pwc-archive/papers-with-abstracts huggingface dataset:
Model-based versus model-free feeding control and water quality monitoring for fish growth tracking in aquaculture systems
Authors: Fahad Aljehani, Ibrahima N'Doye, Taous-Meriem Laleg-Kirati
Date: 2023-06-14 00:00:00
Tasks: model, Model Predictive Control, Q-Learning

The high concentration level of the environmental factors, such as a high ammonia concentration and pH level, affect the water quality, affecting fish's survival and mass death. Therefore, there is a critical need to develop control strategies to determine optimal, efficient, and reliable feeding and water quality monitoring processes. In this paper, we revisit the representative fish growth model describing the total biomass change by incorporating the fish population density and mortality. Since the measurement data of the total biomass and population from the aquaculture systems are limited and difficult to obtain, we validate the new dynamic population model with the individual fish growth data for tracking control purposes. We specifically focus on relative feeding as a manipulated variable to design traditional and optimal control to track the desired weight reference within the sub-optimal temperature and dissolved oxygen profiles under different levels of unionized ammonia exposure. Then, we propose a Q-learning approach that learns an optimal feeding control policy from the simulated data of the fish growth weight trajectories while managing the ammonia effects. The proposed Q-learning feeding control prevents fish mortality and achieves good tracking errors of the fish weight under the different levels of unionized ammonia. However, it maintains a relative food consumption that potentially underfeeds the fish. Finally, we propose an optimal algorithm that optimizes the feeding and water quality of the dynamic fish population growth process. We also show that the model predictive control decreases fish mortality and reduces food consumption in all different cases of unionized ammonia exposure.
Feeding control and water quality monitoring in aquaculture systems: Opportunities and challenges
Authors: Fahad Aljehani, Ibrahima N'Doye, Taous-Meriem Laleg-Kirati
Date: 2023-06-14 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning

Aquaculture systems can benefit from the recent development of advanced control strategies to reduce operating costs and fish loss and increase growth production efficiency, resulting in fish welfare and health. Monitoring the water quality and controlling feeding are fundamental elements of balancing fish productivity and shaping the fish growth process. Currently, most fish-feeding processes are conducted manually in different phases and rely on time-consuming and challenging artificial discrimination. The feeding control approach influences fish growth and breeding through the feed conversion rate; hence, controlling these feeding parameters is crucial for enhancing fish welfare and minimizing general fishery costs. The high concentration of environmental factors, such as a high ammonia concentration and pH, affect the water quality and fish survival. Therefore, there is a critical need to develop control strategies to determine optimal, efficient, and reliable feeding processes and monitor water quality. This paper reviews the main control design techniques for fish growth in aquaculture systems, namely algorithms that optimize the feeding and water quality of a dynamic fish growth process. Specifically, we review model-based control approaches and model-free reinforcement learning strategies to optimize the growth and survival of the fish or track a desired reference live-weight growth trajectory. The model-free framework uses an approximate fish growth dynamic model and does not satisfy constraints. We discuss how model-based approaches can support a reinforcement learning framework to efficiently handle constraint satisfaction and find better trajectories and policies from value-based reinforcement learning.
Prevalence of multistability and nonstationarity in driven chemical networks
Authors: Zachary G. Nicolaou, Schuyler B. Nicholson, Adilson E. Motter, Jason R. Green
Date: 2023-06-15 00:00:00

