pwc-archive/papers-with-abstracts huggingface dataset:
Strategic energy flows in input-output relations: a temporal multilayer approach
Authors: Gian Paolo Clemente, Alessandra Cornaro, Rosanna Grassi, Giorgio Rizzini
Date: 2022-12-22 00:00:00

The energy consumption, the transfer of resources through the international trade, the transition towards renewable energies and the environmental sustainability appear as key drivers in order to evaluate the resilience of the energy systems. Concerning the consumptions, in the literature a great attention has been paid to direct energy, but the production of goods and services also involves indirect energy. Hence, in this work we consider different types of embodied energy sources and the time evolution of the sectors' and countries' interactions. Flows are indeed used to construct a directed and weighted temporal multilayer network based respectively on renewable and non-renewable sources, where sectors are nodes and layers are countries. We provide a methodological approach for analysing the network reliability and resilience and for identifying critical sectors and economies in the system by applying the Multi-Dimensional HITS algorithm. Then, we evaluate central arcs in the network at each time period by proposing a novel topological indicator based on the maximum flow problem. In this way, we provide a full view of economies, sectors and connections that play a relevant role over time in the network and whose removal could heavily affect the stability of the system. We provide a numerical analysis based on the embodied energy flows among countries and sectors in the period from 1990 to 2016. Results prove that the methods are effective in catching the different patterns between renewable and non-renewable energy sources.
Enhancing Resilience: Model-based Simulations
Authors: d'Artis Kancs
Date: 2022-12-21 00:00:00
Tasks: counterfactual, model

Since several years, the fragility of global supply chains (GSCs) is at historically high levels. In the same time, the landscape of hybrid threats is expanding; new forms of hybrid threats create different types of uncertainties. This paper aims to understand the potential consequences of uncertain events - like natural disasters, pandemics, hybrid and/or military aggression - on GSC resilience and robustness. Leveraging a parsimonious supply chain model, we analyse how the organisational structure of GSCs interacts with uncertainty, and how risk-aversion vs. ambiguity-aversion, vertical integration vs. upstream outsourcing, resilience vs. efficiency trade-offs drive a wedge between decentralised and centralised optimal GSC diversification strategies in presence of externalities. Parameterising the scalable data model with World-Input Output Tables, we simulate the survival probability of a GSC and implications for supply chain robustness and resilience. The presented model-based simulations provide an interoperable and directly comparable conceptualisation of positive and normative effects of counterfactual resilience and robustness policy choices under individually optimal (decentralised) and socially optimal (centralised) GSC organisation structures.
Macro carbon price prediction with support vector regression and Paris accord targets
Authors: Jinhui Li
Date: 2022-11-30 00:00:00
Tasks: Decision Making, Management, Prediction, regression

Carbon neutralization is an urgent task in society because of the global warming threat. And carbon trading is an essential market mechanics to solve carbon reduction targets. Macro carbon price prediction is vital in the useful management and decision-making of the carbon market. We focus on the EU carbon market and we choose oil price, coal price, gas price, and DAX index to be the four market factors in predicting carbon price, and also we select carbon emission targets from Paris Accord as the political factor in the carbon market in terms of the macro view of the carbon price prediction. Thus we use these five factors as inputs to predict the future carbon yearly price in 2030 with the support vector regression models. We use grid search and cross validation to guarantee the prediction performance of our models. We believe this model will have great applications in the macro carbon price prediction.
HMM-based data augmentation for E2E systems for building conversational speech synthesis systems
Authors: Ishika Gupta, Anusha Prakash, Jom Kuriakose, Hema A. Murthy
Date: 2022-12-22 00:00:00
Tasks: Data Augmentation, Language Modeling, Language Modelling, Speech Synthesis, text-to-speech, Text to Speech

