pwc-archive/papers-with-abstracts huggingface dataset:
How will Language Modelers like ChatGPT Affect Occupations and Industries?
Authors: Ed Felten, Manav Raj, Robert Seamans
Date: 2023-03-02 00:00:00
Tasks: Language Modeling, Language Modelling

Recent dramatic increases in AI language modeling capabilities has led to many questions about the effect of these technologies on the economy. In this paper we present a methodology to systematically assess the extent to which occupations, industries and geographies are exposed to advances in AI language modeling capabilities. We find that the top occupations exposed to language modeling include telemarketers and a variety of post-secondary teachers such as English language and literature, foreign language and literature, and history teachers. We find the top industries exposed to advances in language modeling are legal services and securities, commodities, and investments. We also find a positive correlation between wages and exposure to AI language modeling.
Green Hydrogen Cost-Potentials for Global Trade
Authors: David Franzmann, Heidi Heinrichs, Felix Lippkau, Thushara Addanki, Christoph Winkler, Patrick Buchenberg, Thomas Hamacher, Markus Blesl, Jochen Linßen, Detlef Stolten
Date: 2023-03-01 00:00:00

Green hydrogen is expected to be traded globally in future greenhouse gas neutral energy systems. However, there is still a lack of temporally- and spatially-explicit cost-potentials for green hydrogen considering the full process chain, which are necessary for creating effective global strategies. Therefore, this study provides such detailed cost-potential-curves for 28 selected countries worldwide until 2050, using an optimizing energy systems approach based on open-field photovoltaics (PV) and onshore wind. The results reveal huge hydrogen potentials (>1,500 PWhLHV/a) and 79 PWhLHV/a at costs below 2.30 EUR/kg in 2050, dominated by solar-rich countries in Africa and the Middle East. Decentralized PV-based hydrogen production, even in wind-rich countries, is always preferred. Supplying sustainable water for hydrogen production is needed while having minor impact on hydrogen cost. Additional costs for imports from democratic regions are only total 7% higher. Hence, such regions could boost the geostrategic security of supply for greenhouse gas neutral energy systems.
Rationalizing Path-Independent Choice Rules
Authors: Koji Yokote, Isa E. Hafalir, Fuhito Kojima, M. Bumin Yenmez
Date: 2023-03-02 00:00:00

Path independence is arguably one of the most important choice rule properties in economic theory. We show that a choice rule is path independent if and only if it is rationalizable by a utility function satisfying ordinal concavity, a concept closely related to concavity notions in discrete mathematics. We also provide a rationalization result for choice rules that satisfy path independence and the law of aggregate demand.
Mr.Keynes and the... Complexity! A suggested agent-based version of the General Theory of Employment, Interest and Money
Authors: Alessio Emanuele Biondo
Date: 2023-03-02 00:00:00

This paper presents a model with the aim to follow, as closely as possible, the rationale of the macroeconomic model advanced by J.M. Keynes in his famous "The General Theory of Employment, Interest and Money", in order to provide a viable tool for macroeconomic research. Keynes' main result will be shown, i.e., to determine the level of income and employment starting from the marginal efficiency of capital and the marginal propensity to consume, given the interest rate. Elements of the model will be described by referring to the original text. The sequentiality in model operation will prove quintessential in order to describe the complex nature of macroeconomic systems.
Real-time Audio Video Enhancement \\with a Microphone Array and Headphones
Authors: Jacob Kealey, Anthony Gosselin, Étienne Deshaies-Samson, Francis Cardinal, Félix Ducharme-Turcotte, Olivier Bergeron, Amélie Rioux-Joyal, Jérémy Bélec, François Grondin
Date: 2023-03-02 00:00:00
Tasks: Face Detection, Speech Enhancement, Video Enhancement

