Miracle-dz/UniFAR-FTUs huggingface dataset:
Reducing errors for root-MUSIC-based methods in uniform circular arrays

Root-MUSIC can be applied to uniform circular array (UCA) to achieve computationally efficient direction of arrival (DOA) estimation via beamspace transformation (BT). When the number of sensors of a UCA is small, the residual errors introduced by the BT will have significant values, resulting in performance degradation for DOA estimation. To solve this problem, an algorithm that enables the modification of the beamspace sample covariance matrix (BSCM) by considering the residual components is proposed. First, the residual components in the BSCM are calculated based on the initial DOAs estimated by the UCA root-MUSIC-based methods. Then the BSCM is modified by removing the undesirable terms. Finally, better DOA estimation performance is obtained by using the revised BSCM. The computational complexity and estimation error are derived. The significant advantages of the proposed algorithm are demonstrated by the simulation results.
Efficient human motion capture data annotation via multi-view spatiotemporal feature fusion

The availability of large motion capture (mocap) data has sparked a great motivation for computer animation, and the task of automatically annotating complex mocap sequences plays an important role in the efficient motion analysis. To this end, this study presents an efficient human mocap data annotation approach by using multi-view spatiotemporal feature fusion. First, the authors exploit an improved hierarchical aligned cluster analysis algorithm to divide the unknown human mocap sequence into several sub-motion clips, and each sub-motion clip incorporates a particular semantic meaning. Then, the two kinds of multi-view features, namely most informative central distances and most informative geometric angles, are discriminatively extracted and temporally modelled by a Fourier temporal pyramid to complementarily characterise each motion clip. Finally, the authors utilise the discriminant correlation analysis to fuse these two types of motion features and further employ an extreme learning machine to annotate each sub-motion clip. The extensive experiments tested on the public available database have demonstrated the effectiveness of the proposed approach in comparison with the existing counterparts.
Brain-Computer Interfacing with Emotion-Inducing imagery: a Pilot Study

Using neural correlates of intentionally induced human emotions may offer alternative imagery strategies to control brain-computer interface (BCI) applications. In this paper, self-induced emotions i.e., emotions induced by participants performing sad or happy related emotional imagery, are compared to motor imagery (MI) in a two-class electroencephalogram (EEG)-based BCI. The BCI setup included a multistage signal-processing framework allowing online continuous feedback presentation in a game involving one-dimensional control of game character. With seven participants, the highest online accuracies were 90% for emotion-inducing imagery (EII) and 80% for MI. Offline and online results analysis showed no significant differences in MI and EII performance. The results suggest that EII may be suitable for intentional control in BCI paradigms and offer a viable alternative for some BCI users.
Penetrating the Fog: the Path to Efficient CNN Models

With the increasing demand to deploy convolutional neural networks (CNNs) on mobile platforms, the sparse kernel approach was proposed, which could save more parameters than the standard convolution while maintaining accuracy. However, despite the great potential, no prior research has pointed out how to craft an sparse kernel design with such potential (i.e., effective design), and all prior works just adopt simple combinations of existing sparse kernels such as group convolution. Meanwhile due to the large design space it is also impossible to try all combinations of existing sparse kernels. In this paper, we are the first in the field to consider how to craft an effective sparse kernel design by eliminating the large design space. Specifically, we present a sparse kernel scheme to illustrate how to reduce the space from three aspects. First, in terms of composition we remove designs composed of repeated layers. Second, to remove designs with large accuracy degradation, we find an unified property named information field behind various sparse kernel designs, which could directly indicate the final accuracy. Last, we remove designs in two cases where a better parameter efficiency could be achieved. Additionally, we provide detailed efficiency analysis on the final four designs in our scheme. Experimental results validate the idea of our scheme by showing that our scheme is able to find designs which are more efficient in using parameters and computation with similar or higher accuracy.
A Guided Task for Cognitive brain-Computer Interfaces

