pwc-archive/papers-with-abstracts huggingface dataset:
Preferences and Attitudes towards Debt Collection: A Cross-Generational Investigation
Authors: Minou Goetze, Christina Herdt, Ricarda Conrad, Stephan Stricker
Date: 2023-03-09 00:00:00

Preliminary research indicated that an increasing number of young adults end up in debt collection. Yet, debt collection agencies (DCAs) are still lacking knowledge on how to approach these consumers. A large-scale mixed-methods survey of consumers in Germany (N = 996) was conducted to investigate preference shifts from traditional to digital payment, and communication channels; and attitude shifts towards financial institutions. Our results show that, indeed, younger consumers are more likely to prefer digital payment methods (e.g., Paypal, Apple Pay), while older consumers are more likely to prefer traditional payment methods such as manual transfer. In the case of communication channels, we found that older consumers were more likely to prefer letters than younger consumers. Additional factors that had an influence on payment and communication preferences include gender, income and living in an urban area. Finally, we observed attitude shifts of younger consumers by exhibiting more openness when talking about their debt than older consumers. In summary, our findings show that consumers' preferences are influenced by individual differences, specifically age, and we discuss how DCAs can leverage these insights to optimize their processes.
Direct comparison or indirect comparison via a series of counterfactual decompositions?
Authors: Anna Naszodi
Date: 2023-03-08 00:00:00
Tasks: counterfactual

We illustrate the point with an empirical analysis of assortative mating in the US, namely, that the outcome of comparing two distant groups can be sensitive to whether comparing the groups directly, or indirectly via a series of counterfactual decompositions involving the groups' comparisons to some intermediate groups. We argue that the latter approach is typically more fit for its purpose.
Socioeconomics of Urban Travel in the U.S.: Evidence from the 2017 NHTS
Authors: Xize Wang, John L. Renne
Date: 2023-03-08 00:00:00

Using the 2017 National Household Travel Survey (NHTS), this study analyzes America's urban travel trends compared with earlier nationwide travel surveys, and examines the variations in travel behaviors among a range of socioeconomic groups. The most noticeable trend for the 2017 NHTS is that although private automobiles continue to be the dominant travel mode in American cities, the share of car trips has slightly and steadily decreased since its peak in 2001. In contrast, the share of transit, non-motorized, and taxicab (including ride-hailing) trips has steadily increased. Besides this overall trend, there are important variations in travel behaviors across income, home ownership, ethnicity, gender, age, and life-cycle stages. Although the trends in transit development, shared mobility, e-commerce, and lifestyle changes offer optimism about American cities becoming more multimodal, policymakers should consider these differences in socioeconomic factors and try to provide more equitable access to sustainable mobility across different socioeconomic groups.
Finding Regularized Competitive Equilibria of Heterogeneous Agent Macroeconomic Models with Reinforcement Learning
Authors: Ruitu Xu, Yifei Min, Tianhao Wang, Zhaoran Wang, Michael I. Jordan, Zhuoran Yang
Date: 2023-02-24 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning (RL)

We study a heterogeneous agent macroeconomic model with an infinite number of households and firms competing in a labor market. Each household earns income and engages in consumption at each time step while aiming to maximize a concave utility subject to the underlying market conditions. The households aim to find the optimal saving strategy that maximizes their discounted cumulative utility given the market condition, while the firms determine the market conditions through maximizing corporate profit based on the household population behavior. The model captures a wide range of applications in macroeconomic studies, and we propose a data-driven reinforcement learning framework that finds the regularized competitive equilibrium of the model. The proposed algorithm enjoys theoretical guarantees in converging to the equilibrium of the market at a sub-linear rate.
Fairer Shootouts in Soccer: The $m-n$ Rule
Authors: Steven J. Brams, Mehmet S. Ismail, D. Marc Kilgour
Date: 2023-02-11 00:00:00

