Giovanni Cabass, Digvijay Wadekar, Matias Zaldarriaga
et al.
Matched filtering for signal detection in noisy data requires template banks that capture variation in signal waveforms while minimizing computational cost. Dimensionality reduction of signal waveforms can be important for building efficient template banks. In various domains of physics, dimensionality reduction is very commonly performed using linear methods such as singular value decomposition (SVD). This can, however, introduce redundancies if the signals span curved, nonlinear manifolds in parameter space. Alternatively, autoencoders are a type of neural networks that can be used for non-linear dimensionality reduction. We use a variation of the autoencoder which preserves the metric in its latent space ($g_{ij}^{\text{latent}} \approx g_{ij}^{\text{physical}}$); this enables template banks to be constructed by simply placing a uniform grid in the autoencoder's low-dimensional latent space. We apply our method for creating geometric template banks for gravitational wave searches and show that our banks require fewer dimensions compared to using the SVD basis. Our method can also be useful for other applications requiring dimensionality reduction, such as gravitational waveform modeling, fast parameter estimation and model-independent tests of general relativity. Finally, we discuss extensions to other domains including cosmological parameter estimation, and we show tests of our method in extreme cases of periodic signal manifolds.
Anomaly detection, which aims to identify anomalies deviating from normal patterns, is challenging due to the limited amount of normal data available. Unlike most existing unified methods that rely on carefully designed image feature extractors and memory banks to capture logical relationships between objects, we introduce a text memory bank to enhance the detection of logical anomalies. Specifically, we propose a Three-Memory framework for Unified structural and logical Anomaly Detection (TMUAD). First, we build a class-level text memory bank for logical anomaly detection by the proposed logic-aware text extractor, which can capture rich logical descriptions of objects from input images. Second, we construct an object-level image memory bank that preserves complete object contours by extracting features from segmented objects. Third, we employ visual encoders to extract patch-level image features for constructing a patch-level memory bank for structural anomaly detection. These three complementary memory banks are used to retrieve and compare normal images that are most similar to the query image, compute anomaly scores at multiple levels, and fuse them into a final anomaly score. By unifying structural and logical anomaly detection through collaborative memory banks, TMUAD achieves state-of-the-art performance across seven publicly available datasets involving industrial and medical domains. The model and code are available at https://github.com/SIA-IDE/TMUAD.
In the last decades, during the unfolding of momentous events, we saw the growing awareness of the need for a transition to a sustainable economy. The radical economic and social change will require the mobilization of colossal resources, and hence the alignment of the financial system to this goal. Climate change also affects the functions of central banks. First of all, it can affect macroeconomic conditions, forcing a different approach to monetary policy and financial stability. Secondly, it affects the risks of banks and other financial intermediaries, thus financial supervision. Finally, the transition also affects the payment system. In this work, we will try to sum up the main topics concerning the links between central banks, the transition, and the possible objections to them. We conclude that an efficient transition requires that the financial markets business model become sustainable, a goal that becomes more difficult in the new geo-political situation.
The advancement of information technology has driven a significant transformation in the banking sector through digital banking, which has now become the backbone of modern financial services. Digital banking offers efficiency, ease of transactions, and reduced operational costs. Despite these benefits, challenges remain, particularly the high initial investment costs and the complexity of customer adoption. Without a well-designed user acceptance strategy, substantial investments risk being underutilized. Therefore, a deep understanding of the factors influencing digital banking adoption is crucial to ensure the effectiveness of digital transformation initiatives. Previous studies have examined the acceptance and use of digital banking using popular models such as TAM, UTAUT, and UTAUT2. However, fragmented findings—caused by variations in results and the inclusion of additional variables—pose challenges for generalization. This study aims to develop a more comprehensive model of digital banking acceptance through a systematic review and meta-analysis. The results indicate that most core constructs of UTAUT2—such as Performance Expectancy, Effort Expectancy, Facilitating Conditions, Social Influence, Habit, Price Value, and Hedonic Motivation—are significant. Furthermore, external variables such as Trust, Perceived Security, Enterprise Image, Promotions, and Perceived Risk also play a role, thereby extending the model beyond the generic framework. The proposed model is expected to enrich the development of technology acceptance theory by introducing a context-specific framework for digital banking. It also provides strategic guidance for the banking industry to enhance adoption through targeted interventions on the most influential variables. Consequently, this model can serve as a stronger foundation for both institutional practices and future research in the field.
