pwc-archive/papers-with-abstracts huggingface dataset:
Threshold Regression in Heterogeneous Panel Data with Interactive Fixed Effects
Authors: Marco Barassi, Yiannis Karavias, Chongxian Zhu
Date: 2023-08-08 00:00:00
Tasks: regression

This paper introduces unit-specific heterogeneity in panel data threshold regression. Both slope coefficients and threshold parameters are allowed to vary by unit. The heterogeneous threshold parameters manifest via a unit-specific empirical quantile transformation of a common underlying threshold parameter which is estimated efficiently from the whole panel. In the errors, the unobserved heterogeneity of the panel takes the general form of interactive fixed effects. The newly introduced parameter heterogeneity has implications for model identification, estimation, interpretation, and asymptotic inference. The assumption of a shrinking threshold magnitude now implies shrinking heterogeneity and leads to faster estimator rates of convergence than previously encountered. The asymptotic theory for the proposed estimators is derived and Monte Carlo simulations demonstrate its usefulness in small samples. The new model is employed to examine the Feldstein-Horioka puzzle and it is found that the trade liberalization policies of the 80's significantly impacted cross-country capital mobility.
Global evidence on the income elasticity of willingness to pay, relative price changes and public natural capital values
Authors: Moritz A. Drupp, Zachary M. Turk, Ben Groom, Jonas Heckenhahn
Date: 2023-08-08 00:00:00

While the global economy continues to grow, ecosystem services tend to stagnate or decline. Economic theory has shown how such shifts in relative scarcities can be reflected in project appraisal and accounting, but empirical evidence has been sparse to put theory into practice. To estimate relative price changes in ecosystem services to be used for making such adjustments, we perform a global meta-analysis of contingent valuation studies to derive income elasticities of marginal willingness to pay (WTP) for ecosystem services to proxy the degree of limited substitutability. Based on 735 income-WTP pairs from 396 studies, we find an income elasticity of WTP of around 0.6. Combined with good-specific growth rates, we estimate relative price change of ecosystem services of around 1.7 percent per year. In an application to natural capital valuation of forest ecosystem services by the World Bank, we show that natural capital should be uplifted by around 40 percent. Our assessment of aggregate public natural capital yields a larger value adjustment of between 58 and 97 percent, depending on the discount rate. We discuss implications for policy appraisal and for estimates of natural capital in comprehensive wealth accounts.
Investigating Speaker Embedding Disentanglement on Natural Read Speech
Authors: Michael Kuhlmann, Adrian Meise, Fritz Seebauer, Petra Wagner, Reinhold Haeb-Umbach
Date: 2023-08-08 00:00:00
Tasks: Disentanglement, Fairness, Representation Learning

Disentanglement is the task of learning representations that identify and separate factors that explain the variation observed in data. Disentangled representations are useful to increase the generalizability, explainability, and fairness of data-driven models. Only little is known about how well such disentanglement works for speech representations. A major challenge when tackling disentanglement for speech representations are the unknown generative factors underlying the speech signal. In this work, we investigate to what degree speech representations encoding speaker identity can be disentangled. To quantify disentanglement, we identify acoustic features that are highly speaker-variant and can serve as proxies for the factors of variation underlying speech. We find that disentanglement of the speaker embedding is limited when trained with standard objectives promoting disentanglement but can be improved over vanilla representation learning to some extent.
Reconfigurable and Static EM Skins on Vehicles for Localization
Authors: Dario Tagliaferri, Marouan Mizmizi, Giacomo Oliveri, Umberto Spagnolini, Andrea Massa
Date: 2023-08-08 00:00:00