Winning the coin toss at the end of a tied soccer game gives a team the right to choose whether to kick either first or second on all five rounds of penalty kicks, when each team is allowed one kick per round. There is considerable evidence that the right to make this choice, which is usually to kick first, gives a team a significant advantage. To make the outcome of a tied game fairer, we suggest a rule that handicaps the team that kicks first (A), requiring it to succeed on one more penalty kick than the team that kicks second (B). We call this the $m - n$ rule and, more specifically, propose $(m, n)$ = (5, 4): For A to win, it must successfully kick 5 goals before the end of the round in which B kicks its 4th; for B to win, it must succeed on 4 penalty kicks before A succeeds on 5. If both teams reach (5, 4) on the same round -- when they both kick successfully at (4, 3) -- then the game is decided by round-by-round "sudden death," whereby the winner is the first team to score in a subsequent round when the other team does not. We show that this rule is fair in tending to equalize the ability of each team to win a tied game in a penalty shootout. We also discuss a related rule that precludes the teams from reaching (5, 4) at the same time, obviating the need for sudden death and extra rounds.
Deep Equilibrium Learning of Explicit Regularizers for Imaging Inverse Problems
Authors: Zihao Zou, Jiaming Liu, Brendt Wohlberg, Ulugbek S. Kamilov
Date: 2023-03-09 00:00:00
Tasks: Image Reconstruction

There has been significant recent interest in the use of deep learning for regularizing imaging inverse problems. Most work in the area has focused on regularization imposed implicitly by convolutional neural networks (CNNs) pre-trained for image reconstruction. In this work, we follow an alternative line of work based on learning explicit regularization functionals that promote preferred solutions. We develop the Explicit Learned Deep Equilibrium Regularizer (ELDER) method for learning explicit regularizers that minimize a mean-squared error (MSE) metric. ELDER is based on a regularization functional parameterized by a CNN and a deep equilibrium learning (DEQ) method for training the functional to be MSE-optimal at the fixed points of the reconstruction algorithm. The explicit regularizer enables ELDER to directly inherit fundamental convergence results from optimization theory. On the other hand, DEQ training enables ELDER to improve over existing explicit regularizers without prohibitive memory complexity during training. We use ELDER to train several approaches to parameterizing explicit regularizers and test their performance on three distinct imaging inverse problems. Our results show that ELDER can greatly improve the quality of explicit regularizers compared to existing methods, and show that learning explicit regularizers does not compromise performance relative to methods based on implicit regularization.
Multispectral Image Compression Based on HEVC Using Pel-Recursive Inter-Band Prediction
Authors: Anna Meyer, Nils Genser, André Kaup
Date: 2023-03-09 00:00:00
Tasks: Image Compression

Recent developments in optical sensors enable a wide range of applications for multispectral imaging, e.g., in surveillance, optical sorting, and life-science instrumentation. Increasing spatial and spectral resolution allows creating higher quality products, however, it poses challenges in handling such large amounts of data. Consequently, specialized compression techniques for multispectral images are required. High Efficiency Video Coding (HEVC) is known to be the state of the art in efficiency for both video coding and still image coding. In this paper, we propose a cross-spectral compression scheme for efficiently coding multispectral data based on HEVC. Extending intra picture prediction by a novel inter-band predictor, spectral as well as spatial redundancies can be effectively exploited. Dependencies among the current band and further spectral references are considered jointly by adaptive linear regression modeling. The proposed backward prediction scheme does not require additional side information for decoding. We show that our novel approach is able to outperform state-of-the-art lossy compression techniques in terms of rate-distortion performance. On different data sets, average Bj{\o}ntegaard delta rate savings of 82 % and 55 % compared to HEVC and a reference method from literature are achieved, respectively.
A novel Cross-Component Context Model for End-to-End Wavelet Image Coding
Authors: Anna Meyer, André Kaup
Date: 2023-03-09 00:00:00