NIOATA (CHIREAC) ROXANA-MIHAELA, BĂRBĂCIORU IULIANA CARMEN, FILIP ROBERT DORIN
The main objective of this paper is to analyze how the principles of behavioral psychology influence the
financial choices of consumers in the banking sector. In this study, we aim to explore and highlight the impact of
various cognitive biases, emotional affect and other psychological factors on financial decisions, particularly in the
context of consumers' interaction with financial institutions. Through this analysis, we will pursue a deeper
understanding of the decision-making process in relation to key banking products, bank loans, savings and investments,
and how these may be shaped by non-rational psychological factors. At the same time, the paper aims to address a
number of fundamental questions, such as "How will cognitive biases, the anchoring effect or risk aversion, influence
our and consumers' financial choices?" or "To what extent will the application of nudge techniques in the banking
sector influence or encourage financial choices to be more rational or beneficial for consumers?".
Commercial geography. Economic geography, Economics as a science
Huizheng Liu, Muhammad Afaq Haider Jafri, Shuo Xu
et al.
Abstract Central Bank Digital Currencies (CBDCs) have gained significant attention as potential innovations in the global financial landscape. The incorporation of artificial intelligence (AI) in the financial sector has brought transformative changes, particularly in the adoption and usage of CBDCs. Therefore, this study explores the impact of artificial intelligence (AI) on consumers’ intentions toward adopting CBDCs in the Chinese banking sector through digital technology awareness, addressing privacy concerns, and ease of use with the moderating role of government support. This research study examined the relationships of a sample of 420 employees in the Chinese banking sector. The current study uses the partial least squares structural equation modeling (PLS-SEM) method to assess these parameters. The findings show that artificial intelligence positively impacts consumers’ willingness to use CBDCs in the Chinese banking sector through digital technology awareness, addressing privacy concerns, and ease of use. Furthermore, government support significantly influences the link between AI, digital technology awareness, addressing privacy concerns, and ease of use. This study contributes to the literature on digital currency adoption by highlighting the critical role of AI in enhancing user experiences and trust in financial innovations. It also provides practical insights for policymakers and financial institutions to leverage AI technologies to strategically foster CBDCs adoption in China.
History of scholarship and learning. The humanities, Social Sciences
We study properties of the \textit{continuum seed-bank coalescent} proposed by \cite{jiao2023wright} such as \textit{not coming down from infinity} and \textit{bounds on the expected time to the most recent common ancestor}. We also provide results on the limiting distributions and propose some interesting open problems.
Olena Zarutska, Roman Pavlov, Tetiana Pavlova
et al.
This article examines the peculiarities of the management of assets and liabilities of Ukrainian banks in the conditions of significant structural transformations of the resource base during the period of martial law. The analysis is carried out at the level of homogeneous structural and functional groups of banks (SFGBs), which are formed using published reporting data and the application of Kohonen's self-organizing map (SOM). Accumulation of statistical data has been carried out for 5 years, special attention is paid to structural changes in the resource base and directions of placement of bank assets over the past two years.
Over the past two years, the bank has been under the influence of shock factors affecting assets and liabilities. At the beginning of 2022, there was an outflow of funds from bank accounts, which was gradually compensated by the inflow of current funds from corporations and the population of individuals. In 2023, the National Bank of Ukraine actively stimulated the development of the term resource base, the basis for ensuring the growth of credit operations. Transactions with state securities continue to grow in the structure of bank assets. The priority task of the banking system remains the financial support of business, but in the conditions of a full-scale war, such development of credit operations is limited. It is expedient to study the strategy of banks by combining the structure of assets and liabilities according to similar characteristics and analyzing the dynamics of groups. Observation of homogeneous groups confirms their stable nature, features of strategy, risk profile and development priorities. It has been proven that banks within homogeneous SFGBs demonstrate similar behaviour in the formation of management strategies and reactions to internal and external shocks. At the macro level, the SOM structure allows you to quantitatively assess the main processes taking place in the banking system, conduct comparisons with maps, and identify problems and priorities in the management of bank assets and liabilities. The SFGB method allows you to evaluate the trajectory of individual banks on the map and develop recommendations for improving the strategy of managing assets and liabilities.
