pwc-archive/papers-with-abstracts huggingface dataset:
Information loss from dimensionality reduction in 5D-Gaussian spectral data
Authors: A. Schelle, H. Lüling
Date: 2023-01-22 00:00:00
Tasks: Dimensionality Reduction

Understanding the loss of information in spectral analytics is a crucial first step towards finding root causes for failures and uncertainties using spectral data in artificial intelligence models built from modern complex data science applications. Here, we show from an elementary Shannon entropy model analysis with quantum statistics of Gaussian distributed spectral data, that the relative loss of information from dimensionality reduction due to the projection of an initial five-dimensional dataset onto two-dimensional diagrams is less than one percent in the parameter range of small data sets with sample sizes on the order of few hundred data samples. From our analysis, we also conclude that the density and expectation value of the entropy probability distribution increases with the sample number and sample size using artificial data models derived from random sampling Monte Carlo simulation methods.
Dynamic Full-Field Optical Coherence Tomography module adapted to commercial microscopes for longitudinal in vitro cell culture study
Authors: Tual Monfort, Salvatore Azzollini, Jeremy Brogard, Marilou Clémençon, Amélie Slembrouck-Brec, Valerie Forster, Serge Picaud, Olivier Goureau, Sacha Reichman, Olivier Thouvenin, Kate Grieve
Date: 2023-01-30 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction

Dynamic full-field optical coherence tomography (D-FFOCT) has recently emerged as a label-free imaging tool, capable of resolving cell types and organelles within 3D live samples, whilst monitoring their activity at tens of milliseconds resolution. Here, a D-FFOCT module design is presented which can be coupled to a commercial microscope with a stage top incubator, allowing non-invasive label-free longitudinal imaging over periods of minutes to weeks on the same sample. Long term volumetric imaging on human induced pluripotent stem cell-derived retinal organoids is demonstrated, highlighting tissue and cell organisation as well as cell shape, motility and division. Imaging on retinal explants highlights single 3D cone and rod structures. An optimal workflow for data acquisition, postprocessing and saving is demonstrated, resulting in a time gain factor of 10 compared to prior state of the art. Finally, a method to increase D-FFOCT signal-to-noise ratio is demonstrated, allowing rapid organoid screening.
DeFi: data-driven characterisation of Uniswap v3 ecosystem & an ideal crypto law for liquidity pools
Authors: Deborah Miori, Mihai Cucuringu
Date: 2022-12-20 00:00:00

Uniswap is a Constant Product Market Maker built around liquidity pools, where pairs of tokens are exchanged subject to a fee that is proportional to the size of transactions. At the time of writing, there exist more than 6,000 pools associated with Uniswap v3, implying that empirical investigations on the full ecosystem can easily become computationally expensive. Thus, we propose a systematic workflow to extract and analyse a meaningful but computationally tractable sub-universe of liquidity pools. Leveraging on the 34 pools found relevant for the six-months time window January-June 2022, we then investigate the related liquidity consumption behaviour of market participants. We propose to represent each liquidity taker by a suitably constructed transaction graph, which is a fully connected network where nodes are the liquidity taker's executed transactions, and edges contain weights encoding the time elapsed between any two transactions. We extend the NLP-inspired graph2vec algorithm to the weighted undirected setting, and employ it to obtain an embedding of the set of graphs. This embedding allows us to extract seven clusters of liquidity takers, with equivalent behavioural patters and interpretable trading preferences. We conclude our work by testing for relationships between the characteristic mechanisms of each pool, i.e. liquidity provision, consumption, and price variation. We introduce a related ideal crypto law, inspired from the ideal gas law of thermodynamics, and demonstrate that pools adhering to this law are healthier trading venues in terms of sensitivity of liquidity and agents' activity. Regulators and practitioners could benefit from our model by developing related pool health monitoring tools.
A Note on the Estimation of Job Amenities and Labor Productivity
Authors: Arnaud Dupuy, Alfred Galichon
Date: 2023-01-29 00:00:00

