IMPROVED MULTIPLE BIRDSONG TRACKING WITH DISTRIBUTION DERIVATIVE METHOD AND MARKOV RENEWAL PROCESS CLUSTERING

DS & MP are supported by an EPSRC Leadership Fellowship EP/G007144/1

Float-polishing process and analysis of float-polished quartz

A fluid-mechanical model is developed for the float-polishing process. In this model laminar flow between the sample and the lap results in pressure gradients at the grooves that support the sample on a fluid layer. The laminar fluid motion also produces supersmooth, damage-free surfaces. Quartz substrates for applications in high-stress environments were float polished, and their surfaces were analyzed by optical scatterometry, photoacoustic spectroscopy, and atomic force microscopy. The removal of 100 µm of material by a lapping-polishing process, with final float polishing, left low levels of subsurface damage, with a surface roughness of approximately 0.2-nm rms

Short-term prediction of photovoltaic power generation using Gaussian process regression

Photovoltaic (PV) power is affected by weather conditions, making the power generated from the PV systems uncertain. Solving this problem would help improve the reliability and cost effectiveness of the grid, and could help reduce reliance on fossil fuel plants. The present paper focuses on evaluating predictions of the energy generated by PV systems in the United Kingdom Gaussian process regression (GPR). Gaussian process regression is a Bayesian non-parametric model that can provide predictions along with the uncertainty in the predicted value, which can be very useful in applications with a high degree of uncertainty. The model is evaluated for short-term forecasts of 48 hours against three main factors -- training period, sky area coverage and kernel model selection -- and for very short-term forecasts of four hours against sky area. We also compare very short-term forecasts in terms of cloud coverage within the prediction period and only initial cloud coverage as a predictor

Learning Timbre Analogies from Unlabelled Data by Multivariate Tree Regression

This is the Author's Original Manuscript of an article whose final and definitive form, the Version of Record, has been published in the Journal of New Music Research, November 2011, copyright Taylor & Francis. The published article is available online at http://www.tandfonline.com/10.1080/09298215.2011.596938

Comparison between transformers and convolutional models for fine-grained classification of insects

Fine-grained classification is challenging due to the difficulty of finding discriminatory features. This problem is exacerbated when applied to identifying species within the same taxonomical class. This is because species are often sharing morphological characteristics that make them difficult to differentiate. We consider the taxonomical class of Insecta. The identification of insects is essential in biodiversity monitoring as they are one of the inhabitants at the base of many ecosystems. Citizen science is doing brilliant work of collecting images of insects in the wild giving the possibility to experts to create improved distribution maps in all countries. We have billions of images that need to be automatically classified and deep neural network algorithms are one of the main techniques explored for fine-grained tasks. At the SOTA, the field of deep learning algorithms is extremely fruitful, so how to identify the algorithm to use? We focus on Odonata and Coleoptera orders, and we propose an initial comparative study to analyse the two best-known layer structures for computer vision: transformer and convolutional layers. We compare the performance of T2TViT, a fully transformer-base, EfficientNet, a fully convolutional-base, and ViTAE, a hybrid. We analyse the performance of the three models in identical conditions evaluating the performance per species, per morph together with sex, the inference time, and the overall performance with unbalanced datasets of images from smartphones. Although we observe high performances with all three families of models, our analysis shows that the hybrid model outperforms the fully convolutional-base and fully transformer-base models on accuracy performance and the fully transformer-base model outperforms the others on inference speed and, these prove the transformer to be robust to the shortage of samples and to be faster at inference time

Delayed Decision-making in Real-time Beatbox Percussion Classification

This is an electronic version of an article published in Journal of New Music Research, 39(3), 203-213, 2010. doi:10.1080/09298215.2010.512979. Journal of New Music Research is available online at: www.tandfonline.com/openurl?genre=article&issn=1744-5027&volume=39&issue=3&spage=20

Down-regulation of human topoisomerase IIα expression correlates with relative amounts of specificity factors Sp1 and Sp3 bound at proximal and distal promoter regions

