Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning Class-level Prototypes for Few-shot Learning

Few-shot learning aims to recognize new categories using very few labeled samples. Although few-shot learning has witnessed promising development in recent years, most existing methods adopt an average operation to calculate prototypes, thus limited by the outlier samples. In this work, we propose a simple yet effective framework for few-shot classification, which can learn to generate preferable prototypes from few support data, with the help of an episodic prototype generator module. The generated prototype is meant to be close to a certain \textit{\targetproto{}} and is less influenced by outlier samples. Extensive experiments demonstrate the effectiveness of this module, and our approach gets a significant raise over baseline models, and get a competitive result compared to previous methods on \textit{mini}ImageNet, \textit{tiered}ImageNet, and cross-domain (\textit{mini}ImageNet $\rightarrow$ CUB-200-2011) datasets.
The Role of Coupling Radius in 1-D Parallel and 2-D SQUID and SQIF Arrays

We investigate theoretically the effect of the coupling radius on the transfer function in 1D parallel and 2D SQUID arrays with different number of Josephson junctions in parallel and series at 77 K. Our results show a plateauing of the array maximum transfer function with the number of junctions in parallel. The plateauing defines the array coupling radius which we show increases with decreasing the normalised inductive reactance of the SQUID loop. The coupling radius is found to be independent of the number of junctions in series. Finally, we investigate the voltage versus magnetic field response and maximum transfer function of one 1D and two 2D SQIF arrays with different SQUID loop area distributions.
Decoding Object-Based Auditory Attention from Source-Reconstructed MEG Alpha Oscillations

How do we attend to relevant auditory information in complex naturalistic scenes? Much research has focused on detecting which information is attended, without regarding underlying top-down control mechanisms. Studies investigating attentional control generally manipulate and cue specific features in simple stimuli. However, in naturalistic scenes it is impossible to dissociate relevant from irrelevant information based on low-level features. Instead, the brain has to parse and select auditory objects of interest. The neural underpinnings of object-based auditory attention remain not well understood. Here we recorded MEG while 15 healthy human subjects (9 female) prepared for the repetition of an auditory object presented in one of two overlapping naturalistic auditory streams. The stream containing the repetition was prospectively cued with 70% validity. Crucially, this task could not be solved by attending low-level features, but only by processing the objects fully. We trained a linear classifier on the cortical distribution of source-reconstructed oscillatory activity to distinguish which auditory stream was attended. We could successfully classify the attended stream from alpha (8–14 Hz) activity in anticipation of repetition onset. Importantly, attention could only be classified from trials in which subjects subsequently detected the repetition, but not from miss trials. Behavioral relevance was further supported by a correlation between classification accuracy and detection performance. Decodability was not sustained throughout stimulus presentation, but peaked shortly before repetition onset, suggesting that attention acted transiently according to temporal expectations. We thus demonstrate anticipatory alpha oscillations to underlie top-down control of object-based auditory attention in complex naturalistic scenes. SIGNIFICANCE STATEMENT In everyday life, we often find ourselves bombarded with auditory information, from which we need to select what is relevant to our current goals. Previous research has highlighted how we attend to specific highly controlled aspects of the auditory input. Although invaluable, it is still unclear how this relates to attentional control in naturalistic auditory scenes. Here we used the high precision of magnetoencephalography in space and time to investigate the brain mechanisms underlying top-down control of object-based attention in ecologically valid sound scenes. We show that rhythmic activity in auditory association cortex at a frequency of ∼10 Hz (alpha waves) controls attention to currently relevant segments within the auditory scene and predicts whether these segments are subsequently detected.
Inhibition of autophagy promotes human RSV NS1-induced inflammation and apoptosis in vitro