Electromagnetic skins (EMSs) have been recently considered as a booster for wireless sensing, but their usage on mobile targets is relatively novel and could be of interest when the target reflectivity can/must be increased to improve its detection or the estimation of parameters. In particular, when illuminated by a wide-bandwidth signal (e.g., from a radar operating at millimeter waves), vehicles behave like \textit{extended targets}, since multiple parts of the vehicle's body effectively contribute to the back-scattering. Moreover, in some cases perspective deformations challenge the correct localization of the vehicle. To address these issues, we propose lodging EMSs on vehicles' roof to act as high-reflectivity planar retro-reflectors toward the sensing terminal. The advantage is twofold: \textit{(i)} by introducing a compact high-reflectivity structure on the target, we make vehicles behave like \textit{point targets}, avoiding perspective deformations and related ranging biases and \textit{(ii)} we increase the reflectivity the vehicle, improving localization performance. We detail the EMS design from the system-level to the full-wave-level considering both reconfigurable intelligent surfaces (RIS) and cost-effective static passive electromagnetic skins (SP-EMSs). Localization performance of the EMS-aided sensing system is also assessed by Cram\'er-Rao bound analysis in both narrowband and spatially wideband operating conditions.
Proceedings of the first edition of the International Symposium on Computational Sensing (ISCS23)
Authors: Thomas Feuillen, Amirafshar Moshtaghpour
Date: 2023-08-08 00:00:00

The International Symposium on Computational Sensing (ISCS) brings together researchers from optical microscopy, electron microscopy, RADAR, astronomical imaging, biomedical imaging, remote sensing, and signal processing. With a particular focus on applications and demonstrators, the purpose of this symposium is to be a forum where researchers in computational sensing working in seemingly unrelated applications can learn, discover, and exchange on their new findings and challenges. This 3-day symposium in the heart of Europe features 6 keynotes speakers and is open to extended abstracts for scientific presentations and show-and-tell demonstrations.
A Reinforcement Learning-Based Approach to Graph Discovery in D2D-Enabled Federated Learning
Authors: Satyavrat Wagle, Anindya Bijoy Das, David J. Love, Christopher G. Brinton
Date: 2023-08-07 00:00:00
Tasks: Federated Learning, Reinforcement Learning (RL)

Augmenting federated learning (FL) with direct device-to-device (D2D) communications can help improve convergence speed and reduce model bias through rapid local information exchange. However, data privacy concerns, device trust issues, and unreliable wireless channels each pose challenges to determining an effective yet resource efficient D2D structure. In this paper, we develop a decentralized reinforcement learning (RL) methodology for D2D graph discovery that promotes communication of non-sensitive yet impactful data-points over trusted yet reliable links. Each device functions as an RL agent, training a policy to predict the impact of incoming links. Local (device-level) and global rewards are coupled through message passing within and between device clusters. Numerical experiments confirm the advantages offered by our method in terms of convergence speed and straggler resilience across several datasets and FL schemes.
Saccharina latissima, candy-factory waste, and digestate from full-scale biogas plant as alternative carbohydrate and nutrient sources for lactic acid production
Authors: Eleftheria Papadopoulou, Charlene Vance, Paloma S. Rozene Vallespin, Panagiotis Tsapekos, Irini Angelidaki
Date: 2023-08-07 00:00:00

To substitute petroleum-based materials with bio-based alternatives, microbial fermentation combined with inexpensive biomass is suggested. In this study Saccharina latissima hydrolysate, candy-factory waste, and digestate from full-scale biogas plant were explored as substrates for lactic acid production. The lactic acid bacteria Enterococcus faecium, Lactobacillus plantarum, and Pediococcus pentosaceus were tested as starter cultures. Sugars released from seaweed hydrolysate and candy-waste were successfully utilized by the studied bacterial strains. Additionally, seaweed hydrolysate and digestate served as nutrient supplements supporting microbial fermentation. According to the highest achieved relative lactic acid production, a scaled-up co-fermentation of candy-waste and digestate was performed. Lactic acid reached a concentration of 65.65 g/L, with 61.69% relative lactic acid production, and 1.37 g/L/hour productivity. The findings indicate that lactic acid can be successfully produced from low-cost industrial residues.
Gromov-Wasserstein unsupervised alignment reveals structural correspondences between the color similarity structures of humans and large language models
Authors: Genji Kawakita, Ariel Zeleznikow-Johnston, Naotsugu Tsuchiya, Masafumi Oizumi
Date: 2023-08-08 00:00:00

