pwc-archive/papers-with-abstracts huggingface dataset:
The energy return on investment of whole energy systems: application to Belgium
Authors: Jonathan Dumas, Antoine Dubois, Paolo Thiran, Pierre Jacques, Francesco Contino, Bertrand Cornélusse, Gauthier Limpens
Date: 2022-05-13 00:00:00

Planning the defossilization of energy systems by facilitating high penetration of renewables and maintaining access to abundant and affordable primary energy resources is a nontrivial multi-objective problem. However, so far, most long-term policies to decrease the carbon footprint of our societies consider the cost of the system as the leading indicator in the energy system models. This paper is the first to develop a novel approach by adding the energy return on investment (EROI) to a whole energy system optimization model. We built the database with all EROI technologies and resources considered. In addition, moving away from fossil-based to carbon-neutral energy systems raises the issue of the uncertainty of low-carbon technologies and resource data. Thus, we conducted a global sensitivity analysis to identify the main parameters driving the variations in the EROI of the system. This novel approach can be applied to any energy system, and we use a real-world case study to illustrate the model: the 2035 Belgian energy system for several greenhouse gas emissions targets. The main results are threefold: (i) the EROI of the system decreases from 8.9 to 3.9 when greenhouse gas emissions are reduced by 5; (ii) the renewable fuels - mainly imported renewable gas - represent the largest share of the system primary energy mix; (iii) in the sensitivity analysis, the renewable fuels drive 67% of the variation of the EROI of the system for low greenhouse gas emissions scenarios. The decrease in the EROI raises questions about meeting the climate targets without adverse socio-economic impact. Most countries rely massively on fossil fuels, and they could encounter an EROI decline when shifting to carbon neutrality. Thus, this study demonstrates the importance of considering other criteria, such as EROI, in energy system models.
STAR-RIS-Assisted Hybrid NOMA mmWave Communication: Optimization and Performance Analysis
Authors: Muhammad Faraz Ul Abrar, Muhammad Talha, Rafay Iqbal Ansari, Syed Ali Hassan, Haejoon Jung
Date: 2022-05-13 00:00:00

Simultaneously reflecting and transmitting reconfigurable intelligent surfaces (STAR-RIS) has recently emerged as prominent technology that exploits the transmissive property of RIS to mitigate the half-space coverage limitation of conventional RIS operating on millimeter-wave (mmWave). In this paper, we study a downlink STAR-RIS-based multi-user multiple-input single-output (MU-MISO) mmWave hybrid non-orthogonal multiple access (H-NOMA) wireless network, where a sum-rate maximization problem has been formulated. The design of active and passive beamforming vectors, time and power allocation for H-NOMA is a highly coupled non-convex problem. To handle the problem, we propose an optimization framework based on alternating optimization (AO) that iteratively solves active and passive beamforming sub-problems. Channel correlations and channel strength-based techniques have been proposed for a specific case of two-user optimal clustering and decoding order assignment, respectively, for which analytical solutions to joint power and time allocation for H-NOMA have also been derived. Simulation results show that: 1) the proposed framework leveraging H-NOMA outperforms conventional OMA and NOMA to maximize the achievable sum-rate; 2) using the proposed framework, the supported number of clusters for the given design constraints can be increased considerably; 3) through STAR-RIS, the number of elements can be significantly reduced as compared to conventional RIS to ensure a similar quality-of-service (QoS).
Event-Based Control for Synchronization of Stochastic Linear Systems with Application to Distributed Estimation
Authors: Jiaqi Yan, Yilin Mo, Hideaki Ishii
Date: 2022-05-13 00:00:00