This paper proposes an approach to build a high-quality text-to-speech (TTS) system for technical domains using data augmentation. An end-to-end (E2E) system is trained on hidden Markov model (HMM) based synthesized speech and further fine-tuned with studio-recorded TTS data to improve the timbre of the synthesized voice. The motivation behind the work is that issues of word skips and repetitions are usually absent in HMM systems due to their ability to model the duration distribution of phonemes accurately. Context-dependent pentaphone modeling, along with tree-based clustering and state-tying, takes care of unseen context and out-of-vocabulary words. A language model is also employed to reduce synthesis errors further. Subjective evaluations indicate that speech produced using the proposed system is superior to the baseline E2E synthesis approach in terms of intelligibility when combining complementing attributes from HMM and E2E frameworks. The further analysis highlights the proposed approach's efficacy in low-resource scenarios.
Coded Illumination for 3D Lensless Imaging
Authors: Yucheng Zheng, M. Salman Asif
Date: 2022-12-22 00:00:00
Tasks: 3D Reconstruction, Image Reconstruction

Mask-based lensless cameras offer a novel design for imaging systems by replacing the lens in a conventional camera with a layer of coded mask. Each pixel of the lensless camera encodes the information of the entire 3D scene. Existing methods for 3D reconstruction from lensless measurements suffer from poor spatial and depth resolution. This is partially due to the system ill conditioning that arises because the point-spread functions (PSFs) from different depth planes are very similar. In this paper, we propose to capture multiple measurements of the scene under a sequence of coded illumination patterns to improve the 3D image reconstruction quality. In addition, we put the illumination source at a distance away from the camera. With such baseline distance between the lensless camera and illumination source, the camera observes a slice of the 3D volume, and the PSF of each depth plane becomes more resolvable from each other. We present simulation results along with experimental results with a camera prototype to demonstrate the effectiveness of our approach.
PIM: Video Coding using Perceptual Importance Maps
Authors: Evgenya Pergament, Pulkit Tandon, Oren Rippel, Lubomir Bourdev, Alexander G. Anderson, Bruno Olshausen, Tsachy Weissman, Sachin Katti, Kedar Tatwawadi
Date: 2022-12-20 00:00:00
Tasks: Video Compression

Human perception is at the core of lossy video compression, with numerous approaches developed for perceptual quality assessment and improvement over the past two decades. In the determination of perceptual quality, different spatio-temporal regions of the video differ in their relative importance to the human viewer. However, since it is challenging to infer or even collect such fine-grained information, it is often not used during compression beyond low-level heuristics. We present a framework which facilitates research into fine-grained subjective importance in compressed videos, which we then utilize to improve the rate-distortion performance of an existing video codec (x264). The contributions of this work are threefold: (1) we introduce a web-tool which allows scalable collection of fine-grained perceptual importance, by having users interactively paint spatio-temporal maps over encoded videos; (2) we use this tool to collect a dataset with 178 videos with a total of 14443 frames of human annotated spatio-temporal importance maps over the videos; and (3) we use our curated dataset to train a lightweight machine learning model which can predict these spatio-temporal importance regions. We demonstrate via a subjective study that encoding the videos in our dataset while taking into account the importance maps leads to higher perceptual quality at the same bitrate, with the videos encoded with importance maps preferred $1.8 \times$ over the baseline videos. Similarly, we show that for the 18 videos in test set, the importance maps predicted by our model lead to higher perceptual quality videos, $2 \times$ preferred over the baseline at the same bitrate.
SPIRiT-Diffusion: SPIRiT-driven Score-Based Generative Modeling for Vessel Wall imaging
Authors: Chentao Cao, Zhuo-Xu Cui, Jing Cheng, Sen Jia, Hairong Zheng, Dong Liang, Yanjie Zhu
Date: 2022-12-14 00:00:00
Tasks: Image Generation, MRI Reconstruction