This paper introduces a maximum likelihood estimator of the value of job amenities and labor productivity in a single matching market based on the observation of equilibrium matches and wages. The estimation procedure simultaneously fits both the matching patterns and the wage curve. While our estimator is suited for a wide range of assignment problems, we provide an application to the estimation of the Value of a Statistical Life using compensating wage differentials for the risk of fatal injury on the job. Using US data for 2017, we estimate the Value of Statistical Life at \$ 6.3 million (\$2017).
Multidimensional dynamic factor models
Authors: Matteo Barigozzi, Filippo Pellegrino
Date: 2023-01-29 00:00:00
Tasks: Missing Values, Time Series, Time Series Analysis

This paper generalises dynamic factor models for multidimensional dependent data. In doing so, it develops an interpretable technique to study complex information sources ranging from repeated surveys with a varying number of respondents to panels of satellite images. We specialise our results to model microeconomic data on US households jointly with macroeconomic aggregates. This results in a powerful tool able to generate localised predictions, counterfactuals and impulse response functions for individual households, accounting for traditional time-series complexities depicted in the state-space literature. The model is also compatible with the growing focus of policymakers for real-time economic analysis as it is able to process observations online, while handling missing values and asynchronous data releases.
Future of Supply Chain: Challenges, Trends, and Prospects
Authors: Cristiana L. Lara, John Wassick
Date: 2023-01-30 00:00:00
Tasks: Decision Making

This paper discusses the broad challenges shared by e-commerce and the process industries operating global supply chains. Specifically, we discuss how process industries and e-commerce differ in many aspects but have similar challenges ahead of them in order to remain competitive, keep up with the always increasing requirements of the customers and stakeholders, and gain efficiency. While both industries have been early adopters of decision support tools based on machine intelligence, both share unresolved challenges related to scalability, integration of decision-making over different time horizons (e.g. strategic, tactical and execution-level decisions) and across internal business units, and orchestration of human and computer-based decision-makers. We discuss future trends and research opportunities in the area of supply chain, and suggest that the methods of multi-agent systems supported by rigorous treatment of human decision-making in combination with machine intelligence is a great contender to address these critical challenges.
The impact of surplus sharing on the outcomes of specific investments under negotiated transfer pricing: An agent-based simulation with fuzzy Q-learning agents
Authors: Christian Mitsch
Date: 2023-01-28 00:00:00
Tasks: Decision Making, Q-Learning

This paper focuses on specific investments under negotiated transfer pricing. Reasons for transfer pricing studies are primarily to find conditions that maximize the firm's overall profit, especially in cases with bilateral trading problems with specific investments. However, the transfer pricing problem has been developed in the context where managers are fully individual rational utility maximizers. The underlying assumptions are rather heroic and, in particular, how managers process information under uncertainty, do not perfectly match with human decision-making behavior. Therefore, this paper relaxes key assumptions and studies whether cognitively bounded agents achieve the same results as fully rational utility maximizers and, in particular, whether the recommendations on managerial-compensation arrangements and bargaining infrastructures are designed to maximize headquarters' profit in such a setting. Based on an agent-based simulation with fuzzy Q-learning agents, it is shown that in case of symmetric marginal cost parameters, myopic fuzzy Q-learning agents invest only as much as in the classic hold-up problem, while non-myopic fuzzy Q-learning agents invest optimally. However, in scenarios with non-symmetric marginal cost parameters, a deviation from the previously recommended surplus sharing rules can lead to higher investment decisions and, thus, to an increase in the firm's overall profit.
An Examination of Ranked Choice Voting in the United States, 2004-2022
Authors: Adam Graham-Squire, David McCune
Date: 2023-01-28 00:00:00

From the perspective of social choice theory, ranked-choice voting (RCV) is known to have many flaws. RCV can fail to elect a Condorcet winner and is susceptible to monotonicity paradoxes and the spoiler effect, for example. We use a database of 182 American ranked-choice elections for political office from the years 2004-2022 to investigate empirically how frequently RCV's deficiencies manifest in practice. Our general finding is that RCV's weaknesses are rarely observed in real-world elections, with the exception that ballot exhaustion frequently causes majoritarian failures.
Royal Processions: Incentives, Efficiency and Fairness in Two-sided Matching
Authors: Sophie Bade, Joseph Root
Date: 2023-01-30 00:00:00
Tasks: Fairness, Vocal Bursts Valence Prediction

