The method constructs arbitrage-free option surfaces from noisy quotes using Chebyshev bases and a fog post-fit layer.
problem Constructing arbitrage-free option price surfaces from noisy bid-ask quotes.
method Chebyshev tensor bases, linear sampling, no-arbitrage operators, quadratic objective, OSQP solvers, fog post-fit layer, Hamiltonian energy.
result High inside-spread coverage (98-99%) and low no-arbitrage violations (below 1%) in stable periods, controlled leakage in stressed periods.
Bayesian method predicts runtime metrics for fog manufacturing.
problem Accurate prediction of runtime performance metrics in fog manufacturing.
method Bayesian sparse regression for multivariate mixed responses.
result Enhanced prediction and statistical inferences of runtime metrics.
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.
problem Challenges with conventional federated learning in heterogeneous networks.
method Intelligent distribution of ML model training across nodes from edge devices to cloud servers.
result Enhanced federated learning with multi-layer hybrid framework considering network, heterogeneity, and proximity.
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…
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…
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…
We propose DefogGAN, a generative approach to the problem of inferring state information hidden in the fog of war for real-time strategy (RTS) games. Given a partially observed state, DefogGAN generates defogged images of a game as predictive information. Such information can lead to create a strategic agent for the ga…
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…
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…
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…
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…
A hybrid neural network optimizes AI deployment on edge and cloud for energy efficiency.
problem Energy and resource constraints in edge devices for deep learning models.
method Conditionally deep hybrid neural network with quantized layers at edge and full-precision layers at cloud.
result Early classification at the edge reduces energy consumption by 5.5x on CIFAR-10 dataset.
In this paper, we prove an extended version of the Minkowski Inequality, holding for any smooth bounded set Ω⊂Rn, n≥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…
This paper introduces early exits in neural networks for faster inference.
problem Reducing inference time and preventing overfitting in neural networks.
method Designing and training multi-output neural networks with early exits.
result Significant reductions in inference time and improved robustness.
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…
Geometric quantization extended to arbitrary connected spaces using path integration.
problem Constructing a Prequantum Groupoid for arbitrary connected parasymplectic spaces.
method Define a Total Group of Periods and a Prequantum Groupoid with connected isotropy.
result The Prequantum Groupoid Tω is isomorphic to the group of symmetries of the Dynamical System. 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…
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…
New framework tackles deep financial reporting bottleneck by improving hallucination and coherence.
problem Statistical smoothing trap in LLMs limits deep financial reporting quality.
method DeepNews Framework integrates information foraging, schema-guided planning, and adversarial prompting.
result DeepNews system achieves 25% acceptance rate in blind test, significantly outperforming SOTA.
Optimizes QoS in FSO links over South Africa using ensemble learning.
problem Impact of weather on QoS in FSO links.
method Ensemble learning models (Random Forest, ADaBoost Regression, Stacking Regression, Gradient Boost Regression, Multilayer Neural Network) applied to meteorological data.
result Significant enhancement in QoS with RMSE and R-squared values of 0.0032 and 0.9906 respectively at George.