Large Language Models (LLMs), such as the General Pre-trained Transformer (GPT), have shown remarkable performance in various cognitive tasks. However, it remains unclear whether these models have the ability to accurately infer human perceptual representations. Previous research has addressed this question by quantifying correlations between similarity response patterns of humans and LLMs. Correlation provides a measure of similarity, but it relies pre-defined item labels and does not distinguish category- and item- level similarity, falling short of characterizing detailed structural correspondence between humans and LLMs. To assess their structural equivalence in more detail, we propose the use of an unsupervised alignment method based on Gromov-Wasserstein optimal transport (GWOT). GWOT allows for the comparison of similarity structures without relying on pre-defined label correspondences and can reveal fine-grained structural similarities and differences that may not be detected by simple correlation analysis. Using a large dataset of similarity judgments of 93 colors, we compared the color similarity structures of humans (color-neurotypical and color-atypical participants) and two GPT models (GPT-3.5 and GPT-4). Our results show that the similarity structure of color-neurotypical participants can be remarkably well aligned with that of GPT-4 and, to a lesser extent, to that of GPT-3.5. These results contribute to the methodological advancements of comparing LLMs with human perception, and highlight the potential of unsupervised alignment methods to reveal detailed structural correspondences. This work has been published in Scientific Reports, DOI: https://doi.org/10.1038/s41598-024-65604-1.
Harnessing Artificial Intelligence To Reduce Phototoxicity in Live Imaging
Authors: Estibaliz Gómez-de-Mariscal, Mario Del Rosario, Joanna W Pylvänäinen, Guillaume Jacquemet, Ricardo Henriques
Date: 2023-08-08 00:00:00
Tasks: Super-Resolution

Fluorescence microscopy, widely used in the study of living cells, tissues, and organisms, often faces the challenge of photodamage. This is primarily caused by the interaction between light and biochemical components during the imaging process, leading to compromised accuracy and reliability of biological results. Methods necessitating extended high-intensity illumination, such as super-resolution microscopy or thick sample imaging, are particularly susceptible to this issue. As part of the solution to these problems, advanced imaging approaches involving artificial intelligence (AI) have been developed. Here we underscore the necessity of establishing constraints to maintain light-induced damage at levels that permit cells to sustain their live behaviour. From this perspective, data-driven live-cell imaging bears significant potential in aiding the development of AI-enhanced photodamage-aware microscopy. These technologies could streamline precise observations of natural biological dynamics while minimising phototoxicity risks.
Extended mean-field control problems with multi-dimensional singular controls
Authors: Robert Denkert, Ulrich Horst
Date: 2023-08-08 00:00:00

We consider extended mean-field control problems with multi-dimensional singular controls. A key challenge when analysing singular controls are jump costs. When controls are one-dimensional, jump costs are most naturally computed by linear interpolation. When the controls are multi-dimensional the situation is more complex, especially when the model parameters depend on an additional mean-field interaction term, in which case one needs to "jointly" and "consistently" interpolate jumps both on a distributional and a pathwise level. This is achieved by introducing the novel concept of two-layer parametrisations of stochastic processes. Two-layer parametrisations allow us to equivalently rewrite rewards in terms of continuous functions of parametrisations of the control process and to derive an explicit representation of rewards in terms of minimal jump costs. From this we derive a DPP for extended mean-field control problems with multi-dimensional singular controls. Under the additional assumption that the value function is continuous we characterise the value function as the minimal super-solution to a certain quasi-variational inequality in the Wasserstein space.
Options are also options on options: how to smile with Black-Scholes
Authors: Claude Martini, Arianna Mingone
Date: 2023-08-08 00:00:00

