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.
Paper uses RL to optimize SFC deployment and VNF management in NFV networks.
We aim to predict and explain service failures in supply-chain networks, more precisely among last-mile pickup and delivery services to customers. We analyze a dataset of 500,000 services using (1) supervised classification with Random Forests, and (2) Association Rules. Our classifier reaches an average sensitivity of…
This paper uses robust optimization to analyze supply chain resilience.
Risk assessment is a major challenge for supply chain managers, as it potentially affects business factors such as service costs, supplier competition and customer expectations. The increasing interconnectivity between organisations has put into focus methods for supply chain cyber risk management. We introduce a gener…
The study provides bounds for geodesic diameter in Euclidean space.
Decentralised fund framework allocates capital via tokenised vaults.
Study analyzes climate-tech investments across 14 sectors.
Our work sheds new light on the role of oil prices in shaping the world economy by investigating flows of goods and services through global value chains between 1960 and 2011, by means of Markov Chain and network analysis. We show that over that time period the international division of labor and trade patterns are tig…
Modern Internet services, such as those at Google, Yahoo!, and Amazon, handle billions of requests per day on clusters of thousands of computers. Because these services operate under strict performance requirements, a statistical understanding of their performance is of great practical interest. Such services are model…
Orchestrating the Twin Transition in GBS: A Socio-Technical Framework
With the growing demand for data connectivity, network service providers are faced with the task of reducing their capital and operational expenses while simultaneously improving network performance and addressing the increased connectivity demand. Although Network Function Virtualization (NFV) has been identified as a…
Study examines cryptoasset service providers in Austria, revealing global integration and distinct responses to market shocks.
The paper bounds generalization errors for deep neural networks with Markov datasets.
The virtualization of compute and network resources enables an unseen flexibility for deploying network services. A wide spectrum of emerging technologies allows an ever-growing range of orchestration possibilities in cloud-based environments. But in this context it remains challenging to rhyme dynamic cloud configurat…
Game theory helps analyze ESOs/EBIs in production and service sectors.
Wide-AdGraph detects ads and trackers using a graph of resource requests.
We develop a complexity measure for large-scale economic systems based on Shannon's concept of entropy. By adopting Leontief's perspective of the production process as a circular flow, we formulate the process as a Markov chain. Then we derive a measure of economic complexity as the average number of bits required to e…
Study optimizes smart contract adoption under high demand variability using Negative Binomial models.
Graph learning categorizes DeFi services into similar functionalities.
This paper investigates a paradigm for offering artificial intelligence as a service (AI-aaS) on software-defined infrastructures (SDIs). The increasing complexity of networking and computing infrastructures is already driving the introduction of automation in networking and cloud computing management systems. Here we …
New method simulates sticky boundaries in multidimensional diffusions.
How does supply uncertainty affect the structure of supply chain networks? To answer this question we consider a setting where retailers and suppliers must establish a costly relationship with each other prior to engaging in trade. Suppliers, with uncertain yield, announce wholesale prices, while retailers must decide …
The paper assesses VASPs' solvency using multiple data sources.
Modeling trading volume curves using hierarchical Poisson processes.
AI enhances financial services but humans are irreplaceable for empathy, presence, and ethics.
In this paper we propose a novel approach for learning from data using rule based fuzzy inference systems where the model parameters are estimated using Bayesian inference and Markov Chain Monte Carlo (MCMC) techniques. We show the applicability of the method for regression and classification tasks using synthetic data…
The advance of modern sensor technologies enables collection of multi-stream longitudinal data where multiple signals from different units are collected in real-time. In this article, we present a non-parametric approach to predict the evolution of multi-stream longitudinal data for an in-service unit through borrowing…
Automated service classification plays a crucial role in service discovery, selection, and composition. Machine learning has been widely used for service classification in recent years. However, the performance of conventional machine learning methods highly depends on the quality of manual feature engineering. In this…
Deep learning model reduces food waste by stabilizing online food delivery supply chains.
We give a new proof of the Morse Homology Theorem by constructing a chain complex associated to a Morse-Bott-Smale function that reduces to the Morse-Smale-Witten chain complex when the function is Morse-Smale and to the chain complex of smooth singular -cube chains when the function is constant. We show that the ho…
OTT services are replacing traditional telecom services, affecting revenue streams.
Study on identifying AMP chain graph models under known and unknown component decompositions.
Economic complexity reflects the amount of knowledge that is embedded in the productive structure of an economy. By combining tools from network science and econometrics, a robust and stable relationship between a country's productive structure and its economic growth has been established. Here we report that not only …
The paper models blockchain queues and trading dynamics, finding conditions for transaction priority and price impact.
This papers presents a deep learning-based framework to predict crowdsourced service availability spatially and temporally. A novel two-stage prediction model is introduced based on historical spatio-temporal traces of mobile crowdsourced services. The prediction model first clusters mobile crowdsourced services into r…
This paper presents approaches to determine a network based pricing for 3D printing services in the context of a two-sided manufacturing-as-a-service marketplace. The intent is to provide cost analytics to enable service bureaus to better compete in the market by moving away from setting ad-hoc and subjective prices. A…
This paper tackles fair same-day delivery service by optimizing regional service rates.
DeepSIP predicts network failures' impact using CNN from syslog and traffic data.
Service system dynamics occur at the interplay between customer behaviour and a service provider's response. This kind of dynamics can effectively be modeled within the framework of queuing theory where customers' arrivals are described by point process models. However, these approaches are limited by parametric assump…
Voice-enabled interactions provide more human-like experiences in many popular IoT systems. Cloud-based speech analysis services extract useful information from voice input using speech recognition techniques. The voice signal is a rich resource that discloses several possible states of a speaker, such as emotional sta…
In his 2011 work, Maas has shown that the law of any time-reversible continuous-time Markov chain with finite state space evolves like a gradient flow of the relative entropy with respect to its stationary distribution. In this work we show the converse to the above by showing that if the relative law of a Markov chain…
AI classifies tourist events for better service.
The paper analyzes transaction fees on blockchains using a priority queue model.
New insights into how to inspect and learn from multi-stage processes and AI reasoning.
An online learning framework optimizes pricing and capacity in service systems.
This work analyzes how users and services adapt to reduce risk, leading to specialization.
Study analyzes smart contract adoption under bounded risk, showing stable adoption but fragile financial outcomes.
Compound examines decentralized lending users and their short loan durations.