Miracle-dz/UniFAR-FTUs huggingface dataset:
A limit theorem for small cliques in inhomogeneous random graphs

The theory of graphons comes with a natural sampling procedure, which results in an inhomogeneous variant of the Erdős–Rényi random graph, called W ‐random graphs. We prove, via the method of moments, a limit theorem for the number of r ‐cliques in such random graphs. We show that, whereas in the case of dense Erdős–Rényi random graphs the fluctuations are normal of order nr−1 , the fluctuations in the setting of W ‐random graphs may be of order 0,nr−1 , or nr−0.5 . Furthermore, when the fluctuations are of order nr−0.5 they are normal, while when the fluctuations are of order nr−1 they exhibit either normal or a particular type of chi‐square behavior whose parameters relate to spectral properties of W . These results can also be deduced from a general setting, based on the projection method. In addition to providing alternative proofs, our approach makes direct links to the theory of graphons.
High-throughput immunoassays for SARS-CoV-2 – considerable differences in performance when comparing three methods

Abstract Background The recently launched high-throughput assays for detecting antibodies against SARS-CoV-2 has contributed to the managing strategies for the COVID-19 pandemic. This study aimed to investigate the performance of three high-throughput assays and one rapid lateral flow test relative to regulatory authorities' recommended criteria. Methods A total of 315 samples, including 150 pre-pandemic samples, 152 samples from SARS-CoV-2 RT-PCR positive individuals and 13 potentially cross-reactive samples were analysed with SARS-CoV-2 IgG (Abbott, Abbott Park, IL), Elecsys Anti-SARS-CoV-2 (Roche, Solna, Sweden), LIAISON SARS-CoV-2 S1/S2 IgG (DiaSorin, Saluggia, Italy) and 2019-nCOV IgG/IgM Rapid Test (Dynamiker Biotechnology Co., Tianjin, China). Results All assays performed with a high level of specificity ranging from 96.7% to 99.3%. Sensitivity differed more between the assays, Roche exhibiting the highest sensitivity of 98.7%. The corresponding figures for Abbott, DiaSorin and Dynamiker Biotechnology were 80.9%, 89.0% and 72.4%, respectively. Conclusions The results of the evaluated SARS-CoV-2 assays vary considerably, as well as their ability to fulfil the performance criteria proposed by regulatory authorities. Introduction into clinical use in low-prevalent settings, should, therefore, be made with caution.
Cutoff of IceCube neutrino spectrum due to t-channel resonant absorption by CνB

The non-observation of neutrinos by the IceCube at the Glashow resonance energy of 6.3 PeV has been a long standing unresolved issue. In this paper we propose a t-channel neutrino absorption by the CνB, which causes a cutoff at 4.5 PeV neutrino energy, to explain the IceCube observations. We present a neutrinophilic 2HDM where the neutrino masses are generated by a low scale seesaw mechanism. A 𝒪(10) MeV scalar mediates the interactions between left and right handed neutrinos and generates the t-channel diagram used for explaining the absence of Glashow resonance. The same scalar mediates the annihilation of the dark matter and generates the correct relic density.
Bounding Box Regression With Uncertainty for Accurate Object Detection

Large-scale object detection datasets (e.g., MS-COCO) try to define the ground truth bounding boxes as clear as possible. However, we observe that ambiguities are still introduced when labeling the bounding boxes. In this paper, we propose a novel bounding box regression loss for learning bounding box transformation and localization variance together. Our loss greatly improves the localization accuracies of various architectures with nearly no additional computation. The learned localization variance allows us to merge neighboring bounding boxes during non-maximum suppression (NMS), which further improves the localization performance. On MS-COCO, we boost the Average Precision (AP) of VGG-16 Faster R-CNN from 23.6% to 29.1%. More importantly, for ResNet-50-FPN Mask R-CNN, our method improves the AP and AP90 by 1.8% and 6.2% respectively, which significantly outperforms previous state-of-the-art bounding box refinement methods. Our code and models are available at github.com/yihui-he/KL-Loss
Prevalence and predictors of music performance anxiety in adolescent learners: Contributions of individual, task-related and environmental factors