We study the set of incentive compatible and efficient two-sided matching mechanisms. We classify all such mechanisms under an additional assumption -- "gender-neutrality" -- which guarantees that the two sides be treated symmetrically. All group strategy-proof, efficient and gender-neutral mechanisms are recursive and the outcome is decided in a sequence of rounds. In each round, two agents are selected, one from each side. These agents are either "matched-by-default" or "unmatched-by-default." In the former case either of the selected agents can unilaterally force the other to match with them while in the latter case they may only match together if both agree. In either case, if this pair of agents is not matched together, each gets their top choice among the set of remaining agents. As an important step in the characterization, we first show that in one-sided matching all group strategy-proof and efficient mechanisms are sequential dictatorships. An immediate corollary is that there are no individually rational, group strategy-proof and efficient one-sided matching mechanisms.
Equilibria and their stability in an asymmetric duopoly model of Kopel
Authors: Xiaoliang Li, Kongyan Chen
Date: 2023-01-30 00:00:00

In this paper, we investigate the equilibria and their stability in an asymmetric duopoly model of Kopel by using several tools based on symbolic computations. We explore the possible positions of the equilibria in Kopel's model. We discuss the possibility of the existence of multiple positive equilibria and establish a necessary and sufficient condition for a given number of equilibria to exist. Furthermore, if the two duopolists adopt the best response reactions or homogeneous adaptive expectations, we establish rigorous conditions for the existence of distinct numbers of positive equilibria for the first time.
Fair congested assignment problem
Authors: Anna Bogomolnaia, Herve Moulin
Date: 2023-01-28 00:00:00
Tasks: Fairness

We propose a fair and efficient solution for assigning agents to m posts subject to congestion, when agents care about both their post and its congestion. Examples include assigning jobs to busy servers, students to crowded schools or crowded classes, commuters to congested routes, workers to crowded office spaces or to team projects etc... Congestion is anonymous (it only depends on the number n of agents in a given post). A canonical interpretation of ex ante fairness allows each agent to choose m post-specific caps on the congestion they tolerate: these requests are mutually feasible if and only if the sum of the caps is n. For ex post fairness we impose a competitive requirement close to envy freeness: taking the congestion profile as given each agent is assigned to one of her best posts. If a competitive assignment exists, it delivers unique congestion and welfare profiles and is also efficient and ex ante fair. In a fractional (randomised or time sharing) version of our model, a unique competitive congestion profile always exists. It is approximately implemented by a mixture of ex post deterministic assignments: with an approxination factor equal to the largest utility loss from one more unit of congestion, the latter deliver identical welfare profiles and are weakly efficient. Our approach to ex ante fairness generalises to the model where each agent's congestion is weighted. Now the caps on posts depend only upon own weight and total congestion, not on the number of other agents contributing to it. Remarkably in both models these caps are feasible if and only if they give to each agent the right to veto all but (1/m) of their feasible allocations.
Cursed Sequential Equilibrium
Authors: Meng-Jhang Fong, Po-Hsuan Lin, Thomas R. Palfrey
Date: 2023-01-27 00:00:00
Tasks: Form

This paper develops a framework to extend the strategic form analysis of cursed equilibrium (CE) developed by Eyster and Rabin (2005) to multi-stage games. The approach uses behavioral strategies rather than normal form mixed strategies, and imposes sequential rationality. We define cursed sequential equilibrium (CSE) and compare it to sequential equilibrium and standard normal-form CE. We provide a general characterization of CSE and establish its properties. We apply CSE to five applications in economics and political science. These applications illustrate a wide range of differences between CSE and Bayesian Nash equilibrium or CE: in signaling games; games with preplay communication; reputation building; sequential voting; and the dirty faces game where higher order beliefs play a key role. A common theme in several of these applications is showing how and why CSE implies systematically different behavior than Bayesian Nash equilibrium in dynamic games of incomplete information with private values, while CE coincides with Bayesian Nash equilibrium for such games.
MYRiAD: A Multi-Array Room Acoustic Database
Authors: Thomas Dietzen, Randall Ali, Maja Taseska, Toon van Waterschoot
Date: 2023-01-30 00:00:00
Tasks: Audio Signal Processing

