pwc-archive/papers-with-abstracts huggingface dataset:
Empirical evidence on the Euler equation for investment in the US
Authors: Guido Ascari, Qazi Haque, Leandro M. Magnusson, Sophocles Mavroeidis
Date: 2021-07-19 00:00:00

Is the typical specification of the Euler equation for investment employed in DSGE models consistent with aggregate macro data? Using state-of-the-art econometric methods that are robust to weak instruments and exploit information in possible structural changes, the answer is yes. Unfortunately, however, there is very little information about the values of these parameters in aggregate data because investment is unresponsive to changes in capital utilization and the real interest rate. In DSGE models, the investment adjustment cost and the persistence of the investment-specific technology shock parameters are mainly identified by, respectively, the cross-equation restrictions and the dynamics implied by the structure of the model.
Monitoring multidimensional phenomena with a multicriteria composite performance interval approach
Authors: Ana Garcia-Bernabeu, Adolfo Hilario-Caballero
Date: 2021-07-18 00:00:00

In the last two decades, composite indicators' construction to measure and compare multidimensional phenomena in a broad spectrum of domains has increased considerably. Different methodological approaches are used to summarize huge data sets of information in a single figure. This paper proposes a new approach that consists of computing a multicriteria composite performance interval based on different aggregation rules. The suggested approach provides an additional layer of information as the performance interval displays a lower bound from a non-compensability perspective, and an upper bound allowing for full-compensability. The outstanding features of this proposal are: (i) a distance-based multicriteria technique is taken as the baseline to construct the multicriteria performance interval (ii) the aggregation of distances/separation measures is made using particular cases of Minkowski's $L_p$ metrics; (iii) the span of the multicriteria performance interval can be considered as a sign of the dimensions or indicators balance.
A Probit Estimation of Urban Bases of Environmental Awareness: Evidence from Sylhet City, Bangladesh
Authors: Mohammad Masud Alam, AFM Zakaria
Date: 2021-07-18 00:00:00

This paper evaluates the significant factors contributing to environmental awareness among individuals living in the urban area of Sylhet, Bangladesh. Ordered Probit(OPM) estimation is applied on the value of ten measures of individual environmental concern. The estimated results of OPM reveal the dominance of higher education, higher income, and full-employment status on environmental concern and environmentally responsible behavior. Younger and more educated respondents tended to be more knowledgeable and concerned than older and less educated respondents. The marginal effect of household size, middle-income level income, and part-time employment status of the survey respondents played a less significant role in the degree of environmental awareness. Findings also validate the "age hypothesis" proposed by Van Liere and Dunlap (1980), and the gender effect reveals an insignificant role in determining the degree of environmental concern. Environmental awareness among urban individuals with higher income increased linearly with environmental awareness programs which may have significant policy importance, such as environmental awareness programs for old-aged and less-educated individuals, and may lead to increased taxation on higher income groups to mitigate city areas' pollution problems.
Sampling dynamics and stable mixing in hawk-dove games
Authors: Srinivas Arigapudi, Yuval Heller, Amnon Schreiber
Date: 2021-07-18 00:00:00

The hawk-dove game admits two types of equilibria: an asymmetric pure equilibrium in which players in one population play hawk and players in the other population play dove, and an inefficient symmetric mixed equilibrium, in which hawks are frequently matched against each other. The existing literature shows that populations will converge to playing one of the pure equilibria from almost any initial state. By contrast, we show that plausible sampling dynamics, in which agents occasionally revise their actions by observing either opponents' behavior or payoffs in a few past interactions, can induce the opposite result: global convergence to one of the inefficient mixed stationary states.
Sustainability of Global Economy as a Quantum Circuit
Authors: Antonino Claudio Bonan
Date: 2021-07-13 00:00:00

In economy, viewed as a quantum system working as a circuit, each process at the microscale is a quantum gate among agents. The global configuration of economy is addressed by optimizing the sustainability of the whole circuit. This is done in terms of geodesics, starting from some approximations. A similar yet somehow different approach is applied for the closed system of the whole and for economy as an open system. Computations may partly be explicit, especially when the reality is represented in a simplified way. The circuit can be also optimized by minimizing its complexity, with a partly similar formalism, yet generally not along the same paths.
Scalable Image Coding for Humans and Machines
Authors: Hyomin Choi, Ivan V. Bajic
Date: 2021-07-18 00:00:00
Tasks: Autonomous Navigation