Brain computer interfaces are an important tool to enable communication for patients suffering from amyotrophic lateral sclerosis. Yet most existing devices use cognitive functions that are impaired in later stages of the disease. Systems based on higher cognitive processes provide new possibilities for this field. However, many patients in the target group suffer from memory impairments. As many higher cognitive processes involve memory, this could be an interference. The present study investigates the differences between a currently existing cognitive paradigm using autobiographic memories and a new paradigm using a guided imagery story. Both are developed to be used in patients and try to target the same processes, with the new paradigm being less dependent on memory functions. Assessment of EEGand behavioral data in healthy subjects results in two working paradigms for brain computer interface control. Higher classification accuracies and more favorable behavioral ratings are achieved by the previously existing paradigm.
ELISA using a recombinant chimera of ESAT-6/MPB70/MPB83 for Mycobacterium bovis diagnosis in naturally infected cattle

Bovine tuberculosis (bTB) control programs generally rely on intradermal tuberculin tests for the antemortem diagnosis of Mycobacterium bovis infection in cattle, but these tests detect only a portion of the infected animals. The aim of the present study was to evaluate the diagnostic coverage of a combination of the bTB antemortem techniques known as the comparative intradermal tuberculin test (CITT) and an ELISA based on a recombinant chimera of ESAT-6/MPB70/MPB83 as the antigen in cattle. The results were compared to postmortem findings based on M. bovis culturing and PCR. Paired comparisons of all data (n=92) demonstrated that ELISA and LST results compared to the culturing results did not present significant differences (P=0.27 on McNemar’s test and P=0.12 on Fisher’s exact test, respectively). Using culturing as the gold standard, the sensitivity and specificity of ELISA were 79.5% (95% CI: 64.5–89.2%) and 75.5% (95% CI: 62.4–85.1%), respectively, whereas LST demonstrated 100% sensitivity (95% CI: 91.03–100%) and 92.5% specificity (95% CI: 82.1–97.0%). The ELISA results did not reveal significant differences in relation to the LST results (P>0.99 on Fisher’s exact test). Using the latter as the gold standard, the sensitivity and specificity of ELISA were 79.1% (95% CI: 64.8–88.6%) and 79.6% (95% CI: 66.4–88.5%), respectively. The use of ELISA with the recombinant chimera of ESAT-6/MPB70/MPB83 as the antigen complements the diagnostic coverage provided by CITT and increases the removal of infected animals from herds.
Ratiometric fluorescence nanosensors based on core-shell structured carbon/CdTe quantum dots and surface molecularly imprinted polymers for the detection of sulfadiazine.

Sulfadiazine is an environmental pollutant derived from abuse of antibiotics. Its content in environmental water is closely related to human health. Thus, a novel dual-emission surface molecularly imprinted nanosensor is designed for the specific adsorption and detection of sulfadiazine. In the system, blue emissive carbon quantum dots wrapped with silica served as the internal reference signal for eliminating background interference, while red emissive thioglycolic acid modified CdTe quantum dots (CdTe QDs), which are low dimensional semiconductor materials by the combination of cadmium and tellurium with excellent optical properties, were encapsulated in the imprinted layer to offer recognition signal. The fluorescence of CdTe quantum dots was quenched and the fluorescence quenching degree of carbon quantum dots was inconspicuous with the increase of concentration of sulfadiazine, thereby reflecting the color change. The detection of sulfadiazine was successfully achieved in a concentration range of 0.25-20 μmol/L with detection limit of 0.042 μmol/L and nanosensors had specific recognition for sulfadiazine over its analogues. Compared to single-emission fluorescence sensors, ratiometric fluorescence nanosensors had wider linear range and higher detection accuracy. Furthermore, the nanosensors were also successfully applied for the determination of sulfadiazine in real water and milk samples with acceptable recoveries. The study provides a feasible method for the detection of sulfadiazine and a reference for the detection of sulfonamides.
Association of Chronic Kidney Disease With Allopurinol Use in Gout Treatment

