and timely information about the electricity consumption of households. These technologies can also be used to forecast the household's electricity consumption (a.k.a. the load). In this paper, we investigate the use of Variational Mode Decomposition and deep learning techniques to improve the accuracy of the load forecasting problem. Although this problem has been studied in the literature, selecting an appropriate decomposition level and a deep learning technique providing better forecasting performance have garnered comparatively less attention. This study bridges this gap by studying the effect of six decomposition levels and five distinct deep learning networks. The raw load profiles are first decomposed into intrinsic mode functions using the Variational Mode Decomposition in order to mitigate their non-stationary aspect. Then, day, hour, and past electricity consumption data are fed as a three-dimensional input sequence to a four-level Wavelet Decomposition Network model. Finally, the forecast sequences related to the different intrinsic mode functions are combined to form the aggregate forecast sequence. The proposed method was assessed using load profiles of five Moroccan households from the Moroccan buildings' electricity consumption dataset (MORED) and was benchmarked against state-of-the-art time-series models and a baseline persistence model.