Human respiratory syncytial virus (RSV) is a major health challenge due to the lack of a safe and effective vaccine and antiviral drugs. RSV non-structural protein 1 (NS1) is the main inhibitor of antiviral signaling pathways in RSV infection; however, the underlying mechanism is unclear. The aim of the present study was to investigate of the role of NS1 and its relationship with autophagy. NS1-Flag plasmid was transfected into A549 cells and the levels of inflammatory cytokines, autophagy markers and apoptosis were detected. In addition, the cells were treated with an autophagy inhibitor, 3-methyladenine for 12 h prior to transfection with the NS1 plasmid to explore the role of autophagy in NS1-transfected cells. The results showed that the production of inflammatory cytokines and autophagy was induced in NS1-transfected cells, and indicated that autophagy prevents the production of cytokines and the activation of apoptosis. Furthermore, the results demonstrated that NS1 activated autophagy partly through the mTOR-p70 S6 kinase signaling pathway. The results suggest that autophagy induced by NS1 transfection through the mTOR pathway can hinder the production of inflammatory cytokines and interferon-α and inhibit cell apoptosis, which may help to explain why autophagy has been shown to be beneficial to viral replication in most studies.
Low Rank Saddle Free Newton: A Scalable Method for Stochastic Nonconvex Optimization

In modern deep learning, highly subsampled stochastic approximation (SA) methods are preferred to sample average approximation (SAA) methods because of large data sets as well as generalization properties. Additionally, due to perceived costs of forming and factorizing Hessians, second order methods are not used for these problems. In this work we motivate the extension of Newton methods to the SA regime, and argue for the use of the scalable low rank saddle free Newton (LRSFN) method, which avoids forming the Hessian in favor of making a low rank approximation. Additionally, LRSFN can facilitate fast escape from indefinite regions leading to better optimization solutions. In the SA setting, iterative updates are dominated by stochastic noise, and stability of the method is key. We introduce a continuous time stability analysis framework, and use it to demonstrate that stochastic errors for Newton methods can be greatly amplified by ill-conditioned Hessians. The LRSFN method mitigates this stability issue via Levenberg-Marquardt damping. However, generally the analysis shows that second order methods with stochastic Hessian and gradient information may need to take small steps, unlike in deterministic problems. Numerical results show that LRSFN can escape indefinite regions that other methods have issues with; and even under restrictive step length conditions, LRSFN can outperform popular first order methods on large scale deep learning tasks in terms of generalizability for equivalent computational work.
Genetic Polymorphisms of Pesticide-Metabolizing Enzymes and Transporters in Agricultural Workers and Thyroid Hormone Levels

Purpose Chronic exposure to pesticides has been associated with thyroid dysfunction owing to their endocrine disruption ability. Genetic variations in genes encoding phase I and II enzymes and phase III transporters are partly responsible for individual responses to chemical pesticides. This study investigated the association between variations in genes involved in pesticide metabolism and altered thyroid hormone concentrations. Methods We assessed thyroid-stimulating hormone (TSH), free triiodothyronine (FT3), and free thyroxine (FT4) in organic agriculture workers (n = 216) and workers who used chemical pesticides (n = 229). A questionnaire was used to collect sociodemographic, pesticide exposure, and health status data. Blood samples were analyzed for TSH, FT3, and FT4. Genomic DNA was extracted and genotyped using the TaqMan real-time PCR genotyping assay and restriction fragment length polymorphism method for 15 metabolically related genes. Results Significant differences in the TSH (1.58 vs 1.12 µIU/mL) and FT3 (0.34 vs 0.31 ng/dL) concentrations between the chemical and organic worker groups were observed. The frequencies of all single nucleotide polymorphisms were in Hardy–Weinberg equilibrium and were mostly consistent with Asian populations. The findings showed the association between SNPs of enzymes and transporters and TSH, FT3, and FT4. The odd ratio and adjusted odd ratio (with sex, age, smoking status, alcohol consumption and exposure parameters) for subclinical thyroid disease by the variant alleles CYP1A1 rs1048943, CYP2B6 rs2279343, CYP2C19 rs4244285, NAT2 rs1799931, and PON1 rs662 in the chemical workers compared with the organic workers were found (P values < 0.05). Conclusion This is the first study to assess gene–environment interactions in Thai agricultural workers by investigating disruptions of the hypothalamic–pituitary–thyroid axis. The investigated SNP profiles revealed several gene–thyroid hormone associations in which even low levels of pesticide exposure could disturb thyroid homeostasis. These findings provide a foundation for planning future studies investigating associations between complex diseases and occupational pesticide exposure.
Numerical investigation of non-condensable gas effect on vapor bubble collapse

