Strong convergence and control condition of modified Halpern iterations in Banach spaces

Let C be a nonempty closed convex subset of a real Banach space X which has a uniformly Gâteaux differentiable norm. Let T∈ΓC and f∈ΠC. Assume that {xt} converges strongly to a fixed point z of T as t→0, where xt is the unique element of C which satisfies xt=tf(xt)+(1−t)Txt. Let {αn} and {βn} be two real sequences in (0,1) which satisfy the following conditions: (C1)lim⁡n→∞αn=0;(C2)∑n=0∞αn=∞;(C6)0<lim⁡inf⁡n→∞βn≤lim⁡sup⁡n→∞βn<1. For arbitrary x0∈C, let the sequence {xn} be defined iteratively by yn=αnf(xn)+(1−αn)Txn, n≥0, xn+1=βnxn+(1−βn)yn, n≥0. Then {xn} converges strongly to a fixed point of T

An Acoustic Study of English Word Stress of Amdo English Learners

This paper analyses the mastery of English word stress of China’s Tibetan Amdo English learners, by means of acoustic phonetics. According to the “Negative Transfer” theory, as the mother language of Amdo doesn’t have word stress, this will put negative influence to the learning of English stress and their pronunciation of it will be poor. However, the result of this study shows that these learners’ grasp of English word stress is better than prediction, with an overall accuracy of 70% percent. Among the findings, two noticeable research result was discovered, which are the Amdo speakers’ pronunciation of English words with stress on the first syllable (for words with multi-syllables), compound words with stress on the first word are quite problematic, and these speakers has no awareness of “stress shift”. These findings are very helpful to Amdo English learners and their eachers and could be further used in pedagogy designs

Deep recurrent spiking neural networks capture both static and dynamic representations of the visual cortex under movie stimuli

In the real world, visual stimuli received by the biological visual system are predominantly dynamic rather than static. A better understanding of how the visual cortex represents movie stimuli could provide deeper insight into the information processing mechanisms of the visual system. Although some progress has been made in modeling neural responses to natural movies with deep neural networks, the visual representations of static and dynamic information under such time-series visual stimuli remain to be further explored. In this work, considering abundant recurrent connections in the mouse visual system, we design a recurrent module based on the hierarchy of the mouse cortex and add it into Deep Spiking Neural Networks, which have been demonstrated to be a more compelling computational model for the visual cortex. Using Time-Series Representational Similarity Analysis, we measure the representational similarity between networks and mouse cortical regions under natural movie stimuli. Subsequently, we conduct a comparison of the representational similarity across recurrent/feedforward networks and image/video training tasks. Trained on the video action recognition task, recurrent SNN achieves the highest representational similarity and significantly outperforms feedforward SNN trained on the same task by 15% and the recurrent SNN trained on the image classification task by 8%. We investigate how static and dynamic representations of SNNs influence the similarity, as a way to explain the importance of these two forms of representations in biological neural coding. Taken together, our work is the first to apply deep recurrent SNNs to model the mouse visual cortex under movie stimuli and we establish that these networks are competent to capture both static and dynamic representations and make contributions to understanding the movie information processing mechanisms of the visual cortex

Neural Pairwise Ranking Factorization Machine for Item Recommendation

The factorization machine models attract significant attention from academia and industry because they can model the context information and improve the performance of recommendation. However, traditional factorization machine models generally adopt the point-wise learning method to learn the model parameters as well as only model the linear interactions between features. They fail to capture the complex interactions among features, which degrades the performance of factorization machine models. In this paper, we propose a neural pairwise ranking factorization machine for item recommendation, which integrates the multi-layer perceptual neural networks into the pairwise ranking factorization machine model. Specifically, to capture the high-order and nonlinear interactions among features, we stack a multi-layer perceptual neural network over the bi-interaction layer, which encodes the second-order interactions between features. Moreover, the pair-wise ranking model is adopted to learn the relative preferences of users rather than predict the absolute scores. Experimental results on real world datasets show that our proposed neural pairwise ranking factorization machine outperforms the traditional factorization machine models

Deep Spiking Neural Networks with High Representation Similarity Model Visual Pathways of Macaque and Mouse

