pwc-archive/papers-with-abstracts huggingface dataset:
Effect on New Loan Repayment Fine Clause on Bank Jaya Artha's Customer Satisfaction and Recommendation
Authors: Mustaqim Adamrah, Yos Sunitiyoso
Date: 2024-01-09 00:00:00

The growing population of older people in Indonesia--the world's fourth-most populous country--makes a larger cake for the pension business, including in the banking sector. PT Bank Jaya Artha is one of the Indonesian banks that provide products and services for people who are set to enter retirement age. In the wake of tight competition in the pension business market, Bank Jaya Artha has since 2019 imposed a fine of three times of installments in addition to 5% of outstanding debt on customers planning to repay their debt in a mission to prevent them from leaving for competitors. While the clause is not included in loan agreements signed before the implementation, it applies to past loan agreements as well. This, in turn, has led to customer complaints. The research is meant to find out how the implementation of the unconsented clause has affected customer satisfaction and willingness to recommend the bank and what the bank should do to become more customer-centric, according to customers. Using a design thinking framework, the research collects quantitative and qualitative data from the bank's pension customers through questionnaires and forum group discussions. Statistical analysis is utilized on quantitative data from questionnaires, and content analysis is utilized on qualitative data from questionnaires. A narrative analysis is also used to explain qualitative data from forum group discussions. The result shows that there are problems in the way the bank communicates information to customers, particularly information about the loan repayment fine. Lack of transparency, a reactive approach instead of a proactive one, the obscurity of the information, and the time the information is delivered have affected customers' satisfaction toward the bank.
Opportunities to upgrade the scientific disciplines space
Authors: Nestor Gandelman, Osiris Jorge Parcero, Flavia Roldan
Date: 2024-01-09 00:00:00

Knowledge generated in a given scientific domain may spill over into other close scientific disciplines, thereby improving performance. Using bibliometric data from the SCImago database drawn from a sample of 174 countries, we implement a measure of proximity based on revealed comparative advantage (RCA). Our estimates show that proximity between disciplines positively and significantly affects the RCA growth rate. This impact is larger on disciplines that currently do not have RCA.
Ecosystem orchestration practices for industrial firms: A qualitative meta-analysis, framework development and research agenda
Authors: Lei Shen, Qingyue Shi, Vinit Parida, Marin Jovanovic
Date: 2024-01-09 00:00:00
Tasks: Navigate

This study ventures into the dynamic realm of ecosystem orchestration for industrial firms, emphasizing its significance in maintaining competitive advantage in the digital era. The fragmented research on this important subject poses challenges for firms aiming to navigate and capitalize on ecosystem orchestration. To bridge this knowledge gap, we conducted a comprehensive qualitative meta-analysis of 31 case studies and identified multifaceted orchestration practices employed by industrial firms. The core contribution of this research is the illumination of five interdependent but interrelated orchestration practices: strategic design, relational, resource integration, technological, and innovation. Together, these practices are synthesized into an integrative framework termed the "Stirring Model," which serves as a practical guide to the orchestration practices. Furthermore, the conceptual framework clarifies the synergy between the identified practices and highlights their collective impact. This study proposes theoretical and practical implications for ecosystem orchestration literature and suggests avenues for further research.
Entrepreneurial Intention and UAE Youth: Unique Influencers of Entrepreneurial Intentions in an Emerging Country Context
Authors: N. Y. Al Saiqal, James. C. Ryabn, Osiris Jorge Parcero
Date: 2024-01-08 00:00:00

The United Arab Emirates (UAE) is a young, oil-rich country, where national youth display a clear preference for public sector employment. Growing youth unemployment reinforces the importance of non-government employment, including entrepreneurship. This study investigates UAE national youth intentions towards entrepreneurship through the Theory of Planned Behavior and the Entrepreneurial Intention Questionnaire (EIQ). Analysis (N=544) identifies the direct influence of attitude and perceived behavioral control, and indirect influence of subjective norms on entrepreneurship intention. Results also examine several demographic variables and highlight the potential importance of family and social groups in promoting entrepreneurial intentions in this emerging country context.
Optimal National policies towards multinationals when local regions can choose between firm-specific and non-firm-specific policies
Authors: Osiris Jorge Parcero
Date: 2024-01-08 00:00:00