Diffusion model is the most advanced method in image generation and has been successfully applied to MRI reconstruction. However, the existing methods do not consider the characteristics of multi-coil acquisition of MRI data. Therefore, we give a new diffusion model, called SPIRiT-Diffusion, based on the SPIRiT iterative reconstruction algorithm. Specifically, SPIRiT-Diffusion characterizes the prior distribution of coil-by-coil images by score matching and characterizes the k-space redundant prior between coils based on self-consistency. With sufficient prior constraint utilized, we achieve superior reconstruction results on the joint Intracranial and Carotid Vessel Wall imaging dataset.
Nervus: A Comprehensive Deep Learning Classification, Regression, and Prognostication Tool for both Medical Image and Clinical Data Analysis
Authors: Toshimasa Matsumoto, Shannon L Walston, Yukio Miki, Daiju Ueda
Date: 2022-12-12 00:00:00

The goal of our research is to create a comprehensive and flexible library that is easy to use for medical imaging research, and capable of handling grayscale images, multiple inputs (both images and tabular data), and multi-label tasks. We have named it Nervus. Based on the PyTorch library, which is suitable for AI for research purposes, we created a four-part model to handle comprehensive inputs and outputs. Nervus consists of four parts. First is the dataloader, then the feature extractor, the feature mixer, and finally the classifier. The dataloader preprocesses the input data, the feature extractor extracts the features between the training data and ground truth labels, feature mixer mixes the features of the extractors, and the classifier classifies the input data from feature mixer based on the task. We have created Nervus, which is a comprehensive and flexible model library that is easy to use for medical imaging research which can handle grayscale images, multi-inputs and multi-label tasks. This will be helpful for researchers in the field of radiology.
Analysis of Explainable Artificial Intelligence Methods on Medical Image Classification
Authors: Vinay Jogani, Joy Purohit, Ishaan Shivhare, Seema C Shrawne
Date: 2022-12-10 00:00:00
Tasks: Decision Making, Explainable artificial intelligence, Explainable Artificial Intelligence (XAI), image-classification, Image Classification, Medical Image Classification

The use of deep learning in computer vision tasks such as image classification has led to a rapid increase in the performance of such systems. Due to this substantial increment in the utility of these systems, the use of artificial intelligence in many critical tasks has exploded. In the medical domain, medical image classification systems are being adopted due to their high accuracy and near parity with human physicians in many tasks. However, these artificial intelligence systems are extremely complex and are considered black boxes by scientists, due to the difficulty in interpreting what exactly led to the predictions made by these models. When these systems are being used to assist high-stakes decision-making, it is extremely important to be able to understand, verify and justify the conclusions reached by the model. The research techniques being used to gain insight into the black-box models are in the field of explainable artificial intelligence (XAI). In this paper, we evaluated three different XAI methods across two convolutional neural network models trained to classify lung cancer from histopathological images. We visualized the outputs and analyzed the performance of these methods, in order to better understand how to apply explainable artificial intelligence in the medical domain.
Multi-Frequency Channel Modeling for Millimeter Wave and THz Wireless Communication via Generative Adversarial Networks
Authors: Yaqi Hu, Mingsheng Yin, William Xia, Sundeep Rangan, Marco Mezzavilla
Date: 2022-12-22 00:00:00
Tasks: Diversity, Generative Adversarial Network

Modern cellular systems rely increasingly on simultaneous communication in multiple discontinuous bands for macro-diversity and increased bandwidth. Multi-frequency communication is particularly crucial in the millimeter wave (mmWave) and Terahertz (THz) frequencies, as these bands are often coupled with lower frequencies for robustness. Evaluation of these systems requires statistical models that can capture the joint distribution of the channel paths across multiple frequencies. This paper presents a general neural network based methodology for training multi-frequency double directional statistical channel models. In the proposed approach, each is described as a multi-clustered set, and a generative adversarial network (GAN) is trained to generate random multi-cluster profiles where the generated cluster data includes the angles and delay of the clusters along with the vectors of random received powers, angular, and delay spread at different frequencies. The model can be readily applied for multi-frequency link or network layer simulation. The methodology is demonstrated on modeling urban micro-cellular links at 28 and 140 GHz trained from extensive ray tracing data. The methodology makes minimal statistical assumptions and experiments show the model can capture interesting statistical relationships between frequencies.
Electromagnetic Based Communication Model for Dynamic Metasurface Antennas
Authors: Robin Jess Williams, Pablo Ramirez-Espinosa, Jide Yuan, Elisabeth de Carvalho
Date: 2022-12-22 00:00:00
Tasks: model

