A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Bayesian approach improves rain field reconstruction using CMLs and DMs.
Framework predicts and prepares for rain-induced microwave link attenuation.
CNNs improve InSAR image denoising and coherence estimation.
Study on spin random fields using chaos decomposition for cosmic microwave background modeling.
Microwave-based breast cancer detection has been proposed as a complementary approach to compensate for some drawbacks of existing breast cancer detection techniques. Among the existing microwave breast cancer detection methods, machine learning-type algorithms have recently become more popular. These focus on detectin…
CNNs improve InSAR coherence classification.
Traditional vision-based hand gesture recognition systems is limited under dark circumstances. In this paper, we build a hand gesture recognition system based on microwave transceiver and deep learning algorithm. A Doppler radar sensor with dual receiving channels at 5.8GHz is used to acquire a big database of hand ges…
CosmoVAE uses deep learning to fill in missing parts of the cosmic microwave background map.
We consider how microlocal methods developed for tomographic problems can be used to detect singularities of the Lorentzian metric of the Universe using measurements of the Cosmic Microwave Background radiation. The physical model we study is mathematically rigorous but highly idealized.
Study of cosmic microwave background polarization using spin random fields.
The underlying stochastic nature of the requirements for the Solvency II regulations has introduced significant challenges if the required calculations are to be performed correctly, without resorting to excessive approximations, within practical timescales. It is generally acknowledged by practising actuaries within U…
Analyzes Indian commercial dynamism using time series data.
Study examines factors influencing lending to SMEs by Kenyan banks.
The paper characterizes the geometry and topology of spin random fields.
ResUNet-CMB neural network reconstructs CMB effects from noisy data.
Study uses neural networks to predict credit risk in banks.
Aiming at quantifying and evaluating the regional commercial environment along with the level of economic development among cities in mainland China, the concept of China City Commercial Environment Credit Index(CEI) was first introduced and established in 2010. In this manuscript, a historical review and detailed intr…
The study compares profitability of conventional and Islamic banks in Bangladesh.
GDiff tackles blind denoising with Gibbs sampling and Monte Carlo inference.
Inside the EU, the commercial integration of the CEE countries has gained remarkable momentum before the crisis appearance, but it has slightly slowed down afterwards. Consequently, the interest in identifying the factors supporting the commercial integration process is high. Recent findings in the new trade theory sug…
This paper develops a machine learning model to assess credit risk in UAE commercial banks.
Article offers models for choosing sale-leaseback vs debt.
Large speech dataset for commercial use with 9.98% word error rate.
This paper argues that there has not been enough discussion in the field of applications of Gaussian Process for the fast moving consumer goods industry. Yet, this technique can be important as it e.g., can provide automatic feature relevance determination and the posterior mean can unlock insights on the data. Signifi…
Study shows visual feedback and monetary incentives reduce plugload energy consumption in commercial buildings.
Understanding urban growth is one with understanding how society evolves to satisfy the needs of its individuals in sharing a common space and adapting to the territory. We propose here a quantitative analysis of the historical development of a large urban area by investigating the spatial distribution and the age of c…
Study examines credit risk's impact on Vietnamese banks' financial performance.
AI enhances bank credit risk management through deep learning and data analysis.
Entity linking is the task of mapping potentially ambiguous terms in text to their constituent entities in a knowledge base like Wikipedia. This is useful for organizing content, extracting structured data from textual documents, and in machine learning relevance applications like semantic search, knowledge graph const…
FinTech negatively impacts Chinese banks' financial sustainability.
As economic entities become increasingly interconnected, a shock in a financial network can provoke significant cascading failures throughout the system. To study the systemic risk of financial systems, we create a bi-partite banking network model composed of banks and bank assets and propose a cascading failure model …
New algorithm extracts device profiles for short-term power predictions in commercial buildings.
Next-generation cosmic microwave background (CMB) experiments will have lower noise and therefore increased sensitivity, enabling improved constraints on fundamental physics parameters such as the sum of neutrino masses and the tensor-to-scalar ratio r. Achieving competitive constraints on these parameters requires hig…
Improves node classification in graphs with active learning.
Study analyzes factors affecting capital adequacy in Bangladesh's banks.
Optimizes QoS in FSO links over South Africa using ensemble learning.
DeFi exploits lead to reduced CP spreads, contrary to contagion hypothesis.
In recent years, a rapidly growing literature has focussed on the construction of wavelet systems to analyze functions defined on the sphere. Our purpose in this paper is to generalize these constructions to situations where sections of line bundles, rather than ordinary scalar-valued functions, are considered. In part…
Recent studies incorporate Nesterov's accelerated gradient method for the acceleration of gradient based training. The Nesterov's Accelerated Quasi-Newton (NAQ) method has shown to drastically improve the convergence speed compared to the conventional quasi-Newton method. This paper implements NAQ for non-convex optimi…
Study compares three performance metrics of Bangladeshi banks.
We carry out the harmonic analysis on four Platonic spherical three-manifolds with different topologies. Starting out from the homotopies (Everitt 2004), we convert them into deck operations, acting on the simply connected three-sphere as the cover, and obtain the corresponding variety of deck groups. For each topology…
Using the new data from the OECD-WTO world network of economic activities we construct the Google matrix of this directed network and perform its detailed analysis. The network contains 58 countries and 37 activity sectors for years 1995 and 2008. The construction of , based on Markov chain transitions, treats a…
Developed a cost and revenue model for HEMS to estimate breakeven transport volumes under different reimbursement and labor cost assumptions.
We describe a method for selecting relevant new training data for the LSTM-based domain selection component of our personal assistant system. Adding more annotated training data for any ML system typically improves accuracy, but only if it provides examples not already adequately covered in the existing data. However, …
Mobile payment incentives optimized using merchant transaction networks.
High frequency trading has led to widespread efforts to reduce information propagation delays between physically distant exchanges. Using relativistically correct millisecond-resolution tick data, we document a 3-millisecond decrease in one-way communication time between the Chicago and New York areas that has occurred…
Developed Taylor series for muscle-finger system analysis.
The paper uses CPI growth rates to improve LGD predictions for CRE loans.