At present, and increasingly so in the future, much of the captured visual content will not be seen by humans. Instead, it will be used for automated machine vision analytics and may require occasional human viewing. Examples of such applications include traffic monitoring, visual surveillance, autonomous navigation, and industrial machine vision. To address such requirements, we develop an end-to-end learned image codec whose latent space is designed to support scalability from simpler to more complicated tasks. The simplest task is assigned to a subset of the latent space (the base layer), while more complicated tasks make use of additional subsets of the latent space, i.e., both the base and enhancement layer(s). For the experiments, we establish a 2-layer and a 3-layer model, each of which offers input reconstruction for human vision, plus machine vision task(s), and compare them with relevant benchmarks. The experiments show that our scalable codecs offer 37%-80% bitrate savings on machine vision tasks compared to best alternatives, while being comparable to state-of-the-art image codecs in terms of input reconstruction.
Managing Interference and Leveraging Secondary Reflections Amongst Multiple IRSs
Authors: Tu V. Nguyen, Diep N. Nguyen
Date: 2021-07-19 00:00:00

Intelligent reflecting surface (IRS) has recently been emerging as an enabler for smart radio environment in which passive antenna arrays can be used to actively tailor/control the radio propagation. With multiple IRSs being launched to support various group of users, it is critical to jointly optimize the phase-shifts of all IRSs to mitigate the interference as well as to leverage the secondary reflections amongst IRSs. This work considers the uplink of multiple users that are grouped and supported by multiple IRSs to a multi-antenna base station. Each IRS with multiple controllable phase-shift elements is intended to serve a group of near-by users. We first formulate the minimum achievable rate maximization problem by jointly optimizing phase-shifts of elements from all IRSs and the received beamformers at the MIMO base station. The problem turns out to be non-convex. We then derive its solution using the alternating optimization mechanism. Our simulations show that by properly managing interference and leveraging the secondary reflections amongst IRSs, there is a great benefit of deploying more IRSs to support different groups of users to achieve a higher rate per user. In contrast, without properly managing the secondary reflections, increasing the number of IRSs can adversely impact the network throughput, especially for higher transmit power.
Joint Power and Gain Allocation in MDM-WDM Optical Communication Networks Based on Enhanced Gaussian Noise Model
Authors: Mohammad Ali Amirabadi, Mohammad Hossein Kahaei, S. Alireza Nezamalhosseini
Date: 2021-07-19 00:00:00

Achieving reliable communication over different channels and modes is one of the main goals of Mode Division Multiplexing-Wavelength Division Multiplexing (MDM-WDM) communication networks. The reliability can be described by minimum Signal to Noise Ratio (SNR) margin which dependents on launched power, Multimode-Erbium Doped Fiber Amplification (MM-EDFA) gain, and MMF nonlinearity. In this paper, an analytical model for MMF nonlinearity is derived based on Enhanced Gaussian Noise (EGN) model formulation by considering carrier phase estimation and the first four dispersion terms. The proposed EGN model is verified through the split step Fourier method simulation. Considering a multi-node linear network, the joint optimized power and gain allocation based on minimum SNR margin maximization is formulated. The practical constraints including MM-EDFA saturation power and maximum gain are considered and the problem is solved by using convex optimization. Three scenarios are assumed including best equal power, optimized power, and joint optimized power and gain. In the first scenario, equal powers are considered for different channels and modes with equal MM-EDFA gain in all spans. It is worth mentioning that the MM-EDFA gain is equal to span loss. In the second scenario, different powers are allocated to different channels and modes with equal MM-EDFA gain in all spans. In the third case, allocated powers to each channel and mode are optimized. Moreover, the MM-EDFA gain for each span is optimized separately.
A Multi-Channel Ratio-of-Ratios Method for Noncontact Hand Video Based SpO$_2$ Monitoring Using Smartphone Cameras
Authors: Xin Tian, Chau-Wai Wong, Sushant M. Ranadive, Min Wu
Date: 2021-07-18 00:00:00

