Transformer-based CNNs: mining temporal context information for multi-sound COVID-19 diagnosis

A paper in EMBC 202

The Impending Demise Of LIFO: History, Threats, Implications, And Potential Remedies

Since its approval by congress in 1939, the last-in-last-out (LIFO) inventory cost flow assumption has historically been utilized by a significant portion of U.S. companies for both tax and financial reporting purposes. However, despite its extensive use and wide acceptance in practice, the LIFO inventory method is currently endangered by two powerful movements that make its future existence far from certain. The first of these movements is the ongoing convergence of U.S. and international accounting standards. Whether future global harmonization of accounting practice come from continued convergence or outright adoption of international accounting standards, this harmonization poses a threat to the continued use of LIFO since LIFO is prohibited under international accounting rules. The second movement is grounded in governmental attempts to lessen the current federal budget deficit. The elimination of LIFO has been targeted as a way of reducing the deficit within the Obama administration’s deficit reduction efforts. The momentum of these two threats to LIFO makes the topic of LIFO’s future ripe for discussion. This study discusses the history of LIFO, illuminates the current threats the method faces, and outlines the most common remedies that have been proposed to mitigate the financial impact faced by companies that will be negatively affected by any such elimination of the method

CovNet: A Transfer Learning Framework for Automatic COVID-19 Detection From Crowd-Sourced Cough Sounds

Since the COronaVIrus Disease 2019 (COVID-19) outbreak, developing a digital diagnostic tool to detect COVID-19 from respiratory sounds with computer audition has become an essential topic due to its advantages of being swift, low-cost, and eco-friendly. However, prior studies mainly focused on small-scale COVID-19 datasets. To build a robust model, the large-scale multi-sound FluSense dataset is utilised to help detect COVID-19 from cough sounds in this study. Due to the gap between FluSense and the COVID-19-related datasets consisting of cough only, the transfer learning framework (namely CovNet) is proposed and applied rather than simply augmenting the training data with FluSense. The CovNet contains (i) a parameter transferring strategy and (ii) an embedding incorporation strategy. Specifically, to validate the CovNet's effectiveness, it is used to transfer knowledge from FluSense to COUGHVID, a large-scale cough sound database of COVID-19 negative and COVID-19 positive individuals. The trained model on FluSense and COUGHVID is further applied under the CovNet to another two small-scale cough datasets for COVID-19 detection, the COVID-19 cough sub-challenge (CCS) database in the INTERSPEECH Computational Paralinguistics challengE (ComParE) challenge and the DiCOVA Track-1 database. By training four simple convolutional neural networks (CNNs) in the transfer learning framework, our approach achieves an absolute improvement of 3.57% over the baseline of DiCOVA Track-1 validation of the area under the receiver operating characteristic curve (ROC AUC) and an absolute improvement of 1.73% over the baseline of ComParE CCS test unweighted average recall (UAR). Copyright © 2022 Chang, Jing, Ren and Schuller

Learning complementary representations via attention-based ensemble learning for cough-based COVID-19 recognition

Coughs sounds have shown promising as-potential marker for distinguishing COVID individuals from non-COVID ones. In this paper, we propose an attention-based ensemble learning approach to learn complementary representations from cough samples. Unlike most traditional schemes such as mere maxing or averaging, the proposed approach fairly considers the contribution of the representation generated by each single model. The attention mechanism is further investigated at the feature level and the decision level. Evaluated on the Track-1 test set of the DiCOVA challenge 2021, the experimental results demonstrate that the proposed feature-level attention-based ensemble learning achieves the best performance (Area Under Curve, AUC: 77.96%), resulting in an 8.05% improvement over the challenge baseline.

CovNet: a transfer learning framework for automatic COVID-19 detection from crowd-sourced cough sounds

Since the COronaVIrus Disease 2019 (COVID-19) outbreak, developing a digital diagnostic tool to detect COVID-19 from respiratory sounds with computer audition has become an essential topic due to its advantages of being swift, low-cost, and eco-friendly. However, prior studies mainly focused on small-scale COVID-19 datasets. To build a robust model, the large-scale multi-sound FluSense dataset is utilised to help detect COVID-19 from cough sounds in this study. Due to the gap between FluSense and the COVID-19-related datasets consisting of cough only, the transfer learning framework (namely CovNet) is proposed and applied rather than simply augmenting the training data with FluSense. The CovNet contains (i) a parameter transferring strategy and (ii) an embedding incorporation strategy. Specifically, to validate the CovNet's effectiveness, it is used to transfer knowledge from FluSense to COUGHVID, a large-scale cough sound database of COVID-19 negative and COVID-19 positive individuals. The trained model on FluSense and COUGHVID is further applied under the CovNet to another two small-scale cough datasets for COVID-19 detection, the COVID-19 cough sub-challenge (CCS) database in the INTERSPEECH Computational Paralinguistics challengE (ComParE) challenge and the DiCOVA Track-1 database. By training four simple convolutional neural networks (CNNs) in the transfer learning framework, our approach achieves an absolute improvement of 3.57% over the baseline of DiCOVA Track-1 validation of the area under the receiver operating characteristic curve (ROC AUC) and an absolute improvement of 1.73% over the baseline of ComParE CCS test unweighted average recall (UAR)

Low Mass Dark Matter and Invisible Higgs Width In Darkon Models

The Standard Model (SM) plus a real gauge-singlet scalar field dubbed darkon (SM+D) is the simplest model possessing a weakly interacting massive particle (WIMP) dark-matter candidate. In this model, the parameters are constrained from dark matter relic density and direct searches. The fact that interaction between darkon and SM particles is only mediated by Higgs boson exchange may lead to significant modifications to the Higgs boson properties. If the dark matter mass is smaller than a half of the Higgs boson mass, the Higgs boson can decay into a pair of darkons resulting in a large invisible branching ratio. The Higgs boson will be searched for at the LHC and may well be discovered in the near future. If a Higgs boson with a small invisible decay width will be found, the SM+D model with small dark matter mass will be in trouble. We find that by extending the SM+D to a two-Higgs-doublet model plus a darkon (THDM+D) it is possible to have a Higgs boson with a small invisible branching ratio and at the same time the dark matter can have a low mass. We also comment on other implications of this model.Comment: RevTeX, 15 pages, 11 figures. A few typos corrected and some references adde

Topological Aspect of Knotted Vortex Filaments in Excitable Media

Scroll waves exist ubiquitously in three-dimensional excitable media. It's rotation center can be regarded as a topological object called vortex filament. In three-dimensional space, the vortex filaments usually form closed loops, and even linked and knotted. In this letter, we give a rigorous topological description of knotted vortex filaments. By using the $\phi$-mapping topological current theory, we rewrite the topological current form of the charge density of vortex filaments and use this topological current we reveal that the Hopf invariant of vortex filaments is just the sum of the linking and self-linking numbers of the knotted vortex filaments. We think that the precise expression of the Hopf invariant may imply a new topological constraint on knotted vortex filaments.Comment: 4 pages, no figures, Accepted by Chin. Phys. Let