pwc-archive/papers-with-abstracts huggingface dataset:
A Robust Score-Driven Filter for Multivariate Time Series
Authors: Enzo D'Innocenzo, Alessandra Luati, Mario Mazzocchi
Date: 2020-09-03 00:00:00
Tasks: Time Series, Time Series Analysis

A multivariate score-driven filter is developed to extract signals from noisy vector processes. By assuming that the conditional location vector from a multivariate Student's t distribution changes over time, we construct a robust filter which is able to overcome several issues that naturally arise when modeling heavy-tailed phenomena and, more in general, vectors of dependent non-Gaussian time series. We derive conditions for stationarity and invertibility and estimate the unknown parameters by maximum likelihood (ML). Strong consistency and asymptotic normality of the estimator are proved and the finite sample properties are illustrated by a Monte-Carlo study. From a computational point of view, analytical formulae are derived, which consent to develop estimation procedures based on the Fisher scoring method. The theory is supported by a novel empirical illustration that shows how the model can be effectively applied to estimate consumer prices from home scanner data.
Two Sample Unconditional Quantile Effect
Authors: Atsushi Inoue, Tong Li, Qi Xu
Date: 2021-05-20 00:00:00
Tasks: counterfactual, Vocal Bursts Valence Prediction

This paper proposes a new framework to evaluate unconditional quantile effects (UQE) in a data combination model. The UQE measures the effect of a marginal counterfactual change in the unconditional distribution of a covariate on quantiles of the unconditional distribution of a target outcome. Under rank similarity and conditional independence assumptions, we provide a set of identification results for UQEs when the target covariate is continuously distributed and when it is discrete, respectively. Based on these identification results, we propose semiparametric estimators and establish their large sample properties under primitive conditions. Applying our method to a variant of Mincer's earnings function, we study the counterfactual quantile effect of actual work experience on income.
Estimation of economic losses due to milk fever and efficiency gains if prevented: evidence from Haryana, India
Authors: A. G. A. Cariappa, B. S. Chandel, G. Sankhala, V. Mani, R. Sendhil, A. K. Dixit, B. S. Meena
Date: 2021-05-20 00:00:00

Calcium (Ca) requirement increases tenfold upon parturition in dairy cows & buffaloes and its deficiency leads to a condition called milk fever (MF). Estimation of losses is necessary to understand the depth of the problem and design preventive measures. How much is the economic loss due to MF? What will be the efficiency gain if MF is prevented at the advent of a technology? We answer these questions using survey data and official statistics employing economic surplus model. MF incidence in sample buffaloes and cows was 19% and 28%, respectively. Total economic losses were calculated as a sum total of losses from milk production, mortality of animals and treatment costs. Yearly economic loss due to MF was estimated to be INR 1000 crores (US$ 137 million) in Haryana. Value of milk lost had the highest share in total economic losses (58%), followed by losses due to mortality (29%) and treatment costs (13%). Despite lower MF incidence, losses were higher in buffaloes due to higher milk prices and market value of animals. The efficiency gain accruing to producers if MF is prevented, resulting from increased milk production at decreased costs was estimated at INR 10990 crores (US$ 1.5 billion). As the potential gain if prevented is around 10 times the economic losses, this study calls for the use of preventive technology against MF.
Exploring trade-offs between landscape impact, land use and resource quality for onshore variable renewable energy: an application to Great Britain
Authors: R. McKenna, I. Mulalic, I. Soutar, J. M. Weinand, J. Price, S. Petrovic, K. Mainzer
Date: 2021-05-20 00:00:00

