pwc-archive/papers-with-abstracts huggingface dataset:
Economic Analysis of Smart Roadside Infrastructure Sensors for Connected and Automated Mobility
Authors: Laurent Kloeker, Gregor Joeken, Lutz Eckstein
Date: 2023-07-24 00:00:00

Smart roadside infrastructure sensors in the form of intelligent transportation system stations (ITS-Ss) are increasingly deployed worldwide at relevant traffic nodes. The resulting digital twins of the real environment are suitable for developing and validating connected and automated driving functions and for increasing the operational safety of intelligent vehicles by providing ITS-S real-time data. However, ITS-Ss are very costly to establish and operate. The choice of sensor technology also has an impact on the overall costs as well as on the data quality. So far, there is only insufficient knowledge about the concrete expenses that arise with the construction of different ITS-S setups. Within this work, multiple modular infrastructure sensor setups are investigated with the help of a life cycle cost analysis (LCCA). Their economic efficiency, different user requirements and sensor data qualities are considered. Based on the static cost model, a Monte Carlo simulation is performed, to generate a range of possible project costs and to quantify the financial risks of implementing ITS-S projects of different scales. Due to its modularity, the calculation model is suitable for diverse applications and outputs a distinctive evaluation of the underlying cost-benefit ratio of investigated setups.
Propagation of a carbon price in a credit portfolio through macroeconomic factors
Authors: Géraldine Bouveret, Jean-François Chassagneux, Smail Ibbou, Antoine Jacquier, Lionel Sopgoui
Date: 2023-07-24 00:00:00

We study how the climate transition through a low-carbon economy, implemented by carbon pricing, propagates in a credit portfolio and precisely describe how carbon price dynamics affects credit risk measures such as probability of default, expected and unexpected losses. We adapt a stochastic multisectoral model to take into account the greenhouse gases (GHG) emissions costs of both sectoral firms' production and consumption, as well as sectoral household's consumption. GHG emissions costs are the product of carbon prices, provided by the NGFS transition scenarios, and of GHG emissions. For each sector, our model yields the sensitivity of firms' production and households' consumption to carbon price and the relationships between sectors. It allows us to analyze the short-term effects of the carbon price as opposed to standard IAM (such as REMIND), which are deterministic and only capture long-term trends. Finally, we use a DCF methodology to compute firms' values which we then combine with a structural credit risk model to describe how the carbon price impacts credit risk measures. We obtain that the carbon price distorts the distribution of the firm's value, increases banking fees charged to clients (materialized by the bank provisions), and reduces banks' profitability (translated by the economic capital). In addition, the randomness we introduce provides extra flexibility to take into account uncertainties on the productivity and on the different transition scenarios. We also compute the sensitivities of the credit risk measures with respect to changes in the carbon price, yielding further criteria for a more accurate assessment of climate transition risk in a credit portfolio. This work provides a preliminary methodology to calculate the evolution of credit risk measures of a credit portfolio, starting from a given climate transition scenario described by a carbon price.
Microeconomics of nitrogen fertilization in boreal carbon forestry
Authors: Petri P. Karenlampi
Date: 2023-07-23 00:00:00

Nitrogen fertilization of boreal forests is investigated in terms of microeconomics, as a tool for carbon sequestration. The effects of nitrogen fertilization's timing on the return rate on capital and the expected value of the timber stock are investigated within a set of semi-fertile, spruce-dominated boreal stands, using an inventory-based growth model. Early fertilization tends to shorten rotations, reducing timber stock and carbon storage. The same applies to fertilization after the second thinning. Fertilization applied ten years before stand maturity is profitable and increases the timber stock, but the latter effect is small. Fertilization of mature stands, extending any rotation by ten years, effectively increases the carbon stock. Profitability varies but is increased by fertilization, instead of merely extending the rotation.
Indicator Choice in Pay-for-Performance
Authors: Majid Mahzoon, Ali Shourideh, Ariel Zetlin-Jones
Date: 2023-07-23 00:00:00