In the development of acoustic signal processing algorithms, their evaluation in various acoustic environments is of utmost importance. In order to advance evaluation in realistic and reproducible scenarios, several high-quality acoustic databases have been developed over the years. In this paper, we present another complementary database of acoustic recordings, referred to as the Multi-arraY Room Acoustic Database (MYRiAD). The MYRiAD database is unique in its diversity of microphone configurations suiting a wide range of enhancement and reproduction applications (such as assistive hearing, teleconferencing, or sound zoning), the acoustics of the two recording spaces, and the variety of contained signals including 1214 room impulse responses (RIRs), reproduced speech, music, and stationary noise, as well as recordings of live cocktail parties held in both rooms. The microphone configurations comprise a dummy head (DH) with in-ear omnidirectional microphones, two behind-the-ear (BTE) pieces equipped with 2 omnidirectional microphones each, 5 external omnidirectional microphones (XMs), and two concentric circular microphone arrays (CMAs) consisting of 12 omnidirectional microphones in total. The two recording spaces, namely the SONORA Audio Laboratory (SAL) and the Alamire Interactive Laboratory (AIL), have reverberation times of 2.1s and 0.5s, respectively. Audio signals were reproduced using 10 movable loudspeakers in the SAL and a built-in array of 24 loudspeakers in the AIL. MATLAB and Python scripts are included for accessing the signals as well as microphone and loudspeaker coordinates. The database is publicly available at [1].
Real-Time Acoustic Perception for Automotive Applications
Authors: Jun Yin, Stefano Damiano, Marian Verhelst, Toon van Waterschoot, Andre Guntoro
Date: 2023-01-30 00:00:00
Tasks: Sound Source Localization

In recent years the automotive industry has been strongly promoting the development of smart cars, equipped with multi-modal sensors to gather information about the surroundings, in order to aid human drivers or make autonomous decisions. While the focus has mostly been on visual sensors, also acoustic events are crucial to detect situations that require a change in the driving behavior, such as a car honking, or the sirens of approaching emergency vehicles. In this paper, we summarize the results achieved so far in the Marie Sklodowska-Curie Actions (MSCA) European Industrial Doctorates (EID) project Intelligent Ultra Low-Power Signal Processing for Automotive (I-SPOT). On the algorithmic side, the I-SPOT Project aims to enable detecting, localizing and tracking environmental audio signals by jointly developing microphone array processing and deep learning techniques that specifically target automotive applications. Data generation software has been developed to cover the I-SPOT target scenarios and research challenges. This tool is currently being used to develop low-complexity deep learning techniques for emergency sound detection. On the hardware side, the goal impels workflows for hardware-algorithm co-design to ease the generation of architectures that are sufficiently flexible towards algorithmic evolutions without giving up on efficiency, as well as enable rapid feedback of hardware implications of algorithmic decision. This is pursued though a hierarchical workflow that breaks the hardware-algorithm design space into reasonable subsets, which has been tested for operator-level optimizations on state-of-the-art robust sound source localization for edge devices. Further, several open challenges towards an end-to-end system are clarified for the next stage of I-SPOT.
Detecting and Controlling Smart Lights with LiTalk
Authors: Jagdeep Singh, Dan Watkinson, Tim Farnham, Daniele Puccinelli
Date: 2023-01-30 00:00:00

The rapid increase in demand for wireless controlled Smart Lighting has created a need to automate the mapping between the identifiers for individual light sources and their physical locations. To control Smart Lights, their IDs and physical locations relative to each other must be determined. Nowadays, skilled technicians perform this process manually, which requires a lot of effort, is time-consuming, and incurs high costs, particularly with non-stationary lights. Visible Light Communication has been presented as a possible solution to this problem. This paper presents an approach based on Visible Light Communication that leverages Machine Learning to automate the mapping process between the identifiers and the relative physical location of Smart Lights. We show that our approach provides a better location-mapping performance compared to existing methods.
A Stochastic Biofilm Disruption Model based on Quorum Sensing Mimickers
Authors: Fatih Gulec, Andrew W. Eckford
Date: 2023-01-30 00:00:00

