Multi-level Attention Model for Weakly Supervised Audio Classification

In this paper, we propose a multi-level attention model to solve the weakly labelled audio classification problem. The objective of audio classification is to predict the presence or absence of audio events in an audio clip. Recently, Google published a large scale weakly labelled dataset called Audio Set, where each audio clip contains only the presence or absence of the audio events, without the onset and offset time of the audio events. Our multi-level attention model is an extension to the previously proposed single-level attention model. It consists of several attention modules applied on intermediate neural network layers. The output of these attention modules are concatenated to a vector followed by a multi-label classifier to make the final prediction of each class. Experiments shown that our model achieves a mean average precision (mAP) of 0.360, outperforms the state-of-the-art single-level attention model of 0.327 and Google baseline of 0.314.Comment: 5 pages, 3 figures, Submitted to Eusipco 201

Direct Observation of Photoinduced Charge Separation in Ruthenium Complex/Ni(OH)\u3csub\u3e2\u3c/sub\u3e Nanoparticle Hybrid

Ni(OH)2 have emerged as important functional materials for solar fuel conversion because of their potential as cost-effective bifunctional catalysts for both hydrogen and oxygen evolution reactions. However, their roles as photocatalysts in the photoinduced charge separation (CS) reactions remain unexplored. In this paper, we investigate the CS dynamics of a newly designed hybrid catalyst by integrating a Ru complex with Ni(OH)2 nanoparticles (NPs). Using time resolved X-ray absorption spectroscopy (XTA), we directly observed the formation of the reduced Ni metal site (~60 ps), unambiguously demonstrating CS process in the hybrid through ultrafast electron transfer from Ru complex to Ni(OH)2 NPs. Compared to the ultrafast CS process, the charge recombination in the hybrid is ultraslow (≫50 ns). These results not only suggest the possibility of developing Ni(OH)2 as solar fuel catalysts, but also represent the first time direct observation of efficient CS in a hybrid catalyst using XTA

Pull-out performance of densified wood dowels embedded into glued laminated timber

International audienceDue to the corrosion of fasteners by water-based preservatives, the preserved timber in outdoor environments can decrease the service life of the metal fasteners. In addition, the segregation of timber members and metal fasteners is also difficult during the demolition of timber structures. Wooden fasteners can be a promising alternative to metal fasteners because they have favorable resistance against corrosion and are more naturally harmonized with timber members. This paper studied the pull-out performance of dried densified wood (DW) dowels embedded into glued laminated timber (glulam) parallel to the grain with three different embedded lengths in two ambient environments with a temperature of 20°C and relative humidity (RH) of 65% and with a temperature of 20°C and relative humidity of 85%. The hygro- scopic swelling of the dried DW dowels with a long embedded length can provide the favorable friction locking to transfer the axial load

Privacy Leakage on DNNs: A Survey of Model Inversion Attacks and Defenses

Model Inversion (MI) attacks aim to disclose private information about the training data by abusing access to the pre-trained models. These attacks enable adversaries to reconstruct high-fidelity data that closely aligns with the private training data, which has raised significant privacy concerns. Despite the rapid advances in the field, we lack a comprehensive overview of existing MI attacks and defenses. To fill this gap, this paper thoroughly investigates this field and presents a holistic survey. Firstly, our work briefly reviews the traditional MI on machine learning scenarios. We then elaborately analyze and compare numerous recent attacks and defenses on \textbf{D}eep \textbf{N}eural \textbf{N}etworks (DNNs) across multiple modalities and learning tasks

Noise reduction in centrifugal pump as turbine: influence of leaning blade or tongue

To reduce the interior/exterior flow-induced noise in centrifugal pump as turbine (PAT), based on the relation between in-phase hydrodynamic action and radiated noise, the angle formula in ideal condition by individually leaning blade was proposed. Considering the joint leaning effect, the formulas associated with parallel- and counter-leaning blade and tongue were presented. The corresponding active noise reduction approaches were put forward without losing hydraulic performance. For noise simulation, the numerical method for interior noise and finite element model for casing structure were checked, with good agreements achieved. The boundary element method (BEM) and finite element method (FEM) were used to investigate the interior/exterior noise characteristics of PATs by varying the shapes of blade or tongue. Researches show that the interior noise due to casing source can reflect joint action of multiple sources. It is feasible to predict the interior noise induced only by casing source through BEM. The leaned blade can keep PAT’s original performance, the leaned tongue can significantly improve the efficiency overall operating range with an increase from 0.67 % to 1.81 %, and the combined counter-leaning blade and tongue can increase the efficiency in larger flow rates. They can all reduce sound pressure level (SPL) at fundamental frequency and total sound energy, and the noise reduction effect under joint action is best

Brachial Plexus Nerve Trunk Segmentation Using Deep Learning: A Comparative Study with Doctors' Manual Segmentation

Ultrasound-guided nerve block anesthesia (UGNB) is a high-tech visual nerve block anesthesia method that can observe the target nerve and its surrounding structures, the puncture needle's advancement, and local anesthetics spread in real-time. The key in UGNB is nerve identification. With the help of deep learning methods, the automatic identification or segmentation of nerves can be realized, assisting doctors in completing nerve block anesthesia accurately and efficiently. Here, we establish a public dataset containing 320 ultrasound images of brachial plexus (BP). Three experienced doctors jointly produce the BP segmentation ground truth and label brachial plexus trunks. We design a brachial plexus segmentation system (BPSegSys) based on deep learning. BPSegSys achieves experienced-doctor-level nerve identification performance in various experiments. We evaluate BPSegSys' performance in terms of intersection-over-union (IoU), a commonly used performance measure for segmentation experiments. Considering three dataset groups in our established public dataset, the IoU of BPSegSys are 0.5238, 0.4715, and 0.5029, respectively, which exceed the IoU 0.5205, 0.4704, and 0.4979 of experienced doctors. In addition, we show that BPSegSys can help doctors identify brachial plexus trunks more accurately, with IoU improvement up to 27%, which has significant clinical application value.Comment: 9 page