Importance Clinicians are often cautious about use of allopurinol in patients with gout when renal function declines. Objective To assess the association of allopurinol use in gout with the risk of developing chronic kidney disease stage 3 or higher. Design, Setting, and Participants A time-stratified propensity score–matched, population-based, prospective cohort study of individuals with newly diagnosed gout who initiated allopurinol (≥300 mg/d) compared with those who did not initiate allopurinol, using the Health Improvement Network (THIN), a United Kingdom general practitioner electronic health records database, was carried out. The data were analyzed using Cox proportional hazards regression. Among adults aged 18 to 89 years with newly diagnosed gout, we propensity score matched 4760 initiators of allopurinol (≥300 mg/d) to the same number of noninitiators of allopurinol, excluding those with chronic kidney disease stage 3 or higher or urate-lowering therapy use before their gout diagnosis. Exposures Allopurinol initiation at a dose of 300 mg or more per day. Main Outcomes and Measures Development of chronic kidney disease stage 3 or higher. Results Of the 4760 allopurinol initiators (3975 men, 785 women) and same number of noninitiators (3971 men, 789 women), 579 and 623, respectively, developed chronic kidney disease stage 3 or higher, with a mean follow-up time of 5 and 4 years, mean age of 57 years, and mean body mass index (calculated as weight in kilograms divided by height in meters squared) of 30 for both groups. Use of allopurinol of at least 300 mg/d was associated with lower risk of developing chronic kidney disease stage 3 or higher compared with nonusers, with a hazard ratio (HR) of 0.87 (95% CI, 0.77-0.97). Allopurinol initiation at less than 300 mg/d was not associated with renal function decline (HR, 1.00; 95% CI, 0.91-1.09). Conclusions and Relevance In this large cohort, allopurinol initiation of at least 300 mg/d was associated with a lower risk of renal function deterioration. Because allopurinol does not appear to be associated with renal function decline, clinicians should consider evaluating other potential causes when patients with gout experience renal function decline.
Improving Learning from Label proportions by Reducing the Feature Dimensionality

Learning from label proportions (LLP) is a recently introduced unsupervised classification method for event-related potential (ERP) based brain-computer interfaces. It estimates the target and non-target means of brain signal epochs based on a known proportion of these classes in different subsets of the data, which can be generated by interleaving different stimulus sequences, e.g., in a visual ERP speller. In contrast to other unsupervised methods, estimations obtained by LLP have the theoretical property of converging to the correct class means. However, the convergence is rather slow. In this paper, we investigate the effect of varying EEG channel numbers as a simple form of regularization onto the performance of LLP classifiers by offline analyses. We found that reduced channel sets can outperform the full set both in terms of single event classification rate and symbol selection accuracy. This is especially pronounced in the initial learning phase. These findings suggest that LLP classification can be significantly improved by reducing the feature dimensionality.
Cutting Throughput on the Edge: App-Aware Placement in Fog Computing

Fog computing extends cloud computing technology to the edge of the infrastructure to let IoT applications access objects' data with reduced latency, location awareness and dynamic computation. By displacing workloads from the central cloud to the edge devices, fog computing overcomes communication bottlenecks avoiding raw data transfer to the central cloud, thus paving the way for the next generation IoT-based applications. In this paper we study scheduling and placement of applications in fog computing, which is key to ensure profitability for the involved stakeholders. We consider a scenario where the emerging microservice architecture allows for the design of applications as cascades of coupled microservice modules. It results into a mixed integer non linear problem involving constraints on both application data flows and computation placement. Due to the complexity of the original problem, we resort to a simplified version, which is further solved using a greedy algorithm. This algorithm is the core placement logic of the FogAtlas platform, a fog computing platform based on existing virtualization technologies. Extensive numerical results validate the model and the scalability of the proposed solution, showing it attains performance close to the optimal solution and, in our real implementation, it scales well with respect to the number of served applications.
Fast and global detection of periodic sequence repeats in large genomic resources