We study the classic principal-agent model when the signal observed by the principal is chosen by the agent. We fully characterize the optimal information structure from an agent's perspective in a general moral hazard setting with limited liability. Due to endogeneity of the contract chosen by the principal, the agent's choice of information is non-trivial. We show that the agent's problem can be mapped into a geometrical game between the principal and the agent in the space of likelihood ratios. We use this representation result to show that coarse contracts are sufficient: The agent can achieve her best with binary signals. Additionally, we can characterize conditions under which the agent is able to extract the entire surplus and implement the first-best efficient allocation. Finally, we show that when effort and performance are one-dimensional, under a general class of models, threshold signals are optimal. Our theory can thus provide a rationale for coarseness of contracts based on the bargaining power of the agent in negotiations.
Sharing Credit for Joint Research
Authors: Nicholas Wu
Date: 2023-07-22 00:00:00

How can one efficiently share payoffs with collaborators when participating in risky research? First, I show that efficiency can be achieved by allocating payoffs asymmetrically between the researcher who makes a breakthrough ("winner") and the others, even if agents cannot observe others' effort. When the winner's identity is non-contractible, allocating credit based on effort at time of breakthrough also suffices to achieve efficiency; so the terminal effort profile, rather than the full history of effort, is a sufficient statistic. These findings suggest that simple mechanisms using minimal information are robust and effective in addressing inefficiencies in strategic experimentation.
Conditional Residual Coding: A Remedy for Bottleneck Problems in Conditional Inter Frame Coding
Authors: Fabian Brand, Jürgen Seiler, André Kaup
Date: 2023-07-24 00:00:00
Tasks: Video Compression

Conditional coding is a new video coding paradigm enabled by neural-network-based compression. It can be shown that conditional coding is in theory better than the traditional residual coding, which is widely used in video compression standards like HEVC or VVC. However, on closer inspection, it becomes clear that conditional coders can suffer from information bottlenecks in the prediction path, i.e., that due to the data processing inequality not all information from the prediction signal can be passed to the reconstructed signal, thereby impairing the coder performance. In this paper we propose the conditional residual coding concept, which we derive from information theoretical properties of the conditional coder. This coder significantly reduces the influence of bottlenecks, while maintaining the theoretical performance of the conditional coder. We provide a theoretical analysis of the coding paradigm and demonstrate the performance of the conditional residual coder in a practical example. We show that conditional residual coders alleviate the disadvantages of conditional coders while being able to maintain their advantages over residual coders. In the spectrum of residual and conditional coding, we can therefore consider them as ``the best from both worlds''.
Deep Homography Prediction for Endoscopic Camera Motion Imitation Learning
Authors: Martin Huber, Sebastien Ourselin, Christos Bergeles, Tom Vercauteren
Date: 2023-07-24 00:00:00
Tasks: Image Registration, Imitation Learning, Prediction

In this work, we investigate laparoscopic camera motion automation through imitation learning from retrospective videos of laparoscopic interventions. A novel method is introduced that learns to augment a surgeon's behavior in image space through object motion invariant image registration via homographies. Contrary to existing approaches, no geometric assumptions are made and no depth information is necessary, enabling immediate translation to a robotic setup. Deviating from the dominant approach in the literature which consist of following a surgical tool, we do not handcraft the objective and no priors are imposed on the surgical scene, allowing the method to discover unbiased policies. In this new research field, significant improvements are demonstrated over two baselines on the Cholec80 and HeiChole datasets, showcasing an improvement of 47% over camera motion continuation. The method is further shown to indeed predict camera motion correctly on the public motion classification labels of the AutoLaparo dataset. All code is made accessible on GitHub.
Synthetic white balancing for intra-operative hyperspectral imaging
Authors: Anisha Bahl, Conor C. Horgan, Mirek Janatka, Oscar J. MacCormac, Philip Noonan, Yijing Xie, Jianrong Qiu, Nicola Cavalcanti, Philipp Fürnstahl, Michael Ebner, Mads S. Bergholt, Jonathan Shapey, Tom Vercauteren
Date: 2023-07-24 00:00:00