The ambitious Net Zero aspirations of Great Britain (GB) require massive and rapid developments of Variable Renewable Energy (VRE) technologies. GB possesses substantial resources for these technologies, but questions remain about which VRE should be exploited where. This study explores the trade-offs between landscape impact, land use competition and resource quality for onshore wind as well as ground- and roof-mounted photovoltaic (PV) systems for GB. These trade-offs constrain the technical and economic potentials for these technologies at the Local Authority level. Our approach combines techno-economic and geospatial analyses with crowd-sourced scenicness data to quantify landscape aesthetics. Despite strong correlations between scenicness and planning application outcomes for onshore wind, no such relationship exists for ground-mounted PV. The innovative method for rooftop-PV assessment combines bottom-up analysis of four cities with a top-down approach at the national level. The results show large technical potentials that are strongly constrained by both landscape and land use aspects. This equates to about 1324 TWh of onshore wind, 153 TWh of rooftop PV and 1200-7093 TWh ground-mounted PV, depending on scenario. We conclude with five recommendations that focus around aligning energy and planning policies for VRE technologies across multiple scales and governance arenas.
State-Promoted Investment for Industrial Reforms: an Information Design Approach
Authors: Keeyoung Rhee, Myungkyu Shim, Ji Zhang
Date: 2021-05-20 00:00:00

We analyze the optimal strategy for a government to promote large-scale investment projects under information frictions. Specifically, we propose a model where the government collects information on probability of each investment and discloses it to private investors a la Kamenica and Gentzkow (2011). We derive the government's optimal information policy, which is characterized as threshold values for the unknown probability of the projects released to the private investors, and study how the underlying features of the economy affect the optimal policies. We find that when multiple projects are available, the government promotes the project with a bigger spillover effect by fully revealing the true state of the economy only when its probability is substantially high. Moreover, the development of the financial/information market also affects the optimal rule.
Improving Acquisition Speed of X-Ray Ptychography through Spatial Undersampling and Regularization
Authors: Prasan Shedligeri, Florian Schiffers, Semih Barutcu, Pablo Ruiz, Aggelos K Katsaggelos, Oliver Cossairt
Date: 2021-05-20 00:00:00
Tasks: Retrieval

X-ray ptychography is one of the versatile techniques for nanometer resolution imaging. The magnitude of the diffraction patterns is recorded on a detector and the phase of the diffraction patterns is estimated using phase retrieval techniques. Most phase retrieval algorithms make the solution well-posed by relying on the constraints imposed by the overlapping region between neighboring diffraction pattern samples. As the overlap between neighboring diffraction patterns reduces, the problem becomes ill-posed and the object cannot be recovered. To avoid the ill-posedness, we investigate the effect of regularizing the phase retrieval algorithm with image priors for various overlap ratios between the neighboring diffraction patterns. We show that the object can be faithfully reconstructed at low overlap ratios by regularizing the phase retrieval algorithm with image priors such as Total-Variation and Structure Tensor Prior. We also show the effectiveness of our proposed algorithm on real data acquired from an IC chip with a coherent X-ray beam.
Coding Standards as Anchors for the CVPR CLIC video track
Authors: Théo Ladune, Pierrick Philippe
Date: 2021-05-20 00:00:00
Tasks: Image Compression

In 2021, a new track has been initiated in the Challenge for Learned Image Compression~: the video track. This category proposes to explore technologies for the compression of short video clips at 1 Mbit/s. This paper proposes to generate coded videos using the latest standardized video coders, especially Versatile Video Coding (VVC). The objective is not only to measure the progress made by learning techniques compared to the state of the art video coders, but also to quantify their progress from years to years. With this in mind, this paper documents how to generate the video sequences fulfilling the requirements of this challenge, in a reproducible way, targeting the maximum performance for VVC.
Fully Digital and Hybrid Beamforming Design For Millimeter-Wave MIMO-OFDM Two-Way Relaying Systems
Authors: Sepideh Gherekhloo, Khaled Ardah, Martin Haardt
Date: 2021-05-20 00:00:00

In this work, we consider the design of hybrid analog-digital (HAD) multi-carrier MIMO-OFDM two-way relaying systems, where the relay station is equipped with a HAD amplify-and-forward architecture and every mobile station is equipped with a fully-digital beamforming architecture. We propose a sub-optimal solution by reformulating the original non-convex problem as a constrained Tucker2 decomposition with the objective of minimizing the sum Euclidean-norm between the HAD amplification matrices and their fully-digital counterparts. For the fully-digital amplification matrix design, we use a Frobenius-norm maximization of the effective channels on every subcarrier and propose an effective solution applicable for multi-stream communication scenarios. After that, we propose an alternating maximization (AltMax) HAD solution by exploiting the tensor structure of the reformulated problem. Simulation results are provided, where we show that the proposed fully-digital and AltMax-based HAD amplification matrix designs outperform some benchmark methods, especially for multi-stream communication scenarios.
The multi-channel potentiostat: Development and Evaluation of a Scalable Mini-Potentiostat array for investigating electrochemical reaction mechanisms
Authors: Pattawong Pansodtee, John Selberg, Manping Jia, Mohammad Jafari, Harika Dechiraju, Thomas Thomsen, Marcella Gomez, Marco Rolandi, Mircea Teodorescu
Date: 2021-05-20 00:00:00