Periodically repeating DNA and protein elements are involved in various important biological events including genomic evolution, gene regulation, protein complex formation, and immunity. Notably, the currently used genome editing tools such as ZFNs, TALENs, and CRISPRs are also all associated with periodically repeating biomolecules of natural organisms. Despite the biological importance of periodically repeating sequences and the expectation that new genome editing modules could be discovered from such periodical repeats, no software that globally detects such structured elements in large genomic resources in a high-throughput and unsupervised manner has been developed. Here, we developed new software, SPADE (Search for Patterned DNA Elements), that exhaustively explores periodic DNA and protein repeats from large-scale genomic datasets based on k-mer periodicity evaluation. SPADE precisely captured reported genome-editing-associated sequences and other protein families involving repeating domains with significantly better performance than the other software designed for limited sets of repetitive biomolecular sequences.
Batch Active Preference-Based Learning of Reward Functions

Data generation and labeling are usually an expensive part of learning for robotics. While active learning methods are commonly used to tackle the former problem, preference-based learning is a concept that attempts to solve the latter by querying users with preference questions. In this paper, we will develop a new algorithm, batch active preference-based learning, that enables efficient learning of reward functions using as few data samples as possible while still having short query generation times. We introduce several approximations to the batch active learning problem, and provide theoretical guarantees for the convergence of our algorithms. Finally, we present our experimental results for a variety of robotics tasks in simulation. Our results suggest that our batch active learning algorithm requires only a few queries that are computed in a short amount of time. We then showcase our algorithm in a study to learn human users' preferences.
Sharing a mental number line across individuals? The role of body position and empathy in joint numerical cognition

A growing body of research shows that the human brain acts differently when performing a task together with another person than when performing the same task alone. In this study, we investigated the influence of a co-actor on numerical cognition using a joint random number generation (RNG) task. We found that participants generated relatively smaller numbers when they were located to the left (vs. right) of a co-actor (Experiment 1), as if the two individuals shared a mental number line and predominantly selected numbers corresponding to their relative body position. Moreover, the mere presence of another person on the left or right side or the processing of numbers from loudspeaker on the left or right side had no influence on the magnitude of generated numbers (Experiment 2), suggesting that a bias in RNG only emerged during interpersonal interactions. Interestingly, the effect of relative body position on RNG was driven by participants with high trait empathic concern towards others, pointing towards a mediating role of feelings of sympathy for joint compatibility effects. Finally, the spatial bias emerged only after the co-actors swapped their spatial position, suggesting that joint spatial representations are constructed only after the spatial reference frame became salient. In contrast to previous studies, our findings cannot be explained by action co-representation because the consecutive production of numbers does not involve conflict at the motor response level. Our results therefore suggest that spatial reference coding, rather than motor mirroring, can determine joint compatibility effects. Our results demonstrate how physical properties of interpersonal situations, such as the relative body position, shape seemingly abstract cognition.
Sexual behaviors of HIV-infected pregnant women and factors associated with sexual transmitted infection in South Africa.