External flows of energy, entropy, and matter can cause sudden transitions in the stability of biological and industrial systems, fundamentally altering their dynamical function. How might we control and design these transitions in chemical reaction networks? Here, we analyze transitions giving rise to complex behavior in random reaction networks subject to external driving forces. In the absence of driving, we characterize the uniqueness of the steady state and identify the percolation of a giant connected component in these networks as the number of reactions increases. When subject to chemical driving (influx and outflux of chemical species), the steady state can undergo bifurcations, leading to multistability or oscillatory dynamics. By quantifying the prevalence of these bifurcations, we show how chemical driving and network sparsity tend to promote the emergence of these complex dynamics and increased rates of entropy production. We show that catalysis also plays an important role in the emergence of complexity, strongly correlating with the prevalence of bifurcations. Our results suggest that coupling a minimal number of chemical signatures with external driving can lead to features present in biochemical processes and abiogenesis.
Deep Learning-Based Spatiotemporal Multi-Event Reconstruction for Delay Line Detectors
Authors: Marco Knipfer, Stefan Meier, Jonas Heimerl, Peter Hommelhoff, Sergei Gleyzer
Date: 2023-06-13 00:00:00

Accurate observation of two or more particles within a very narrow time window has always been a challenge in modern physics. It creates the possibility of correlation experiments, such as the ground-breaking Hanbury Brown-Twiss experiment, leading to new physical insights. For low-energy electrons, one possibility is to use a microchannel plate with subsequent delay lines for the readout of the incident particle hits, a setup called a Delay Line Detector. The spatial and temporal coordinates of more than one particle can be fully reconstructed outside a region called the dead radius. For interesting events, where two electrons are close in space and time, the determination of the individual positions of the electrons requires elaborate peak finding algorithms. While classical methods work well with single particle hits, they fail to identify and reconstruct events caused by multiple nearby particles. To address this challenge, we present a new spatiotemporal machine learning model to identify and reconstruct the position and time of such multi-hit particle signals. This model achieves a much better resolution for nearby particle hits compared to the classical approach, removing some of the artifacts and reducing the dead radius by half. We show that machine learning models can be effective in improving the spatiotemporal performance of delay line detectors.
Comparing intervention measures in a model of a disease outbreak on a university campus
Authors: Alex Best, Prerna Singh
Date: 2023-06-16 00:00:00

A number of theoretical models have been developed in recent years modelling epidemic spread in educational settings such as universities to help inform re-opening strategies during the Covid-19 pandemic. However, these studies have had differing conclusions as to the most effective non-pharmaceutical interventions. They also largely assumed permanent acquired immunity, meaning we have less understanding of how disease dynamics will play out when immunity wanes. Here we complement these studies by developing and analysing a stochastic simulation model of disease spread on a university campus where we allow immunity to wane, expoloring the effectiveness of different interventions. We find that the two most effective interventions to limit the severity of a disease outbreak are reducing extra-household mixing and surveillance testing backed-up by a moderate isolation period. We find that contact tracing only has a limited effect, while reducing class sizes only has much effect if extra-household mixing is already low. We identify a range of measures that can not only limit an outbreak but prevent it entirely, and also comment on the variation in measures of severity that emerge from our stochastic simulations. We hope that our model may help in designing effective strategies for universities in future disease outbreaks.
Testing for Peer Effects without Specifying the Network Structure
Authors: Hyunseok Jung, Xiaodong Liu
Date: 2023-06-16 00:00:00
Tasks: valid

This paper proposes an Anderson-Rubin (AR) test for the presence of peer effects in panel data without the need to specify the network structure. The unrestricted model of our test is a linear panel data model of social interactions with dyad-specific peer effect coefficients for all potential peers. The proposed AR test evaluates if these peer effect coefficients are all zero. As the number of peer effect coefficients increases with the sample size, so does the number of instrumental variables (IVs) employed to test the restrictions under the null, rendering Bekker's many-IV environment. By extending existing many-IV asymptotic results to panel data, we establish the asymptotic validity of the proposed AR test. Our Monte Carlo simulations show the robustness and superior performance of the proposed test compared to some existing tests with misspecified networks. We provide two applications to demonstrate its empirical relevance.
CSREU: A Novel Dataset about Corporate Social Responsibility and Performance Indicators
Authors: Erion Çano, Xhesilda Vogli
Date: 2023-06-16 00:00:00

