Quantitative hierarchical representation and comparison of hand grasps from electromyography and kinematic data

Motivation: Modeling human grasping and hand movements is important for robotics, prosthetics and rehabilitation. Several qualitative taxonomies of hand grasps have been proposed in scientific literature. However it is not clear how well they correspond to subjects movements. Objective: In this work we quantitatively analyze the similarity between hand movements in 40 subjects using different features. Methods: Publicly available data from 40 healthy subjects were used for this study. The data include electromyography and kinematic data recorded while the subjects perform 20 hand grasps. The kinematic and myoelectric signal was windowed and several signal features were extracted. Then, for each subject, a set of hierarchical trees was computed for the hand grasps. The obtained results were compared in order to evaluate differences between features and different subjects. Results: The comparison of the signal feature taxonomies revealed a relation among the same subject. The comparison of the subject taxonomies highlighted also a similarity shared between subjects except for rare cases. Conclusions: The results suggest that quantitative hierarchical representations of hand movements can be performed with the proposed approach and the results from different subjects and features can be compared. The presented approach suggests a way to perform a systematic analysis of hand movements and to create a quantitative taxonomy of hand movements

Présentation: Détermination et prédication, Hommage à Naoyo Furukawa

Présentation des concepts complémentaires de prédication et détermination et des articles contenus dans le numéro à ce titre de "Langue Française" 171, septembre 2011

Visualizing and Interpreting Feature Reuse of Pretrained CNNs for Histopathology

Reusing the parameters of networks pretrained on large scale datasets of natural images, such as ImageNet, is a common technique in the medical imaging domain. The large variability of objects and classes is, however, drastically reduced in most medical applications where images are dominated by repetitive patterns with, at times, subtle differences between the classes. This paper takes the example of finetuning a pretrained convolutional network on a histopathology task. Because of the reduced visual variability in this application domain, the network mostly learns to detect textures and simple patterns. As a result, the complex structures that maximize the channel activations of deep layers in the pretrained network are not present after finetuning. The learned features seem to be used by the network to spot atypical nuclei in the images, as shown by class activation maps. Finally, texture measures appear discriminative after finetuning, as shown by accurate Regression Concept Vectors

Disentangling Neuron Representations with Concept Vectors

Mechanistic interpretability aims to understand how models store representations by breaking down neural networks into interpretable units. However, the occurrence of polysemantic neurons, or neurons that respond to multiple unrelated features, makes interpreting individual neurons challenging. This has led to the search for meaningful vectors, known as concept vectors, in activation space instead of individual neurons. The main contribution of this paper is a method to disentangle polysemantic neurons into concept vectors encapsulating distinct features. Our method can search for fine-grained concepts according to the user's desired level of concept separation. The analysis shows that polysemantic neurons can be disentangled into directions consisting of linear combinations of neurons. Our evaluations show that the concept vectors found encode coherent, human-understandable features

Learning Interpretable Microscopic Features of Tumor by Multi-task Adversarial CNNs Improves Generalization

Adopting Convolutional Neural Networks (CNNs) in the daily routine of primary diagnosis requires not only near-perfect precision, but also a sufficient degree of generalization to data acquisition shifts and transparency. Existing CNN models act as black boxes, not ensuring to the physicians that important diagnostic features are used by the model. Building on top of successfully existing techniques such as multi-task learning, domain adversarial training and concept-based interpretability, this paper addresses the challenge of introducing diagnostic factors in the training objectives. Here we show that our architecture, by learning end-to-end an uncertainty-based weighting combination of multi-task and adversarial losses, is encouraged to focus on pathology features such as density and pleomorphism of nuclei, e.g. variations in size and appearance, while discarding misleading features such as staining differences. Our results on breast lymph node tissue show significantly improved generalization in the detection of tumorous tissue, with best average AUC 0.89 (0.01) against the baseline AUC 0.86 (0.005). By applying the interpretability technique of linearly probing intermediate representations, we also demonstrate that interpretable pathology features such as nuclei density are learned by the proposed CNN architecture, confirming the increased transparency of this model. This result is a starting point towards building interpretable multi-task architectures that are robust to data heterogeneity. Our code is available at https://bit.ly/356yQ2u.Comment: 21 pages, 4 figure

The Arecibo L-band Feed Array Zone of Avoidance Survey I: Precursor Observations through the Inner and Outer Galaxy