Hyperspectral imaging shows promise for surgical applications to non-invasively provide spatially-resolved, spectral information. For calibration purposes, a white reference image of a highly-reflective Lambertian surface should be obtained under the same imaging conditions. Standard white references are not sterilizable, and so are unsuitable for surgical environments. We demonstrate the necessity for in situ white references and address this by proposing a novel, sterile, synthetic reference construction algorithm. The use of references obtained at different distances and lighting conditions to the subject were examined. Spectral and color reconstructions were compared with standard measurements qualitatively and quantitatively, using $\Delta E$ and normalised RMSE respectively. The algorithm forms a composite image from a video of a standard sterile ruler, whose imperfect reflectivity is compensated for. The reference is modelled as the product of independent spatial and spectral components, and a scalar factor accounting for gain, exposure, and light intensity. Evaluation of synthetic references against ideal but non-sterile references is performed using the same metrics alongside pixel-by-pixel errors. Finally, intraoperative integration is assessed though cadaveric experiments. Improper white balancing leads to increases in all quantitative and qualitative errors. Synthetic references achieve median pixel-by-pixel errors lower than 6.5% and produce similar reconstructions and errors to an ideal reference. The algorithm integrated well into surgical workflow, achieving median pixel-by-pixel errors of 4.77%, while maintaining good spectral and color reconstruction.
Low-complexity Overfitted Neural Image Codec
Authors: Thomas Leguay, Théo Ladune, Pierrick Philippe, Gordon Clare, Félix Henry
Date: 2023-07-24 00:00:00
Tasks: Decoder

We propose a neural image codec at reduced complexity which overfits the decoder parameters to each input image. While autoencoders perform up to a million multiplications per decoded pixel, the proposed approach only requires 2300 multiplications per pixel. Albeit low-complexity, the method rivals autoencoder performance and surpasses HEVC performance under various coding conditions. Additional lightweight modules and an improved training process provide a 14% rate reduction with respect to previous overfitted codecs, while offering a similar complexity. This work is made open-source at https://orange-opensource.github.io/Cool-Chic/
Bayesian Based Unrolling for Reconstruction and Super-resolution of Single-Photon Lidar Systems
Authors: Abderrahim Halimi, JaKeoung Koo, Stephen McLaughlin
Date: 2023-07-24 00:00:00
Tasks: Super-Resolution

Deploying 3D single-photon Lidar imaging in real world applications faces several challenges due to imaging in high noise environments and with sensors having limited resolution. This paper presents a deep learning algorithm based on unrolling a Bayesian model for the reconstruction and super-resolution of 3D single-photon Lidar. The resulting algorithm benefits from the advantages of both statistical and learning based frameworks, providing best estimates with improved network interpretability. Compared to existing learning-based solutions, the proposed architecture requires a reduced number of trainable parameters, is more robust to noise and mismodelling of the system impulse response function, and provides richer information about the estimates including uncertainty measures. Results on synthetic and real data show competitive results regarding the quality of the inference and computational complexity when compared to state-of-the-art algorithms. This short paper is based on contributions published in [1] and [2].
4D Feet: Registering Walking Foot Shapes Using Attention Enhanced Dynamic-Synchronized Graph Convolutional LSTM Network
Authors: Farzam Tajdari, Toon Huysmans, Xinhe Yao, Jun Xu, Yu Song
Date: 2023-07-23 00:00:00

