An Improvement to the Domain Adaptation Bound in a PAC-Bayesian context

This paper provides a theoretical analysis of domain adaptation based on the PAC-Bayesian theory. We propose an improvement of the previous domain adaptation bound obtained by Germain et al. in two ways. We first give another generalization bound tighter and easier to interpret. Moreover, we provide a new analysis of the constant term appearing in the bound that can be of high interest for developing new algorithmic solutions.Comment: NIPS 2014 Workshop on Transfer and Multi-task learning: Theory Meets Practice, Dec 2014, Montr{\'e}al, Canad

The Countable Character of Uncountable Graphs

We show that a graph can always be decomposed into edge-disjoint subgraphs of countable cardinality in which the edge-connectivities and edge-separations of the original graph are preserved up to countable cardinals. We also show that the vertex set of any graph can be endowed with a well-ordering which has a certain compactness property with respect to edge-separation

and

We introduce the notion of an edge-end and characterize those countable graphs which have edge-end-faithful spanning trees. We also prove that for a natural class of graphs, there always exists a tree which is faithful on the undominated ends and rayless over the dominated does. 1997 Academic Press 1

On cop-win graphs

Following a question of Anstee and Farber we investigate the possibility that all bridged graphs are cop-win. We show that in nite chordal graphs, even of diameter two, need not be cop-win and point to some interesting questions, some of which we answer

Fatigability of Lower Limb Muscles during Walking in Chronic Obstructive Pulmonary Disease

Background: Patients with chronic obstructive pulmonary disease (COPD) perceive much less quadriceps fatigue during walking compare to cycling. Whether other lower limb muscles could develop fatigue during walking is unknown. The purpose of this study was to assess the electrical activity of five lower limb muscles during a 6-minute walking test in 11 healthy subjects and in 10 patients with COPD matched for age and activity level. Methods: Surface electromyographic (EMG) data were recorded in five muscle groups (soleus, gastrocnemius (GM), tibialis anterior, vastus lateralis and rectus femoris) of the right leg during the walking test. The EMG median frequency of all contractions at minute 2 and 6 were averaged for each muscle group. Ventilation, oxygen consumption and CO2 production were also continuously measured throughout the test. Results: Although the walking distance (494 &#177; 116 vs. 625 &#177; 50 m; P < 0.01) and the walking speed (1.7 &#177; 0.4 vs. 2.1 &#177; 1.2 m&#183;s-1; P < 0.01) were reduced in COPD compared with controls, patients worked at a higher percentage of their estimated maximum voluntary ventilation during the test (118 &#177; 32 % vs. 51 &#177; 14 %; P < 0.01). The time course of the EMG median frequency from minute 2 to 6 differed between patients with COPD and healthy controls for the soleus, GM and tibialis anterior suggesting the occurrence of a muscle fatiguing profile in COPD. Conclusions: Evidences of a fatiguing profile was found in three lower limb muscle groups during walking in COPD despite a slower walking speed compared to healthy controls

Unsupervised Domain Adversarial Self-Calibration for Electromyography-Based Gesture Recognition

Surface electromyography (sEMG) provides an intuitive and non-invasive interface from which to control machines. However, preserving the myoelectric control system’s performance over multiple days is challenging, due to the transient nature of the signals obtained with this recording technique. In practice, if the system is to remain usable, a time-consuming and periodic recalibration is necessary. In the case where the sEMG interface is employed every few days, the user might need to do this recalibration before every use. Thus, severely limiting the practicality of such a control method. Consequently, this paper proposes tackling the especially challenging task of unsupervised adaptation of sEMG signals, when multiple days have elapsed between each recording, by introducing Self-Calibrating Asynchronous Domain Adversarial Neural Network (SCADANN). SCADANN is compared with two state-of-the-art self-calibrating algorithms developed specifically for deep learning within the context of EMG-based gesture recognition and three state-of-the-art domain adversarial algorithms. The comparison is made both on an offline and a dynamic dataset (20 participants per dataset), using two different deep network architectures with two different input modalities (temporal-spatial descriptors and spectrograms). Overall, SCADANN is shown to substantially and systematically improves classification performances over no recalibration and obtains the highest average accuracy for all tested cases across all methods

Deep Learning for Electromyographic Hand Gesture Signal Classification Using Transfer Learning

In recent years, deep learning algorithms have become increasingly more prominent for their unparalleled ability to automatically learn discriminant features from large amounts of data. However, within the field of electromyography-based gesture recognition, deep learning algorithms are seldom employed as they require an unreasonable amount of effort from a single person, to generate tens of thousands of examples. This paper's hypothesis is that general, informative features can be learned from the large amounts of data generated by aggregating the signals of multiple users, thus reducing the recording burden while enhancing gesture recognition. Consequently, this paper proposes applying transfer learning on aggregated data from multiple users while leveraging the capacity of deep learning algorithms to learn discriminant features from large datasets. Two datasets comprised 19 and 17 able-bodied participants, respectively (the first one is employed for pre-training), were recorded for this work, using the Myo armband. A third Myo armband dataset was taken from the NinaPro database and is comprised ten able-bodied participants. Three different deep learning networks employing three different modalities as input (raw EMG, spectrograms, and continuous wavelet transform (CWT)) are tested on the second and third dataset. The proposed transfer learning scheme is shown to systematically and significantly enhance the performance for all three networks on the two datasets, achieving an offline accuracy of 98.31% for 7 gestures over 17 participants for the CWT-based ConvNet and 68.98% for 18 gestures over 10 participants for the raw EMG-based ConvNet. Finally, a use-case study employing eight able-bodied participants suggests that real-time feedback allows users to adapt their muscle activation strategy which reduces the degradation in accuracy normally experienced over time