Dynamic metasurface antennas (DMAs) arise as a promising technology in the field of massive multiple-input multiple-output (mMIMO) systems, offering the possibility of integrating a large number of antennas in a limited -- and potentially large -- aperture while keeping the required number of radio-frequency (RF) chains under control. Although envisioned as practical realizations of mMIMO systems, DMAs represent a new paradigm in the design of signal processing techniques (such as beamforming) due to the constraints inherent to their physical implementation, for which no complete models are available yet. In this work, we propose a complete and electromagnetic-compliant narrowband communication model for a generic DMA based system. Specifically, the model accounts for: i) the wave propagation and reflections throughout the waveguides that feed the antenna elements, ii) the mutual coupling both through the air and the waveguides, and iii) the insertion losses. Also, we integrate the electromagnetic model in the conventional digital communication model, providing a complete and useful framework to design and characterize the performance of these systems. Finally, the accuracy of the model is verified through full-wave simulations.
Multidimensional Graph Neural Networks for Wireless Communications
Authors: ShengJie Liu, Jia Guo, Chenyang Yang
Date: 2022-12-22 00:00:00

Graph neural networks (GNNs) have been shown promising in improving the efficiency of learning communication policies by leveraging their permutation properties. Nonetheless, existing works design GNNs only for specific wireless policies, lacking a systematical approach for modeling graph and selecting structure. Based on the observation that the mismatched permutation property from the policies and the information loss during the update of hidden representations have large impact on the learning performance and efficiency, in this paper we propose a unified framework to learn permutable wireless policies with multidimensional GNNs. To avoid the information loss, the GNNs update the hidden representations of hyper-edges. To exploit all possible permutations of a policy, we provide a method to identify vertices in a graph. We also investigate the permutability of wireless channels that affects the sample efficiency, and show how to trade off the training, inference, and designing complexities of GNNs. We take precoding in different systems as examples to demonstrate how to apply the framework. Simulation results show that the proposed GNNs can achieve close performance to numerical algorithms, and require much fewer training samples and trainable parameters to achieve the same learning performance as the commonly used convolutional neural networks.
Joint Beamforming and PD Orientation Design for Mobile Visible Light Communications
Authors: Shuai Ma, Jing Wang, Chun Du, Hang Li, Xiaodong Liu, Youlong Wu, Naofal Al-Dhahir, Shiyin Li
Date: 2022-12-21 00:00:00

In this paper, we propose joint beamforming and photo-detector (PD) orientation (BO) optimization schemes for mobile visible light communication (VLC) with the orientation adjustable receiver (OAR). Since VLC is sensitive to line-of-sight propagation, we first establish the OAR model and the human body blockage model for mobile VLC user equipment (UE). To guarantee the quality of service (QoS) of mobile VLC, we jointly optimize BO with minimal UE the power consumption for both fixed and random UE orientation cases. For the fixed UE orientation case, since the {transmit} beamforming and the PD orientation are mutually coupled, the joint BO optimization problem is nonconvex and intractable. To address this challenge, we propose an alternating optimization algorithm to obtain the transmit beamforming and the PD orientation. For the random UE orientation case, we further propose a robust alternating BO optimization algorithm to ensure the worst-case QoS requirement of the mobile UE. Finally, the performance of joint BO optimization design schemes are evaluated for mobile VLC through numerical experiments.
Minimum Phase Linear Antenna Array Design
Authors: Jan C Olivier, Etienne Barnard
Date: 2022-12-21 00:00:00