This paper looks at a country's optimal central-government optimal policy in a setting where its two identical local jurisdictions compete to attract footloose multinationals to their sites, and where the considered multinationals strictly prefer this country to the rest of the world. For the sake of realism the model allows the local jurisdictions to choose between firm-specific and non-firm-specific policies. We show that the implementation of the jurisdictional firm-specific policy is weakly welfare dominant. Hence the frequent calls for the central government to ban the former type of policies go against the advice of this paper.
Becoming a Knowledge Economy: the Case of Qatar, UAE and 17 Benchmark Countries
Authors: Osiris Parcero, James Christopher Ryan
Date: 2024-01-08 00:00:00

This paper assesses the performance of Qatar and the United Arab Emirates (UAE) in terms of their achievements towards becoming knowledge-based economies. This is done through a comparison against 17 benchmark countries using a four pillars' framework comprising; (1) information and communication technology, (2) education, (3) innovation and (4) economy and regime. Results indicate that the UAE ranks slightly better than the median rank of the 19 compared countries while Qatar ranks somewhat below. Results also indicate that both countries lag considerably behind knowledge economy leaders; particularly evidenced in the innovation pillar. Policy recommendations are mainly addressed at further developing the two countries' research culture as well as improving the incentives to attract top quality researchers and highly talented workers.
Economic Forces in Stock Returns
Authors: Yue Chen, Mohan Li
Date: 2024-01-06 00:00:00

When analyzing the components influencing the stock prices, it is commonly believed that economic activities play an important role. More specifically, asset prices are more sensitive to the systematic economic news that impose a pervasive effect on the whole market. Moreover, the investors will not be rewarded for bearing idiosyncratic risks as such risks are diversifiable. In the paper Economic Forces and the Stock Market 1986, the authors introduced an attribution model to identify the specific systematic economic forces influencing the market. They first defined and examined five classic factors from previous research papers: Industrial Production, Unanticipated Inflation, Change in Expected Inflation, Risk Premia, and The Term Structure. By adding in new factors, the Market Indices, Consumptions and Oil Prices, one by one, they examined the significant contribution of each factor to the stock return. The paper concluded that the stock returns are exposed to the systematic economic news, and they are priced with respect to their risk exposure. Also, the significant factors can be identified by simply adopting their model. Driven by such motivation, we conduct an attribution analysis based on the general framework of their model to further prove the importance of the economic factors and identify the specific identity of significant factors.
Human Activity Segmentation Challenge @ ECML/PKDD’23
Authors: Arik Ermshaus, Patrick Schäfer, Anthony Bagnall, Thomas Guyet, Georgiana Ifrim, Vincent Lemaire, Ulf Leser, Colin Leverger, Simon Malinowski
Date: 2023-09-18 00:00:00
Proceeding: Advanced Analytics and Learning on Temporal Data 2023 9
Tasks: Activity Recognition, Change Point Detection, Human Activity Recognition, Segmentation, Semantic Segmentation, Time Series, Time Series Analysis

Time series segmentation (TSS) is a research problem that focuses on dividing long multivariate sensor data into smaller, homogeneous subsequences. This task is critical for various real-world data analysis applications, such as energy consumption monitoring, climate change assessment, and human activity recognition (HAR). Despite its importance, existing methods demonstrate limited efficacy on real-world multivariate time series data. To advance the field, we organized the Human Activity Segmentation Challenge at ECML/PKDD and AALTD 2023, featuring 57 participants. Collaborating with 15 bachelor computer science students, we gathered and annotated 10.7 h of real-world human motion sensor data. The challenge required participants to segment the resulting 250 multivariate time series into an unknown number of variable-sized activities. The top-8 approaches outperformed existing baselines, but show only limited improvements, capped at 1.9% points. The segmentation of real-world mobile sensing recordings remains challenging. We release the labelled challenge data for future research.
Should Politicians be Informed? Targeted Benefits and Heterogeneous Voters
Authors: Maxim Senkov, Arseniy Samsonov
Date: 2024-01-08 00:00:00