BACKGROUND Sexual behaviors in HIV-infected pregnant women in South Africa are not well understood. METHODS HIV-infected pregnant women were recruited into a prospective cohort at first antenatal care visit. Sociodemographic information and self-collected vulvovaginal swab samples were collected from participants. Vulvovaginal swab samples were tested for Chlamydia trachomatis (CT), Neisseria gonorrhoea (NG) and Trichomonas vaginalis (TV) using GeneXpert®. We investigated sexual behaviors, alcohol use and factors associated with condomless sex during pregnancy and prevalent sexually transmitted infection (STI) among our cohort. We report descriptive, univariate and multivariable logistic regression results of sexual behaviors and alcohol use, factors associated with condomless sex at last sex, and having any STI during pregnancy adjusting for a priori confounders. RESULTS We recruited and enrolled 430 HIV-infected pregnant women. Median age was 30-years; median gestational age was 20-weeks. 89% of women reported sex during pregnancy. At last sex, 68% reported condomless sex; 18% reported having >1 sex partner in the past 12-months. Adjusting for age, income and relationship status, condom use at last sex was associated with prior knowledge of HIV status (aOR=2.46;95%CI=1.54-3.92) and being in a concordant HIV-positive (aOR=3.17;95%CI=1.84-5.50), or serodiscordant relationship (aOR=6.50;95%CI=3.59-11.80). The prevalence of any STI was 41% (95%CI=36%-45%). Adjusting for mothers' age and employment, odds of having a STI increased if the woman reported alcohol use during pregnancy (aOR=1.96;95%CI=1.06-3.64) or if the father of the child was a non-cohabiting or casual partner (aOR=1.42;95%CI=0.97-2.03). CONCLUSION Almost all HIV-infected pregnant women were sexually active during pregnancy and most women reported condomless sex at last sex. Condom use was associated with knowledge of serostatus and/or partner's serostatus before first ANC visit. Factors associated with having a STIs included: alcohol use during pregnancy and father of child being a non-cohabiting partner.
Incident cognitive impairment: longitudinal changes in molecular, structural and cognitive biomarkers

Longer periods are needed to examine how biomarker changes occur relative to incident sporadic cognitive impairment. We evaluated molecular (CSF and imaging), structural, and cognitive biomarkers to predict incident cognitive impairment and examined longitudinal biomarker changes before and after symptomatic onset. Data from participants who were cognitively normal, underwent amyloid imaging using Pittsburgh compound B and/or CSF studies, and at least two clinical assessments were used. Stepwise Cox proportional hazards models tested associations of molecular (Pittsburgh compound B; CSF amyloid-β42, tau, ptau181, tau/amyloid-β42, ptau181/amyloid-β42), structural (normalized hippocampal volume, normalized whole brain volume), and cognitive (Animal Naming, Trail Making A, Trail Making B, Selective Reminding Test - Free Recall) biomarkers with time to Clinical Dementia Rating (CDR) > 0. Cognitively normal participants (n = 664), aged 42 to 90 years (mean ± standard deviation = 71.4 ± 9.2) were followed for up to 16.9 years (mean ± standard deviation = 6.2 ± 3.5 years). Of these, 145 (21.8%) participants developed a CDR > 0. At time of incident cognitive impairment, molecular, structural, and cognitive markers were abnormal for CDR > 0 compared to CDR = 0. Linear mixed models indicated rates of change in molecular biomarkers were similar for CDR = 0 and CDR > 0, suggesting that the separation in values between CDR = 0 and CDR > 0 must have occurred prior to the observation period. Rate of decline for structural and cognitive biomarkers was faster for CDR > 0 compared to CDR = 0 (P < 0.0001). Structural and cognitive biomarkers for CDR > 0 diverged from CDR 0 at 9 and 12 years before incident cognitive impairment, respectively. Within those who developed CDR > 0, a natural separation occurred for Pittsburgh compound B values. In particular, CDR > 0 who had at least one APOE ɛ4 allele had higher, and more rapid increase in Pittsburgh compound B, while APOE ɛ2 was observed to have slower increases in Pittsburgh compound B. Of molecular biomarker-positive participants followed for at least 10 years (n = 16-23), ∼70% remained CDR = 0 over the follow-up period. In conclusion, conversion from cognitively normal to CDR > 0 is characterized by not only the magnitude of molecular biomarkers but also rate of change in cognitive and structural biomarkers. Findings support theoretical models of biomarker changes seen during transition to cognitive impairment using longitudinal data and provide a potential time for changes seen during this transition. These findings support the use of molecular biomarkers for trial inclusion and cognitive/structural biomarkers for evaluating trial outcomes. Finally, results support a potential role for APOE ɛ in modulating amyloid accumulation in CDR > 0 with APOE ɛ4 being deleterious and APOE ɛ2 protective.