Currently, no formal music performance anxiety (MPA) prevalence rates exist for children and adolescent musicians. Factors contributing to distinct MPA experiences (maladaptive versus adaptive) are also not clear and there is a lack of research investigating cultural variability in MPA experiences. The study aimed to investigate the prevalence of MPA and factors affecting MPA levels in adolescent musicians. Comparisons between sexes (boys, girls), age groups (12–15, 16–19 years) and nationalities (Cypriot, British) were conducted to elucidate the association between sex and MPA, explore the developmental trajectory of MPA and investigate cultural variability in its prevalence and manifestation. Profiles of musicians experiencing high, moderate and low MPA were examined to investigate discriminating characteristics. A sample of 410 musicians in Cyprus and the United Kingdom completed the Young Musicians’ Performance Questionnaire, including the Adolescent Musicians’ Performance Anxiety Scale (AMPAS). Overall, 11% of participants experienced high levels of MPA. Anxiety was affected by the cultural context as group differences and different developmental trajectories were observed in the two nationalities. Girls experienced higher MPA levels than boys, although when sex was included in a hierarchical regression with other variables, it was not a significant predictor. Approximately 60% of the variance in MPA was explained by variables that related to individual characteristics affecting susceptibility to anxiety, task-efficacy, and the performance environment. Highly anxious participants were more likely to experience maladaptive MPA. The findings support the development of personalized and culturally responsive interventions addressing individual needs on the basis of distinct musician profiles.
Entropic Spectral Learning for Large-Scale Graphs

Graph spectra have been successfully used to classify network types, compute the similarity between graphs, and determine the number of communities in a network. For large graphs, where an eigen-decomposition is infeasible, iterative moment matched approximations to the spectra and kernel smoothing are typically used. We show that the underlying moment information is lost when using kernel smoothing. We further propose a spectral density approximation based on the method of Maximum Entropy, for which we develop a new algorithm. This method matches moments exactly and is everywhere positive. We demonstrate its effectiveness and superiority over existing approaches in learning graph spectra, via experiments on both synthetic networks, such as the Erdős-Renyi and Barabasi-Albert random graphs, and real-world networks, such as the social networks for Orkut, YouTube, and Amazon from the SNAP dataset.
The Effects of Overweight and Obesity on Assisted Reproduction Technology Outcomes

The aim of the present study was to assess the impact of professional nutrition assistance on assisted reproduction technology (ART) outcomes in overweight or obese patients with polycystic ovarian syndrome (PCOS). The study represents a retrospective analysis of fertilization rates, embryo quality and gestations after ART in seven PCOS patients, five obese and two overweight. The women attended a private Fertility Center in Brazil between the years 2010 and 2016. Out of the seven patients, the three that reached a successful gestation were the ones that underwent comprehensive lifestyle changes, taking care of their diet for a more prolonged period of time and reached an ideal weight loss during the nutrition counseling period.
Population assignment reveals low migratory connectivity in a weakly structured songbird

Understanding migratory connectivity is essential for determining the drivers behind population dynamics and for implementing effective conservation strategies for migratory species. Genetic markers provide a means to describe migratory connectivity; however, they can be uninformative for species with weak population genetic structure, which has limited their application. Here, we demonstrated a genomic approach to describing migratory connectivity in the prothonotary warbler, Protonotaria citrea, a Neotropical songbird of conservation concern. Using 26,189 single nucleotide polymorphisms (SNPs), we revealed regional genetic structure between the Mississippi River Valley and the Atlantic Seaboard with overall weak genetic differentiation among populations (FST = 0.0055; 95% CI: 0.0051–0.0059). Genetic variation had a stronger association with geographic rather than environmental factors, with each explaining 14.5% and 8.2% of genetic variation, respectively. By varying the numbers of genomic markers used in population assignment models with individuals of known provenance, we identified a maximum assignment accuracy (89.7% to site, 94.3% to region) using a subset of 600 highly differentiated SNPs. We then assigned samples from nonbreeding sites to breeding region and found low migratory connectivity. Our results highlight the importance of filtering markers for informative loci in models of population assignment. Quantifying migratory connectivity for weakly structured species will be useful for expanding studies to a wider range of migratory species across taxonomic groups and may contribute to a deeper understanding of the evolution of migratory strategies.
Impact of myocardial infarction on intraventricular vortex and flow energetics assessed using computational simulations

