Bioformers: Embedding Transformers for Ultra-Low Power sEMG-based Gesture Recognition

Human-machine interaction is gaining traction in rehabilitation tasks, such as controlling prosthetic hands or robotic arms. Gesture recognition exploiting surface electromyographic (sEMG) signals is one of the most promising approaches, given that sEMG signal acquisition is non-invasive and is directly related to muscle contraction. However, the analysis of these signals still presents many challenges since similar gestures result in similar muscle contractions. Thus the resulting signal shapes are almost identical, leading to low classification accuracy. To tackle this challenge, complex neural networks are employed, which require large memory footprints, consume relatively high energy and limit the maximum battery life of devices used for classification. This work addresses this problem with the introduction of the Bioformers. This new family of ultra-small attention-based architectures approaches state-of-the-art performance while reducing the number of parameters and operations of 4.9x. Additionally, by introducing a new inter-subjects pre-training, we improve the accuracy of our best Bioformer by 3.39%, matching state-of-the-art accuracy without any additional inference cost.Deploying our best performing Bioformer on a Parallel, Ultra-Low Power (PULP) microcontroller unit (MCU), the GreenWaves GAP8, we achieve an inference latency and energy of 2.72 ms and 0.14 mJ, respectively, 8.0x lower than the previous state-of-the-art neural network, while occupying just 94.2 kB of memory

Chiral corrections to the SU(2)×SU(2)SU(2)\times SU(2) Gell-Mann-Oakes-Renner relation

The next to leading order chiral corrections to the $SU(2)\times SU(2)$ Gell-Mann-Oakes-Renner (GMOR) relation are obtained using the pseudoscalar correlator to five-loop order in perturbative QCD, together with new finite energy sum rules (FESR) incorporating polynomial, Legendre type, integration kernels. The purpose of these kernels is to suppress hadronic contributions in the region where they are least known. This reduces considerably the systematic uncertainties arising from the lack of direct experimental information on the hadronic resonance spectral function. Three different methods are used to compute the FESR contour integral in the complex energy (squared) s-plane, i.e. Fixed Order Perturbation Theory, Contour Improved Perturbation Theory, and a fixed renormalization scale scheme. We obtain for the corrections to the GMOR relation, $\delta_\pi$, the value $\delta_\pi = (6.2, \pm 1.6)$. This result is substantially more accurate than previous determinations based on QCD sum rules; it is also more reliable as it is basically free of systematic uncertainties. It implies a light quark condensate $\simeq \equiv |_{2\,\mathrm{GeV}} = (- 267 \pm 5 MeV)^3$. As a byproduct, the chiral perturbation theory (unphysical) low energy constant $H^r_2$ is predicted to be $H^r_2 (\nu_\chi = M_\rho) = - (5.1 \pm 1.8)\times 10^{-3}$, or $H^r_2 (\nu_\chi = M_\eta) = - (5.7 \pm 2.0)\times 10^{-3}$.Comment: A comment about the value of the strong coupling has been added at the end of Section 4. No change in results or conslusion

Corrections to the SU(3)×SU(3){\bf SU(3)\times SU(3)} Gell-Mann-Oakes-Renner relation and chiral couplings L8rL^r_8 and H2rH^r_2

Next to leading order corrections to the $SU(3) \times SU(3)$ Gell-Mann-Oakes-Renner relation (GMOR) are obtained using weighted QCD Finite Energy Sum Rules (FESR) involving the pseudoscalar current correlator. Two types of integration kernels in the FESR are used to suppress the contribution of the kaon radial excitations to the hadronic spectral function, one with local and the other with global constraints. The result for the pseudoscalar current correlator at zero momentum is $\psi_5(0) = (2.8 \pm 0.3) \times 10^{-3} GeV^{4}$, leading to the chiral corrections to GMOR: $\delta_K = (55 \pm 5)$. The resulting uncertainties are mostly due to variations in the upper limit of integration in the FESR, within the stability regions, and to a much lesser extent due to the uncertainties in the strong coupling and the strange quark mass. Higher order quark mass corrections, vacuum condensates, and the hadronic resonance sector play a negligible role in this determination. These results confirm an independent determination from chiral perturbation theory giving also very large corrections, i.e. roughly an order of magnitude larger than the corresponding corrections in chiral $SU(2) \times SU(2)$. Combining these results with our previous determination of the corrections to GMOR in chiral $SU(2) \times SU(2)$, $\delta_\pi$, we are able to determine two low energy constants of chiral perturbation theory, i.e. $L^r_8 = (1.0 \pm 0.3) \times 10^{-3}$, and $H^r_2 = - (4.7 \pm 0.6) \times 10^{-3}$, both at the scale of the $\rho$-meson mass.Comment: Revised version with minor correction

Maximum-Reward Motion in a Stochastic Environment: The Nonequilibrium Statistical Mechanics Perspective

We consider the problem of computing the maximum-reward motion in a reward field in an online setting. We assume that the robot has a limited perception range, and it discovers the reward field on the fly. We analyze the performance of a simple, practical lattice-based algorithm with respect to the perception range. Our main result is that, with very little perception range, the robot can collect as much reward as if it could see the whole reward field, under certain assumptions. Along the way, we establish novel connections between this class of problems and certain fundamental problems of nonequilibrium statistical mechanics . We demonstrate our results in simulation examples