We numerically investigate the effect of non-condensable gas inside a vapor bubble on bubble dynamics, collapse pressure and pressure impact of spherical and aspherical bubble collapses. Free gas inside a vapor bubble has a damping effect that can weaken the pressure wave and enhance the bubble rebound. To estimate this effect numerically, we derive and validate a multi-component model for vapor bubbles containing gas. For the cavitating liquid and the non-condensable gas, we employ a homogeneous mixture model with a coupled equation of state for all components. The cavitation model for the cavitating liquid is a barotropic thermodynamic equilibrium model. Compressibility of all phases is considered in order to capture the shock wave of the bubble collapse. After validating the model with an analytical energy partitioning model, simulations of collapsing wall-attached bubbles with different stand-off distances are performed. The effect of the non-condensable gas on rebound and damping of the emitted shock wave is well captured.
Method Calls Frequency-Based Tie-Breaking Strategy For Software Fault Localization

In Spectrum-Based Fault Localization (SBFL), a suspiciousness score is assigned to each code element based on test coverage and test outcomes. The scores are then used to rank the code elements relative to each other in order to aid the programmer during the debugging process when seeking the source of a fault. However, probably none of the known SBFL formulae are guaranteed to produce different scores for all the program elements, hence ties emerge between the code elements. Based on our experiments, ties in SBFL are prevalent: in Defects4J, 54–56% of buggy methods are members of ties, i.e., there is at least one other method with the same score in these cases (but typically much more, on average 6), and this inevitably reduces the effectiveness of any SBFL approach. In this work, we present a technique to break ties in such cases based on the so-called method calls frequencies. This counts the number of different contexts of method calls (both as callees and as callers) in failing test cases. The intuition is that if a method appears in many different calling contexts during a failing test case, it will be more suspicious and get a higher rank position compared to other methods with the same scores. This method can be applied to any underlying SBFL formula, and can favourably break the occurring ranks in the ties in many cases. The experimental results show that our novel tie-breaking strategy achieved a significant reduction in both size and number of critical ties in our benchmark. In 72-73% of the cases, the ties were completely eliminated and the average reduction rate was more than 80%.
Development of refined clarifier operating diagrams using an updated settling characteristics database

An analysis of five sludge-settling characteristics data sets is presented, representing data from 51 separate suspended growth bio logical wastewater treatment processes and including 214 separate runs and over 1 500 individual data points. The results indicate that the data can be characterized using a single relationship between the initial sludge settling velocity ( Kj), the initial sludge concentration (Ci), and the sludge volume index (SVI). The predictive capability of this relationship is in dependently verified. These results suggest that this single relationship can be used to accurately characterize the settling characteristics of an activated sludge based only on the measured SVI. A comparison of al ternate sludge-settling indexes indicates that the predictive capability of the relationship can be further improved by use of the stirred SVI3 5 analysis rather than the SVI. More data are required to refine relationships developed based on the SSVI3 5 and the diluted SVI. Secondary clarifier operating diagrams based on the developed relationships are presented. Water Environ. Res., 67, 000 (1995).
Models In a Spelling Bee: Language Models Implicitly Learn the Character Composition of Tokens