Flow energetics have been proposed as early indicators of progressive left ventricular (LV) functional impairment in patients with myocardial infarction (MI), but its correlation with individual MI parameters has not been fully explored. Using electro–fluid‐structure interaction LV models, this study investigated the correlation between four MI parameters: infarct size, infarct multiplicity, regional enhancement of contractility at the viable myocardium area (RECVM), and LV mechanical dyssynchrony (LVMD) with intraventricular vortex and flow energetics. In LV with small infarcts, our results showed that infarct appearance amplified the energy dissipation index (DI), where substantial viscous energy loss was observed in areas with high flow velocity and near the infarct‐vortex interface. The LV with small multiple infarcts and RECVM showed remarkable DI increment during systole and diastole. In correlation analysis, the systolic kinetic energy fluctuation index (E′) was positively related to ejection fraction (EF) (R2 = 0.982) but negatively correlated with diastolic E′ (R2 = 0.970). Diastolic E′ was inversely correlated with vortex kinetic energy (R2 = 0.960) and vortex depth (R2 = 0.876). We showed an excessive systolic DI could differentiate infarcted LV with normal EF from healthy LV. Strong flow acceleration, LVMD, and vortex‐infarct interactions were predominant factors that induced excessive DI in infarcted LVs. Instead of causing undesired flow turbulence, high systolic E′ suggested the existence of energetic flow acceleration, while high diastolic E′ implied an inefficient diastolic filling. Thus, systolic E′ is not a suitable early indicator for progressive LV dysfunction in MI patients, while diastolic E′ may be a useful index to indicate diastolic impairment in these patients.
Mucor cheongyangensis, a new species isolated from the surface of Lycorma delicatula in Korea

A new species of Mucor, isolated from the surface of Lycorma delicatula collected at Cheongyang in the Chungnam Province of Korea, is described and compared with morphologically similar taxa. The phylogenetic analysis of the internal transcribed spacer (ITS) and the large subunit (28S) rDNA sequences revealed that this isolate is closely related to M. orantomantidis and M. guilliermondii. However, the new isolate differs from those by having larger sporangia (24.5–125 × 23.5–120 µm), rhizoid-like structures, and production of secondary sporangia from vesicles outside of the original sporangium. Here, this novel fungal taxon is proposed as Mucor cheongyangensis sp. nov.
Bone marrow and plasma FGF‐23 in heart failure patients: novel insights into the heart–bone axis

Fibroblast growth factor 23 (FGF‐23) is known to be elevated in patients with congestive heart failure (CHF). As FGF‐23 is expressed in the bone but can also be expressed in the myocardium, the origin of serum FGF‐23 in CHF remains unclear. It is also unclear if FGF‐23 expressed in the bone is associated with outcome in CHF. The aim of the present study was to investigate FGF‐23 levels measured in bone marrow plasma (FGF‐23‐BM) and in peripheral blood (FGF‐23‐P) in CHF patients to gain further insights into the heart–bone axis of FGF‐23 expression. We also investigated possible associations between FGF‐23‐BM as well as FGF‐23‐P and outcome in CHF patients.
Effects of Chinese word structure on object categorization in Chinese–English bilinguals

ABSTRACT We investigated how verbal labels affect object categorization in bilinguals. In English, most nouns do not provide linguistic clues to their categories (an exception is sunflower), whereas in Chinese, some nouns provide category information morphologically (e.g., 鸵鸟- ostrich and 知更鸟- robin have the morpheme鸟- bird in their Chinese names), while some nouns do not (e.g., 企鹅- penguin and 鸽子- pigeon). We examined the effect of Chinese word structure on bilinguals’ categorization processes in two ERP experiments. Chinese–English bilinguals and English monolinguals judged the membership of atypical (e.g., ostrich, penguin) vs. typical (e.g., robin, pigeon) pictorial (Experiment 1) and English word (Experiment 2) exemplars of categories (e.g., bird). English monolinguals showed typicality effects in RT data, and in the N300 and N400 of ERP data, regardless of whether the object name had a category cue in Chinese. In contrast, Chinese–English bilinguals showed a larger typicality effect for objects without category cues in their name than objects with cues, even when they were tested in English. These results demonstrate that linguistic information in bilinguals’ L1 has an effect on their L2 categorization processes. The findings are explained using the label-feedback hypothesis.
Improved robustness of reinforcement learning policies upon conversion to spiking neuronal network platforms applied to ATARI games