This paper presents a complete hardware and software pipeline for real-time speech enhancement in noisy and reverberant conditions. The device consists of a microphone array and a camera mounted on eyeglasses, connected to an embedded system that enhances speech and plays back the audio in headphones, with a latency of maximum 120 msec. The proposed approach relies on face detection, tracking and verification to enhance the speech of a target speaker using a beamformer and a postfiltering neural network. Results demonstrate the feasibility of the approach, and opens the door to the exploration and validation of a wide range of beamformer and speech enhancement methods for real-time speech enhancement.
Ego-noise reduction of a mobile robot using noise spatial covariance matrix learning and minimum variance distortionless response
Authors: Pierre-Olivier Lagacé, François Ferland, François Grondin
Date: 2023-03-01 00:00:00
Tasks: Event Detection

The performance of speech and events recognition systems significantly improved recently thanks to deep learning methods. However, some of these tasks remain challenging when algorithms are deployed on robots due to the unseen mechanical noise and electrical interference generated by their actuators while training the neural networks. Ego-noise reduction as a preprocessing step therefore can help solve this issue when using pre-trained speech and event recognition algorithms on robots. In this paper, we propose a new method to reduce ego-noise using only a microphone array and less than two minute of noise recordings. Using Principal Component Analysis (PCA), the best covariance matrix candidate is selected from a dictionary created online during calibration and used with the Minimum Variance Distortionless Response (MVDR) beamformer. Results show that the proposed method runs in real-time, improves the signal-to-distortion ratio (SDR) by up to 10 dB, decreases the word error rate (WER) by 55\% in some cases and increases the Average Precision (AP) of event detection by up to 0.2.
Leveraging Redundancy in Multiple Audio Signals for Far-Field Speech Recognition
Authors: Feng-Ju Chang, Anastasios Alexandridis, Rupak Vignesh Swaminathan, Martin Radfar, Harish Mallidi, Maurizio Omologo, Athanasios Mouchtaris, Brian King, Roland Maas
Date: 2023-03-01 00:00:00
Tasks: Acoustic echo cancellation, Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

To achieve robust far-field automatic speech recognition (ASR), existing techniques typically employ an acoustic front end (AFE) cascaded with a neural transducer (NT) ASR model. The AFE output, however, could be unreliable, as the beamforming output in AFE is steered to a wrong direction. A promising way to address this issue is to exploit the microphone signals before the beamforming stage and after the acoustic echo cancellation (post-AEC) in AFE. We argue that both, post-AEC and AFE outputs, are complementary and it is possible to leverage the redundancy between these signals to compensate for potential AFE processing errors. We present two fusion networks to explore this redundancy and aggregate these multi-channel (MC) signals: (1) Frequency-LSTM based, and (2) Convolutional Neural Network based fusion networks. We augment the MC fusion networks to a conformer transducer model and train it in an end-to-end fashion. Our experimental results on commercial virtual assistant tasks demonstrate that using the AFE output and two post-AEC signals with fusion networks offers up to 25.9% word error rate (WER) relative improvement over the model using the AFE output only, at the cost of <= 2% parameter increase.
Comprehensive Comparisons of Uniform Quantization in Deep Image Compression
Authors: Koki Tsubota, Kiyoharu Aizawa
Date: 2023-03-01 00:00:00
Tasks: Decoder, Image Compression, Quantization

In deep image compression, uniform quantization is applied to latent representations obtained by using an auto-encoder architecture for reducing bits and entropy coding. Quantization is a problem encountered in the end-to-end training of deep image compression. Quantization's gradient is zero, and it cannot backpropagate meaningful gradients. Many methods have been proposed to address the approximations of quantization to obtain gradients. However, there have not been equitable comparisons among them. In this study, we comprehensively compare the existing approximations of uniform quantization. Furthermore, we evaluate possible combinations of quantizers for the decoder and the entropy model, as the approximated quantizers can be different for them. We conduct experiments using three network architectures on two test datasets. The experimental results reveal that the best approximated quantization differs by the network architectures, and the best approximations of the three are different from the original ones used for the architectures. We also show that the combination of quantizers that uses universal quantization for the entropy model and differentiable soft quantization for the decoder is a comparatively good choice for different architectures and datasets.
Motion Estimation for Fisheye Video With an Application to Temporal Resolution Enhancement
Authors: Andrea Eichenseer, Michel Bätz, André Kaup
Date: 2023-03-01 00:00:00
Tasks: Motion Estimation