Quorum sensing (QS) mimickers can be used as an effective tool to disrupt biofilms which consist of communicating bacteria and extracellular polymeric substances. In this paper, a stochastic biofilm disruption model based on the usage of QS mimickers is proposed. A chemical reaction network (CRN) involving four different states is employed to model the biological processes during the biofilm formation and its disruption via QS mimickers. In addition, a state-based stochastic simulation algorithm is proposed to simulate this CRN. The proposed model is validated by the in vitro experimental results of Pseudomonas aeruginosa biofilm and its disruption by rosmarinic acid as the QS mimicker. Our results show that there is an uncertainty in state transitions due to the effect of the randomness in the CRN. In addition to the QS activation threshold, the presented work demonstrates that there are underlying two more thresholds for the disruption of EPS and bacteria, which provides a realistic modeling for biofilm disruption with QS mimickers.
ISAC-Enabled V2I Networks Based on 5G NR: How Much Can the Overhead Be Reduced?
Authors: Yunxin Li, Fan Liu, Zhen Du, Weijie Yuan, Christos Masouros
Date: 2023-01-30 00:00:00
Tasks: ISAC, Management

The emergence of the fifth-generation (5G) New Radio (NR) brings additional possibilities to vehicle-to-everything (V2X) network with improved quality of services. In order to obtain accurate channel state information (CSI) in high-mobility V2X networks, pilot signals and frequent handover between vehicles and infrastructures are required to establish and maintain the communication link, which increases the overheads and reduces the communication throughput. To address this issue, integrated sensing and communications (ISAC) was employed at the base station (BS) in the vehicle-to-infrastructure (V2I) network to reduce a certain amount of overheads, thus improve the spectral efficiency. Nevertheless, the exact amount of overheads reduction remains unclear, particularly for practical NR based V2X networks. In this paper, we study a link-level NR based V2I system employing ISAC signaling to facilitate the communication beam management, where the Extended Kalman filtering (EKF) algorithm is performed to realize the functions of tracking and predicting the motion of the vehicle. We provide detailed analysis on the overheads reduction with the aid of ISAC, and show that up to 43.24% overheads can be reduced under assigned NR frame structure. In addition, numerical results are provided to validate the improved performance on the beam tracking and communication throughput.
Disproving Sum-Difference Co-Array Property
Authors: Jisheng Dai, Hing Cheung So
Date: 2023-01-29 00:00:00
Tasks: valid

The recently published paper by Gupta and Agrawal [1] exploited the sum-difference co-array (SDCA) to enhance the virtual aperture of sparse arrays. We argue that the key SDCA property established in [1] requires a critical necessary and sufficient condition that is valid for a very rare case only.
State Machine-based Waveforms for Channels With 1-Bit Quantization and Oversampling With Time-Instance Zero-Crossing Modulation
Authors: Diana M. V. Melo, Lukas T. N. Landau, Rodrigo C. de Lamare
Date: 2023-01-28 00:00:00
Tasks: Quantization

Systems with 1-bit quantization and oversampling are promising for the Internet of Things (IoT) devices in order to reduce the power consumption of the analog-to-digital-converters. The novel time-instance zero-crossing (TI ZX) modulation is a promising approach for this kind of channels but existing studies rely on optimization problems with high computational complexity and delay. In this work, we propose a practical waveform design based on the established TI ZX modulation for a multiuser multi-input multi-output (MIMO) downlink scenario with 1-bit quantization and temporal oversampling at the receivers. In this sense, the proposed temporal transmit signals are constructed by concatenating segments of coefficients which convey the information into the time-instances of zero-crossings according to the TI ZX mapping rules. The proposed waveform design is compared with other methods from the literature. The methods are compared in terms of bit error rate and normalized power spectral density. Numerical results show that the proposed technique is suitable for multiuser MIMO system with 1-bit quantization while tolerating some small amount of out-of-band radiation.