The paper proves ADL mechanisms face a trilemma and optimizes them for fairness, revenue, and exchange solvency.
problem The impossibility of a perpetual futures exchange achieving solvency, revenue, and fairness.
method Formal model of ADL, proving trilemma, and analyzing three ADL mechanisms.
result Optimized ADL mechanisms can reduce trader losses while maintaining exchange solvency.
This paper formalizes autodeleveraging as online learning, providing robustness results and algorithms for better performance.
problem Autodeleveraging as a mechanism to restore solvency in perpetual futures markets when liquidation and insurance buffers are insufficient.
method Formalizes autodeleveraging as online learning on a PNL-haircut domain, using an algorithm to recover solvency.
result The optimized algorithm achieves about 2.6% of an upper bound on regret, reducing overshoot to $3M.
New complexity measure ADL connects to classical complexity measures.
problem Deriving generalization bounds for neural networks.
method Exploring ADL's relationship to Covering Numbers and VC Dimension.
result ADL is equivalent to Covering Numbers and VC Dimension for real-valued functions.
The feasibility of deep neural networks (DNNs) to address data stream problems still requires intensive study because of the static and offline nature of conventional deep learning approaches. A deep continual learning algorithm, namely autonomous deep learning (ADL), is proposed in this paper. Unlike traditional deep …
Optimizes cryptocurrency exchanges' risk management by reducing positions based on leverage.
problem Managing risk in cryptocurrency futures exchanges during large price moves.
method Formulates ADL as an optimization problem to minimize risk of loss, using a water-filling rule to equalize leverage.
result The optimal ADL policy minimizes maximum leverage among participants, providing a transparent and implementable benchmark.
Pre-trained model from healthy ADLs improves gait pattern classification for Parkinson's disease.
problem Limited training data for deep learning models in healthcare applications.
method Used convolutional autoencoder to extract features from healthy ADLs data and trained a multi-layer perceptron model for Parkinson's disease classification.
result Features extracted from healthy ADLs data can be used to train an effective classification model for Parkinson's disease.
New sEMG dataset for ADL activities recognized with high accuracy.
problem Developing a robust dataset for sEMG-based ADL activity recognition.
method Acquired sEMG data from 25 subjects performing 22 ADL activities. Used 4 classifiers with various feature sets.
result SVM classifier achieved 83.21% accuracy on 5 FAABOS categories.
This paper introduces a novel online inference method for high-dimensional GLMs.
problem Real-time analysis of sequentially collected data in high-dimensional settings.
method Adaptive stochastic gradient descent with online debiasing for dynamic objective functions.
result Established the asymptotic normality of the Adaptive Debiased Lasso (ADL) estimator.
Inspired by the hierarchical hidden Markov models (HHMM), we present the hierarchical semi-Markov conditional random field (HSCRF), a generalisation of embedded undirectedMarkov chains tomodel complex hierarchical, nestedMarkov processes. It is parameterised in a discriminative framework and has polynomial time algorit…
This work tackles missing annotations in large sensor datasets.
problem Missing annotations in large sensor datasets negatively affect supervised learning system performance.
method Proposes and evaluates three paradigms to handle gaps: dropping, single label, and unique label, along with a hybrid combination.
result Evaluation of proposed paradigms and hybrid combination shows significant performance improvement.
The emergence of an ageing population is a significant public health concern. This has led to an increase in the number of people living with progressive neurodegenerative disorders like dementia. Consequently, the strain this is places on health and social care services means providing 24-hour monitoring is not sustai…
Study uses 1D-CNNs to forecast mortality in ELSA survey.
problem Forecasting mortality in the middle-aged and older population.
method 1D-CNNs applied to longitudinal data with various over/undersampling and activation functions.
result Swish nonlinearity outperforms other functions in forecasting mortality.
Adaptive Langevin dynamics reduces bias in Bayesian inference with mini-batching.
problem Bias in posterior sampling due to mini-batching in Bayesian inference.
method Adaptive Langevin dynamics with dynamical friction to correct noise.
result Quantified bias in posterior distribution due to mini-batching.
New indicator detects financial strain through smart meter data.
problem Fuel poverty in households, affecting millions.
method Smart meters and machine learning for behavior measurement.
result Early detection of financial strain in households.
The paper presents a method to reduce arm motion complexity for prosthetics and robotics.
problem Reducing the complexity of human arm motions for robotic and prosthetic control.
method Data-driven techniques including DTW, DBA, Ward's distance, batch-DTW, and fPCA.
result Representative motion clusters and averages for different arm DOF levels.
mmFall detects falls using mmWave radar and a hybrid VRAE, achieving high accuracy.
problem Detecting falls accurately and privately using mmWave radar.
method Uses mmWave radar for body point cloud and centroid, combined with a hybrid VRAE for anomaly detection.
result Achieves 98% fall detection with 2 false alarms out of 50 falls.