Accuracy of Bilingual Chinese-speakers using search systems

Internet users have substantial trust in search engine’s ability to rank the re-sults by the relevance to the query. This paper is seeking to understand how three factors affect the accuracy for native bilingual Chinese-speaking inter-net users. The factors are proficiency of English, the position of relevant in-formation on search engine result page (SERP) and system language. Sub-jects in this research interacted with simulated search engines and they were asked to identify the best results on SERP. The results show that the system language or English ability alone do not affect subjects’ performance, only if those two factors work together effect on subjects’ performance on finding results on SERP. Rank basis exists in bilingual Chinese-speakers and they tent to click on the results on higher locations. Target location in different system language only matters in the group of subjects within average Eng-lish ability, not in the group of low or high English ability subjects. But the accuracy performances are reversed in low English ability compare to high English ability

Single-ancilla ground state preparation via Lindbladians

We design an early fault-tolerant quantum algorithm for ground state preparation. As a Monte Carlo-style quantum algorithm, our method features a Lindbladian where the target state is stationary, and its evolution can be efficiently implemented using just one ancilla qubit. Our algorithm can prepare the ground state even when the initial state has zero overlap with the ground state, bypassing the most significant limitation of methods like quantum phase estimation. As a variant, we also propose a discrete-time algorithm, which demonstrates even better efficiency, providing a near-optimal simulation cost for the simulation time and precision. Numerical simulation using Ising models and Hubbard models demonstrates the efficacy and applicability of our method

Learning Two-Stream CNN for Multi-Modal Age-related Macular Degeneration Categorization

This paper tackles automated categorization of Age-related Macular Degeneration (AMD), a common macular disease among people over 50. Previous research efforts mainly focus on AMD categorization with a single-modal input, let it be a color fundus image or an OCT image. By contrast, we consider AMD categorization given a multi-modal input, a direction that is clinically meaningful yet mostly unexplored. Contrary to the prior art that takes a traditional approach of feature extraction plus classifier training that cannot be jointly optimized, we opt for end-to-end multi-modal Convolutional Neural Networks (MM-CNN). Our MM-CNN is instantiated by a two-stream CNN, with spatially-invariant fusion to combine information from the fundus and OCT streams. In order to visually interpret the contribution of the individual modalities to the final prediction, we extend the class activation mapping (CAM) technique to the multi-modal scenario. For effective training of MM-CNN, we develop two data augmentation methods. One is GAN-based fundus / OCT image synthesis, with our novel use of CAMs as conditional input of a high-resolution image-to-image translation GAN. The other method is Loose Pairing, which pairs a fundus image and an OCT image on the basis of their classes instead of eye identities. Experiments on a clinical dataset consisting of 1,099 color fundus images and 1,290 OCT images acquired from 1,099 distinct eyes verify the effectiveness of the proposed solution for multi-modal AMD categorization

Potential roles of the gut microbiota in the manifestations of drug use disorders

Drug use disorders (DUDs) not only cause serious harm to users but also cause huge economic, security, and public health burdens to families and society. Recently, several studies have shown that gut microbiota (GM) can affect the central nervous system and brain functions. In this review, we focus on the potential role of the GM in the different stages of DUDs. First, the GM may induce individuals to seek novel substances. Second, the gut microbiota is involved in the decomposition and absorption of drugs. Symptoms of individuals who suffer from DUDs are also related to intestinal microorganisms. Third, the effects of the GM and its metabolites on drug relapse are mainly reflected in the reward effect and drug memory. In conclusion, recent studies have preliminarily explored the relationship between GM and DUDs. This review deepens our understanding of the mechanisms of DUDs and provides important information for the future development of clinical treatment for DUDs

Machine Learning-Enabled IoT Security: Open Issues and Challenges Under Advanced Persistent Threats

Despite its technological benefits, Internet of Things (IoT) has cyber weaknesses due to the vulnerabilities in the wireless medium. Machine learning (ML)-based methods are widely used against cyber threats in IoT networks with promising performance. Advanced persistent threat (APT) is prominent for cybercriminals to compromise networks, and it is crucial to long-term and harmful characteristics. However, it is difficult to apply ML-based approaches to identify APT attacks to obtain a promising detection performance due to an extremely small percentage among normal traffic. There are limited surveys to fully investigate APT attacks in IoT networks due to the lack of public datasets with all types of APT attacks. It is worth to bridge the state-of-the-art in network attack detection with APT attack detection in a comprehensive review article. This survey article reviews the security challenges in IoT networks and presents the well-known attacks, APT attacks, and threat models in IoT systems. Meanwhile, signature-based, anomaly-based, and hybrid intrusion detection systems are summarized for IoT networks. The article highlights statistical insights regarding frequently applied ML-based methods against network intrusion alongside the number of attacks types detected. Finally, open issues and challenges for common network intrusion and APT attacks are presented for future research.Comment: ACM Computing Surveys, 2022, 35 pages, 10 Figures, 8 Table

Association of Toll-Like Receptor 4 Gene Polymorphism and Expression with Urinary Tract Infection Types in Adults

Background: Innate immunity of which Toll-like receptor (TLR) 4 and CXCR1 are key elements plays a central role in the development of urinary tract infection (UTI). Although the relation between the genetics of TLR4 and CXCR1 and UTI is investigated partly, the polymorphisms and expression of TLR4 and CXCR1 in different types of UTI in adults are not extremely clear. Methodology/Principal Findings: This study investigates the presence of TLR4 A (896) G and CXCR1 G (2608) C polymorphisms in 129 UTI patients using RFLP-PCR. Gene and allelic prevalence were compared with 248 healthy controls. Flow cytometry was used to detect TLR4 and CXCR1 expression in the monocytes of UTI patients and healthy controls. TLR4 (896) AG genotype and TLR4 (896) G allele had higher prevalence in UTI (especially in acute cystitis and urethritis) patients, whereas CXCR1 (2608) GC genotype and CXCR1 (2608) C allele had lower prevalence in UTI patients than controls. TLR4 expression was significantly lower in chronic UTI patients than in acute pyelonephritis or healthy controls. CXCR1 expression was similar in both controls and patients. TLR4 expression in chronic UTI patients after astragalus treatment was higher than pre-treatment. Conclusions: The results indicate the relationship between the carrier status of TLR4 (896) G alleles and the development of UTI, especially acute cystitis and urethritis, in adults. TLR4 expression levels are correlated with chronic UTI