A potentiostat is an essential piece of analytical equipment for studying electrochemical devices and reactions. As the design of electrochemical devices evolve applications for systems with multiple working electrodes have become more common. These applications drive a need for low-cost multi-channel potentiostat systems. We have developed a portable low-cost scalable system with a modular design that can support 8 to 64 channels at a cost as low as \$8 per channel. This design can replace the functionality of commercial potentiostats which cost upwards of \$10k for certain applications. Each channel in the multi-channel potentiostat has an independent adjustable voltage source with a built-in ammeter and switch, making the device flexible for various configurations. The multi-channel potentiostat is designed for low current applications (nA range), but its purpose can change by varying its shunt resistor value. The system can either function as a standalone device or remotely controlled. We demonstrate the functionality of this system for the control of a 24-channel bioelectronic ion pump for open- and closed- loop control of pH.
Recognition of food-texture attributes using an in-ear microphone
Authors: Vasileios Papapanagiotou, Christos Diou, Janet van den Boer, Monica Mars, Anastasios Delopoulos
Date: 2021-05-20 00:00:00
Tasks: Attribute

Food texture is a complex property; various sensory attributes such as perceived crispiness and wetness have been identified as ways to quantify it. Objective and automatic recognition of these attributes has applications in multiple fields, including health sciences and food engineering. In this work we use an in-ear microphone, commonly used for chewing detection, and propose algorithms for recognizing three food-texture attributes, specifically crispiness, wetness (moisture), and chewiness. We use binary SVMs, one for each attribute, and propose two algorithms: one that recognizes each texture attribute at the chew level and one at the chewing-bout level. We evaluate the proposed algorithms using leave-one-subject-out cross-validation on a dataset with 9 subjects. We also evaluate them using leave-one-food-type-out cross-validation, in order to examine the generalization of our approach to new, unknown food types. Our approach performs very well in recognizing crispiness (0.95 weighted accuracy on new subjects and 0.93 on new food types) and demonstrates promising results for objective and automatic recognition of wetness and chewiness.
Analysis of long-term transients and detection of early warning signs of major population changes in a two-timescale ecosystem
Authors: Susmita Sadhu
Date: 2021-05-19 00:00:00
Tasks: Form

Identifying early warning signs of sudden population changes and mechanisms leading to regime shifts are highly desirable in population biology. In this paper, a two-trophic ecosystem comprising of two species of predators, competing for their common prey, with explicit interference competition is considered. With proper rescaling, the model is portrayed as a singularly perturbed system with fast prey dynamics and slow dynamics of the predators. In a parameter regime near singular Hopf bifurcation, chaotic mixed-mode oscillations (MMOs), featuring concatenation of small and large amplitude oscillations are observed as long-lasting transients before the system approaches its asymptotic state. To analyze the dynamical cause that initiates a large amplitude oscillation in an MMO orbit, the model is reduced to a suitable normal form near the singular-Hopf point. The normal form possesses a separatrix surface that separates two different types of oscillations. A large amplitude oscillation is initiated if a trajectory moves from the "inner" to the "outer side" of this surface. A set of conditions on the normal form variables are obtained to determine whether a trajectory would exhibit another cycle of MMO dynamics before experiencing a regime shift (i.e. approaching its asymptotic state). These conditions can serve as early warning signs for a sudden population shift in an ecosystem.
Valuation of European Options under an Uncertain Market Price of Volatility Risk
Authors: Bartosz Jaroszkowski, Max Jensen
Date: 2021-05-20 00:00:00
Tasks: Uncertainty Quantification