We compare two scenarios in a model where politicians offer local public goods to heterogeneous voters: one where politicians have access to data on voters and thus can target specific ones, and another where politicians only decide on the level of spending. When the budget is small, or the public good has a high value, access to voter information leads the winner to focus on poorer voters, enhancing voter welfare. With a larger budget or less crucial public goods, politicians target a narrow group of swing voters, which harms the voter welfare.
Deep Covariance Alignment for Domain Adaptive Remote Sensing Image Segmentation
Authors: Linshan Wu, Ming Lu, Leyuan Fang
Date: 2024-01-09 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

Unsupervised domain adaptive (UDA) image segmentation has recently gained increasing attention, aiming to improve the generalization capability for transferring knowledge from the source domain to the target domain. However, in high spatial resolution remote sensing image (RSI), the same category from different domains (\emph{e.g.}, urban and rural) can appear to be totally different with extremely inconsistent distributions, which heavily limits the UDA accuracy. To address this problem, in this paper, we propose a novel Deep Covariance Alignment (DCA) model for UDA RSI segmentation. The DCA can explicitly align category features to learn shared domain-invariant discriminative feature representations, which enhances the ability of model generalization. Specifically, a Category Feature Pooling (CFP) module is first employed to extract category features by combining the coarse outputs and the deep features. Then, we leverage a novel Covariance Regularization (CR) to enforce the intra-category features to be closer and the inter-category features to be further separate. Compared with the existing category alignment methods, our CR aims to regularize the correlation between different dimensions of the features and thus performs more robustly when dealing with the divergent category features of imbalanced and inconsistent distributions. Finally, we propose a stagewise procedure to train the DCA in order to alleviate the error accumulation. Experiments on both Rural-to-Urban and Urban-to-Rural scenarios of the LoveDA dataset demonstrate the superiority of our proposed DCA over other state-of-the-art UDA segmentation methods. Code is available at https://github.com/Luffy03/DCA.
OrthoSeisnet: Seismic Inversion through Orthogonal Multi-scale Frequency Domain U-Net for Geophysical Exploration
Authors: Supriyo Chakraborty, Aurobinda Routray, Sanjay Bhargav Dharavath, Tanmoy Dam
Date: 2024-01-09 00:00:00
Tasks: Seismic Inversion, SSIM

Seismic inversion is crucial in hydrocarbon exploration, particularly for detecting hydrocarbons in thin layers. However, the detection of sparse thin layers within seismic datasets presents a significant challenge due to the ill-posed nature and poor non-linearity of the problem. While data-driven deep learning algorithms have shown promise, effectively addressing sparsity remains a critical area for improvement. To overcome this limitation, we propose OrthoSeisnet, a novel technique that integrates a multi-scale frequency domain transform within the U-Net framework. OrthoSeisnet aims to enhance the interpretability and resolution of seismic images, enabling the identification and utilization of sparse frequency components associated with hydrocarbon-bearing layers. By leveraging orthogonal basis functions and decoupling frequency components, OrthoSeisnet effectively improves data sparsity. We evaluate the performance of OrthoSeisnet using synthetic and real datasets obtained from the Krishna-Godavari basin. Orthoseisnet outperforms the traditional method through extensive performance analysis utilizing commonly used measures, such as mean absolute error (MAE), mean squared error (MSE), and structural similarity index (SSIM) https://github.com/supriyo100/Orthoseisnet.
A New Spatial Block-Correlation Model for Fluid Antenna Systems
Authors: Pablo Ramirez-Espinosa, David Morales-Jimenez, Kai-Kit Wong
Date: 2024-01-09 00:00:00