Deep artificial neural networks (ANNs) play a major role in modeling the visual pathways of primate and rodent. However, they highly simplify the computational properties of neurons compared to their biological counterparts. Instead, Spiking Neural Networks (SNNs) are more biologically plausible models since spiking neurons encode information with time sequences of spikes, just like biological neurons do. However, there is a lack of studies on visual pathways with deep SNNs models. In this study, we model the visual cortex with deep SNNs for the first time, and also with a wide range of state-of-the-art deep CNNs and ViTs for comparison. Using three similarity metrics, we conduct neural representation similarity experiments on three neural datasets collected from two species under three types of stimuli. Based on extensive similarity analyses, we further investigate the functional hierarchy and mechanisms across species. Almost all similarity scores of SNNs are higher than their counterparts of CNNs with an average of 6.6%. Depths of the layers with the highest similarity scores exhibit little differences across mouse cortical regions, but vary significantly across macaque regions, suggesting that the visual processing structure of mice is more regionally homogeneous than that of macaques. Besides, the multi-branch structures observed in some top mouse brain-like neural networks provide computational evidence of parallel processing streams in mice, and the different performance in fitting macaque neural representations under different stimuli exhibits the functional specialization of information processing in macaques. Taken together, our study demonstrates that SNNs could serve as promising candidates to better model and explain the functional hierarchy and mechanisms of the visual system.Comment: Accepted by Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI-23

High prevalence of a globally disseminated hypervirulent clone, Staphylococcus aureus CC121, with reduced vancomycin susceptibility in community settings in China

Objectives: Most vancomycin-intermediate Staphylococcus aureus (VISA) and heterogeneous VISA (hVISA) are derived from hospital-associated MRSA due to treatment failure; however, the prevalence of hVISA/VISA in community settings remains unclear. Methods: Four hundred and seventy-six community-associated isolates were collected between 2010 and 2011 during national surveillance for antimicrobial resistance in 31 county hospitals across China. Drug susceptibility evaluation and mecA detection were performed by using broth microdilution and PCR analysis, respectively. hVISA/VISA were identified by using macro-Etest and a modified population analysis profile (PAP)-AUC method. The genetic features of all hVISA/VISA isolates were genotyped. Results: Among 476 isolates, MRSA and MSSA accounted for 19.7% (n = 94) and 80.3% (n = 382), respectively. Two VISA and 36 hVISA isolates were identified by PAP-AUC testing. The VISA isolates and 29 of the hVISA isolates were MRSA. The proportion of hVISA/VISA was significantly higher in MRSA (30.9%) than in MSSA (1.8%). The hVISA/VISA isolates were assigned to 18 STs classified into seven clonal complexes (CCs). CC121 (n = 12) followed by ST239 (n = 11) was the most prevalent hVISA/VISA clone. All ST239-hVISA/VISA were MRSA, while 12 CC121-hVISA isolates included 6 MSSA and 6 MRSA isolates. SCCmec III was predominant among MRSA-hVISA/VISA isolates. agr I and agr IV were detected in ST239 and CC121, respectively. All except two strains were positive for Panton-Valentine leucocidin genes. Conclusions: To the best of our knowledge, this is the first report of CC121 as a prevalent hVISA clone in community settings, highlighting the necessity of surveillance and stricter infection control measures for this globally disseminated lineage

PCR-Free Detection of Genetically Modified Organisms Using Magnetic Capture Technology and Fluorescence Cross-Correlation Spectroscopy

The safety of genetically modified organisms (GMOs) has attracted much attention recently. Polymerase chain reaction (PCR) amplification is a common method used in the identification of GMOs. However, a major disadvantage of PCR is the potential amplification of non-target DNA, causing false-positive identification. Thus, there remains a need for a simple, reliable and ultrasensitive method to identify and quantify GMO in crops. This report is to introduce a magnetic bead-based PCR-free method for rapid detection of GMOs using dual-color fluorescence cross-correlation spectroscopy (FCCS). The cauliflower mosaic virus 35S (CaMV35S) promoter commonly used in transgenic products was targeted. CaMV35S target was captured by a biotin-labeled nucleic acid probe and then purified using streptavidin-coated magnetic beads through biotin-streptavidin linkage. The purified target DNA fragment was hybridized with two nucleic acid probes labeled respectively by Rhodamine Green and Cy5 dyes. Finally, FCCS was used to detect and quantify the target DNA fragment through simultaneously detecting the fluorescence emissions from the two dyes. In our study, GMOs in genetically engineered soybeans and tomatoes were detected, using the magnetic bead-based PCR-free FCCS method. A detection limit of 50 pM GMOs target was achieved and PCR-free detection of GMOs from 5 µg genomic DNA with magnetic capture technology was accomplished. Also, the accuracy of GMO determination by the FCCS method is verified by spectrophotometry at 260 nm using PCR amplified target DNA fragment from GM tomato. The new method is rapid and effective as demonstrated in our experiments and can be easily extended to high-throughput and automatic screening format. We believe that the new magnetic bead-assisted FCCS detection technique will be a useful tool for PCR-free GMOs identification and other specific nucleic acids