Corporate Social Responsibility (CSR) has become an important topic that is gaining academic interest. This research paper presents CSREU, a new dataset with attributes of 115 European companies, which includes several performance indicators and the respective CSR disclosure scores computed using the Global Reporting Initiative (GRI) framework. We also examine the correlations between some of the financial indicators and the CSR disclosure scores of the companies. According to our results, these correlations are weak and deeper analysis is required to draw convincing conclusions about the potential impact of CSR disclosure on financial performance. We hope that the newly created data and our preliminary results will help and foster research in this field.
Perceived university support and environment as a factor of entrepreneurial intention: Evidence from Western Transdanubia Region
Authors: Attila Lajos Makai, Tibor Dőry
Date: 2023-06-16 00:00:00

The exploration of entrepreneurship has become a priority for scientific research in recent years. Understanding this phenomenon is particularly important for the transformation of entrepreneurship into action, which is a key factor in early-stage entrepreneurial activity. This gains particular relevance in the university environment, where, in addition to the conventional teaching and research functions, the entrepreneurial university operation based on open innovation, as well as the enhancement of entrepreneurial attitudes of researchers and students, are receiving increased attention. This study is based on a survey conducted among students attending a Hungarian university of applied science in Western Transdanubia Region who have demonstrated their existing entrepreneurial commitment by joining a national startup training and incubation programme. The main research question of the study is to what extent student entrepreneurship intention is influenced by the environment of the entrepreneurial university ecosystem and the support services available at the university. A further question is whether these factors are able to mitigate the negative effects of internal cognitive and external barriers by enhancing entrepreneurial attitudes and perceived behavioural control. The relatively large number of students involved in the programme allows the data to be analysed using SEM modelling. The results indicate a strong covariance between the perceived university support and environment among students. Another observation is the distinct effect of these institutional factors on perceived behavioural control of students.
Algorithmic Collusion in Auctions: Evidence from Controlled Laboratory Experiments
Authors: Pranjal Rawat
Date: 2023-06-15 00:00:00
Tasks: Q-Learning

Algorithms are increasingly being used to automate participation in online markets. Banchio and Skrzypacz (2022) demonstrate how exploration under identical valuation in first-price auctions may lead to spontaneous coupling into sub-competitive bidding. However, it is an open question if these findings extend to affiliated values, optimal exploration, and specifically which algorithmic details play a role in facilitating algorithmic collusion. This paper contributes to the literature by generating robust stylized facts to cover these gaps. I conduct a set of fully randomized experiments in a controlled laboratory setup and apply double machine learning to estimate granular conditional treatment effects of auction design on seller revenues. I find that first-price auctions lead to lower seller revenues and higher seller regret under identical values, affiliated values, and under both Q-learning and Bandits. There is more possibility of such tacit collusion under fewer bidders, Boltzmann exploration, asynchronous updating, and longer episodes; while high reserve prices can offset this. This evidence suggests that programmatic auctions, e.g. the Google Ad Exchange, which depend on first-price auctions, might be susceptible to coordinated bid suppression and significant revenue losses.
Robust Predictions in Games with Rational Inattention
Authors: Tommaso Denti, Doron Ravid
Date: 2023-06-16 00:00:00

We derive robust predictions in games involving flexible information acquisition, also known as rational inattention (Sims 2003). These predictions remain accurate regardless of the specific methods players employ to gather information. Compared to scenarios where information is predetermined, rational inattention reduces welfare and introduces additional constraints on behavior. We show these constraints generically do not bind; the two knowledge regimes are behaviorally indistinguishable in most environments. Yet, we demonstrate the welfare difference they generate is substantial: optimal policy depends on whether one assumes information is given or acquired. We provide the necessary tools for policy analysis in this context.
Knowledge Distillation for Efficient Audio-Visual Video Captioning
Authors: Özkan Çaylı, Xubo Liu, Volkan Kılıç, Wenwu Wang
Date: 2023-06-16 00:00:00
Tasks: Audio-Visual Video Captioning, Caption Generation, Knowledge Distillation, Video Captioning

