Event-based Face Detection and Tracking in the Blink of an Eye

We present the first purely event-based method for face detection using the high temporal resolution of an event-based camera. We will rely on a new feature that has never been used for such a task that relies on detecting eye blinks. Eye blinks are a unique natural dynamic signature of human faces that is captured well by event-based sensors that rely on relative changes of luminance. Although an eye blink can be captured with conventional cameras, we will show that the dynamics of eye blinks combined with the fact that two eyes act simultaneously allows to derive a robust methodology for face detection at a low computational cost and high temporal resolution. We show that eye blinks have a unique temporal signature over time that can be easily detected by correlating the acquired local activity with a generic temporal model of eye blinks that has been generated from a wide population of users. We furthermore show that once the face is reliably detected it is possible to apply a probabilistic framework to track the spatial position of a face for each incoming event while updating the position of trackers. Results are shown for several indoor and outdoor experiments. We will also release an annotated data set that can be used for future work on the topic

Adversarial attacks on spiking convolutional neural networks for event-based vision

Event-based dynamic vision sensors provide very sparse output in the form of spikes, which makes them suitable for low-power applications. Convolutional spiking neural networks model such event-based data and develop their full energy-saving potential when deployed on asynchronous neuromorphic hardware. Event-based vision being a nascent field, the sensitivity of spiking neural networks to potentially malicious adversarial attacks has received little attention so far. We show how white-box adversarial attack algorithms can be adapted to the discrete and sparse nature of event-based visual data, and demonstrate smaller perturbation magnitudes at higher success rates than the current state-of-the-art algorithms. For the first time, we also verify the effectiveness of these perturbations directly on neuromorphic hardware. Finally, we discuss the properties of the resulting perturbations, the effect of adversarial training as a defense strategy, and future directions

Signal characteristics of focal bone marrow lesions in patients with multiple myeloma using whole body T1w-TSE, T2w-STIR and diffusion-weighted imaging with background suppression

Objective: This study analyses the diagnostic potential of Diffusion-Weighted Imaging with Background Suppression (DWIBS) in the detection of focal bone marrow lesions from multiple myeloma. The signal and contrast properties of DWIBS are evaluated in correlation with the serum concentration of M-component (MC) and compared with established T1- and T2-weighted sequences. Methods: Data from 103 consecutive studies in 81 patients are analysed retrospectively. Signal intensities and apparent Diffusion Coefficients (ADC) of 79 focal lesions in the lumbar spine or pelvis of 38 patients are determined and contrast-to-noise-ratio (CNR) is calculated. Data from patients with low (20g/dL) MC are evaluated separately. Results: Signal intensities of focal myeloma lesions on T2w-STIR vary significantly depending on the MC, which leads to a loss in CNR in patients with high MC. No signal variation is observed for T1w-TSE and DWIBS. The CNR values provided by DWIBS in patients with high MC are slightly higher than those of T2w-STIR. ADC values in patients with low MC are significantly higher than in patients with high MC. Conclusion: Whole-body DWIBS has the potential to improve the conspicuity of focal myeloma lesions and provides additional biological information by ADC quantificatio

Impact of bio-augmentation with Sphingomonas sp. strain TTNP3 in membrane bioreactors degrading nonylphenol

This study evaluates the potential of bio-augmentation to improve the degradation of recalcitrant nonylphenol during the wastewater treatment in membrane bioreactors (MBR). One MBR containing activated sludge was bio-augmented using multistep inoculation with freeze dried Sphingomonas sp. strain TTNP3, whereas a second control reactor contained activated sludge solely. The 14C-labeled-nonylphenol isomer (4-[1-ethyl-1,3-dimethylpentyl]phenol) was applied as a single pulse. Bio-augmentation resulted in an immediate increase of dissolved radioactivity in the effluent in comparison to the control reactor (13% and 2% of initially applied radioactivity after 1day, respectively). After 5days of operation, the retentate of the bio-augmented reactor contained only 7% of the initial radioactivity in contrast to 50% in the control reactor. The radioactivity associated to the mixed liquor suspended solids, i.e., the suspension of biomass and other solids on the retentate side of the membrane, was mainly found as non-extractable residues that were increasingly formed during prolonged reactor operation, especially for the control MBR. HPLC-LSC and GC-MSn analyses revealed that the bio-augmented reactor produced more polar hydroquinone as main degradation intermediate, whereas the control reactor effluent contained a complex mixture of apolar compounds with shortened oxidized alkyl chains. Thus, the apparent differences in the behavior of nonylphenol between the reactors were due to the catabolism of nonylphenol conferred by bio-augmentation with Sphingomonas sp. strain TTNP

Preoperative staging of non-small-cell lung cancer: comparison of whole-body diffusion-weighted magnetic resonance imaging and 18F-fluorodeoxyglucose-positron emission tomography/computed tomography

