arXiv research

Bayesian method predicts runtime metrics for fog manufacturing.

Smart mobility management would be an important prerequisite for future fog computing systems. In this research, we propose a learning-based handover optimization for the Internet of Vehicles that would assist the smooth transition of device connections and offloaded tasks between fog nodes. To accomplish this, we make…

Fog learning distributes ML model training across heterogeneous devices and networks.

Machine Learning (ML) algorithms, like Convolutional Neural Networks (CNN), Support Vector Machines (SVM), etc. have become widespread and can achieve high statistical performance. However their accuracy decreases significantly in energy-constrained mobile and embedded systems space, where all computations need to be c…

Task offloading is a promising technology to exploit the benefits of fog computing. An effective task offloading strategy is needed to utilize the computational resources efficiently. In this paper, we endeavor to seek an online task offloading strategy to minimize the long-term latency. In particular, we formulate a s…

The method constructs arbitrage-free option surfaces from noisy quotes using Chebyshev bases and a fog post-fit layer.

Task offloading is an emerging technology in fog-enabled networks. It allows users to transmit tasks to neighbor fog nodes so as to utilize the computing resources of the networks. In this paper, we investigate a stochastic task offloading model and propose a multi-armed bandit framework to formulate this model. We con…

In this paper, the distributed edge caching problem in fog radio access networks (F-RANs) is investigated. By considering the unknown spatio-temporal content popularity and user preference, a user request model based on hidden Markov process is proposed to characterize the fluctuant spatio-temporal traffic demands in F…

DefogGAN predicts hidden RTS game information to aid strategic decision-making.

A hybrid neural network optimizes AI deployment on edge and cloud for energy efficiency.

Freezing of gait (FoG) is a common gait disability in Parkinson's disease, that usually appears in its advanced stage. Freeze episodes are associated with falls, injuries, and psychological consequences, negatively affecting the patients' quality of life. For detecting FoG episodes automatically, a highly accurate dete…

Footfall based biometric system is perhaps the only person identification technique which does not hinder the natural movement of an individual. This is a clear edge over all other biometric systems which require a formidable amount of human intervention and encroach upon an individual's privacy to some extent or the o…

Small, carefully crafted perturbations called adversarial perturbations can easily fool neural networks. However, these perturbations are largely additive and not naturally found. We turn our attention to the field of Autonomous navigation wherein adverse weather conditions such as fog have a drastic effect on the pred…

This paper introduces early exits in neural networks for faster inference.

With the advent of huges volumes of data produced in the form of fast streams, real-time machine learning has become a challenge of relevance emerging in a plethora of real-world applications. Processing such fast streams often demands high memory and processing resources. In addition, they can be affected by non-stati…

Recurrent Neural Networks (RNNs) have been shown to be valuable for constructing Intrusion Detection Systems (IDSs) for network data. They allow determining if a flow is malicious or not already before it is over, making it possible to take action immediately. However, considering the large number of packets that has t…

In this paper, we prove an extended version of the Minkowski Inequality, holding for any smooth bounded set $Ω\subset \mathbb R^n$, $n\geq 3$. Our proof relies on the discovery of effective monotonicity formulas holding along the level set flow of the $p$-capacitary potentials associated with $Ω$, for every $p$ suffici…

Geometric quantization extended to arbitrary connected spaces using path integration.

The Internet of Things (IoT) extends the Internet connectivity into billions of IoT devices around the world, where the IoT devices collect and share information to reflect status of the physical world. The Autonomous Control System (ACS), on the other hand, performs control functions on the physical systems without ex…

New framework tackles deep financial reporting bottleneck by improving hallucination and coherence.

Optimizes QoS in FSO links over South Africa using ensemble learning.

Quantum computing speeds up multi-period asset allocation.

The paper analyzes the pricing of a new compute futures asset.

Quantum computing offers energy savings over classical computing.

The paper introduces reservoir computing models for complex systems.

Defines computable learning for binary classification over metric spaces.

Automatic computation speeds up crosscap number calculation for alternating knots.

TKFT models computation via smooth vector fields, simulating functions in a single dynamical step.

Most neural networks utilize the same amount of compute for every example independent of the inherent complexity of the input. Further, methods that adapt the amount of computation to the example focus on finding a fixed inference-time computational graph per example, ignoring any external computational budgets or vary…

Stochastic reservoir computing is shown to be a universal approximator.

Machine learning impacts computational math, offering new functions approximations.

Coded computation techniques provide robustness against straggling servers in distributed computing, with the following limitations: First, they increase decoding complexity. Second, they ignore computations carried out by straggling servers; and they are typically designed to recover the full gradient, and thus, canno…

Method for computing Khovanov homology of tangles.

With the rapid growth of the data volume and the fast increasing of the computational model complexity in the scenario of cloud computing, it becomes an important topic that how to handle users' requests by scheduling computational jobs and assigning the resources in data center. In order to have a better perception of…

This paper simplifies computing higher-order $U$-statistics efficiently.

Survey on computational models in dynamical systems, including new universality concepts.

Computations for prime knots up to 11 crossings.

Survey on quantum computing and neural networks.

New methods improve Reservoir Computing for chaotic time series prediction.

Quantum computing promises faster bioinformatics, but challenges remain.

We present two paradigms relating algebraic, topological and quantum computational statistics for the topological model for quantum computation. In particular we suggest correspondences between the computational power of topological quantum computers, computational complexity of link invariants and images of braid grou…

As inductive inference and machine learning methods in computer science see continued success, researchers are aiming to describe ever more complex probabilistic models and inference algorithms. It is natural to ask whether there is a universal computational procedure for probabilistic inference. We investigate the com…

Quantum computing promises new financial modeling.

We look into computational aspects of two classical knot invariants. We look for ways of simplifying the computation of the coloring invariant and of the Alexander module. We support our ideas with explicit computations on pretzel knots.

Study error bounds in evaluating distributional computational graphs.

Quantum computing techniques improve graph analysis and community detection.

New method speeds up knot computations in 3D.

PALMS reconstructs large-scale networks efficiently with parallel computing.