arXiv research

Research presents a dataset and algorithm for optimizing bus timetables in New Delhi.

Accurate expected time of arrival (ETA) information is crucial in maintaining the quality of service of public transit. Recent advances in artificial intelligence (AI) has led to more effective models for ETA estimation that rely heavily on a large GPS datasets. More importantly, these are mainly cabs based datasets wh…

Accurate and reliable travel time predictions in public transport networks are essential for delivering an attractive service that is able to compete with other modes of transport in urban areas. The traditional application of this information, where arrival and departure predictions are displayed on digital boards, is…

This paper evaluates various bus arrival time prediction models.

BusTr predicts bus travel times from real-time traffic forecasts.

For power grid operations, a large body of research focuses on using generation redispatching, load shedding or demand side management flexibilities. However, a less costly and potentially more flexible option would be grid topology reconfiguration, as already partially exploited by Coreso (European RSC) and RTE (Frenc…

Two neural network models analyze bus system efficiency and demand.

This study predicts parking availability using multi-source data and a self-supervised learning enhanced transformer.

The wave equation $u_{tt} = c^2 u_{xx}$ is generally regarded as a linear approximation to the equation describing the amplitude of a transversely vibrating elastic string in the plane. But, as is shown in \cite{BC96}, the assumption of transverse vibration in fact implies that the wave equation describes the vibration…

Two novel models predict bus travel times with uncertainty, improving connection assurance.

Thompson Sampling with bilateral uncertainty improves performance in Bayesian Optimization.

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…

Artificial neural network (ANN) provides superior accuracy for nonlinear alternating current (AC) state estimation (SE) in smart grid over traditional methods. However, research has discovered that ANN could be easily fooled by adversarial examples. In this paper, we initiate a new study of adversarial false data injec…

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 …

DDSTN improves breast cancer diagnosis by leveraging imbalanced ultrasound modalities.

Given a complete, smooth metric measure space $(M,g,e^{-f}dv)$ with the Bakry-Émery Ricci curvature bounded from below, various gradient estimates for solutions of the following general $f$-heat equations $u_t=Δ_f u+au\log u+bu +Au^p+Bu^{-q}$ and \[ u_t=Δ_f u+Ae^{pu}+Be^{-pu}+D \] are studied. As by-product, we obt…

In this work we investigate approaches to reconstruct generator models from measurements available at the generator terminal bus using machine learning (ML) techniques. The goal is to develop an emulator which is trained online and is capable of fast predictive computations. The training is illustrated on synthetic dat…

Proposes BU-SPO method to improve text classification robustness.

Optimizes bus schedules to improve on-time performance.

Capsule networks improve performance on image classification tasks with fewer parameters.

Route Choice Models predict the route choices of travelers traversing an urban area. Most of the route choice models link route characteristics of alternative routes to those chosen by the drivers. The models play an important role in prediction of traffic levels on different routes and thus assist in development of ef…

Diverse fault types, fast re-closures, and complicated transient states after a fault event make real-time fault location in power grids challenging. Existing localization techniques in this area rely on simplistic assumptions, such as static loads, or require much higher sampling rates or total measurement availabilit…

JAMPR learns to solve complex VRP with time windows.

A large GPS dataset reveals that a single route often covers 60% of travel observations.

Global routing has been a historically challenging problem in electronic circuit design, where the challenge is to connect a large and arbitrary number of circuit components with wires without violating the design rules for the printed circuit boards or integrated circuits. Similar routing problems also exist in the de…

We propose and systematically evaluate three strategies for training dynamically-routed artificial neural networks: graphs of learned transformations through which different input signals may take different paths. Though some approaches have advantages over others, the resulting networks are often qualitatively similar…

A new router uses attention-based reinforcement learning to solve detailed routing problems efficiently.

The DEBS Grand Challenge 2018 is set in the context of maritime route prediction. Vessel routes are modeled as streams of Automatic Identification System (AIS) data points selected from real-world tracking data. The challenge requires to correctly estimate the destination ports and arrival times of vessel trips, as ear…

Unified routing and arbitrage with concave continuation.

A new approach integrates inventory prediction and routing optimization for better supply chain management.

The increasing complexity of the power grid, due to higher penetration of distributed resources and the growing availability of interconnected, distributed metering devices re- quires novel tools for providing a unified and consistent view of the system. A computational framework for power systems data fusion, based on…

Sparse routing networks with co-training prevent catastrophic forgetting in continual learning.

Neural LNS improves vehicle routing performance.

Polestar optimizes public transportation routes for efficiency and user satisfaction.

A recently proposed method in deep learning groups multiple neurons to capsules such that each capsule represents an object or part of an object. Routing algorithms route the output of capsules from lower-level layers to upper-level layers. In this paper, we prove that state-of-the-art routing procedures decrease the e…

Capsules are the multidimensional analogue to scalar neurons in neural networks, and because they are multidimensional, much more complex routing schemes can be used to pass information forward through the network than what can be used in traditional neural networks. This work treats capsules as collections of neurons …

We compute the rings $H^*(N;\mathbb{F}_2)$ for $N$ a closed $\mathbb{S}ol^3$-manifold and then determine the Borsuk-Ulam indices $BU(N,φ)$ with $φ\not=0$ in $H^1(N;\mathbb{F}_2)$.

Capsule networks improve at detecting changes in compositionality with routing.

This study develops methods to coordinate travel routes to reduce congestion.

Optimizes query routing to LLMs under cost and resource constraints.

New operations defined on moduli spaces for bundles with orientations.

The paper provides gradient estimates for a specific equation on Riemannian manifolds.

Study finds average 2.02 bps loss in automated market maker routing.

A new method uses Gaussian Processes to solve power flow problems with uncertain renewable and load inputs.

This paper presents a spatiotemporal unsupervised feature learning method for cause identification of electromagnetic transient events (EMTE) in power grids. The proposed method is formulated based on the availability of time-synchronized high-frequency measurement, and using the convolutional neural network (CNN) as t…

CARROT optimizes LLM routing by choosing the cheapest and most accurate model.

Enhanced route planning with probabilistic prediction and uncertainty sets.

New machine learning pipeline solves dynamic vehicle routing problems efficiently.