Objective: To investigate the diagnostic value of whole-body magnetic resonance imaging (MRI) including diffusion-weighted imaging with background signal suppression (DWIBS) for preoperative assessment of non-small-cell lung cancer (NSCLC) in comparison to 18F-fluorodeoxyglucose 18FDG) positron emission tomography/computed tomography (PET/CT). Methods: Thirty-three patients with suspected NSCLC were enrolled. Patients were examined before surgery with PET/CT and whole-body MRI including T1-weighted turbo spin echo (TSE), T2-weighted short tau inversion recovery (STIR) and DWIBS sequences (b = 0/800). Histological or cytological specimens were taken as standard of reference. Results: Whole-body MRI with DWIBS as well as PET/CT provided diagnostic image quality in all cases. Sensitivity for primary tumour detection: MRI 93%, PET/CT 98%. T-staging accuracy: MRI 63%, PET/CT 56%. N-staging accuracy: MRI 66%, PET/CT 71%. UICC staging accuracy: MRI 66%, PET/CT 74%. Sensitivity for metastatic involvement of individual lymph node groups: MRI 44%, PET/CT 47%. Specificity for individual non-metastatic lymph node groups: MRI 93%, PET/CT 96%. Assessment accuracy for individual lymph node groups: MRI 85%, PET/CT 88%. Observer agreement rate for UICC staging: MRI 74%, PET/CT 90%. Conclusion: Whole-body MRI with DWIBS provides comparable results to PET/CT in staging of NSCLC, but shows no superiority. Most relevant challenges for both techniques are T-staging accuracy and sensitivity for metastatic lymph node involvement. Key Points : • Numerous radiological methods are available for the crucial staging of lung cancer • Whole-body DWIBS MRI provides comparable results to PET/CT in NSCLC staging. • No evident superiority of whole-body DWIBS over PET/CT in NSCLC staging. • Challenges for both techniques are T-staging and detection of small metastase

Training Spiking Neural Networks Using Lessons From Deep Learning

The brain is the perfect place to look for inspiration to develop more efficient neural networks. The inner workings of our synapses and neurons provide a glimpse at what the future of deep learning might look like. This paper serves as a tutorial and perspective showing how to apply the lessons learnt from several decades of research in deep learning, gradient descent, backpropagation and neuroscience to biologically plausible spiking neural neural networks. We also explore the delicate interplay between encoding data as spikes and the learning process; the challenges and solutions of applying gradient-based learning to spiking neural networks; the subtle link between temporal backpropagation and spike timing dependent plasticity, and how deep learning might move towards biologically plausible online learning. Some ideas are well accepted and commonly used amongst the neuromorphic engineering community, while others are presented or justified for the first time here. A series of companion interactive tutorials complementary to this paper using our Python package, snnTorch, are also made available: https://snntorch.readthedocs.io/en/latest/tutorials/index.htm

Adversarial attacks on spiking convolutional neural networks for event-based vision

Event-based dynamic vision sensors provide very sparse output in the form of spikes, which makes them suitable for low-power applications. Convolutional spiking neural networks model such event-based data and develop their full energy-saving potential when deployed on asynchronous neuromorphic hardware. Event-based vision being a nascent field, the sensitivity of spiking neural networks to potentially malicious adversarial attacks has received little attention so far. We show how white-box adversarial attack algorithms can be adapted to the discrete and sparse nature of event-based visual data, and demonstrate smaller perturbation magnitudes at higher success rates than the current state-of-the-art algorithms. For the first time, we also verify the effectiveness of these perturbations directly on neuromorphic hardware. Finally, we discuss the properties of the resulting perturbations, the effect of adversarial training as a defense strategy, and future directions

Tailoring crosstalk between localized 1D spin-wave nanochannels using focused ion beams

1D spin-wave conduits are envisioned as nanoscale components of magnonics-based logic and computing schemes for future generation electronics. `A-la-carte methods of versatile control of the local magnetization dynamics in such nanochannels are highly desired for efficient steering of the spin waves in magnonic devices. Here, we present a study of localized dynamical modes in 1-$\mu$m-wide Permalloy conduits probed by microresonator ferromagnetic resonance technique. We clearly observe the lowest-energy edge mode in the microstrip after its edges were finely trimmed by means of focused Ne$^+$ ion irradiation. Furthermore, after milling the microstrip along its long axis by focused ion beams, creating consecutively $\sim$50 and $\sim$100 nm gaps, additional resonances emerge and are attributed to modes localized at the inner edges of the separated strips. To visualize the mode distribution, spatially resolved Brillouin light scattering microscopy was used showing an excellent agreement with the ferromagnetic resonance data and confirming the mode localization at the outer/inner edges of the strips depending on the magnitude of the applied magnetic field. Micromagnetic simulations confirm that the lowest-energy modes are localized within $\sim$15-nm-wide regions at the edges of the strips and their frequencies can be tuned in a wide range (up to 5 GHz) by changing the magnetostatic coupling (i.e. spatial separation) between the microstrips.Comment: 10 pages, 4 figure

Investigation by thermodesorption spectroscopy of hydrogen accumulation features in Zr-1%Nb zirconium alloy during gas-phase hydrogenation

Modern trends in human development require more and more electricity to maintain the pace of economic and scientific development. One of the most environmentally friendly methods of electricity production is the use of nuclear power plants (NPPs). In connection with high impact on structural materials of nuclear power plants cores there is uncontrolled degradation of core material. Understanding the degradation processes will help predict and prevent various man-made disasters. The purpose of this work is to study the accumulation of hydrogen in the zirconium alloy Zr-1%Nb (E110). In this work, we studied the processes of hydrogen sorption and desorption by the zirconium alloy Zr-1%Nb