4D scans of dynamic deformable human body parts help researchers have a better understanding of spatiotemporal features. However, reconstructing 4D scans based on multiple asynchronous cameras encounters two main challenges: 1) finding the dynamic correspondences among different frames captured by each camera at the timestamps of the camera in terms of dynamic feature recognition, and 2) reconstructing 3D shapes from the combined point clouds captured by different cameras at asynchronous timestamps in terms of multi-view fusion. In this paper, we introduce a generic framework that is able to 1) find and align dynamic features in the 3D scans captured by each camera using the nonrigid iterative closest-farthest points algorithm; 2) synchronize scans captured by asynchronous cameras through a novel ADGC-LSTM-based network, which is capable of aligning 3D scans captured by different cameras to the timeline of a specific camera; and 3) register a high-quality template to synchronized scans at each timestamp to form a high-quality 3D mesh model using a non-rigid registration method. With a newly developed 4D foot scanner, we validate the framework and create the first open-access data-set, namely the 4D feet. It includes 4D shapes (15 fps) of the right and left feet of 58 participants (116 feet in total, including 5147 3D frames), covering significant phases of the gait cycle. The results demonstrate the effectiveness of the proposed framework, especially in synchronizing asynchronous 4D scans using the proposed ADGC-LSTM network.
ELiOT : End-to-end Lidar Odometry using Transformer Framework
Authors: Daegyu Lee, Hyunwoo Nam, D. Hyunchul Shim
Date: 2023-07-22 00:00:00
Tasks: Decoder, Point Cloud Registration

In recent years, deep-learning-based point cloud registration methods have shown significant promise. Furthermore, learning-based 3D detectors have demonstrated their effectiveness in encoding semantic information from LiDAR data. In this paper, we introduce ELiOT, an end-to-end LiDAR odometry framework built on a transformer architecture. Our proposed Self-attention flow embedding network implicitly represents the motion of sequential LiDAR scenes, bypassing the need for 3D-2D projections traditionally used in such tasks. The network pipeline, composed of a 3D transformer encoder-decoder, has shown effectiveness in predicting poses on urban datasets. In terms of translational and rotational errors, our proposed method yields encouraging results, with 7.59% and 2.67% respectively on the KITTI odometry dataset. This is achieved with an end-to-end approach that foregoes the need for conventional geometric concepts.
Conditional Temporal Attention Networks for Neonatal Cortical Surface Reconstruction
Authors: Qiang Ma, Liu Li, Vanessa Kyriakopoulou, Joseph Hajnal, Emma C. Robinson, Bernhard Kainz, Daniel Rueckert
Date: 2023-07-21 00:00:00
Tasks: Surface Reconstruction

Cortical surface reconstruction plays a fundamental role in modeling the rapid brain development during the perinatal period. In this work, we propose Conditional Temporal Attention Network (CoTAN), a fast end-to-end framework for diffeomorphic neonatal cortical surface reconstruction. CoTAN predicts multi-resolution stationary velocity fields (SVF) from neonatal brain magnetic resonance images (MRI). Instead of integrating multiple SVFs, CoTAN introduces attention mechanisms to learn a conditional time-varying velocity field (CTVF) by computing the weighted sum of all SVFs at each integration step. The importance of each SVF, which is estimated by learned attention maps, is conditioned on the age of the neonates and varies with the time step of integration. The proposed CTVF defines a diffeomorphic surface deformation, which reduces mesh self-intersection errors effectively. It only requires 0.21 seconds to deform an initial template mesh to cortical white matter and pial surfaces for each brain hemisphere. CoTAN is validated on the Developing Human Connectome Project (dHCP) dataset with 877 3D brain MR images acquired from preterm and term born neonates. Compared to state-of-the-art baselines, CoTAN achieves superior performance with only 0.12mm geometric error and 0.07% self-intersecting faces. The visualization of our attention maps illustrates that CoTAN indeed learns coarse-to-fine surface deformations automatically without intermediate supervision.
NLOS Localization Exploiting Frequency-selective Metasurfaces
Authors: Marina Lotti, Giacomo Calesini, Davide Dardari
Date: 2023-07-24 00:00:00
Tasks: Position