We observe that a European Call option with strike $L > K$ can be seen as a Call option with strike $L-K$ on a Call option with strike $K$. Under no arbitrage assumptions, this yields immediately that the prices of the two contracts are the same, in full generality. We study in detail the relative pricing function which gives the price of the Call on Call option as a function of its underlying Call option, and provide quasi-closed formula for those new pricing functions in the Carr-Pelts-Tehranchi family [Carr and Pelts, Duality, Deltas, and Derivatives Pricing, 2015] and [Tehranchi, A Black-Scholes inequality: applications and generalisations, Finance Stoch, 2020] that includes the Black-Scholes model as a particular case. We also study the properties of the function that maps the price normalized by the underlier, viewed as a function of the moneyness, to the normalized relative price, which allows us to produce several new closed formulas. In connection to the symmetry transformation of a smile, we build a lift of the relative pricing function in the case of an underlier that does not vanish. We finally provide some properties of the implied volatility smiles of Calls on Calls and lifted Calls on Calls in the Black-Scholes model.
Regularity in forex returns during financial distress: Evidence from India
Authors: Radhika Prosad Datta
Date: 2023-08-08 00:00:00

This paper uses the concepts of entropy to study the regularity/irregularity of the returns from the Indian Foreign exchange (forex) markets. The Approximate Entropy and Sample Entropy statistics which measure the level of repeatability in the data are used to quantify the randomness in the forex returns from the time period 2006 to 2021. The main objective of the research is to see how the randomness of the foreign exchange returns evolve over the given time period particularly during periods of high financial instability or turbulence in the global financial market. With this objective we look at 2 major financial upheavals, the subprime crisis also known as the Global Financial Crisis (GFC) during 2006-2007 and the recent Covid-19 pandemic during 2020-2021. Our empirical results overwhelmingly confirm our working hypothesis that regularity in the returns of the major Indian foreign exchange rates increases during times of financial crisis. This is evidenced by a decrease in the values of the sample entropy and approximate entropy before and after/during the financial crisis period for the majority of the exchange rates. Our empirical results also show that Sample Entropy is a better measure of regularity than Approximate Entropy for the Indian forex rates which is in agreement with the theoretical predictions.
Applications of Machine Learning to Modelling and Analysing Dynamical Systems
Authors: Vedanta Thapar
Date: 2023-07-22 00:00:00

We explore the use of Physics Informed Neural Networks to analyse nonlinear Hamiltonian Dynamical Systems with a first integral of motion. In this work, we propose an architecture which combines existing Hamiltonian Neural Network structures into Adaptable Symplectic Recurrent Neural Networks which preserve Hamilton's equations as well as the symplectic structure of phase space while predicting dynamics for the entire parameter space. This architecture is found to significantly outperform previously proposed neural networks when predicting Hamiltonian dynamics especially in potentials which contain multiple parameters. We demonstrate its robustness using the nonlinear Henon-Heiles potential under chaotic, quasiperiodic and periodic conditions. The second problem we tackle is whether we can use the high dimensional nonlinear capabilities of neural networks to predict the dynamics of a Hamiltonian system given only partial information of the same. Hence we attempt to take advantage of Long Short Term Memory networks to implement Takens' embedding theorem and construct a delay embedding of the system followed by mapping the topologically invariant attractor to the true form. This architecture is then layered with Adaptable Symplectic nets to allow for predictions which preserve the structure of Hamilton's equations. We show that this method works efficiently for single parameter potentials and provides accurate predictions even over long periods of time.
Goodness-of-Fit of Attributed Probabilistic Graph Generative Models
Authors: Pablo Robles-Granda, Katherine Tsai, Oluwasanmi Koyejo
Date: 2023-07-28 00:00:00

Probabilistic generative models of graphs are important tools that enable representation and sampling. Many recent works have created probabilistic models of graphs that are capable of representing not only entity interactions but also their attributes. However, given a generative model of random attributed graph(s), the general conditions that establish goodness of fit are not clear a-priori. In this paper, we define goodness of fit in terms of the mean square contingency coefficient for random binary networks. For this statistic, we outline a procedure for assessing the quality of the structure of a learned attributed graph by ensuring that the discrepancy of the mean square contingency coefficient (constant, or random) is minimal with high probability. We apply these criteria to verify the representation capability of a probabilistic generative model for various popular types of graph models.
AMaizeD: An End to End Pipeline for Automatic Maize Disease Detection
Authors: Anish Mall, Sanchit Kabra, Ankur Lhila, Pawan Ajmera
Date: 2023-07-23 00:00:00