Positron emission tomography imaging of coronary atherosclerosis

Inflammation has a central role in the progression of coronary atherosclerosis. Recent developments in cardiovascular imaging with the advent of hybrid positron emission tomography have provided a window into the molecular pathophysiology underlying coronary plaque inflammation. Using novel radiotracers targeted at specific cellular pathways, the potential exists to observe inflammation, apoptosis, cellular hypoxia, microcalcification and angiogenesis in vivo. Several clinical studies are now underway assessing the ability of this hybrid imaging modality to inform about atherosclerotic disease activity and the prediction of future cardiovascular risk. A better understanding of the molecular mechanisms governing coronary atherosclerosis may be the first step toward offering patients a more stratified, personalized approach to treatment

Pharmacological reversal of endothelin-1 mediated constriction of the spiral modiolar artery: a potential new treatment for sudden sensorineural hearing loss

BACKGROUND: Vasospasm of the spiral modiolar artery (SMA) may cause ischemic stroke of the inner ear. Endothelin-1 (ET-1) induces a strong, long-lasting constriction of the SMA by increasing contractile apparatus Ca(2+ )sensitivity via Rho-kinase. We therefore tested several Rho-kinase inhibitors and a cell-permeable analogue of cAMP (dbcAMP) for their ability to reverse ET-1-induced constriction and Ca(2+)-sensitization. METHODS: The present study employed SMA isolated from gerbil temporal bones. Ca(2+)sensitivity was evaluated by correlating vascular diameter and smooth muscle cell [Ca(2+)](i), measured by fluo-4-microfluorometry and videomicroscopy. RESULTS: The Rho-kinase inhibitors Y-27632, fasudil, and hydroxy-fasudil reversed ET-1-induced vasoconstriction with an IC(50 )of 3, 15, and 111 μmol/L, respectively. DbcAMP stimulated a dose-dependent vasodilation (Ec(50 )= 1 mmol/L) and a reduction of [Ca(2+)](i )(EC(50 )= 0.3 μmol/L) of ET-1-preconstricted vessels (1 nmol/L). Fasudil and dbcAMP both reversed the ET-1-induced increase in Ca(2+ )sensitivity. CONCLUSION: Rho-kinase inhibition and dbcAMP reversed ET-1-induced vasoconstriction and Ca(2+)-sensitization. Therefore, Rho-kinase inhibitors or cAMP modulators could possess promise as pharmacological tools for the treatment of ET-1-induced constriction, ischemic stroke and sudden hearing loss

Reviewer agreement trends from four years of electronic submissions of conference abstract

BACKGROUND: The purpose of this study was to determine the inter-rater agreement between reviewers on the quality of abstract submissions to an annual national scientific meeting (Canadian Association of Emergency Physicians; CAEP) to identify factors associated with low agreement. METHODS: All abstracts were submitted using an on-line system and assessed by three volunteer CAEP reviewers blinded to the abstracts' source. Reviewers used an on-line form specific for each type of study design to score abstracts based on nine criteria, each contributing from two to six points toward the total (maximum 24). The final score was determined to be the mean of the three reviewers' scores using Intraclass Correlation Coefficient (ICC). RESULTS: 495 Abstracts were received electronically during the four-year period, 2001 – 2004, increasing from 94 abstracts in 2001 to 165 in 2004. The mean score for submitted abstracts over the four years was 14.4 (95% CI: 14.1–14.6). While there was no significant difference between mean total scores over the four years (p = 0.23), the ICC increased from fair (0.36; 95% CI: 0.24–0.49) to moderate (0.59; 95% CI: 0.50–0.68). Reviewers agreed less on individual criteria than on the total score in general, and less on subjective than objective criteria. CONCLUSION: The correlation between reviewers' total scores suggests general recognition of "high quality" and "low quality" abstracts. Criteria based on the presence/absence of objective methodological parameters (i.e., blinding in a controlled clinical trial) resulted in higher inter-rater agreement than the more subjective and opinion-based criteria. In future abstract competitions, defining criteria more objectively so that reviewers can base their responses on empirical evidence may lead to increased consistency of scoring and, presumably, increased fairness to submitters

A comparison of univariate, vector, bilinear autoregressive, and band power features for brain–computer interfaces

Selecting suitable feature types is crucial to obtain good overall brain–computer interface performance. Popular feature types include logarithmic band power (logBP), autoregressive (AR) parameters, time-domain parameters, and wavelet-based methods. In this study, we focused on different variants of AR models and compare performance with logBP features. In particular, we analyzed univariate, vector, and bilinear AR models. We used four-class motor imagery data from nine healthy users over two sessions. We used the first session to optimize parameters such as model order and frequency bands. We then evaluated optimized feature extraction methods on the unseen second session. We found that band power yields significantly higher classification accuracies than AR methods. However, we did not update the bias of the classifiers for the second session in our analysis procedure. When updating the bias at the beginning of a new session, we found no significant differences between all methods anymore. Furthermore, our results indicate that subject-specific optimization is not better than globally optimized parameters. The comparison within the AR methods showed that the vector model is significantly better than both univariate and bilinear variants. Finally, adding the prediction error variance to the feature space significantly improved classification results