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.
AI model enhances grid monitoring with synchro-waveform tech.
We present a simple, fast, and accurate method for pricing a variety of discretely monitored options in the Black-Scholes framework, including autocallable structured products, single and double barrier options, and Bermudan options. The method is based on a quadrature technique, and it employs only elementary calculat…
There is an increasing need for monitoring and controlling uncertainties brought by distributed energy resources in distribution grids. For such goal, accurate multi-phase topology is the basis for correlating measurements in unbalanced distribution networks. Unfortunately, such topology knowledge is often unavailable …
The increasing penetration of distributed energy resources poses numerous reliability issues to the urban distribution grid. The topology estimation is a critical step to ensure the robustness of distribution grid operation. However, the bus connectivity and grid topology estimation are usually hard in distribution gri…
Distribution grid is the medium and low voltage part of a large power system. Structurally, the majority of distribution networks operate radially, such that energized lines form a collection of trees, i.e. forest, with a substation being at the root of any tree. The operational topology/forest may change from time to …
Automates detection of fast-ramped flexibility events for DSOs.
New method identifies distribution grid outages using smart meter data.
The growing integration of distributed energy resources (DERs) in urban distribution grids raises various reliability issues due to DER's uncertain and complex behaviors. With a large-scale DER penetration, traditional outage detection methods, which rely on customers making phone calls and smart meters' "last gasp" si…
In the monitoring of a complex electric grid, it is of paramount importance to provide operators with early warnings of anomalies detected on the network, along with a precise classification and diagnosis of the specific fault type. In this paper, we propose a novel multi-stage early warning system prototype for electr…
Contemporary power grids are being challenged by rapid voltage fluctuations that are caused by large-scale deployment of renewable generation, electric vehicles, and demand response programs. In this context, monitoring the grid's operating conditions in real time becomes increasingly critical. With the emergent large …
This paper presents a study on power grid disturbance classification by Deep Learning (DL). A real synchrophasor set composing of three different types of disturbance events from the Frequency Monitoring Network (FNET) is used. An image embedding technique called Gramian Angular Field is applied to transform each time …
New method detects anomalies in computing centers' logs.
Paper uses ML to predict insulator flashover risk.
Model predicts one-year NDVI for Four Corners region.
We present a grid diagram analogue of Carter, Rieger and Saito's smooth movie theorem. Specifically, we give definitions for grid movies, grid movie isotopies and present a definition of grid planar isotopy as a particular subset of the grid diagram moves: stabilization, destabilization and commutation. We show that gr…
We show how spectral filters can improve the convergence of numerical schemes which use discrete Hilbert transforms based on a sinc function expansion, and thus ultimately on the fast Fourier transform. This is relevant, for example, for the computation of fluctuation identities, which give the distribution of the maxi…
The paper introduces triple grid diagrams to construct Lagrangian surfaces in complex projective space.
Half grid diagrams prove every link can be represented by a special type of grid diagram.
Grid homology confirms the Upsilon invariant in knot theory.
GridPyM handles grid diagrams for knot theory.
Grid homology theory for spatial graphs extends skein sequence.
Extends knot invariant to filtered grid complexes.
New method finds grid diagrams for many fibered knots.
Grid homology properties for MOY graphs studied.
Effective utilization of photovoltaic (PV) plants requires weather variability robust global solar radiation (GSR) forecasting models. Random weather turbulence phenomena coupled with assumptions of clear sky model as suggested by Hottel pose significant challenges to parametric & non-parametric models in GSR conversio…
Grid homology invariant proved for lens space links.
New trading strategy beats traditional grid in crypto markets.
New method constructs moduli spaces of Lagrangian surfaces in CP^2 from grid diagrams.
Develops equivariant grid homology for strongly invertible knots.
AI systems that explain their decisions can be monitored for harmful intentions.
Grid homology shows knot unknotting lower bound.
Computes homology of an obstruction chain complex in grid homology.
SKI accelerates GP inference with sparse grids to handle higher dimensions.
Modern smart grids rely on advanced metering infrastructure (AMI) networks for monitoring and billing purposes. However, such an approach suffers from electricity theft cyberattacks. Different from the existing research that utilizes shallow, static, and customer-specific-based electricity theft detectors, this paper p…
The paper studies grid homology for spatial graphs and proves a Künneth formula for connected sums.
A scalable Gaussian process clustering method for large datasets.
Hexagon grid patterns emerge from conformal isometry in grid cell neural networks.
Minimal grid diagrams for 12-crossing prime knots identified.
Engine forecasts NO2, O3, PM2.5, PM10 with high accuracy.
It will be shown that according to theorems of K. Menger, every neuron grid if identified with a curve is able to preserve the adopted qualitative structure of a data space. Furthermore, if this identification is made, the neuron grid structure can always be mapped to a subset of a universal neuron grid which is constr…
The paper analyzes how grid cells perform path integration and learns hexagon grid patterns.
Power grids are one of the most important components of infrastructure in today's world. Every nation is dependent on the security and stability of its own power grid to provide electricity to the households and industries. A malfunction of even a small part of a power grid can cause loss of productivity, revenue and i…
The paper studies how grid cell patterns emerge in neural networks.
This paper proposes a multi-grid method for learning energy-based generative ConvNet models of images. For each grid, we learn an energy-based probabilistic model where the energy function is defined by a bottom-up convolutional neural network (ConvNet or CNN). Learning such a model requires generating synthesized exam…
Combinatorial proof of grid homology properties.
The topology of a power grid affects its dynamic operation and settlement in the electricity market. Real-time topology identification can enable faster control action following an emergency scenario like failure of a line. This article discusses a graphical model framework for topology estimation in bulk power grids (…
Simple online monitor detects unsafe LLM outputs.
Study finds cherry-picking load shaping strategies outperforms others in reducing grid CO2 emissions.