Surveying wide areas with only one camera is a typical scenario in surveillance and automotive applications. Ultra wide-angle fisheye cameras employed to that end produce video data with characteristics that differ significantly from conventional rectilinear imagery as obtained by perspective pinhole cameras. Those characteristics are not considered in typical image and video processing algorithms such as motion estimation, where translation is assumed to be the predominant kind of motion. This contribution introduces an adapted technique for use in block-based motion estimation that takes into the account the projection function of fisheye cameras and thus compensates for the non-perspective properties of fisheye videos. By including suitable projections, the translational motion model that would otherwise only hold for perspective material is exploited, leading to improved motion estimation results without altering the source material. In addition, we discuss extensions that allow for a better prediction of the peripheral image areas, where motion estimation falters due to spatial constraints, and further include calibration information to account for lens properties deviating from the theoretical function. Simulations and experiments are conducted on synthetic as well as real-world fisheye video sequences that are part of a data set created in the context of this paper. Average synthetic and real-world gains of 1.45 and 1.51 dB in luminance PSNR are achieved compared against conventional block matching. Furthermore, the proposed fisheye motion estimation method is successfully applied to motion compensated temporal resolution enhancement, where average gains amount to 0.79 and 0.76 dB.
Temporal Scalability of Dynamic Volume Data Using Mesh Compensated Wavelet Lifting
Authors: Wolfgang Schnurrer, Niklas Pallast, Thomas Richter, André Kaup
Date: 2023-03-01 00:00:00
Tasks: Computed Tomography (CT), Motion Compensation

Due to their high resolution, dynamic medical 2D+t and 3D+t volumes from computed tomography (CT) and magnetic resonance tomography (MR) reach a size which makes them very unhandy for teleradiologic applications. A lossless scalable representation offers the advantage of a down-scaled version which can be used for orientation or previewing, while the remaining information for reconstructing the full resolution is transmitted on demand. The wavelet transform offers the desired scalability. A very high quality of the lowpass sub-band is crucial in order to use it as a down-scaled representation. We propose an approach based on compensated wavelet lifting for obtaining a scalable representation of dynamic CT and MR volumes with very high quality. The mesh compensation is feasible to model the displacement in dynamic volumes which is mainly given by expansion and contraction of tissue over time. To achieve this, we propose an optimized estimation of the mesh compensation parameters to optimally fit for dynamic volumes. Within the lifting structure, the inversion of the motion compensation is crucial in the update step. We propose to take this inversion directly into account during the estimation step and can improve the quality of the lowpass sub-band by 0.63 and 0.43 dB on average for our tested dynamic CT and MR volumes at the cost of an increase of the rate by 2.4% and 1.2% on average.
A Step Closer Towards 5G mmWave-based Multipath Positioning in Dense Urban Environments
Authors: Qamar Bader, Sharief Saleh, Mohamed Elhabiby, Aboelmagd Noureldin
Date: 2023-03-02 00:00:00
Tasks: Ensemble Learning

5G mmWave technology can turn multipath into a friend, as multipath components become highly resolvable in the time and angle domains. Multipath signals have not only been used in the literature to position the user equipment (UE) but also to create a map of the surrounding environment. Yet, many multipath-based methods in the literature share a common assumption, which entails that multipath signals are caused by single-bounce reflections only, which is not usually the case. There are very few methods in the literature that accurately filters out higher-order reflections, which renders the exploitation of multipath signals challenging. This paper proposes an ensemble learning-based model for classifying signal paths based on their order of reflection using 5G channel parameters. The model is trained on a large dataset of 3.6 million observations obtained from a quasi-real ray-tracing based 5G simulator that utilizes 3D maps of real-world downtown environments. The trained model had a testing accuracy of 99.5%. A single-bounce reflection-based positioning method was used to validate the positioning error. The trained model enabled the positioning solution to maintain sub-30cm level accuracy 97% of the time.
Evaluation Method and Design Guidance for Direction Finding Antenna Systems
Authors: Lukas Grundmann, Dirk Manteuffel
Date: 2023-03-02 00:00:00