In contrast to traditional compression techniques performing linear transforms, the latent space of popular compressive autoencoders is obtained from a learned nonlinear mapping and hard to interpret. In this paper, we explore a promising alternative approach for neural compression, with an autoencoder whose latent space represents a nonlinear wavelet decomposition. Previous work has shown that neural wavelet image coding can outperform HEVC. However, the approach codes color components independently, thereby ignoring inter-component dependencies. Hence, we propose a novel cross-component context model (CCM). With CCM, the entropy model for the chroma latent space can be conditioned on previously coded components exploiting correlations in the learned wavelet space. The proposed CCM outperforms the baseline model with average Bj{\o}ntegaard delta rate savings of 2.6 % and 1.6 % for the Kodak and Tecnick image sets. Also, our method is competitive with VVC and learning-based methods.
Structural Similarity: When to Use Deep Generative Models on Imbalanced Image Dataset Augmentation
Authors: Chenqi Guo, Fabian Benitez-Quiroz, Qianli Feng, Aleix Martinez
Date: 2023-03-08 00:00:00
Tasks: Data Augmentation, SSIM

Improving the performance on an imbalanced training set is one of the main challenges in nowadays Machine Learning. One way to augment and thus re-balance the image dataset is through existing deep generative models, like class-conditional Generative Adversarial Networks (cGAN) or Diffusion Models by synthesizing images on each of the tail-class. Our experiments on imbalanced image dataset classification show that, the validation accuracy improvement with such re-balancing method is related to the image similarity between different classes. Thus, to quantify this image dataset class similarity, we propose a measurement called Super-Sub Class Structural Similarity (SSIM-supSubCls) based on Structural Similarity (SSIM). A deep generative model data augmentation classification (GM-augCls) pipeline is also provided to verify this metric correlates with the accuracy enhancement. We further quantify the relationship between them, discovering that the accuracy improvement decays exponentially with respect to SSIM-supSubCls values.
Dynamical Analysis of a Lotka-Volterra Competition Model with both Allee and Fear Effect
Authors: Shangming Chen, Fengde Chen, Vaibhava Srivastava, Rana D. Parshad
Date: 2023-03-08 00:00:00

Population ecology theory is replete with density dependent processes. However trait-mediated or behavioral indirect interactions can both reinforce or oppose density-dependent effects. This paper presents the first two species competitive ODE and PDE systems where an Allee effect, which is a density dependent process and the fear effect, which is non-consumptive and behavioral are both present. The stability of the equilibria is discussed analytically using the qualitative theory of ordinary differential equations. It is found that the Allee effect and the fear effect change the extinction dynamics of the system and the number of positive equilibrium points, but they do not affect the stability of the positive equilibria. We also observe some special dynamics that induce bifurcations in the system by varying the Allee or fear parameter. Interestingly we find that the Allee effect working in conjunction with the fear effect, can bring about several qualitative changes to the dynamical behavior of the system with only the fear effect in place, in regimes of small fear. That is, for small amounts of the fear parameter, it can change a competitive exclusion type situation to a strong competition type situation. It can also change a weak competition type situation to a bi-stability type situation. However for large fear regimes the Allee effect reinforces the dynamics driven by the fear effect. The analysis of the corresponding spatially explicit model is also presented. To this end the comparison principle for parabolic PDE is used. The conclusions of this paper have strong implications for conservation biology, biological control as well as the preservation of biodiversity.
Molecular detection and antimicrobial activity of Endophytic fungi isolated from a medical plant Rosmarinus officinalis
Authors: Shimal Yonuis Abdulhadi, Ghazwan Qasim Hasan, Raghad Nawaf Gergees
Date: 2023-03-09 00:00:00

Endophytes are tiny organisms present in living tissues of distinct plants and have been extensively studied for their endophytic microbial complement. Roots of Rosmarinus officinalis were subjected to the isolation of endophytic fungi and screened for antimicrobial activity against Gram-positive (Staphylococcus aureus and Bacillus subtilis) and Gram-negative (Escherichia coli, Pseudomonas aeruginosa, Klebsiella pneumoniae) bacteria. Genomic DNA from active fungal strain of Trichoderma harzianum was isolated, and the internal transcribed spacer (ITS) region was amplified using ITS4 and ITS5 primers and sequenced for genetic inference in fungus. The crude extract of T. harzianum isolate with Ethyl acetate was showed significant antimicrobial activity against P. aeruginosa, S. aureus, K. pneumonia, B. subtilis and E. coli. The antimicrobial activity was highest against P. aeruginosa at concentration of 40 microgram/ ml, followed by S. aureus and K. pneumonia at the same concentration. The lowest antimicrobial activity was against by S. aureus at concentration of 60 microgram/ ml. The current study is confirmed that the antimicrobial activity is due to bioactive compounds founded in endophytic fungi.
Emergent rate-based dynamics in duplicate-free populations of spiking neurons
Authors: Valentin Schmutz, Johanni Brea, Wulfram Gerstner
Date: 2023-03-09 00:00:00