This paper studies the synchronization of stochastic linear systems which are subject to a general class of noises, in the sense that the noises are bounded in covariance but might be correlated with the states of agents and among each other. We propose an event-based control protocol for achieving the synchronization among agents in the mean square sense and theoretically analyze the performance of it by using a stochastic Lyapunov function, where the stability of $c$-martingales is particularly developed to handle the challenges brought by the general model of noises and the event-triggering mechanism. The proposed event-based synchronization algorithm is then applied to solve the problem of distributed estimation in sensor network. Specifically, by losslessly decomposing the optimal Kalman filter, it is shown that the problem of distributed estimation can be resolved by using the algorithms designed for achieving the synchronization of stochastic linear systems. As such, an event-based distributed estimation algorithm is developed, where each sensor performs local filtering solely using its own measurement, together with the proposed event-based synchronization algorithm to fuse the local estimates of neighboring nodes. With the reduced communication frequency, the designed estimator is proved to be stable under the minimal requirements of network connectivity and collective system observability.
A New Hybrid Multi-Objective Scheduling Model for Hierarchical Hub and Flexible Flow Shop Problems
Authors: Sina Aghakhani, Mohammad Sadra Rajabi
Date: 2022-05-13 00:00:00
Tasks: Scheduling

Technologies and lifestyles have been increasingly geared toward consumerism in recent years. Accordingly, it is both the price and the delivery time that matter most to the ultimate customers of commercial enterprises. Consequently, the importance of having an optimal delivery time is becoming increasingly evident these days. Scheduling can be used to optimize supply chains and production systems in this manner, which is one practical method for lowering costs and boosting productivity. This paper suggests a multi-objective scheduling model for hierarchical hub structures (HHS) with three levels of service. The factory and customers hub (second level) and central are on the first level in which the factory has a Flexible Flow Shop (FFS) environment. The noncentral hub (third level) is responsible for the delivery of products made in the factory to customers. Customer nodes and factories are connected separately to the second level, and the non-central hubs are connected to the third level. The model's objective is to minimize transportation and production costs and product arrival times. To validate and evaluate the model, small instances have been solved and analyzed in detail with the weighted sum and e-constraint methods. Consequently, based on the ideal mean distance (MID) metric, the two methods were compared for the designed instances. As NP-hardness causes the previously proposed methods to solve large-scale problems to be time-consuming, a meta-heuristic method was developed to solve the large-scale problem.
Interface Networks for Failure Localization in Power Systems
Authors: Chen Liang, Alessandro Zocca, Steven H. Low, Adam Wierman
Date: 2022-05-12 00:00:00

Transmission power systems usually consist of interconnected sub-grids that are operated relatively independently. When a failure happens, it is desirable to localize its impact within the sub-grid where the failure occurs. This paper introduces three interface networks to connect sub-grids, achieving better failure localization while maintaining robust network connectivity. The proposed interface networks are validated with numerical experiments on the IEEE 118-bus test network under both DC and AC power flow models.
Prototype Development and Validation of a Beam-Divergence Control System for Free-Space Laser Communications
Authors: Alberto Carrasco-Casado, Koichi Shiratama, Dimitar Kolev, Phuc V. Trinh, Tetsuharu Fuse, Shingo Fuse, Koji Kawaguchi, Yusuke Hashimoto, Masamitsu Hyodo, Takashi Sakamoto, Terufusa Kunisada, Morio Toyoshima
Date: 2022-05-09 00:00:00

Being able to dynamically control the transmitted-beam divergence can bring important advantages in free-space optical communications. Specifically, this technique can help to optimize the overall communications performance when the optimum laser-beam divergence is not fixed or known. This is the case in most realistic space laser communication systems, since the optimum beam divergence depends on multiple factors that can vary with time, such as the link distance, or cannot be accurately known, such as the actual pointing accuracy. A dynamic beam-divergence control allows to optimize the link performance for every platform, scenario, and condition. NICT is currently working towards the development of a series of versatile lasercom terminals that can fit a variety of conditions, for which the adaptive element of the transmitted beam divergence is a key element. This manuscript presents a prototype of a beam-divergence control system designed and developed by NICT and Tamron to evaluate this technique and to be later integrated within the lasercom terminals. The basic design of the prototype is introduced as well as the first validation tests that demonstrate its performance.
Modelling stellar activity with Gaussian process regression networks
Authors: J. D. Camacho, J. P. Faria, P. T. P. Viana
Date: 2022-05-13 00:00:00
Tasks: Gaussian Processes, regression, Time Series, Time Series Analysis