We propose a model to quantify the effect of parameter uncertainty on the option price in the Heston model. More precisely, we present a Hamilton-Jacobi-Bellman framework which allows us to evaluate best and worst case scenarios under an uncertain market price of volatility risk. For the numerical approximation the Hamilton--Jacobi--Bellman equation is reformulated to enable the solution with a finite element method. A case study with butterfly options exhibits how the dependence of Delta on the magnitude of the uncertainty is nonlinear and highly varied across the parameter regime. Keywords: Uncertain market price, Volatility risk, Hamilton-Jacobi-Bellman equation, Finite element method, Uncertainty quantification
Dependence Modeling and Risk Assessment of a Financial Portfolio with ARMA-APARCH-EVT models based on HACs
Authors: Dodo Natatou Moutari, Hassane Abba Mallam, Diakarya Barro, Bisso Saley
Date: 2021-05-17 00:00:00

This study aims to widen the sphere of pratical applicability of the HAC model combined with the ARMA-APARCH volatility forecast model and the extreme values theory. A sequential process of modeling of the VaR of a portfolio based on the ARMA-APARCH-EVT-HAC model was discussed. The empirical analysis conducted with data from international stock market indices clearly illustrates the performance and accuracy of modeling based on HACs.
Empirical Analysis of Image Caption Generation using Deep Learning
Authors: Aditya Bhattacharya, Eshwar Shamanna Girishekar, Padmakar Anil Deshpande
Date: 2021-05-14 00:00:00
Tasks: Caption Generation, Decoder, Deep Learning, Image Captioning, Word Embeddings

Automated image captioning is one of the applications of Deep Learning which involves fusion of work done in computer vision and natural language processing, and it is typically performed using Encoder-Decoder architectures. In this project, we have implemented and experimented with various flavors of multi-modal image captioning networks where ResNet101, DenseNet121 and VGG19 based CNN Encoders and Attention based LSTM Decoders were explored. We have studied the effect of beam size and the use of pretrained word embeddings and compared them to baseline CNN encoder and RNN decoder architecture. The goal is to analyze the performance of each approach using various evaluation metrics including BLEU, CIDEr, ROUGE and METEOR. We have also explored model explainability using Visual Attention Maps (VAM) to highlight parts of the images which has maximum contribution for predicting each word of the generated caption.
Joint Face Image Restoration and Frontalization for Recognition
Authors: Xiaoguang Tu, Jian Zhao, Qiankun Liu, Wenjie Ai, Guodong Guo, Zhifeng Li, Wei Liu, Jiashi Feng
Date: 2021-05-12 00:00:00
Tasks: Decoder, Face Recognition, Image Restoration

In real-world scenarios, many factors may harm face recognition performance, e.g., large pose, bad illumination,low resolution, blur and noise. To address these challenges, previous efforts usually first restore the low-quality faces to high-quality ones and then perform face recognition. However, most of these methods are stage-wise, which is sub-optimal and deviates from the reality. In this paper, we address all these challenges jointly for unconstrained face recognition. We propose an Multi-Degradation Face Restoration (MDFR) model to restore frontalized high-quality faces from the given low-quality ones under arbitrary facial poses, with three distinct novelties. First, MDFR is a well-designed encoder-decoder architecture which extracts feature representation from an input face image with arbitrary low-quality factors and restores it to a high-quality counterpart. Second, MDFR introduces a pose residual learning strategy along with a 3D-based Pose Normalization Module (PNM), which can perceive the pose gap between the input initial pose and its real-frontal pose to guide the face frontalization. Finally, MDFR can generate frontalized high-quality face images by a single unified network, showing a strong capability of preserving face identity. Qualitative and quantitative experiments on both controlled and in-the-wild benchmarks demonstrate the superiority of MDFR over state-of-the-art methods on both face frontalization and face restoration.
An IoT-Based Framework for Remote Fall Monitoring
Authors: Ayman Al-Kababji, Abbes Amira, Faycal Bensaali, Abdulah Jarouf, Lisan Shidqi, Hamza Djelouat
Date: 2021-03-10 00:00:00
Tasks: Specificity

