Genomic Methods for Bacterial Infection Identification

Hospital-acquired infections (HAIs) have high mortality rates around the world and are a challenge to medical science due to rapid mutation rates in their pathogens. A new methodology is proposed to identify bacterial species causing HAIs based on sets of universal biomarkers for next-generation microarray designs (i.e., nxh chips), rather than a priori selections of biomarkers. This method allows arbitrary organisms to be classified based on readouts of their DNA sequences, including whole genomes. The underlying models are based on the biochemistry of DNA, unlike traditional edit-distance based alignments. Furthermore, the methodology is fairly robust to genetic mutations, which are likely to reduce accuracy. Standard machine learning methods (neural networks, self-organizing maps, and random forests) produce results to identify HAIs on nxh chips that are very competitive, if not superior, to current standards in the field. The potential feasibility of translating these techniques to a clinical test is also discussed

Secrecy performance of TAS/SC-based multi-hop harvest-to-transmit cognitive WSNs under joint constraint of interference and hardware imperfection

In this paper, we evaluate the secrecy performance of multi-hop cognitive wireless sensor networks (WSNs). In the secondary network, a source transmits its data to a destination via the multi-hop relaying model using the transmit antenna selection (TAS)/selection combining (SC) technique at each hop, in the presence of an eavesdropper who wants to receive the data illegally. The secondary transmitters, including the source and intermediate relays, have to harvest energy from radio-frequency signals of a power beacon for transmitting the source data. Moreover, their transmit power must be adjusted to satisfy the quality of service (QoS) of the primary network. Under the joint impact of hardware imperfection and interference constraint, expressions for the transmit power for the secondary transmitters are derived. We also derive exact and asymptotic expressions of secrecy outage probability (SOP) and probability of non-zero secrecy capacity (PNSC) for the proposed protocol over Rayleigh fading channel. The derivations are then verified by Monte Carlo simulations.Web of Science195art. no. 116

Inexact proximal methods for weakly convex functions

This paper proposes and develops inexact proximal methods for finding stationary points of the sum of a smooth function and a nonsmooth weakly convex one, where an error is present in the calculation of the proximal mapping of the nonsmooth term. A general framework for finding zeros of a continuous mapping is derived from our previous paper on this subject to establish convergence properties of the inexact proximal point method when the smooth term is vanished and of the inexact proximal gradient method when the smooth term satisfies a descent condition. The inexact proximal point method achieves global convergence with constructive convergence rates when the Moreau envelope of the objective function satisfies the Kurdyka-Lojasiewicz (KL) property. Meanwhile, when the smooth term is twice continuously differentiable with a Lipschitz continuous gradient and a differentiable approximation of the objective function satisfies the KL property, the inexact proximal gradient method achieves the global convergence of iterates with constructive convergence rates.Comment: 26 pages, 3 table

Enhancing Accuracy-Privacy Trade-off in Differentially Private Split Learning

Split learning (SL) aims to protect user data privacy by distributing deep models between client-server and keeping private data locally. Only processed or `smashed' data can be transmitted from the clients to the server during the SL process. However, recently proposed model inversion attacks can recover the original data from the smashed data. In order to enhance privacy protection against such attacks, a strategy is to adopt differential privacy (DP), which involves safeguarding the smashed data at the expense of some accuracy loss. This paper presents the first investigation into the impact on accuracy when training multiple clients in SL with various privacy requirements. Subsequently, we propose an approach that reviews the DP noise distributions of other clients during client training to address the identified accuracy degradation. We also examine the application of DP to the local model of SL to gain insights into the trade-off between accuracy and privacy. Specifically, findings reveal that introducing noise in the later local layers offers the most favorable balance between accuracy and privacy. Drawing from our insights in the shallower layers, we propose an approach to reduce the size of smashed data to minimize data leakage while maintaining higher accuracy, optimizing the accuracy-privacy trade-off. Additionally, a smaller size of smashed data reduces communication overhead on the client side, mitigating one of the notable drawbacks of SL. Experiments with popular datasets demonstrate that our proposed approaches provide an optimal trade-off for incorporating DP into SL, ultimately enhancing training accuracy for multi-client SL with varying privacy requirements

Performance of cluster-based cognitive multihop networks under joint impact of hardware noises and non-identical primary co-channel interference

In this paper, we evaluate outage probability (OP) of a cluster-based multi-hop protocol operating on an underlay cognitive radio (CR) mode. The primary network consists of multiple independent transmit/receive pairs, and the primary transmitters seriously cause co-channel interference (CCI) to the secondary receivers. To improve the outage performance for the secondary network under the joint impact of the CCI and hardware imperfection, we employ the best relay selection at each hop. Moreover, the destination is equipped with multiple antennas and uses the selection combining (SC) technique to enhance the reliability of the data transmission at the last hop. For performance evaluation, we first derive an exact formula of OP for the primary network which is used to calculate the transmit power of the secondary transmitters. Next, an exact closed-form expression of the end-to-end OP for the secondary network is derived over Rayleigh fading channels. We then perform Monte-Carlo simulations to validate the derivations. The results present that the CCI caused by the primary operations significantly impacts on the outage performance of the secondary network

Inexact reduced gradient methods in smooth nonconvex optimization

This paper proposes and develops new line search methods with inexact gradient information for finding stationary points of nonconvex continuously differentiable functions on finite-dimensional spaces. Some abstract convergence results for a broad class of line search methods are reviewed and extended. A general scheme for inexact reduced gradient (IRG) methods with different stepsize selections are proposed to construct sequences of iterates with stationary accumulation points. Convergence results with convergence rates for the developed IRG methods are established under the Kurdyka-Lojasiewicz property. The conducted numerical experiments confirm the efficiency of the proposed algorithms

ZnO/CdS Bilayer used for Electrode in Photovoltaic Device

In this article we present the fabrication and characterization of the nanoporous ZnO and/or ZnO/CdS thin films onto indium doped-tin oxide (ITO) substrates, based on the thermal evaporation technique followed by thermal treatment. The preparation method was relatively simple and low-cost for large scale uniform coating to produce clean, dense and strong adhesion to substrate thin films. The nanostructured ZnO and ZnO/CdS thin films were characterized by X-ray diffraction (XRD) and field emission scanning electron microscope (FE-SEM). The nanostructured ZnO/CdS bilayer film was used in a photo-electrochemical (PEC) cell as a working electrode and a Pt net as a counter electrode. The results show that the photovoltaic cell with nanostructured ZnO/CdS bilayer film electrode has significantly improved photoelectric capability in comparison with that of ZnO electrode