Powered by position-flexible antennas, the emerging fluid antenna system (FAS) technology is postulated as a key enabler for massive connectivity in 6G networks. The free movement of antenna elements enables the opportunistic minimization of interference, allowing several users to share the same radio channel without the need of precoding. However, the true potential of FAS is still unknown due to the extremely high spatial correlation of the wireless channel between very close-by antenna positions. To unveil the multiplexing capabilities of FAS, proper (simple yet accurate) modeling of the spatial correlation is prominently needed. Realistic classical models such as Jakes's are prohibitively complex, rendering intractable analyses, while state-of-the-art approximations often are too simplistic and poorly accurate. Aiming to fill this gap, we here propose a general framework to approximate spatial correlation by block-diagonal matrices, motivated by the well-known block fading assumption and by statistical results on large correlation matrices. The proposed block-correlation model makes the performance analysis possible, and tightly approximates the results obtained with realistic models (Jakes's and Clarke's). Our framework is leveraged to analyze fluid antenna multiple access (FAMA) systems, evaluating their performance for both one- and two-dimensional fluid antennas.
Epidemiological dynamics in populations structured by neighbourhoods and households
Authors: Abby Barlow, Ben Adams, Sarah Penington
Date: 2024-01-09 00:00:00
Tasks: Epidemiology

Epidemiological dynamics are affected by the spatial and demographic structure of the host population. Households and neighbourhoods are known to be important groupings but little is known about the epidemiological interplay between them. In order to explore the implications for infectious disease epidemiology of households with similar demographic structures clustered in space we develop a multi-scale epidemic model consisting of neighbourhoods of households. In our analysis we focus on key parameters which control household size, the importance of transmission within households relative to outside of them, and the degree to which the non-household transmission is localised within neighbourhoods. We construct the household reproduction number $R_*$ over all neighbourhoods and derive the analytic probability of an outbreak occurring from a single infected individual in a specific neighbourhood. We find that reduced localisation of transmission within neighbourhoods reduces $R_*$ when household size differs between neighbourhoods. This effect is amplified by larger differences between household sizes and larger divergence between transmission rates within households and outside of them. However, the impact of neighbourhoods with larger household sizes on an individual's risk of infection is mainly limited to the individuals that reside in those neighbourhoods. We consider various surveillance scenarios and show that household size information from the initial infectious cases is often more important than neighbourhood information while household size and neighbourhood localisation influences the sequence of neighbourhoods in which an outbreak is observed.
High throughput screening for LC3/GABARAP binders utilizing fluorescence polarization assay
Authors: Martin P. Schwalm, Johannes Dopfer, Stefan Knapp, Vladimir V. Rogov
Date: 2024-01-09 00:00:00

The characterization of interactions between autophagy modifiers (Atg8-family proteins) and their natural ligands (peptides and proteins) or small molecules is important for a detailed understanding of selective autophagy mechanisms and for design of potential Atg8 inhibitors that affect the autophagy processes in cells. The fluorescence polarization (FP) assay is a rapid, cost-effective and robust method which provides affinity and selectivity information of small molecules and peptide ligands targeting human Atg8 proteins. This chapter introduces the basic principles of FP assays are introduced. In addition, a case study for FP small molecule and peptide interactions with human Atg8 proteins (LC3/GABARAPs) is described. Finally, data analysis and quality control of FP studies are discussed for the proper calculation of Ki values of the measured compounds.
How does the inner retinal network shape the ganglion cells receptive field : a computational study
Authors: Evgenia Kartsaki, Gerrit Hilgen, Evelyne Sernagor, Bruno Cessac
Date: 2024-01-09 00:00:00

We consider a model of basic inner retinal connectivity where bipolar and amacrine cells interconnect, and both cell types project onto ganglion cells, modulating their response output to the brain visual areas. We derive an analytical formula for the spatio-temporal response of retinal ganglion cells to stimuli taking into account the effects of amacrine cells inhibition. This analysis reveals two important functional parameters of the network: (i) the intensity of the interactions between bipolar and amacrine cells, and, (ii) the characteristic time scale of these responses. Both parameters have a profound combined impact on the spatiotemporal features of RGC responses to light. %We show that, depending on these parameters value, retinal ganglion cells can change their spatio-temporal response (e.g. from monophasic to biphasic). The validity of the model is confirmed by faithfully reproducing pharmacogenetic experimental results obtained by stimulating excitatory DREADDs (Designer Receptors Exclusively Activated by Designer Drugs) expressed on ganglion cells and amacrine cells subclasses, thereby modifying the inner retinal network activity to visual stimuli in a complex, entangled manner. Our mathematical model allows us to explore and decipher these complex effects in a manner that would not be feasible experimentally and provides novel insights in retinal dynamics.
Quasi-equilibrium states and phase transitions in biological evolution
Authors: Artem Romanenko, Vitaly Vanchurin
Date: 2024-01-09 00:00:00