Standard pretrained language models operateon sequences of subword tokens without direct access to the characters that compose eachtoken’s string representation. We probe theembedding layer of pretrained language models and show that models learn the internalcharacter composition of whole word and subword tokens to a surprising extent, withoutever seeing the characters coupled with the tokens. Our results show that the embedding layers of RoBERTa and GPT2 each hold enoughinformation to accurately spell up to a thirdof the vocabulary and reach high characterngram overlap across all token types. We further test whether enriching subword modelswith character information can improve language modeling, and observe that this methodhas a near-identical learning curve as training without spelling-based enrichment. Overall, our results suggest that language modeling objectives incentivize the model to implicitly learn some notion of spelling, and that explicitly teaching the model how to spell doesnot appear to enhance its performance on suchtasks.
Chemical Compositions in the Vicinity of Protostars in Ophiuchus

We have analyzed Atacama Large Millimeter/submillimeter Array Cycle 4 Band 6 data toward two young stellar objects (YSOs), Oph-emb5 and Oph-emb9, in the Ophiuchus star-forming region. The YSO Oph-emb5 is located in a relatively quiescent region, whereas Oph-emb9 is irradiated by a nearby bright Herbig Be star. Molecular lines from cyclic-C3H2 (c-C3H2), H2CO, CH3OH, 13CO, C18O, and DCO+ have been detected from both sources, while DCN is detected only in Oph-emb9. Around Oph-emb5, c-C3H2 is enhanced at the west side, relative to the IR source, whereas H2CO and CH3OH are abundant at the east side. In the field of Oph-emb9, moment 0 maps of the c-C3H2 lines show a peak at the eastern edge of the field of view, which is irradiated by the Herbig Be star. Moment 0 maps of CH3OH and H2CO show peaks farther from the bright star. We derive the N(c-C3H2)/N(CH3OH) column density ratios at the peak positions of c-C3H2 and CH3OH near each YSO, which are identified based on their moment 0 maps. The N(c-C3H2)/N(CH3OH) ratio at the c-C3H2 peak is significantly higher than at the CH3OH peak by a factor of ∼19 in Oph-emb9, while the difference in this column density ratio between these two positions is a factor of ∼2.6 in Oph-emb5. These differences are attributed to the efficiency of the photon-dominated region chemistry in Oph-emb9. The higher DCO+ column density and the detection of DCN in Oph-emb9 are also discussed in the context of UV irradiation flux.
Prevalence, Parasite Density and Determinants of Falciparum Malaria Among Febrile Children in Some Peri-Urban Communities in Southwestern Nigeria: A Cross-Sectional Study

Background Malaria remains a serious public health problem worldwide, particularly in tropical and subtropical regions, including Nigeria. This study investigates the prevalence, parasite density and determinants of malaria among symptomatic children in some peri-urban communities in southwestern Nigeria. Methods This was a randomized cross-sectional and hospital-based study. The standard method of microscopy was employed. Thick and thin films were prepared and viewed under a light microscope to identify and quantify malaria parasites. A well-structured and pre-tested questionnaire was used to obtain the subject’s information on the demographic, socio-economic and environmental variables. Results A total of 380 (71.7%) participants were infected with Plasmodium falciparum with a mean parasite density of 1857.11 parasite/µL of blood. Malaria prevalence and mean parasite density were significantly higher among male compared to their female counterparts [80.3% vs 61.4% and 2026.46 vs 1619.63 parasite/µL of blood]. Similarly, age group ≤5 years had the highest malaria prevalence (92.2%) and mean parasite density (2031.66 parasite/µL of blood) than other age groups (AOR 2.281, 95% CI: 1.187–4.384, P < 0.05). The multivariate logistic analysis showed that malaria disease is significantly associated with having mother with no formal education (AOR 12.235, 95% CI: 3.253–46.021, P < 0.05), having well and river as a major source of household water supply (AOR 13.810, 95% CI: 3.012–63.314, P < 0.05 vs AOR 5.639, 95% CI: 1.455–21.853, P < 0.05) and presence of stagnant water around home (AOR 5.22, 95% CI: 2.921–9.332, P < 0.05). Furthermore, protective factors observed include ownership of mosquito bed net (AOR 0.474, 95% CI: 0.223–1.008, P < 0.05) and distance of home to hospital (AOR 0.279, 95% CI: 0.158–0.493, P < 0.05). Conclusion Malaria remains a serious public health problem in the study area. Adopting integrated malaria control measures including educating parents on malaria prevention and control strategies, distributing mosquito bed nets, and establishing larvae source management program is highly imperative.
PAM-DenseNet: A Deep Convolutional Neural Network for Computer-Aided COVID-19 Diagnosis

