Crater Formation and Deuterium Production in Laser Irradiation of Polymers with Implanted Nano-antennas

Recent validation experiments on laser irradiation of polymer foils with and without implanted golden nano-particles are discussed. First we analyze characteristics of craters, formed in the target after its interaction with laser beam. Preliminary experimental results show significant production of deuterons when both the energy of laser pulse and concentration of nano-particles are high enough. We consider the deuteron production via the nuclear transmutation reactions $p+C\rightarrow d+X$ where protons are accelerated by Coulomb field, generated in the target plasma. We argue that maximal proton energy can be above threshold values for these reactions and the deuteron yield may noticeably increase due to presence of nano-particles.Comment: 9 pages, 4 figure

Audio tagging using a linear noise modelling layer

Label noise refers to the presence of inaccurate target labels in a dataset. It is an impediment to the performance of a deep neural network (DNN) as the network tends to overfit to the label noise, hence it becomes imperative to devise a generic methodology to counter the effects of label noise. FSDnoisy18k is an audio dataset collected with the aim of encouraging research on label noise for sound event classification. The dataset contains ~42.5 hours of audio recordings divided across 20 classes, with a small amount of manually verified labels and a large amount of noisy data. Using this dataset, our work intends to explore the potential of modelling the label noise distribution by adding a linear layer on top of a baseline network. The accuracy of the approach is compared to an alternative approach of adopting a noise robust loss function. Results show that modelling the noise distribution improves the accuracy of the baseline network in a similar capacity to the soft bootstrapping loss

Onsets, activity, and events: a multi-task approach for polyphonic sound event modelling

State of the art polyphonic sound event detection (SED) systems function as frame-level multi-label classification models. In the context of dynamic polyphony levels at each frame, sound events interfere with each other which degrade a classifier's ability to learn the exact frequency profile of individual sound events. Frame-level localized classifiers also fail to explicitly model the long-term temporal structure of sound events. Consequently, the event-wise detection performance is less than the segment-wise detection. We define 'temporally precise polyphonic sound event detection' as the subtask of detecting sound event instances with the correct onset. Here, we investigate the effectiveness of sound activity detection (SAD) and onset detection as auxiliary tasks to improve temporal precision in polyphonic SED using multi-task learning. SAD helps to differentiate event activity frames from noisy and silence frames and helps to avoid missed detections at each frame. Onset predictions ensure the start of each event which in turn are used to condition predictions of both SAD and SED. Our experiments on the URBAN-SED dataset show that by conditioning SED with onset detection and SAD, there is over a three-fold relative improvement in event-based F-score

Forging volumetric methods

The last two decades have seen a “volumetric turn” within Anglophone social sciences and humanities scholarship. This turn is premised on the idea that space may be better understood in three-dimensional terms – with complex heights and depths – rather than as a series of two-dimensional areas or surfaces. While there is an increasingly diverse and rich set of scholarship accounting for voluminous complexities in the air, oceans, ice, mountains, and undergrounds, all too often this work foregrounds state and military-led approaches to volume. This has resulted in a limited methodological toolkit through which to explore voluminous complexities as they emerge and extend beyond military and state contexts. Often reliant on elite interviews, archives, and cartographies, there has been little critical discussion of both methodological practice and the “flatness” of research outputs articulating three-dimensional worlds. In this paper we address this by foregrounding the role of immersive and multisensory methodologies (sounding volumes, seeing-sensing drone volumes, and object volumes). To conclude, we offer avenues for further inquiry, including attending to shifting everyday voluminous experiences in the Anthropocene, and the need to diversify the communication of “volume” research

A Geometric Segmentation Approach For The 3d Reconstruction Of

In this paper, an algorithm is proposed to solve the multi-frame structure from motion (MFSfM) problem for monocular video sequences with multiple rigid moving objects. The algorithm uses the epipolar criterion to segment feature trajectories belonging to the background scene and each of the independently moving objects. As a large baseline length is essential for the reliability of the epipolar geometry, the geometric robust information criterion is employed for key-frame selection within the sequences. Once the features are segmented, corresponding objects are reconstructed individually using a sequential algorithm that is capable of prioritizing the frame pairs with respect to their reliability and information content. The experimental results on synthetic and real data demonstrate that our approach has the potential to effectively deal with the multi-body MFSfM problem