Can Spiking Neural Networks (SNNs) approximate the dynamics of Recurrent Neural Networks (RNNs)? Arguments in classical mean-field theory based on laws of large numbers provide a positive answer when each neuron in the network has many "duplicates", i.e. other neurons with almost perfectly correlated inputs. Using a disordered network model that guarantees the absence of duplicates, we show that duplicate-free SNNs can converge to RNNs, thanks to the concentration of measure phenomenon. This result reveals a general mechanism underlying the emergence of rate-based dynamics in large SNNs.
Inter-brain substrates of role switching during mother-child interaction
Authors: Yamin Li, Saishuang Wu, Jiayang Xu, Haiwa Wang, Qi Zhu, Wen Shi, Yue Fang, Fan Jiang, Shanbao Tong, Yunting Zhang, Xiaoli Guo
Date: 2023-03-08 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

Mother-child interaction is highly dynamic and reciprocal. Switching roles in these back-and-forth interactions serves as a crucial feature of reciprocal behaviors while the underlying neural entrainment is still not well-studied. Here, we designed a role-controlled cooperative task with dual EEG recording to study how differently two brains interact when mothers and children hold different roles. When children were actors and mothers were observers, mother-child inter-brain synchrony emerged within the theta oscillations and the frontal lobe, which highly correlated with children's attachment to their mothers. When their roles were reversed, this synchrony was shifted to the alpha oscillations and the central area and associated with mothers' perception of their relationship with their children. The results suggested an observer-actor neural alignment within the actor's oscillations, which was modulated by the actor-toward-observer emotional bonding. Our findings contribute to the understanding of how inter-brain synchrony is established and dynamically changed during mother-child reciprocal interaction.
A variational synthesis of evolutionary and developmental dynamics
Authors: Karl Friston, Daniel Ari Friedman, Axel Constant, V. Bleu Knight, Thomas Parr, John O. Campbell
Date: 2023-03-08 00:00:00
Tasks: Model Selection

This paper introduces a variational formulation of natural selection, paying special attention to the nature of "things" and the way that different "kinds" of "things" are individuated from - and influence - each other. We use the Bayesian mechanics of particular partitions to understand how slow phylogenetic processes constrain - and are constrained by - fast, phenotypic processes. The main result is a formulation of adaptive fitness as a path integral of phenotypic fitness. Paths of least action, at the phenotypic and phylogenetic scales, can then be read as inference and learning processes, respectively. In this view, a phenotype actively infers the state of its econiche under a generative model, whose parameters are learned via natural (bayesian model selection). The ensuing variational synthesis features some unexpected aspects. Perhaps the most notable is that it is not possible to describe or model a population of conspecifics per se. Rather, it is necessary to consider populations - and nested meta-populations - of different natural kinds that influence each other. This paper is limited to a description of the mathematical apparatus and accompanying ideas. Subsequent work will use these methods for simulations and numerical analyses - and identify points of contact with related mathematical formulations of evolution.
Structure-based approach can identify driver nodes in ensembles of biologically-inspired Boolean networks
Authors: Eli Newby, Jorge Gómez Tejeda Zañudo, Réka Albert
Date: 2023-03-08 00:00:00
Tasks: Feedback Vertex Set (FVS)

