115 research outputs found
Visual Gaze Estimation by Joint Head and Eye Information
International audienc
Bounded Influence Approaches to Constrained Mixed Vector Autoregressive Models
The proliferation of many clinical studies obtaining multiple biophysical signals from several individuals repeatedly in time is increasingly recognized, a recognition generating growth in statistical models that analyze cross-sectional time series data. In general, these statistical models try to answer two questions: (i) intra-individual dynamics of the response and its relation to some covariates; and, (ii) how this dynamics can be aggregated consistently in a group. In response to the first question, we propose a covariate-adjusted constrained Vector Autoregressive model, a technique similar to the STARMAX model (Stoffer, JASA 81, 762-772), to describe serial dependence of observations. In this way, the number of parameters to be estimated is kept minimal while offering flexibility for the model to explore higher order dependence. In response to (ii), we use mixed effects analysis that accommodates modelling of heterogeneity among cross-sections arising from covariate effects that vary from one cross-section to another. Although estimation of the model can proceed using standard maximum likelihood techniques, we believed it is advantageous to use bounded influence procedures in the modelling (such as choosing constraints) and parameter estimation so that the effects of outliers can be controlled. In particular, we use M-estimation with a redescending bounding function because its influence function is always bounded. Furthermore, assuming consistency, this influence function is useful to obtain the limiting distribution of the estimates. However, this distribution may not necessarily yield accurate inference in the presence of contamination as the actual asymptotic distribution might have wider tails. This led us to investigate bootstrap approximation techniques. A sampling scheme based on IID innovations is modified to accommodate the cross-sectional structure of the data. Then the M-estimation is applied to each bootstrap sample naively to obtain the asymptotic distribution of the estimates.We apply these strategies to the extracted BOLD activation from several regions of the brain from a group of individuals to describe joint dynamic behavior between these locations. We used simulated data with both innovation and additive outliers to test whether the estimation procedure is accurate despite contamination
TinyML: Tools, Applications, Challenges, and Future Research Directions
In recent years, Artificial Intelligence (AI) and Machine learning (ML) have
gained significant interest from both, industry and academia. Notably,
conventional ML techniques require enormous amounts of power to meet the
desired accuracy, which has limited their use mainly to high-capability devices
such as network nodes. However, with many advancements in technologies such as
the Internet of Things (IoT) and edge computing, it is desirable to incorporate
ML techniques into resource-constrained embedded devices for distributed and
ubiquitous intelligence. This has motivated the emergence of the TinyML
paradigm which is an embedded ML technique that enables ML applications on
multiple cheap, resource- and power-constrained devices. However, during this
transition towards appropriate implementation of the TinyML technology,
multiple challenges such as processing capacity optimization, improved
reliability, and maintenance of learning models' accuracy require timely
solutions. In this article, various avenues available for TinyML implementation
are reviewed. Firstly, a background of TinyML is provided, followed by detailed
discussions on various tools supporting TinyML. Then, state-of-art applications
of TinyML using advanced technologies are detailed. Lastly, various research
challenges and future directions are identified.Comment: 12 pags, 3 tables, 4 figure
Interpretable Motion Planner for Urban Driving via Hierarchical Imitation Learning
Learning-based approaches have achieved remarkable performance in the domain
of autonomous driving. Leveraging the impressive ability of neural networks and
large amounts of human driving data, complex patterns and rules of driving
behavior can be encoded as a model to benefit the autonomous driving system.
Besides, an increasing number of data-driven works have been studied in the
decision-making and motion planning module. However, the reliability and the
stability of the neural network is still full of uncertainty. In this paper, we
introduce a hierarchical planning architecture including a high-level
grid-based behavior planner and a low-level trajectory planner, which is highly
interpretable and controllable. As the high-level planner is responsible for
finding a consistent route, the low-level planner generates a feasible
trajectory. We evaluate our method both in closed-loop simulation and real
world driving, and demonstrate the neural network planner has outstanding
performance in complex urban autonomous driving scenarios.Comment: 6 pages, 8 figures, accepted by IROS202
Pupil Localisation and Eye Centre Estimation using Machine Learning and Computer Vision
Various methods have been used to estimate the pupil location within an image or a real-time video frame in many fields. However, these methods lack the performance specifically in low-resolution images and varying background conditions. We propose a coarse-to-fine pupil localisation method using a composite of machine learning and image processing algorithms. First, a pre-trained model is employed for the facial landmark identification to extract the desired eye-frames within the input image. We then use multi-stage convolution to find the optimal horizontal and vertical coordinates of the pupil within the identified eye-frames. For this purpose, we define an adaptive kernel to deal with the varying resolution and size of input images. Furthermore, a dynamic threshold is calculated recursively for reliable identification of the best-matched candidate. We evaluated our method using various statistical and standard metrics along-with a standardized distance metric we introduce first time in this study. Proposed method outperforms previous works in terms of accuracy and reliability when benchmarked on multiple standard datasets. The work has diverse artificial intelligence and industrial applications including human computer interfaces, emotion recognition, psychological profiling, healthcare and automated deception detection
The New Keynesian Microfoundations of Macroeconomics
New Keynesian Macroeconomics (NKM) obeys to the new dogma that macroeconomics should be firmly grounded in First Principles of micro theory. Households are assumed to run an intertemporal optimization calculus with respect to leisure and consumption by making use of perfect financial markets. The supply side is organized so that full employment prevails. Macroeconomic coordination problems between saving and investment are absent. In order to make model predictions more compatible with empirical facts, NKM chooses "ad hoc" microfoundations: utility functions and market structures are designed arbitrarily to allow for persistence of macro variables. NKM's reduced hybrid macro model, with lags and expectational leads, is a useful "work horse", compatible with various micro reasoning. However, NKM's insistence on the representative agent obstructs an understanding of heterogeneous beliefs and learning.Representative Agent, Ramsey Saving, Calvo Pricing, Sticky Information, Rational Expectations, Heterogeneous Beliefs
Datasheets for Machine Learning Sensors
Machine learning (ML) sensors offer a new paradigm for sensing that enables
intelligence at the edge while empowering end-users with greater control of
their data. As these ML sensors play a crucial role in the development of
intelligent devices, clear documentation of their specifications,
functionalities, and limitations is pivotal. This paper introduces a standard
datasheet template for ML sensors and discusses its essential components
including: the system's hardware, ML model and dataset attributes, end-to-end
performance metrics, and environmental impact. We provide an example datasheet
for our own ML sensor and discuss each section in detail. We highlight how
these datasheets can facilitate better understanding and utilization of sensor
data in ML applications, and we provide objective measures upon which system
performance can be evaluated and compared. Together, ML sensors and their
datasheets provide greater privacy, security, transparency, explainability,
auditability, and user-friendliness for ML-enabled embedded systems. We
conclude by emphasizing the need for standardization of datasheets across the
broader ML community to ensure the responsible and effective use of sensor
data
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