Stellar photospheric activity is known to limit the detection and characterisation of extra-solar planets. In particular, the study of Earth-like planets around Sun-like stars requires data analysis methods that can accurately model the stellar activity phenomena affecting radial velocity (RV) measurements. Gaussian Process Regression Networks (GPRNs) offer a principled approach to the analysis of simultaneous time-series, combining the structural properties of Bayesian neural networks with the non-parametric flexibility of Gaussian Processes. Using HARPS-N solar spectroscopic observations encompassing three years, we demonstrate that this framework is capable of jointly modelling RV data and traditional stellar activity indicators. Although we consider only the simplest GPRN configuration, we are able to describe the behaviour of solar RV data at least as accurately as previously published methods. We confirm the correlation between the RV and stellar activity time series reaches a maximum at separations of a few days, and find evidence of non-stationary behaviour in the time series, associated with an approaching solar activity minimum.
A Robust Permutation Test for Subvector Inference in Linear Regressions
Authors: Xavier D'Haultfœuille, Purevdorj Tuvaandorj
Date: 2022-05-13 00:00:00

We develop a new permutation test for inference on a subvector of coefficients in linear models. The test is exact when the regressors and the error terms are independent. Then, we show that the test is asymptotically of correct level, consistent and has power against local alternatives when the independence condition is relaxed, under two main conditions. The first is a slight reinforcement of the usual absence of correlation between the regressors and the error term. The second is that the number of strata, defined by values of the regressors not involved in the subvector test, is small compared to the sample size. The latter implies that the vector of nuisance regressors is discrete. Simulations and empirical illustrations suggest that the test has good power in practice if, indeed, the number of strata is small compared to the sample size.
Causal Estimation of Position Bias in Recommender Systems Using Marketplace Instruments
Authors: Rina Friedberg, Karthik Rajkumar, Jialiang Mao, Qian Yao, YinYin Yu, Min Liu
Date: 2022-05-12 00:00:00
Tasks: Information Retrieval, Position, Recommendation Systems, Retrieval

Information retrieval systems, such as online marketplaces, news feeds, and search engines, are ubiquitous in today's digital society. They facilitate information discovery by ranking retrieved items on predicted relevance, i.e. likelihood of interaction (click, share) between users and items. Typically modeled using past interactions, such rankings have a major drawback: interaction depends on the attention items receive. A highly-relevant item placed outside a user's attention could receive little interaction. This discrepancy between observed interaction and true relevance is termed the position bias. Position bias degrades relevance estimation and when it compounds over time, it can silo users into false relevant items, causing marketplace inefficiencies. Position bias may be identified with randomized experiments, but such an approach can be prohibitive in cost and feasibility. Past research has also suggested propensity score methods, which do not adequately address unobserved confounding; and regression discontinuity designs, which have poor external validity. In this work, we address these concerns by leveraging the abundance of A/B tests in ranking evaluations as instrumental variables. Historical A/B tests allow us to access exogenous variation in rankings without manually introducing them, harming user experience and platform revenue. We demonstrate our methodology in two distinct applications at LinkedIn - feed ads and the People-You-May-Know (PYMK) recommender. The marketplaces comprise users and campaigns on the ads side, and invite senders and recipients on PYMK. By leveraging prior experimentation, we obtain quasi-experimental variation in item rankings that is orthogonal to user relevance. Our method provides robust position effect estimates that handle unobserved confounding well, greater generalizability, and easily extends to other information retrieval systems.
Two strategies for boreal forestry with goodwill in capitalization
Authors: Petri P. Karenlampi
Date: 2022-05-13 00:00:00
Tasks: Vocal Bursts Valence Prediction