Automatically describing audio-visual content with texts, namely video captioning, has received significant attention due to its potential applications across diverse fields. Deep neural networks are the dominant methods, offering state-of-the-art performance. However, these methods are often undeployable in low-power devices like smartphones due to the large size of the model parameters. In this paper, we propose to exploit simple pooling front-end and down-sampling algorithms with knowledge distillation for audio and visual attributes using a reduced number of audio-visual frames. With the help of knowledge distillation from the teacher model, our proposed method greatly reduces the redundant information in audio-visual streams without losing critical contexts for caption generation. Extensive experimental evaluations on the MSR-VTT dataset demonstrate that our proposed approach significantly reduces the inference time by about 80% with a small sacrifice (less than 0.02%) in captioning accuracy.
Use of a humanoid robot for auditory psychophysical testing
Authors: Luke Meyer, Laura Rachman, Gloria Araiza-Illan, Etienne Gaudrain, Deniz Başkent
Date: 2023-06-16 00:00:00

Tasks in psychophysical tests can at times be repetitive and cause individuals to lose engagement during the test. To facilitate engagement, we propose the use of a humanoid NAO robot, named Sam, as an alternative interface for conducting psychophysical tests. Specifically, we aim to evaluate the performance of Sam as an auditory testing interface, given its potential limitations and technical differences, in comparison to the current laptop interface. We examine the results and durations of two voice perception tests, voice cue sensitivity and voice gender categorisation, obtained from both the conventionally used laptop interface and Sam. Both tests investigate the perception and use of two speaker-specific voice cues, fundamental frequency (F0) and vocal tract length (VTL), important for characterising voice gender. Responses are logged on the laptop using a connected mouse, and on Sam using the tactile sensors. Comparison of test results from both interfaces shows functional similarity between the interfaces and replicates findings from previous studies with similar tests. Comparison of test durations shows longer testing times with Sam, primarily due to longer processing times in comparison to the laptop, as well as other design limitations due to the implementation of the test on the robot. Despite the inherent constraints of the NAO robot, such as in sound quality, relatively long processing and testing times, and different methods of response logging, the NAO interface appears to facilitate collecting similar data to the current laptop interface, confirming its potential as an alternative psychophysical test interface for auditory perception tests.
MF-PAM: Accurate Pitch Estimation through Periodicity Analysis and Multi-level Feature Fusion
Authors: Woo-Jin Chung, Doyeon Kim, Soo-Whan Chung, Hong-Goo Kang
Date: 2023-06-16 00:00:00
Tasks: Audio Signal Processing

We introduce Multi-level feature Fusion-based Periodicity Analysis Model (MF-PAM), a novel deep learning-based pitch estimation model that accurately estimates pitch trajectory in noisy and reverberant acoustic environments. Our model leverages the periodic characteristics of audio signals and involves two key steps: extracting pitch periodicity using periodic non-periodic convolution (PNP-Conv) blocks and estimating pitch by aggregating multi-level features using a modified bi-directional feature pyramid network (BiFPN). We evaluate our model on speech and music datasets and achieve superior pitch estimation performance compared to state-of-the-art baselines while using fewer model parameters. Our model achieves 99.20 % accuracy in pitch estimation on a clean musical dataset. Overall, our proposed model provides a promising solution for accurate pitch estimation in challenging acoustic environments and has potential applications in audio signal processing.
Distillation Strategies for Discriminative Speech Recognition Rescoring
Authors: Prashanth Gurunath Shivakumar, Jari Kolehmainen, Yile Gu, Ankur Gandhe, Ariya Rastrow, Ivan Bulyko
Date: 2023-06-15 00:00:00
Tasks: Language Modeling, Language Modelling, Re-Ranking, speech-recognition, Speech Recognition