Currently, several convolutional neural network (CNN)-based methods have been proposed for computer-aided COVID-19 diagnosis based on lung computed tomography (CT) scans. However, the lesions of pneumonia in CT scans have wide variations in appearances, sizes, and locations in the lung regions, and the manifestations of COVID-19 in CT scans are also similar to other types of viral pneumonia, which hinders the further improvement of CNN-based methods. Delineating infection regions manually is a solution to this issue, while excessive workload of physicians during the epidemic makes it difficult for manual delineation. In this article, we propose a CNN called dense connectivity network with parallel attention module (PAM-DenseNet), which can perform well on coarse labels without manually delineated infection regions. The parallel attention module automatically learns to strengthen informative features from both channelwise and spatialwise simultaneously, which can make the network pay more attention to the infection regions without any manual delineation. The dense connectivity structure performs feature maps reuse by introducing direct connections from previous layers to all subsequent layers, which can extract representative features from fewer CT slices. The proposed network is first trained on 3530 lung CT slices selected from 382 COVID-19 lung CT scans, 372 lung CT scans infected by other pneumonia, and 200 normal lung CT scans to obtain a pretrained model for slicewise prediction. We then apply this pretrained model to a CT scans dataset containing 94 COVID-19 CT scans, 93 other pneumonia CT scans, and 93 normal lung scans, and achieve patientwise prediction through a voting mechanism. The experimental results show that the proposed network achieves promising results with an accuracy of 94.29%, a precision of 93.75%, a sensitivity of 95.74%, and a specificity of 96.77%, which is comparable to the methods that are based on manually delineated infection regions.
SARS-CoV-2 transmission in kindergarten to grade 12 schools in the Vancouver Coastal Health region: a descriptive epidemiologic study

Background: There is an urgent need to assess the role of schools in the spread of SARS-CoV-2 in Canada to inform public health measures. We describe the epidemiology of SARS-CoV-2 infection among students and staff in the Vancouver Coastal Health (VCH) region in the first 3 months of the 2020/2021 academic year, and examine the extent of transmission in schools. Methods: This descriptive epidemiologic study using contact tracing data included individuals aged 5 years and older with SARS-CoV-2 infection, reported between Sept. 10 and Dec. 18, 2020, who worked in or attended kindergarten to grade 12 (K–12) schools in person in the VCH region. We described case and cluster characteristics and reported the number of school-based transmissions. Results: During the study period, 699 cases of SARS-CoV-2 infection were reported (55 cases per 10 000 VCH school population). Among cases in VCH resident staff and students, 52.5% (354/674) were linked to a household case or cluster; less than 1.5% (< 10) of infected individuals were hospitalized and none died. Out of 699 cases present at school, 26 clusters with school-based transmission resulted in 55 secondary cases. Staff members accounted for 53.8% of index cases (14/26) while making up 14.3% of the school population (17 742/123 647). Among clusters, 88.5% (23) had fewer than 4 secondary cases. Interpretation: In our population during the study period, there were no deaths and severe disease was rare; furthermore, school-based SARS-CoV-2 transmissions were uncommon and clusters were small. Our results, which relate primarily to symptomatic disease, support the growing body of evidence that schools likely did not play a major role in SARS-CoV-2 spread in 2020.