The paper considers the design of minimum phase discrete linear arrays. The paper introduces recent advances for the design of minimum phase Finite Impulse Response filters, as applied to the design of minimum phase linear arrays. The minimum phase linear array is demonstrated to require the least number of elements of all linear arrays that are able to achieve a given magnitude pattern specification. Three example designs are presented and compared to results from the literature.
Detecting Temporal shape changes with the Euler Characteristic Transform
Authors: Lewis Marsh, Felix Y. Zhou, Xiao Qin, Xin Lu, Helen M. Byrne, Heather A. Harrington
Date: 2022-12-21 00:00:00
Tasks: Topological Data Analysis

Organoids are multi-cellular structures which are cultured in vitro from stem cells to resemble specific organs (e.g., brain, liver) in their three-dimensional composition. Dynamic changes in the shape and composition of these model systems can be used to understand the effect of mutations and treatments in health and disease. In this paper, we propose a new technique in the field of topological data analysis for DEtecting Temporal shape changes with the Euler Characteristic Transform (DETECT). DETECT is a rotationally invariant signature of dynamically changing shapes. We demonstrate our method on a data set of segmented videos of mouse small intestine organoid experiments and show that it outperforms classical shape descriptors. We verify our method on a synthetic organoid data set and illustrate how it generalises to 3D. We conclude that DETECT offers rigorous quantification of organoids and opens up computationally scalable methods for distinguishing different growth regimes and assessing treatment effects.
Subharmonic solutions for a class of predator-prey models with degenerate weights in periodic environments
Authors: Julián López-Gómez, Eduardo Muñoz-Hernández, Fabio Zanolin
Date: 2022-12-21 00:00:00

This paper deals with the existence, multiplicity, minimal complexity and global structure of the subharmonic solutions to a class of planar Hamiltonian systems with periodic coefficients, being the classical predator-prey model of V. Volterra its most paradigmatic example. By means of a topological approach based on techniques from global bifurcation theory, the first part of the paper ascertains their nature, multiplicity and minimal complexity, as well as their global minimal structure, in terms of the configuration of the function coefficients in the setting of the model. The second part of the paper introduces a dynamical system approach based on the theory of topological horseshoes that permits to detect, besides subharmonic solutions, ``chaotic-type'' solutions. As a byproduct of our analysis, the simplest predator-prey prototype models in periodic environments can provoke chaotic dynamics. This cannot occur in cooperative and quasi-cooperative dynamics, as a consequence of the ordering imposed by the maximum principle.
Intransitively winning chess players positions
Authors: Alexander Poddiakov
Date: 2022-12-11 00:00:00
Tasks: Position

Positions of chess players in intransitive (rock-paper-scissors) relations are considered. Namely, position A of White is preferable (it should be chosen if choice is possible) to position B of Black, position B of Black is preferable to position C of White, position C of White is preferable to position D of Black, but position D of Black is preferable to position A of White. Intransitivity of winningness of positions of chess players is considered to be a consequence of complexity of the chess environment -- in contrast with simpler games with transitive positions only. The space of relations between winningness of positions of chess players is non-Euclidean. The Zermelo-von Neumann theorem is complemented by statements about possibility vs. impossibility of building pure winning strategies based on the assumption of transitivity of positions of chess players. Questions about the possibility of intransitive positions of players in other positional games are raised.
Risk Sharing with Deep Neural Networks
Authors: Matteo Burzoni, Alessandro Doldi, Enea Monzio Compagnoni
Date: 2022-12-22 00:00:00
Tasks: Position

We consider the problem of optimally sharing a financial position among agents with potentially different reference risk measures. The problem is equivalent to computing the infimal convolution of the risk metrics and finding the so-called optimal allocations. We propose a neural network-based framework to solve the problem and we prove the convergence of the approximated inf-convolution, as well as the approximated optimal allocations, to the corresponding theoretical values. We support our findings with several numerical experiments.