<p>Abstract</p> <p>Background</p> <p>Topoisomerase IIα has been shown to be down-regulated in doxorubicin-resistant cell lines. The specificity proteins Sp1 and Sp3 have been implicated in regulation of topoisomerase IIα transcription, although the mechanism by which they regulate expression is not fully understood. Sp1 has been shown to bind specifically to both proximal and distal GC elements of the human topoisomerase IIα promoter <it>in vitro</it>, while Sp3 binds only to the distal GC element unless additional flanking sequences are included. While Sp1 is thought to be an activator of human topoisomerase IIα, the functional significance of Sp3 binding is not known. Therefore, we sought to determine the functional relationship between Sp1 and Sp3 binding to the topoisomerase IIα promoter <it>in vivo</it>. We investigated endogenous levels of Sp1, Sp3 and topoisomerase IIα as well as binding of both Sp1 and Sp3 to the GC boxes of the topoisomerase IIα promoter in breast cancer cell lines <it>in vivo </it>after short term doxorubicin exposure.</p> <p>Results</p> <p>Functional effects of Sp1 and Sp3 were studied using transient cotransfection assays using a topoisomerase IIα promoter reporter construct. The <it>in vivo </it>interactions of Sp1 and Sp3 with the GC elements of the topoisomerase IIα promoter were studied in doxorubicin-treated breast cancer cell lines using chromatin immunoprecipitation assays. Relative amounts of endogenous proteins were measured using immunoblotting. <it>In vivo </it>DNA looping mediated by proteins bound at the GC1 and GC2 elements was studied using the chromatin conformation capture assay. Both Sp1 and Sp3 bound to the GC1 and GC2 regions. Sp1 and Sp3 were transcriptional activators and repressors respectively, with Sp3 repression being dominant over Sp1-mediated activation. The GC1 and GC2 elements are linked <it>in vivo </it>to form a loop, thus bringing distal regulatory elements and their cognate transcription factors into close proximity with the transcription start site.</p> <p>Conclusion</p> <p>These observations provide a mechanistic explanation for the modulation of topoisomerase IIα and concomitant down-regulation that can be mediated by topoisomerase II poisons. Competition between Sp1 and Sp3 for the same cognate DNA would result in activation or repression depending on absolute amounts of each transcription factor in cells treated with doxorubicin.</p

Audio Event Detection using Weakly Labeled Data

Acoustic event detection is essential for content analysis and description of multimedia recordings. The majority of current literature on the topic learns the detectors through fully-supervised techniques employing strongly labeled data. However, the labels available for majority of multimedia data are generally weak and do not provide sufficient detail for such methods to be employed. In this paper we propose a framework for learning acoustic event detectors using only weakly labeled data. We first show that audio event detection using weak labels can be formulated as an Multiple Instance Learning problem. We then suggest two frameworks for solving multiple-instance learning, one based on support vector machines, and the other on neural networks. The proposed methods can help in removing the time consuming and expensive process of manually annotating data to facilitate fully supervised learning. Moreover, it can not only detect events in a recording but can also provide temporal locations of events in the recording. This helps in obtaining a complete description of the recording and is notable since temporal information was never known in the first place in weakly labeled data.Comment: ACM Multimedia 201

Lonely adatoms in space

There is a close relation between the problems of second layer nucleation in epitaxial crystal growth and chemical surface reactions, such as hydrogen recombination, on interstellar dust grains. In both cases standard rate equation analysis has been found to fail because the process takes place in a confined geometry. Using scaling arguments developed in the context of second layer nucleation, I present a simple derivation of the hydrogen recombination rate for small and large grains. I clarify the reasons for the failure of rate equations for small grains, and point out a logarithmic correction to the reaction rate when the reaction is limited by the desorption of hydrogen atoms (the second order reaction regime)

ATGNN: Audio Tagging Graph Neural Network

Deep learning models such as CNNs and Transformers have achieved impressive performance for end-to-end audio tagging. Recent works have shown that despite stacking multiple layers, the receptive field of CNNs remains severely limited. Transformers on the other hand are able to map global context through self-attention, but treat the spectrogram as a sequence of patches which is not flexible enough to capture irregular audio objects. In this work, we treat the spectrogram in a more flexible way by considering it as graph structure and process it with a novel graph neural architecture called ATGNN. ATGNN not only combines the capability of CNNs with the global information sharing ability of Graph Neural Networks, but also maps semantic relationships between learnable class embeddings and corresponding spectrogram regions. We evaluate ATGNN on two audio tagging tasks, where it achieves 0.585 mAP on the FSD50K dataset and 0.335 mAP on the AudioSet-balanced dataset, achieving comparable results to Transformer based models with significantly lower number of learnable parameters