We develop a macroscopic description of the evolutionary dynamics by following the temporal dynamics of the total Shannon entropy of sequences, denoted by $S$, and the average Hamming distance between them, denoted by $H$. We argue that a biological system can persist in the so-called quasi-equilibrium state for an extended period, characterized by strong correlations between $S$ and $H$, before undergoing a phase transition to another quasi-equilibrium state. To demonstrate the results, we conducted a statistical analysis of SARS-CoV-2 data from the United Kingdom during the period between March, 2020 and December, 2023. From a purely theoretical perspective, this allows us to systematically study various types of phase transitions described by a discontinuous change in the thermodynamic parameters. From a more practical point of view, the analysis can be used, for example, as an early warning system for pandemics.
Impact of maternal high fat on neurovascular unit of adult offspring
Authors: Cheryl A. Hawkes, Victoria Goss, Elina Zotova, Tual Monfort, Anthony Postle, Sumeet Mahajan, James A. R. Nicoll, Roy O. Weller, Roxana O. Carare
Date: 2024-01-09 00:00:00

Maternal obesity is associated with increased risk of diabetes, cardiovascular disease and hypertension in adult offspring. Midlife hypercholesterolemia and hypertension are risk factors for Alzheimer's disease, suggesting that the ageing brain may be impacted by early life environment. We found that exposure to a high fat diet during gestation and lactation induced changes in multiple components of the neurovascular unit, including a downregulation in apolipoprotein E and fibronectin, an upregulation in markers of astrocytes and perivascular macrophages and altered blood vessel morphology in the brains of adult mice. Feeding of high fat diet after weaning increased lipid droplets in the brain and influenced the fatty acid composition of phosphatidylcholine and phosphatidylethanolamine species, but did not affect the neurovascular unit. Sustained high fat diet over the entire lifespan resulted in additional decreases in levels of pericytes and collagen IV, changes in phospholipid composition and impaired perivascular clearance of Beta-amyloid (A-Beta) from the brain. In humans, vascular A-Beta load was significantly increased in the brains of aged individuals with a history of hypercholesterolemia. These results support a critical role for early dietary influence on the brain vasculature across the lifespan, with consequences for the development of age-related cerebrovascular and neurodegenerative diseases.
Proof of Efficient Liquidity: A Staking Mechanism for Capital Efficient Liquidity
Authors: Arman Abgaryan, Utkarsh Sharma, Joshua Tobkin
Date: 2024-01-09 00:00:00
Tasks: Diversity, POS

The Proof of Efficient Liquidity (PoEL) protocol, designed for specialised Proof of Stake (PoS) consensus-based blockchains that incorporate intrinsic DeFi applications, aims to support sustainable liquidity bootstrapping and network security. This concept seeks to efficiently utilise budgeted staking rewards to attract and sustain liquidity through a risk-structuring engine and incentive allocation strategy, both of which are designed to maximise capital efficiency. The proposed protocol serves the dual objective of: (i) capital creation by attracting risk capital efficiently and maximising its operational utility for intrinsic DeFi applications, thereby asserting sustainability; and (ii) enhancing the adopting blockchain network's economic security by augmenting their staking (PoS) mechanism with a harmonious layer seeking to attract a diversity of digital assets. Finally, the protocol's conceptual framework, as detailed in the appendix, is extended to encompass service fee credits. This extension capitalises on the network's auxiliary services to disperse incentives and attract liquidity, ensuring the network achieves and maintains the critical usage threshold essential for its sustained operational viability and progressive growth.