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 framework predicts post-disruption travel times in metro networks.
Modeling supply chain disruptions from climate hazards with adaptive firms.
Poor economies not only produce less; they typically produce things that involve fewer inputs and fewer intermediate steps. Yet the supply chains of poor countries face more frequent disruptions---delivery failures, faulty parts, delays, power outages, theft, government failures---that systematically thwart the product…
Study disrupts Sicilian Mafia networks using data analysis.
Predicts disruptions in ADITYA tokamak 12 ms in advance.
The multi-scale, mutli-physics nature of fusion plasmas makes predicting plasma events challenging. Recent advances in deep convolutional neural network architectures (CNN) utilizing dilated convolutions enable accurate predictions on sequences which have long-range, multi-scale characteristics, such as the time-series…
We consider problems in which a system receives external \emph{perturbations} from time to time. For instance, the system can be a train network in which particular lines are repeatedly disrupted without warning, having an effect on passenger behavior. The goal is to predict changes in the behavior of the system at par…
Model assesses how supply chain disruptions affect financial stability.
This study analyzes how cryptocurrency networks adapt to financial disruptions.
Adaptive rerouting reshapes impacts of maritime chokepoint disruptions
Study forecasts supply chain disruptions in automotive industry.
Study automates detection of visitation disruptions in ICU patients.
This paper uses robust optimization to analyze supply chain resilience.
Study shows how firms adapt to systemic risk during crises, revealing key players and trade volume predictors.
Despite the robust structure of the Internet, it is still susceptible to disruptive routing updates that prevent network traffic from reaching its destination. Our research shows that BGP announcements that are associated with disruptive updates tend to occur in groups of relatively high frequency, followed by periods …
QTIP improves traffic prediction in sudden disruptions.
A Global Navigation Satellite System (GNSS) uses a constellation of satellites around the earth for accurate navigation, timing, and positioning. Natural phenomena like space weather introduce irregularities in the Earth's ionosphere, disrupting the propagation of the radio signals that GNSS relies upon. Such disruptio…
Training a neural network model can be a lifelong learning process and is a computationally intensive one. A severe adverse effect that may occur in deep neural network models is that they can suffer from catastrophic forgetting during retraining on new data. To avoid such disruptions in the continuous learning, one ap…
Hybrid quantum neural networks predict continuous variables.
Supply chains are the backbone of the global economy. Disruptions to them can be costly. Centrally managed supply chains invest in ensuring their resilience. Decentralized supply chains, however, must rely upon the self-interest of their individual components to maintain the resilience of the entire chain. We examine t…
This paper studies the interrelation between spot and futures prices in the two major rice markets in prewar Japan from the perspective of market efficiency. Applying a non-Bayesian time-varying model approach to the fundamental equation for spot returns and the futures premium, we detect when efficiency reductions in …
Blockchain disrupts corporate finance, but challenges remain.
In deep reinforcement learning (RL), adversarial attacks can trick an agent into unwanted states and disrupt training. We propose a system called Robust Student-DQN (RS-DQN), which permits online robustness training alongside Q networks, while preserving competitive performance. We show that RS-DQN can be combined with…
Paper presents a method to disrupt deep uncertainty estimation without affecting accuracy.
This thesis focuses on gaining linguistic insights into textual discussions on a word level. It was of special interest to distinguish messages that constructively contribute to a discussion from those that are detrimental to them. Thereby, we wanted to determine whether "I"- and "You"-messages are indicators for eithe…
We define the intrinsic scale at which a network begins to reveal its identity as the scale at which subgraphs in the network (created by a random walk) are distinguishable from similar sized subgraphs in a perturbed copy of the network. We conduct an extensive study of intrinsic scale for several networks, ranging fro…
Study forecasts monthly electricity demand using pattern similarity-based methods.
This paper develops a stochastic learning-optimization model for resilient automotive supply chains.
Plasma tomography consists in reconstructing the 2D radiation profile in a poloidal cross-section of a fusion device, based on line-integrated measurements along several lines of sight. The reconstruction process is computationally intensive and, in practice, only a few reconstructions are usually computed per pulse. I…
Researchers disrupt Gaussian model inference to test adversarial attacks.
Study adapts OHLC volatility estimators for monitoring market stress in diverse settings.
Marine buoys aid in the battle against Illegal, Unreported and Unregulated (IUU) fishing by detecting fishing vessels in their vicinity. Marine buoys, however, may be disrupted by natural causes and buoy vandalism. In this paper, we formulate marine buoy placement as a clustering problem, and propose dropout k-means an…
FinTech framework clusters innovations for financial services.
To survive in the dynamically-evolving world, we accumulate knowledge and improve our skills based on experience. In the process, gaining new knowledge does not disrupt our vigilance to external stimuli. In other words, our learning process is 'accumulative' and 'online' without interruption. However, despite the recen…
This paper investigates bias in resampled backtests for financial portfolios, finding it often negligible.
Study analyzes Airbnb booking lead times during global crises using a new metric.
Study uncovers tactical line-breaking passes in football using clustering.
Social media sites are becoming a key factor in politics. These platforms are easy to manipulate for the purpose of distorting information space to confuse and distract voters. Past works to identify disruptive patterns are mostly focused on analyzing the content of tweets. In this study, we jointly embed the informati…
Achieving international food security requires improved understanding of how international trade networks connect countries around the world through the import-export flows of food commodities. The properties of food trade networks are still poorly documented, especially from a multi-network perspective. In particular,…
FinTech negatively impacts Chinese banks' financial sustainability.
Study finds Aave token network has core-periphery structure, with high decentralization predicting better returns.
The global financial system has become highly connected and complex. Has been proven in practice that existing models, measures and reports of financial risk fail to capture some important systemic dimensions. Only lately, advisory boards have been established in high level and regulations are directly targeted to syst…
VEGN uses graph neural networks to predict disease-causing mutations from genetic variants.
Training deep recurrent neural network (RNN) architectures is complicated due to the increased network complexity. This disrupts the learning of higher order abstracts using deep RNN. In case of feed-forward networks training deep structures is simple and faster while learning long-term temporal information is not poss…
AI learns to design chemical processes efficiently.
This paper shows RBMs can maintain strong performance even after extreme pruning, but only if done early in training.
Non-recurring traffic congestion is caused by temporary disruptions, such as accidents, sports games, adverse weather, etc. We use data related to real-time traffic speed, jam factors (a traffic congestion indicator), and events collected over a year from Nashville, TN to train a multi-layered deep neural network. The …
New neural network captures spatial correlations in wind speed predictions.