A deterministic evaluation procedure for multi-port direction finding antennas is proposed. It is based on a direction finding uncertainty parameter, which describes how well different directions of arrival and polarizations are distinguishable. By investigating a simple antenna array, it is shown that the proposed parameter provides additional insight into the behavior of an antenna system, when compared to established methods. Moreover, since the uncertainty parameter is calculated from a set of far fields, it is applicable to port far fields as well as Characteristic Modes. This finding is utilized to derive a design guidance: Starting with a set of Characteristic Mode far fields, the angular distribution of the uncertainty is investigated to verify that no ambiguities are present. Different sets of far fields are compared and the differences regarding their direction finding behavior are visualized and explained using the uncertainty in conjunction with an estimate of the incident field. To quantify these differences, a key performance indicator is introduced that summarizes the direction finding capabilities over a selected angular region. To demonstrate the design process, a multi-mode multi-port antenna with three uncorrelated ports is developed, manufactured and measured.
LSTM-ACB-Based RA for IoT Mixed Traffic
Authors: Herman L. dos Santos, José Carlos Marinello, Cristiano Magalhaes Panazio, Taufik Abrão
Date: 2023-03-02 00:00:00

A novel random access (RA) scheme for mixed URLLC-mMTC traffic scenario is proposed using realistic statistical models, with the use mode presenting long-term traffic regularity. The traffic is predicted by a long short-term memory neural network, which enables a traffic-aware resource slicing aided by contention access class barring-based procedure. The method combines a grant-free (GF) RA scheme with an intermediate step to congestion alleviation. The protocol trade-off is a small overhead while enabling a higher number of decoded received packets thanks to the intermediate step. Numerical results evaluate the system performance for each procedure and combined solution. A comparison with GF benchmark reveals substantial improvement in system performance.
Enabling Low-Overhead Over-the-Air Synchronization Using Online Learning
Authors: Dieter Verbruggen, Hazem Sallouha, Sofie Pollin
Date: 2023-03-02 00:00:00

Accurate network synchronization is a key enabler for services such as coherent transmission, cooperative decoding, and localization in distributed and cell-free networks. Unlike centralized networks, where synchronization is generally needed between a user and a base station, synchronization in distributed networks needs to be maintained between several cooperative devices, which is an inherently challenging task due to hardware imperfections and environmental influences on the clock, such as temperature. As a result, distributed networks have to be frequently synchronized, introducing a significant synchronization overhead. In this paper, we propose an online-LSTM-based model for clock skew and drift compensation, to elongate the period at which synchronization signals are needed, decreasing the synchronization overhead. We conducted comprehensive experimental results to assess the performance of the proposed model. Our measurement-based results show that the proposed model reduces the need for re-synchronization between devices by an order of magnitude, keeping devices synchronized with a precision of at least 10 microseconds with a probability 90%.
Distributed Adaptive Norm Estimation for Blind System Identification in Wireless Sensor Networks
Authors: Matthias Blochberger, Filip Elvander, Randall Ali, Jan Østergaard, Jesper Jensen, Marc Moonen, Toon van Waterschoot
Date: 2023-03-01 00:00:00