In recent years the rise of Islamic banking has been one of the most important trends in the economic sphere, with an estimated 1.5 billion Muslims in the world, this arena has plenty of room for expansion. Conforming to Shariah (Islamic Law) puts a huge demand among Muslims looking for financial products and services that adhere to their beliefs. If it weren’t for the creation of such alter-natives to conventional banking and finance, Muslims would find it hard to participate in our globalized world without violating their religious principles. There are currently over 300 financial Institutions across the global sphere providing some type of Islamic financial product. According to some experts, the assets that are currently being managed under Shariah law, which range from investment to commercial banks and investment funds, are estimated to be no less than 300 billion. Other experts in the industry estimate the assets under mana-gement to be much larger. The FSA (Financial Services Authority), a regulator for financial services based out of London, estimates the total amount associated with Shariah banking to be as much as 500 billion. Even the U.S rating agency, S & P, estimates the sukuk (deed) market has reached over 75 billion and will likely be over 150 billion by the end of the decade. It used to be that Islamic fi-nancial products were more of a niche market but over time they are now considered mainstream, with many well-known interna-tional financial institutions battling to get a little piece of the pie.
Maria Clara Fittipaldi, Adrián González Casanova, Julio Ernesto Nava
We present a lookdown construction for a Moran seed-bank model with variable active and inactive population sizes and we show that the empirical measure of our model coincides with that of the Seed-Bank-Moran Model with latency of Greven, den Hollander and Oomen, 2022. Furthermore, we prove that the time to the most recent common ancestor, starting from $N$ individuals with stationary distribution over its state (active or inactive), has the same asymptotic order as the largest inactivity period. We then obtain an asymptotic distribution of the TMRCA, and use this result to find the first order of the asymptotic distribution of the fixation time of a single beneficial mutant conditioned to invade the whole population, which surprisingly is of order $\ln(N)$.
Credit risk is a crucial topic in the field of financial stability, especially at this time given the profound impact of the ongoing pandemic on the world economy. This study provides insight into the impact of credit risk on the financial performance of 26 commercial banks in Vietnam for the period from 2006 to 2016. The financial performance of commercial banks is measured by return on assets (ROA), return on equity (ROE), and Net interest margin (NIM); credit risk is measured by the Non-performing loan ratio (NPLR); control variables are measured by bank-specific characteristics, including bank size (SIZE), loan loss provision ratio (LLPR), and capital adequacy ratio (CAR), and macroeconomic factors such as annual gross domestic product (GDP) growth and annual inflation rate (INF). The assumption tests show that models have autocorrelation, non-constant variance, and endogeneity. Hence, a dynamic Difference Generalized Method of Moments (dynamic Difference GMM) approach is employed to thoroughly address these problems. The empirical results show that the financial performance of commercial banks measured by ROE and NIM persists from one year to the next. Furthermore, SIZE and NPLR variables have a significant negative effect on ROA and ROE but not on NIM. There is no evidence found in support of the LLPR and CAR variables on models. The effect of GDP growth is statistically significant and positive on ROA, ROE, and NIM, whereas the INF is only found to have a significant positive impact on ROA and NIM.
This study uses religiosity as a moderating variable to analyze the influence of attitudes, subjective norms, perceptions of behavioral control, and financial literacy on the intention to save for the pilgrimage. The research population is the Muslim community on the island of Java. The distribution of questionnaires resulted in 173 respondents by convenience sampling and 157 respondents through purposive sampling. The data analysis technique uses the structural equation model partial least squares (SEM-PLS) with Smart PLS 3.0 software tools. The study results show that attitudes, subjective norms, and financial literacy significantly affect intention, while the perception of behavioral control and religiosity have no effect on intention. Indirectly religiosity cannot strengthen the relationship between the independent variables on the intention to save for the pilgrimage. This research can be used as input in making decisions for Bank Syariah Indonesia (BSI), where factors that have a significant influence can be used as a reference in deciding to increase the intention to save a prospective customer's pilgrimage.
In recent years, Latin American banking sectors have experienced an accelerated process of concentration and foreign penetration that has prompted diverse views regarding its implications for the competitive behavior of banks and for the financial stability of the system as a whole. Exploiting a rich bank-level balance sheet database for eight Latin American countries, we examine the evolution of concentration and foreign penetration indicators and their impact on competition and risk. We find that, while concentration did not reduce competition in the industry, foreign penetration appears to have led to less competitive banking sectors. Moreover, we find banking sector fragility to be positively related to competition and, through this channel, negatively related to foreign participation, despite the fact that foreign banks in the region are associated with higher insolvency risk due to higher leverage ratios and more volatile returns.