The Arecibo L-band Feed Array (ALFA) is being used to conduct a low-Galactic latitude survey, to map the distribution of galaxies and large-scale structures behind the Milky Way through detection of galaxies' neutral hydrogen (HI) 21-cm emission. This Zone of Avoidance (ZOA) survey finds new HI galaxies which lie hidden behind the Milky Way, and also provides redshifts for partially-obscured galaxies known at other wavelengths. Before the commencement of the full survey, two low-latitude precursor regions were observed, totalling 138 square degrees, with 72 HI galaxies detected. Detections through the inner Galaxy generally have no cataloged counterparts in any other waveband, due to the heavy extinction and stellar confusion. Detections through the outer Galaxy are more likely to have 2MASS counterparts. We present the results of these precursor observations, including a catalog of the detected galaxies, with their HI parameters. The survey sensitivity is well described by a flux- and linewidth-dependent signal-to-noise ratio of 6.5. ALFA ZOA galaxies which also have HI measurements in the literature show good agreement between our measurements and previous work. The inner Galaxy precursor region was chosen to overlap the HI Parkes Zone of Avoidance Survey so ALFA performance could be quickly assessed. The outer Galaxy precursor region lies north of the Parkes sky. Low-latitude large-scale structure in this region is revealed, including an overdensity of galaxies near l = 183 deg and between 5000 - 6000 km/s in the ZOA. The full ALFA ZOA survey will be conducted in two phases: a shallow survey using the observing techniques of the precursor observations, and also a deep phase with much longer integration time, with thousands of galaxies predicted for the final catalog.Comment: 26 pages, 7 figures, 2 tables, Astronomical Journal accepte

Dynamic modelling of induced draft cooling towers with parallel heat exchangers, pumps and cooling water network

In the process industries, cooling capacity is an important enabler for the facility to manufacture on specification product. The cooling water network is an important part of the over-all cooling system of the facility. In this paper a cooling water circuit consisting of 3 cooling towers in parallel, 2 cooling water pumps in parallel, and 11 heat exchangers in parallel, is modelled. The model developed is based on first principles and captures the dynamic, non-linear nature of the plant. The modelled plant is further complicated by continuous, as well as Boolean process variables, giving the model a hybrid nature. Energy consumption is included in the model as it is a very important parameter for plant operation. The model is fitted to real industry data by using a particle swarm optimisation approach. The model is suitable to be used for optimisation and control purposes.The National Research Foundation of South Africa (Grant Number 90533).http://www.elsevier.com/locate/jprocont2019-08-01hj2018Electrical, Electronic and Computer Engineerin

Hybrid nonlinear model predictive control of a cooling water network

A Hybrid Nonlinear Model Predictive Control (HNMPC) strategy is developed for temperature control and power consumption minimisation of a cooling water network. The HNMPC uses a gradient descent optimisation algorithm for the continuous manipulated variables, and an enumerated tree traversal algorithm to control and optimise the Boolean manipulated variables. The HNMPC is subjected to disturbances similar to those experienced on a real plant, and its performance compared to a continuous Nonlinear Model Predictive Control (NMPC) and two base case scenarios. Power consumption is minimised, and process temperature disturbances are successfully rejected. Monetary benefits of the HNMPC control strategy are estimated.The National Research Foundation of South Africahttp://www.elsevier.com/locate/conengprac2021-04-01hj2020Electrical, Electronic and Computer Engineerin

Assistive technology for older persons – analyses of data from WHO's rapid assistive technology assessment

With a growing population of older persons globally, the need for mainstream assistive technology (AT) as well as assistive technology specifically intended for older persons is on the increase. The association between age and functional limitations strongly indicates a growing demand due the current demographic development. There was however until recently limited data that can describe the situation, monitor development and compare between countries and populations. Quality data is essential for developing regional, national and international responses to current and future need for AT globally. The Global Report on Assistive Technology (GReAT) was launched on 16th May 2022 and highlights both substantial gaps in provision of AT and AT related services globally and in particular in low- and middle-income countries. As part of the process leading up to the GReAT, World Health Organization and partners developed the "rapid Assistive Technology Assessment" (rATA) survey to enable data collection that for the first time can provide estimates of AT use and need in a global perspective. The purpose of this presentation is to present key indicators from rATA among older persons in the countries that participated in the global data collection.publishedVersio

Novel structural-scale uncertainty measures and error retention curves: application to multiple sclerosis

This paper focuses on the uncertainty estimation for white matter lesions (WML) segmentation in magnetic resonance imaging (MRI). On one side, voxel-scale segmentation errors cause the erroneous delineation of the lesions; on the other side, lesion-scale detection errors lead to wrong lesion counts. Both of these factors are clinically relevant for the assessment of multiple sclerosis patients. This work aims to compare the ability of different voxel- and lesion-scale uncertainty measures to capture errors related to segmentation and lesion detection, respectively. Our main contributions are (i) proposing new measures of lesion-scale uncertainty that do not utilise voxel-scale uncertainties; (ii) extending an error retention curves analysis framework for evaluation of lesion-scale uncertainty measures. Our results obtained on the multi-center testing set of 58 patients demonstrate that the proposed lesion-scale measure achieves the best performance among the analysed measures. All code implementations are provided at https://github.com/NataliiaMolch/MS_WML_uncsComment: 4 pages, 2 figures, 3 tables, ISBI preprin