This paper introduces a new approach to localize user devices located in non-line-of-sight (NLOS) areas using a passive, non-reconfigurable, and frequency-selective metasurface called metaprism. By analyzing the spatial filtering of subcarriers in the orthogonal frequency division multiplexing (OFDM) signal transmitted by each user device, the base station can estimate the device's angle of view, distance, and subsequently its position. Two different criteria are proposed for designing the frequency response of the metaprism, depending on whether the users operate in the far-field or near-field region of the metaprism. Simulation results in the millimeter-wave band demonstrate that the system can achieve an accuracy of less than 2 degrees in angle estimation and in the order of decimeters in position estimation.
Dynamic Sub-array Based Modeling for Large-Scale RIS-assisted mmWave UAV Channels
Authors: Baiping Xiong, Zaichen Zhang, Jiangzhou Wang
Date: 2023-07-24 00:00:00

Large-scale reconfigurable intelligent surface (RIS) can effectively enhance the performance of millimeter wave (mmWave) unmanned aerial vehicle (UAV) to ground communication link with obstructed line-of-sight (LoS) path by exploiting more reflecting units. However, the non-negligible array dimension of large-scale RIS and the mobile property of the terminals bring significant variations in propagation characteristics, making conventional channel models inapplicable. To address this issue, we propose a dynamic sub-array partition scheme to divide the large-scale RIS into sub-arrays by exploiting the Rayleigh distance criterion and the mobile property of the transceivers. Based on the proposed scheme, a novel non-stationary channel model for large-scale RIS auxiliary mmWave UAV-to-ground mobile networks is developed, which outperforms existing models with well balance between model complexity and accuracy. Numerical results are provided to verify our analysis.
Joint Ultra-wideband Characterization of Azimuth, Elevation and Time of Arrival with Toric Arrays
Authors: Alejandro Ramírez-Arroyo, Antonio Alex-Amor, Rubén Medina, Pablo Padilla, Juan F. Valenzuela-Valdés
Date: 2023-07-24 00:00:00

In this paper, we present an analytical framework for the joint characterization of the 3D direction of arrival (DoA), i.e., azimuth and elevation components, and time of arrival (ToA) in multipath environments. The analytical framework is based on the use of nearly frequency-invariant beamformers (FIB) formed by toric arrays. The frequency response of the toric array is expanded as a series of phase modes, which leads to azimuth-time and elevation-time diagrams from which the 3D DoA and the ToA of the incoming waves can be extracted over a wide bandwidth. Firstly, we discuss some practical considerations, advantages and limitations of using the analytical method. Subsequently, we perform a parametric study to analyze the influence of the method parameters on the quality of the estimation. The method is tested in single-path and multipath mm-wave environments over a large bandwidth. The results show that the proposed method improves the quality of the estimation, i.e., decreases the level of the artifacts, compared to other state-of-art FIB approaches based on the use of single/concentric circular and elliptical arrays.
Robust MIMO Detection With Imperfect CSI: A Neural Network Solution
Authors: Yi Sun, Hong Shen, Wei Xu, Nan Hu, Chunming Zhao
Date: 2023-07-24 00:00:00

In this paper, we investigate the design of statistically robust detectors for multi-input multi-output (MIMO) systems subject to imperfect channel state information (CSI). A robust maximum likelihood (ML) detection problem is formulated by taking into consideration the CSI uncertainties caused by both the channel estimation error and the channel variation. To address the challenging discrete optimization problem, we propose an efficient alternating direction method of multipliers (ADMM)-based algorithm, which only requires calculating closed-form solutions in each iteration. Furthermore, a robust detection network RADMMNet is constructed by unfolding the ADMM iterations and employing both model-driven and data-driven philosophies. Moreover, in order to relieve the computational burden, a low-complexity ADMM-based robust detector is developed using the Gaussian approximation, and the corresponding deep unfolding network LCRADMMNet is further established. On the other hand, we also provide a novel robust data-aided Kalman filter (RDAKF)-based channel tracking method, which can effectively refine the CSI accuracy and improve the performance of the proposed robust detectors. Simulation results validate the significant performance advantages of the proposed robust detection networks over the non-robust detectors with different CSI acquisition methods.