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.
Large speech dataset for commercial use with 9.98% word error rate.
Investigations have been performed into using clustering methods in data mining time-series data from smart meters. The problem is to identify patterns and trends in energy usage profiles of commercial and industrial customers over 24-hour periods, and group similar profiles. We tested our method on energy usage data p…
Study shows visual feedback and monetary incentives reduce plugload energy consumption in commercial buildings.
Commercial activity trackers are set to become an essential tool in health research, due to increasing availability in the general population. The corresponding vast amounts of mostly unlabeled data pose a challenge to statistical modeling approaches. To investigate the feasibility of deep learning approaches for unsup…
We consider a class of linear-programming based estimators in reconstructing a sparse signal from linear measurements. Specific formulations of the reconstruction problem considered here include Dantzig selector, basis pursuit (for the case in which the measurements contain no errors), and the fused Dantzig selector (f…
Analyzes Indian commercial dynamism using time series data.
Study examines factors influencing lending to SMEs by Kenyan banks.
Gradient boosted decision trees (GBDT) is the leading algorithm for many commercial and academic data applications. We give a deep analysis of this algorithm, especially the histogram technique, which is a basis for the regulized distribution with compact support. We present three new modifications. 1) Share memory tec…
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.
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.
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…
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.
The ubiquity of smartphone usage in many people's lives make it a rich source of information about a person's mental and cognitive state. In this work we analyze 12 weeks of phone usage data from 113 older adults, 31 with diagnosed cognitive impairment and 82 without. We develop structured models of users' smartphone i…
AI enhances bank credit risk management through deep learning and data analysis.
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.
Method learns software resource usage from snapshots.
This study analyzes how weather impacts bike sharing usage in Washington D.C.
Bayesian approach improves rain field reconstruction using CMLs and DMs.
Paper presents AETN for efficient user modeling from mobile app usage.
Study analyzes factors affecting capital adequacy in Bangladesh's banks.
DeFi exploits lead to reduced CP spreads, contrary to contagion hypothesis.
Most of the current game-theoretic demand-side management methods focus primarily on the scheduling of home appliances, and the related numerical experiments are analyzed under various scenarios to achieve the corresponding Nash-equilibrium (NE) and optimal results. However, not much work is conducted for academic or c…
Proposes AtCoR for predicting bike station usage, improving station network reconfiguration.
Study compares three performance metrics of Bangladeshi banks.
Developed a cost and revenue model for HEMS to estimate breakeven transport volumes under different reimbursement and labor cost assumptions.
This study uses LSTM and SARIMA models to forecast CPU usage in cloud computing.
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, …
Method predicts hardware resource usage by control software with guaranteed linear convergence.
A new model improves homogeneity in burn patient reimbursement.
The paper uses CPI growth rates to improve LGD predictions for CRE loans.
In this study, we focus on the market clearing problem of Turkish day-ahead electricity market. We propose a mathematical model by extending the variety of bid types for different price regions. The commercial solvers may not find any feasible solution for the proposed problem in some instances within the given time li…
Agents buy and sell services. All services are of equal quality. Buyers choose sellers at random. Monetary and fiscal policies are imposed by a central bank and a central government. Credit is supplied by a commercial banking system. Propensities to buy, sell, and lend depend on account balances, interest rates, tax ra…
CTS machines improve screen development in printing industries, reducing costs and increasing profitability.
This paper investigates how machine learning APIs change over time and proposes an efficient method to monitor these changes.
Can textual data be compressed intelligently without losing accuracy in evaluating sentiment? In this study, we propose a novel evolutionary compression algorithm, PARSEC (PARts-of-Speech for sEntiment Compression), which makes use of Parts-of-Speech tags to compress text in a way that sacrifices minimal classification…
Quantum computer optimizes investment portfolios, outperforming traditional methods.
Electric vehicles (EVs) have been gaining popularity due to their environmental friendliness and efficiency. EV charging station networks are scalable solutions for supporting increasing numbers of EVs within modern electric grid constraints, yet few tools exist to aid the physical configuration design of new networks.…
Predicting the click-through rate of an advertisement is a critical component of online advertising platforms. In sponsored search, the click-through rate estimates the probability that a displayed advertisement is clicked by a user after she submits a query to the search engine. Commercial search engines typically rel…
The carbon footprint of algorithms must be measured and transparently reported so computer scientists can take an honest and active role in environmental sustainability. In this paper, we take analyses usually applied at the industrial level and make them accessible for individual computer science researchers with an e…
Small LLMs outperform large ones on simple tasks without extra labelling costs.
Explainable machine learning (ML) enables human learning from ML, human appeal of automated model decisions, regulatory compliance, and security audits of ML models. Explainable ML (i.e. explainable artificial intelligence or XAI) has been implemented in numerous open source and commercial packages and explainable ML i…