Because the attractors of biological networks reflect stable behaviors (e.g., cell phenotypes), identifying control interventions that can drive a system towards its attractors (attractor control) is of particular relevance when controlling biological systems. Driving a network's feedback vertex set (FVS) by node-state override into a state consistent with a target attractor is proven to force every system trajectory to the target attractor, but in biological networks, the FVS is typically larger than can be realistically manipulated. External control of a subset of a biological network's FVS was proposed as a strategy to drive the network to its attractors utilizing fewer interventions; however, the effectiveness of this strategy was only demonstrated on a small set of Boolean models of biological networks. Here, we extend this analysis to ensembles of biologically-inspired Boolean networks. On these models, we use three structural metrics -- PRINCE propagation, modified PRINCE propagation, and CheiRank -- to rank FVS subsets by their predicted attractor control strength. We validate the accuracy of these rankings using three dynamical measures: To Control, Away Control, and logical domain of influence. We also calculate the propagation metrics on effective graphs, which incorporate each Boolean model's functional information into edge weights. While this additional information increases the predicting power of structural metrics, we find that the increase with respect to the unweighted network is limited. The propagation metrics in conjunction with the FVS can be used to identify realizable driver node sets by emulating the dynamics that are prevalent in biological networks. This approach only uses the network's structure, and the driver sets are shown to be robust to the specific dynamical model.
Bayesian at heart: Towards autonomic outflow estimation via generative state-space modelling of heart rate dynamics
Authors: Fernando E. Rosas, Diego Candia-Rivera, Andrea I Luppi, Yike Guo, Pedro A. M. Mediano
Date: 2023-03-08 00:00:00
Tasks: Bayesian Inference, Decision Making

Recent research is revealing how cognitive processes are supported by a complex interplay between the brain and the rest of the body, which can be investigated by the analysis of physiological features such as breathing rhythms, heart rate, and skin conductance. Heart rate dynamics are of particular interest as they provide a way to track the sympathetic and parasympathetic outflow from the autonomic nervous system, which is known to play a key role in modulating attention, memory, decision-making, and emotional processing. However, extracting useful information from heartbeats about the autonomic outflow is still challenging due to the noisy estimates that result from standard signal-processing methods. To advance this state of affairs, we propose a paradigm shift in how we conceptualise and model heart rate: instead of being a mere summary of the observed inter-beat intervals, we introduce a modelling framework that views heart rate as a hidden stochastic process that drives the observed heartbeats. Moreover, by leveraging the rich literature of state-space modelling and Bayesian inference, our proposed framework delivers a description of heart rate dynamics that is not a point estimate but a posterior distribution of a generative model. We illustrate the capabilities of our method by showing that it recapitulates linear properties of conventional heart rate estimators, while exhibiting a better discriminative power for metrics of dynamical complexity compared across different physiological states.
Prevalence and Major Risk Factors of Non-communicable Diseases: A Machine Learning based Cross-Sectional Study
Authors: Mrinmoy Roy, Anica Tasnim Protity, Srabonti Das, Porarthi Dhar
Date: 2023-03-03 00:00:00

Objective: The study aimed to determine the prevalence of several non-communicable diseases (NCD) and analyze risk factors among adult patients seeking nutritional guidance in Dhaka, Bangladesh. Result: Our study observed the relationships between gender, age groups, obesity, and NCDs (DM, CKD, IBS, CVD, CRD, thyroid). The most frequently reported NCD was cardiovascular issues (CVD), which was present in 83.56% of all participants. CVD was more common in male participants. Consequently, male participants had a higher blood pressure distribution than females. Diabetes mellitus (DM), on the other hand, did not have a gender-based inclination. Both CVD and DM had an age-based progression. Our study showed that chronic respiratory illness was more frequent in middle-aged participants than in younger or elderly individuals. Based on the data, every one in five hospitalized patients was obese. We analyzed the co-morbidities and found that 31.5% of the population has only one NCD, 30.1% has two NCDs, and 38.3% has more than two NCDs. Besides, 86.25% of all diabetic patients had cardiovascular issues. All thyroid patients in our study had CVD. Using a t-test, we found a relationship between CKD and thyroid (p-value 0.061). Males under 35 years have a statistically significant relationship between thyroid and chronic respiratory diseases (p-value 0.018). We also found an association between DM and CKD among patients over 65 (p-value 0.038). Moreover, there has been a statistically significant relationship between CKD and Thyroid (P < 0.05) for those below 35 and 35-65. We used a two-way ANOVA test to find the statistically significant interaction of heart issues and chronic respiratory illness, in combination with diabetes. The combination of DM and RTI also affected CKD in male patients over 65 years old.