Distributed signal-processing algorithms in (wireless) sensor networks often aim to decentralize processing tasks to reduce communication cost and computational complexity or avoid reliance on a single device (i.e., fusion center) for processing. In this contribution, we extend a distributed adaptive algorithm for blind system identification that relies on the estimation of a stacked network-wide consensus vector at each node, the computation of which requires either broadcasting or relaying of node-specific values (i.e., local vector norms) to all other nodes. The extended algorithm employs a distributed-averaging-based scheme to estimate the network-wide consensus norm value by only using the local vector norm provided by neighboring sensor nodes. We introduce an adaptive mixing factor between instantaneous and recursive estimates of these norms for adaptivity in a time-varying system. Simulation results show that the extension provides estimation results close to the optimal fully-connected-network or broadcasting case while reducing inter-node transmission significantly.
Track-To-Track Association for Fusion of Dimension-Reduced Estimates
Authors: Robin Forsling, Zoran Sjanic, Fredrik Gustafsson, Gustaf Hendeby
Date: 2023-03-01 00:00:00

Network-centric multitarget tracking under communication constraints is considered, where dimension-reduced track estimates are exchanged. Previous work on target tracking in this subfield has focused on fusion aspects only and derived optimal ways of reducing dimensionality based on fusion performance. In this work we propose a novel problem formalization where estimates are reduced based on association performance. The problem is analyzed theoretically and problem properties are derived. The theoretical analysis leads to an optimization strategy that can be used to partly preserve association quality when reducing the dimensionality of communicated estimates. The applicability of the suggested optimization strategy is demonstrated numerically in a multitarget scenario.
Crowded MTC Random Access in NOMA XL-MIMO
Authors: Thiago Augusto Bruza Alves, Taufik Abrão
Date: 2023-03-01 00:00:00

Massive MIMO is one of the key technologies to support the growth of massive access attempts by devices, such as in massive machine type communication (mMTC). The evolution of antenna array technology brought the recent extra-large scale massive multiple input multiple output (XL-MIMO) systems, seen as a promising technology for providing very high-data rates in high-user density scenarios. Spatial non stationarities and visibility regions (VRs) occur across all huge XL array extension, since its large dimension is of the same order of the distances to the user equipment (UE). We investigate the random access (RA) problem in crowded XL-MIMO scenarios. The proposed nonorthogonal multiple access (NOMA) visible region extra large array (NVR-XL) protocol takes advantage of the power domain NOMA to allow access of two or more users colliding in the same XL sub-array (SA) selecting the same pilot sequence. The NVRXL provides a reduction in the number of attempts to access the network, while improving the average sum-rate, as the number of SA increases.
A Probabilistic Reformulation Technique for Discrete RIS Optimization in Wireless Systems
Authors: Anish Pradhan, Harpreet S. Dhillon
Date: 2023-03-01 00:00:00
Tasks: Quantization

The use of reconfigurable intelligent surfaces (RIS) can improve wireless communication by modifying the wireless link to create virtual line-of-sight links, bypass blockages, suppress interference, and enhance localization. However, enabling the RIS to modify the wireless channel requires careful optimization of the RIS phase-shifts. Although discrete RIS is more practical given hardware limitations, continuous RIS phase-shift optimization has attracted significantly more attention than discrete RIS optimization, which suffers from issues like quantization error and scalability. We develop a comprehensive probabilistic technique to transform discrete optimization problems into optimization problems of continuous domain probability parameters by interpreting the discrete optimization variable as a categorical random vector and computing expectations with respect to those parameters. We rigorously establish that for the unconstrained case, the optimal points of the reformulation and the original problem coincide. For the constrained case, we prove that the transformed problem is a relaxation of the original problem. We apply the proposed technique to two canonical discrete RIS applications: SINR maximization and overhead-aware rate and energy efficiency (EE) maximization. The reformulation enables both stochastic and analytical interpretations of the original problems. The former interpretation yields a stochastic sampling technique, whereas the latter yields an analytical gradient descent (GD) approach that employs closed-form approximations for the expectation. The numerical results demonstrate that the proposed technique is applicable to a variety of discrete RIS optimization problems and outperforms other general approaches, such as closest point projection (CPP) and semidefinite relaxation (SDR) methods.