Second-pass rescoring is employed in most state-of-the-art speech recognition systems. Recently, BERT based models have gained popularity for re-ranking the n-best hypothesis by exploiting the knowledge from masked language model pre-training. Further, fine-tuning with discriminative loss such as minimum word error rate (MWER) has shown to perform better than likelihood-based loss. Streaming applications with low latency requirements impose significant constraints on the size of the models, thereby limiting the word error rate (WER) performance gains. In this paper, we propose effective strategies for distilling from large models discriminatively trained with the MWER objective. We experiment on Librispeech and production scale internal dataset for voice-assistant. Our results demonstrate relative improvements of upto 7% WER over student models trained with MWER. We also show that the proposed distillation can reduce the WER gap between the student and the teacher by 62% upto 100%.
Camera PPG waveforms at the forehead
Authors: A. C. den Brinker, H. H. Der Sarkissian, J. H. Wülbern, B. Balmaekers, M. Padalko, J. Sénégas, R. Springorum, C. Possanzini
Date: 2023-06-16 00:00:00
Tasks: Photoplethysmography (PPG)

In order to obtain insights into the feasibility of replacing ECG-guided triggering in magnetic resonance imaging (MRI) by a system based on video photoplethysmography (PPG), PPG and ECG data were collected from volunteers in an MRI scanner. PPG waveforms obtained using remote camera PPG directed at the forehead are studied in qualitative and quantitative sense over a number of volunteers. The data analysis considers variations in PPG waveforms across volunteers, modelling of the waveforms in Fourier series, dependencies of waveforms and features on the interbeat interval (IBI) and breath-holding, and models for ECG-blind estimation of R-peak position. The main findings are that the PPG waveform depends on the volunteer and that its shape changes with IBI and does not depend on breath-holding in the given scenario. Low-order harmonic models provide accurate approximations to the PPG waveform, where for higher IBI the waveform shows more temporal details. Accurate predictions (20 ms std) of the delays between markers in ECG and PPG appear feasible from a single PPG feature.
Implementation of Real-Time Automotive SAR Imaging
Authors: Marcel Hoffmann, Theresa Noegel, Christian Schüßler, Lars Schwenger, Peter Gulden, Dietmar Fey, Martin Vossiek
Date: 2023-06-16 00:00:00
Tasks: GPU

This paper presents measures to reduce the computation time of automotive synthetic aperture radar (SAR) imaging to achieve real-time capability. For this, the image formation, which is based on the Back-Projection algorithm, was thoroughly analyzed. Various optimizations were individually tested and analyzed on graphics processing units (GPU). Apart from the time reduction gained from these measures, the data size needed for processing was also drastically decreased. With a combination of all measures, a high-resolution SAR image of 30 m by 30 m that combines 8192 chirps can be reconstructed in less than 30 ms using a standard GPU. It is thus demonstrated that a real-time implementation of automotive SAR is possible.
MOV-Modified-FxLMS algorithm with Variable Penalty Factor in a Practical Power Output Constrained Active Control System
Authors: Chung Kwan Lai, Dongyuan Shi, Bhan Lam, Woon-Seng Gan
Date: 2023-06-15 00:00:00

Practical Active Noise Control (ANC) systems typically require a restriction in their maximum output power, to prevent overdriving the loudspeaker and causing system instability. Recently, the minimum output variance filtered-reference least mean square (MOV-FxLMS) algorithm was shown to have optimal control under output constraint with an analytically formulated penalty factor, but it needs offline knowledge of disturbance power and secondary path gain. The constant penalty factor in MOV-FxLMS is also susceptible to variations in disturbance power that could cause output power constraint violations. This paper presents a new variable penalty factor that utilizes the estimated disturbance in the established Modified-FxLMS (MFxLMS) algorithm, resulting in a computationally efficient MOV-MFxLMS algorithm that can adapt to changes in disturbance levels in real-time. Numerical simulation with real noise and plant response showed that the variable penalty factor always manages to meet its maximum power output constraint despite sudden changes in disturbance power, whereas the fixed penalty factor has suffered from a constraint mismatch.