Fall detection is a serious healthcare issue that needs to be solved. Falling without quick medical intervention would lower the chances of survival for the elderly, especially if living alone. Hence, the need is there for developing fall detection algorithms with high accuracy. This paper presents a novel IoT-based system for fall detection that includes a sensing device transmitting data to a mobile application through a cloud-connected gateway device. Then, the focus is shifted to the algorithmic aspect where multiple features are extracted from 3-axis accelerometer data taken from existing datasets. The results emphasize on the significance of Continuous Wavelet Transform (CWT) as an influential feature for determining falls. CWT, Signal Energy (SE), Signal Magnitude Area (SMA), and Signal Vector Magnitude (SVM) features have shown promising classification results using K-Nearest Neighbors (KNN) and E-Nearest Neighbors (ENN). For all performance metrics (accuracy, recall, precision, specificity, and F1 Score), the achieved results are higher than 95% for a dataset of small size, while more than 98.47% score is achieved in the aforementioned criteria over the UniMiB-SHAR dataset by the same algorithms, where the classification time for a single test record is extremely efficient and is real-time
Enhancing safety in water transport system based on Internet of Things for developing countries
Authors: Md Mohaimenuzzaman, SM Monzurur Rahman, Musaed Alhussein, Ghulam Muhammad, Khondaker Abdullah Al Mamun
Date: 2021-03-05 00:00:00

Accidents in inland waterways in developing countries are a regular phenomenon throughout the year causing deaths, injuries, monetary loss, and a significant amount of missing people. In consequence, a lot of families are losing their dear ones leading to much misery. The above context demands an intelligent, safe, and reliable water transport system for the developing countries. The concept of Intelligent Transport System (ITS) can be applied to develop such system; however, there are issues with ITS and Internet of Things (IoT) unlocks a new way of developing it. This paper proposes a model to transform the water transport system into an intelligent system based on IoT. IPv6 based machine-to-machine (M2M) protocol, 3G telecommunication technology, and IEEE 802.15.4 network standard play a significant role in this proposed IoT based system.
Double Q-PID algorithm for mobile robot control
Authors: IgnacioCarlucho, MarianoDe Paula, Gerardo G.Acosta
Date: 2018-11-01 00:00:00
Tasks: Active Learning, Q-Learning

Many expert systems have been developed for self-adaptive PID controllers of mobile robots. However, the high computational requirements of the expert systems layers, developed for the tuning of the PID controllers, still require previous expert knowledge and high efficiency in algorithmic and software execution for real-time applications. To address these problems, in this paper we propose an expert agent-based system, based on a reinforcement learning agent, for self-adapting multiple low-level PID controllers in mobile robots. For the formulation of the artificial expert agent, we develop an incremental model-free algorithm version of the double Q-Learning algorithm for fast on-line adaptation of multiple low-level PID controllers. Fast learning and high on-line adaptability of the artificial expert agent is achieved by means of a proposed incremental active-learning exploration-exploitation procedure, for a non-uniform state space exploration, along with an experience replay mechanism for multiple value functions updates in the double Q-learning algorithm. A comprehensive comparative simulation study and experiments in a real mobile robot demonstrate the high performance of the proposed algorithm for a real-time simultaneous tuning of multiple adaptive low-level PID controllers of mobile robots in real world conditions.
Optimal Wireless Resource Allocation with Random Edge Graph Neural Networks
Authors: Mark Eisen, Alejandro Ribeiro
Date: 2019-09-04 00:00:00
Tasks: Graph Neural Network

We consider the problem of optimally allocating resources across a set of transmitters and receivers in a wireless network. The resulting optimization problem takes the form of constrained statistical learning, in which solutions can be found in a model-free manner by parameterizing the resource allocation policy. Convolutional neural networks architectures are an attractive option for parameterization, as their dimensionality is small and does not scale with network size. We introduce the random edge graph neural network (REGNN), which performs convolutions over random graphs formed by the fading interference patterns in the wireless network. The REGNN-based allocation policies are shown to retain an important permutation equivariance property that makes them amenable to transference to different networks. We further present an unsupervised model-free primal-dual learning algorithm to train the weights of the REGNN. Through numerical simulations, we demonstrate the strong performance REGNNs obtain relative to heuristic benchmarks and their transference capabilities.