Two strategies for boreal forestry with goodwill in estate capitalization are introduced. A strategy focusing on Real Estate (RE) is financially superior to Timber Sales (TS). The feasibility of the RE requires the presence of forest land end users in the real estate market, like insurance companies or investment trusts, and the periodic boundary condition does not apply. Commercial thinnings do not enter the RE strategy in a stand-level discussion. However, they may appear in estates with a variable age structure and enable an extension of stand rotation times.
Dynamic Stochastic Inventory Management in E-Grocery Retailing
Authors: David Winkelmann, Matthias Ulrich, Michael Römer, Roland Langrock, Hermann Jahnke
Date: 2022-05-13 00:00:00
Tasks: Management

E-grocery retailing enables ordering products online to be delivered at a future time slot chosen by the customer. This emerging field of business provides retailers with large and comprehensive new data sets, yet creates several challenges for the inventory management process. For example, the risk of a single item's stock-out leading to a complete cancellation of the shopping process is higher in e-grocery than in traditional store retailing. As a consequence, retailers aim at very high service level targets to provide satisfactory customer service and to ensure long-term business growth. When determining replenishment order quantities, it is of crucial importance to precisely account for the full uncertainty in the inventory process. This requires predictive and prescriptive analytics to (1) estimate suitable underlying probability distributions to represent the uncertainty caused by non-stationary customer demand, shelf lives, and supply, and to (2) integrate those forecasts into a comprehensive multi-period optimisation framework. In this paper, we model this stochastic dynamic problem by a sequential decision process that allows us to avoid simplifying assumptions commonly made in the literature, such as the focus on a single demand period. As the resulting problem will typically be analytically intractable, we propose a stochastic lookahead policy incorporating Monte Carlo techniques to fully propagate the associated uncertainties in order to derive replenishment order quantities. This policy naturally integrates probabilistic forecasts and allows us to explicitly derive the value of accounting for probabilistic information compared to myopic or deterministic approaches in a simulation-based setting. In addition, we evaluate our policy in a case study based on real-world data where underlying probability distributions are estimated from historical data and explanatory variables.
The Value of Information in Stopping Problems
Authors: Ehud Lehrer, Tao Wang
Date: 2022-05-13 00:00:00

We consider stopping problems in which a decision maker (DM) faces an unknown state of nature and decides sequentially whether to stop and take an irreversible action; pay a fee and obtain additional information; or wait without acquiring information. We discuss the value and quality of information. The former is the maximal discounted expected revenue the DM can generate. We show that among all history-dependent fee schemes, the upfront scheme (as opposed, for instance, to pay-for-use) is optimal: it generates the highest possible value of information. The effects on the optimal strategy of obtaining information from a more accurate source and of having a higher discount factor are distinct, as far as expected stopping time and its distribution are concerned. However, these factors have a similar effect in that they both enlarge the set of cases in which the optimal strategy prescribes waiting.
High-Frequency Tunable Resistorless Memcapacitor Emulator and Application
Authors: Pratik Kumar, Sajal K. Paul
Date: 2022-05-13 00:00:00
Tasks: Vocal Bursts Intensity Prediction

In this paper, a new design has been proposed for the realization of high-frequency memcapacitor emulators built with three OTAs. This paper also proposes the application of memcapacitor as an amplitude modulator. Furthermore, applications of memcapacitor as a filter, Oscillator point attractor, and periodic doubler are also shown. The proposed circuits can be configured in both incremental and decremental topology. The proposed circuits and their application claim that the circuit is much simpler in design and can be utilized in both topologies. The performance of all the proposed circuits has been verified on Cadence Virtuoso Spectre using standard CMOS 180nm. Furthermore, post-layout simulations and their comparison have been carried out.
Joint Acoustic Echo Cancellation and Blind Source Extraction based on Independent Vector Extraction
Authors: Thomas Haubner, Zbyněk Koldovský, Walter Kellermann
Date: 2022-05-13 00:00:00
Tasks: Acoustic echo cancellation