Blood oxygen saturation (SpO$_2$) is an important indicator for pulmonary and respiratory functionalities. Clinical findings on COVID-19 show that many patients had dangerously low blood oxygen levels not long before conditions worsened. It is therefore recommended, especially for the vulnerable population, to regularly monitor the blood oxygen level for precaution. Recent works have investigated how ubiquitous smartphone cameras can be used to infer SpO$_2$. Most of these works are contact-based, requiring users to cover a phone's camera and its nearby light source with a finger to capture reemitted light from the illuminated tissue. Contact-based methods may lead to skin irritation and sanitary concerns, especially during a pandemic. In this paper, we propose a noncontact method for SpO$_2$ monitoring using hand videos acquired by smartphones. Considering the optical broadband nature of the red (R), green (G), and blue (B) color channels of the smartphone cameras, we exploit all three channels of RGB sensing to distill the SpO$_2$ information beyond the traditional ratio-of-ratios (RoR) method that uses only two wavelengths. To further facilitate an accurate SpO$_2$ prediction, we design adaptive narrow bandpass filters based on accurately estimated heart rate to obtain the most cardiac-related AC component for each color channel. Experimental results show that our proposed blood oxygen estimation method can reach a mean absolute error of 1.26% when a pulse oximeter is used as a reference, outperforming the traditional RoR method by 25%.
Learning Sparse Privacy-Preserving Representations for Smart Meters Data
Authors: Mohammadhadi Shateri, Francisco Messina, Pablo Piantanida, Fabrice Labeau
Date: 2021-07-17 00:00:00
Tasks: Attribute, Fault Detection, Load Forecasting, Privacy Preserving

Fine-grained Smart Meters (SMs) data recording and communication has enabled several features of Smart Grids (SGs) such as power quality monitoring, load forecasting, fault detection, and so on. In addition, it has benefited the users by giving them more control over their electricity consumption. However, it is well-known that it also discloses sensitive information about the users, i.e., an attacker can infer users' private information by analyzing the SMs data. In this study, we propose a privacy-preserving approach based on non-uniform down-sampling of SMs data. We formulate this as the problem of learning a sparse representation of SMs data with minimum information leakage and maximum utility. The architecture is composed of a releaser, which is a recurrent neural network (RNN), that is trained to generate the sparse representation by masking the SMs data, and an utility and adversary networks (also RNNs), which help the releaser to minimize the leakage of information about the private attribute, while keeping the reconstruction error of the SMs data minimum (i.e., maximum utility). The performance of the proposed technique is assessed based on actual SMs data and compared with uniform down-sampling, random (non-uniform) down-sampling, as well as the state-of-the-art in privacy-preserving methods using a data manipulation approach. It is shown that our method performs better in terms of the privacy-utility trade-off while releasing much less data, thus also being more efficient.
Learning to Equalize OTFS
Authors: Zhou Zhou, Lingjia Liu, Jiarui Xu, Robert Calderbank
Date: 2021-07-17 00:00:00
Tasks: Scheduling

Orthogonal Time Frequency Space (OTFS) is a novel framework that processes modulation symbols via a time-independent channel characterized by the delay-Doppler domain. The conventional waveform, orthogonal frequency division multiplexing (OFDM), requires tracking frequency selective fading channels over the time, whereas OTFS benefits from full time-frequency diversity by leveraging appropriate equalization techniques. In this paper, we consider a neural network-based supervised learning framework for OTFS equalization. Learning of the introduced neural network is conducted in each OTFS frame fulfilling an online learning framework: the training and testing datasets are within the same OTFS-frame over the air. Utilizing reservoir computing, a special recurrent neural network, the resulting one-shot online learning is sufficiently flexible to cope with channel variations among different OTFS frames (e.g., due to the link/rank adaptation and user scheduling in cellular networks). The proposed method does not require explicit channel state information (CSI) and simulation results demonstrate a lower bit error rate (BER) than conventional equalization methods in the low signal-to-noise (SNR) regime under large Doppler spreads. When compared with its neural network-based counterparts for OFDM, the introduced approach for OTFS will lead to a better tradeoff between the processing complexity and the equalization performance.
Developing Synthetic Spectroscopy Noise and Chemometric Database for Computational Classification
Authors: Nicholas J. Napoli
Date: 2021-07-17 00:00:00
Tasks: Classification