This research paper presents AMaizeD: An End to End Pipeline for Automatic Maize Disease Detection, an automated framework for early detection of diseases in maize crops using multispectral imagery obtained from drones. A custom hand-collected dataset focusing specifically on maize crops was meticulously gathered by expert researchers and agronomists. The dataset encompasses a diverse range of maize varieties, cultivation practices, and environmental conditions, capturing various stages of maize growth and disease progression. By leveraging multispectral imagery, the framework benefits from improved spectral resolution and increased sensitivity to subtle changes in plant health. The proposed framework employs a combination of convolutional neural networks (CNNs) as feature extractors and segmentation techniques to identify both the maize plants and their associated diseases. Experimental results demonstrate the effectiveness of the framework in detecting a range of maize diseases, including powdery mildew, anthracnose, and leaf blight. The framework achieves state-of-the-art performance on the custom hand-collected dataset and contributes to the field of automated disease detection in agriculture, offering a practical solution for early identification of diseases in maize crops advanced machine learning techniques and deep learning architectures.
Enhancing image captioning with depth information using a Transformer-based framework
Authors: Aya Mahmoud Ahmed, Mohamed Yousef, Khaled F. Hussain, Yousef Bassyouni Mahdy
Date: 2023-07-24 00:00:00
Tasks: Image Captioning, Image Paragraph Captioning, Scene Understanding, Sentence

Captioning images is a challenging scene-understanding task that connects computer vision and natural language processing. While image captioning models have been successful in producing excellent descriptions, the field has primarily focused on generating a single sentence for 2D images. This paper investigates whether integrating depth information with RGB images can enhance the captioning task and generate better descriptions. For this purpose, we propose a Transformer-based encoder-decoder framework for generating a multi-sentence description of a 3D scene. The RGB image and its corresponding depth map are provided as inputs to our framework, which combines them to produce a better understanding of the input scene. Depth maps could be ground truth or estimated, which makes our framework widely applicable to any RGB captioning dataset. We explored different fusion approaches to fuse RGB and depth images. The experiments are performed on the NYU-v2 dataset and the Stanford image paragraph captioning dataset. During our work with the NYU-v2 dataset, we found inconsistent labeling that prevents the benefit of using depth information to enhance the captioning task. The results were even worse than using RGB images only. As a result, we propose a cleaned version of the NYU-v2 dataset that is more consistent and informative. Our results on both datasets demonstrate that the proposed framework effectively benefits from depth information, whether it is ground truth or estimated, and generates better captions. Code, pre-trained models, and the cleaned version of the NYU-v2 dataset will be made publically available.
GeoTransformer: Fast and Robust Point Cloud Registration with Geometric Transformer
Authors: Zheng Qin, Hao Yu, Changjian Wang, Yulan Guo, Yuxing Peng, Slobodan Ilic, Dewen Hu, Kai Xu
Date: 2023-07-25 00:00:00
Tasks: Image to Point Cloud Registration, Point Cloud Registration, Triplet

We study the problem of extracting accurate correspondences for point cloud registration. Recent keypoint-free methods have shown great potential through bypassing the detection of repeatable keypoints which is difficult to do especially in low-overlap scenarios. They seek correspondences over downsampled superpoints, which are then propagated to dense points. Superpoints are matched based on whether their neighboring patches overlap. Such sparse and loose matching requires contextual features capturing the geometric structure of the point clouds. We propose Geometric Transformer, or GeoTransformer for short, to learn geometric feature for robust superpoint matching. It encodes pair-wise distances and triplet-wise angles, making it invariant to rigid transformation and robust in low-overlap cases. The simplistic design attains surprisingly high matching accuracy such that no RANSAC is required in the estimation of alignment transformation, leading to $100$ times acceleration. Extensive experiments on rich benchmarks encompassing indoor, outdoor, synthetic, multiway and non-rigid demonstrate the efficacy of GeoTransformer. Notably, our method improves the inlier ratio by $18{\sim}31$ percentage points and the registration recall by over $7$ points on the challenging 3DLoMatch benchmark. Our code and models are available at \url{https://github.com/qinzheng93/GeoTransformer}.