We describe a joint acoustic echo cancellation (AEC) and blind source extraction (BSE) approach for multi-microphone acoustic frontends. The proposed algorithm blindly estimates AEC and beamforming filters by maximizing the statistical independence of a non-Gaussian source of interest and a stationary Gaussian background modeling interfering signals and residual echo. Double talk-robust and fast-converging parameter updates are derived from a global maximum-likelihood objective function resulting in a computationally efficient Newton-type update rule. Evaluation with simulated acoustic data confirms the benefit of the proposed joint AEC and beamforming filter estimation in comparison to updating both filters individually.
Evaluation of Video Coding for Machines without Ground Truth
Authors: Kristian Fischer, Markus Hofbauer, Christopher Kuhn, Eckehard Steinbach, André Kaup
Date: 2022-05-13 00:00:00
Tasks: Instance Segmentation, object-detection, Object Detection, Segmentation, Semantic Segmentation

In the emerging field of video coding for machines, video datasets with pristine video quality and high-quality annotations are required for a comprehensive evaluation. However, existing video datasets with detailed annotations are severely limited in size and video quality. Thus, current methods have to either evaluate their codecs on still images or on already compressed data. To mitigate this problem, we propose an evaluation method based on pseudo ground-truth data from the field of semantic segmentation to the evaluation of video coding for machines. Through extensive evaluation, this paper shows that the proposed ground-truth-agnostic evaluation method results in an acceptable absolute measurement error below 0.7 percentage points on the Bjontegaard Delta Rate compared to using the true ground truth for mid-range bitrates. We evaluate on the three tasks of semantic segmentation, instance segmentation, and object detection. Lastly, we utilize the ground-truth-agnostic method to measure the coding performances of the VVC compared against HEVC on the Cityscapes sequences. This reveals that the coding position has a significant influence on the task performance.
Analysis of Neural Image Compression Networks for Machine-to-Machine Communication
Authors: Kristian Fischer, Christian Forsch, Christian Herglotz, André Kaup
Date: 2022-05-13 00:00:00
Tasks: Decoder, Image Compression, SSIM

Video and image coding for machines (VCM) is an emerging field that aims to develop compression methods resulting in optimal bitstreams when the decoded frames are analyzed by a neural network. Several approaches already exist improving classic hybrid codecs for this task. However, neural compression networks (NCNs) have made an enormous progress in coding images over the last years. Thus, it is reasonable to consider such NCNs, when the information sink at the decoder side is a neural network as well. Therefore, we build-up an evaluation framework analyzing the performance of four state-of-the-art NCNs, when a Mask R-CNN is segmenting objects from the decoded image. The compression performance is measured by the weighted average precision for the Cityscapes dataset. Based on that analysis, we find that networks with leaky ReLU as non-linearity and training with SSIM as distortion criteria results in the highest coding gains for the VCM task. Furthermore, it is shown that the GAN-based NCN architecture achieves the best coding performance and even out-performs the recently standardized Versatile Video Coding (VVC) for the given scenario.
Robust Deep Neural Object Detection and Segmentation for Automotive Driving Scenario with Compressed Image Data
Authors: Kristian Fischer, Christian Blum, Christian Herglotz, André Kaup
Date: 2022-05-13 00:00:00
Tasks: Autonomous Driving, object-detection, Object Detection

Deep neural object detection or segmentation networks are commonly trained with pristine, uncompressed data. However, in practical applications the input images are usually deteriorated by compression that is applied to efficiently transmit the data. Thus, we propose to add deteriorated images to the training process in order to increase the robustness of the two state-of-the-art networks Faster and Mask R-CNN. Throughout our paper, we investigate an autonomous driving scenario by evaluating the newly trained models on the Cityscapes dataset that has been compressed with the upcoming video coding standard Versatile Video Coding (VVC). When employing the models that have been trained with the proposed method, the weighted average precision of the R-CNNs can be increased by up to 3.68 percentage points for compressed input images, which corresponds to bitrate savings of nearly 48 %.