There has been little to no work in the area of spectroscopy noise in order to create data sets for analytical algorithms to be challenged on the ability to separate chemicals. We present a framework on how to build off of a sparse about of experimental data in order to expand your chemometric database and create realistic instrumentation noise. The combination of various interactions of chemicals combined with various random permutations of spectroscopy noises enables researchers to better capture and model the multitude of types of signals and variations that can be present within an experimental reading.
Energy-Efficient Tethered UAV Deployment in B5G for Smart Environments and Disaster Recovery
Authors: Abdu Saif, Kaharudin Dimyati, Kamarul Ariffin Noordin, Nor Shahida MohdShah, S. H. Alsamhi, Qazwan Abdullah
Date: 2021-07-17 00:00:00

Due to Unmanned aerial vehicles (UAVs) limitations in processing power and battery lifetime. The tethered UAV (TUAV) offers an attractive approach to answer these shortcomings. Since a tethered connected to UAV is one potential energy solution to provide a stable power supply that connects to the ground would achieve impressive performances in smart environments and disaster recovery. The proposed solution is intended to provide stable energy and increase the coverage area of TUAV for smart environments and disaster recovery. This paper proposed that the tethered connected to UAV will provide the continuous supply and exchange the data with ground terminals. Besides the adjustable tether length, elevation angels act to increase the hovering region, leading to the scalability of coverage in many applications. Moreover, the power consumption and transmission the distance while achieving a trade-off between the hovering and coverage probabilities. The simulation results demonstrate efficient performance in terms of line-of-sight probability, path loss, and coverage probability for scalability coverage smart environments and disaster recovery scenarios. Furthermore, maximum coverage probability is achieved versus increased tethered length because of the gain and fly over a region of maximum tethered.
Anomaly Detection Based on Multiple-Hypothesis Autoencoder
Authors: JoonSung Lee, YeongHyeon Park
Date: 2021-07-07 00:00:00
Tasks: Anomaly Detection

Recently Autoencoder(AE) based models are widely used in the field of anomaly detection. A model trained with normal data generates a larger restoration error for abnormal data. Whether or not abnormal data is determined by observing the restoration error. It takes a lot of cost and time to obtain abnormal data in the industrial field. Therefore the model trains only normal data and detects abnormal data in the inference phase. However, the restoration area for the input data of AE is limited in the latent space. To solve this problem, we propose Multiple-hypothesis Autoencoder(MH-AE) model composed of several decoders. MH-AE model increases the restoration area through contention between decoders. The proposed method shows that the anomaly detection performance is improved compared to the traditional AE for various input datasets.
Automatic Fatou Property of Law-invariant Risk Measures
Authors: Shengzhong Chen, Niushan Gao, Denny Leung, Lei LI
Date: 2021-07-16 00:00:00

In the paper we investigate automatic Fatou property of law-invariant risk measures on a rearrangement-invariant function space $\mathcal{X}$ other than $L^\infty$. The main result is the following characterization: Every real-valued, law-invariant, coherent risk measure on $\mathcal{X}$ has the Fatou property at every random variable $X\in \mathcal{X}$ whose negative tails have vanishing norm (i.e., $\lim_n\|X\mathbf{1}_{\{X\leq -n\}}\|=0$) if and only if $\mathcal{X}$ satisfies the Almost Order Continuous Equidistributional Average (AOCEA) property, namely, $\mathrm{d}(\mathcal{CL}(X),\mathcal{X}_a) =0$ for any $X\in \mathcal{X}_+$, where $ \mathcal{CL}(X)$ is the convex hull of all random variables having the same distribution as $X$ and $\mathcal{X}_a=\{X\in\mathcal{X}:\lim_n \|X\mathbf{1}_{ \{|X|\geq n\} }\| =0\}$. As a consequence, we show that under the AOCEA property, every real-valued, law-invariant, coherent risk measure on $\mathcal{X}$ admits a tractable dual representation at every $X\in \mathcal{X}$ whose negative tails have vanishing norm. Furthermore, we show that the AOCEA property is satisfied by most classical model spaces, including Orlicz spaces, and therefore the foregoing results have wide applications.
Deep Learning with Nonsmooth Objectives
Authors: Vinesha Peiris, Nadezda Sukhorukova, Vera Roshchina
Date: 2021-07-14 00:00:00
Tasks: Deep Learning

We explore the potential for using a nonsmooth loss function based on the max-norm in the training of an artificial neural network. We hypothesise that this may lead to superior classification results in some special cases where the training data is either very small or unbalanced. Our numerical experiments performed on a simple artificial neural network with no hidden layers (a setting immediately amenable to standard nonsmooth optimisation techniques) appear to confirm our hypothesis that uniform approximation based approaches may be more suitable for the datasets with reliable training data that either is limited size or biased in terms of relative cluster sizes.
Cell density controls signal propagation waves in a multicellular synthetic gene circuit
Authors: Marco Santorelli, Pranav Bhamidipati, Andriu Kavanagh, Victoria Fitts, Trusha Sondkar, Matt Thomson, Leonardo Morsut
Date: 2021-07-16 00:00:00

During organismal development, biochemical reaction networks sense and respond to mechanical forces to coordinate embryonic patterning with embryo morphogenesis. Factors such as cortical tension, cell density, and matrix mechanical properties influence differentiation and cell fate decisions by modulating gene regulatory signaling networks. A major goal in synthetic development is to construct gene regulatory circuits that program the patterning and morphogenesis of synthetic multicellular structures. However, in the synthetic context, little is known regarding how the physical properties of the growth environment impact the behavior of synthetic gene circuits. Here, we exploit physical-chemical coupling observed in a synthetic patterning circuit in order to control the size and spatial distribution of patterned synthetic cell sheets. We show that cell density attenuates the propagation of signal between neighboring cells in a multicellular sheet containing a contact-dependent patterning circuit based on the synNotch signaling system. Density-dependent attenuation leads to a signal propagation wave that exhibits distinct qualitative phases of persistent propagation, transient propagation, and no propagation. Through computational modeling, we demonstrate that cell growth parameters determine the phase of propagation observed within a growing cell sheet. Using growth-modulating drugs and spatial density gradients, we control the size of synNotch-activated cell populations and generate tissue-scale activation gradients and kinematic waves. Our study reveals that density-dependent synNotch activity can be exploited to control a synthetic multicellular patterning circuit. More broadly, we show that synthetic gene circuits can be critically impacted by their physical context, providing an alternate means for programming circuit behavior.
Synaptic balancing: a biologically plausible local learning rule that provably increases neural network noise robustness without sacrificing task performance
Authors: Christopher H. Stock, Sarah E. Harvey, Samuel A. Ocko, Surya Ganguli
Date: 2021-07-18 00:00:00

We introduce a novel, biologically plausible local learning rule that provably increases the robustness of neural dynamics to noise in nonlinear recurrent neural networks with homogeneous nonlinearities. Our learning rule achieves higher noise robustness without sacrificing performance on the task and without requiring any knowledge of the particular task. The plasticity dynamics -- an integrable dynamical system operating on the weights of the network -- maintains a multiplicity of conserved quantities, most notably the network's entire temporal map of input to output trajectories. The outcome of our learning rule is a synaptic balancing between the incoming and outgoing synapses of every neuron. This synaptic balancing rule is consistent with many known aspects of experimentally observed heterosynaptic plasticity, and moreover makes new experimentally testable predictions relating plasticity at the incoming and outgoing synapses of individual neurons. Overall, this work provides a novel, practical local learning rule that exactly preserves overall network function and, in doing so, provides new conceptual bridges between the disparate worlds of the neurobiology of heterosynaptic plasticity, the engineering of regularized noise-robust networks, and the mathematics of integrable Lax dynamical systems.