Deep Reinforcement Learning (RL) demonstrates excellent performance on tasks that can be solved by trained policy. It plays a dominant role among cutting-edge machine learning approaches using multi-layer Neural networks (NNs). At the same time, Deep RL suffers from high sensitivity to noisy, incomplete, and misleading input data. Following biological intuition, we involve Spiking Neural Networks (SNNs) to address some deficiencies of deep RL solutions. Previous studies in image classification domain demonstrated that standard NNs (with ReLU nonlinearity) trained using supervised learning can be converted to SNNs with negligible deterioration in performance. In this paper, we extend those conversion results to the domain of Q-Learning NNs trained using RL. We provide a proof of principle of the conversion of standard NN to SNN. In addition, we show that the SNN has improved robustness to occlusion in the input image. Finally, we introduce results with converting full-scale Deep Q-network to SNN, paving the way for future research to robust Deep RL applications.
Simple Applications of BERT for Ad Hoc Document Retrieval

Following recent successes in applying BERT to question answering, we explore simple applications to ad hoc document retrieval. This required confronting the challenge posed by documents that are typically longer than the length of input BERT was designed to handle. We address this issue by applying inference on sentences individually, and then aggregating sentence scores to produce document scores. Experiments on TREC microblog and newswire test collections show that our approach is simple yet effective, as we report the highest average precision on these datasets by neural approaches that we are aware of.
Pushing Information: Realized Uncertainty and Notification Design

We study the dynamic information design problem of a firm seeking to influence consumer checking behavior. The firm's payoffs increase in the frequency of consumer checking, and it can design push notifications to induce consumer checking. The consumer is uncertain about the arrival of information as well as its valuation. In addition to direct consumption utility, the consumer also has preferences over realized uncertainty: she experiences disutility (anxiety) from the variance of the information stock that has arrived, but which remains unchecked. We show that push notifications can lead to more frequent checking compared to no-push, even though it reduces the variance of the information. While push notifications resolve the information arrival uncertainty, they also create an endogenous impulse to check the information immediately. They may allow the firm to create a more efficient spread in the consumer's beliefs/anxiety between zero or a level enough to induce checking. We consider generalized push strategies that allow the firm to strategically add phantom notifications that do not contain useful information. This noisy push strategy can lead to more frequent consumer checking, even though the consumer has rational expectations about the firm's strategy. We extend the model to account for consumer self-control to show that sophisticated consumers have the equilibrium incentive to block notifications. The main results also hold in a model with endogenous prices.
Knowledge Management Mechanisms in E-Commerce: A Study of Online Retailing and Auction Sites

Knowledge management is vital to a company's success in this e-commerce era. Not only has the topic of knowledge management attracted much research attention lately, but the management of customer knowledge is also becoming increasingly important. Despite its significance, few research studies have systematically investigated knowledge management in the e-commerce context. None of the studies we reviewed looked at how knowledge management mechanisms were employed in e-commerce. This research presents a comprehensive analysis of the knowledge management mechanisms employed by 14 e-commerce sites that were rated as being the best by Gomez.com, an independent Internet quality measurement firm. Specifically, we analyzed the knowledge acquisition, knowledge dissemination, and knowledge sharing mechanisms employed by these leading e-commerce sites. As a pioneering research in this area, the findings have important implications for e-commerce companies seeking to enhance their knowledge management practices.
eZTrust: Network-Independent Zero-Trust Perimeterization for Microservices

Emerging microservices-based workloads introduce new security risks in today's data centers as attacks can propagate laterally within the data center relatively easily by exploiting cross-service dependencies. As countermeasures for such attacks, traditional perimeterization approaches, such as network-endpoint-based access control, do not fare well in highly dynamic microservices environments (especially considering the management complexity, scalability and policy granularity of these earlier approaches). In this paper, we propose eZTrust, a network-independent perimeterization approach for microservices. eZTrust allows data center tenants to express access control policies based on fine-grained workload identities, and enables data center operators to enforce such policies reliably and efficiently in a purely network-independent fashion. To this end, we leverage eBPF, the extended Berkeley Packet Filter, to trace authentic workload identities and apply per-packet tagging and verification. We demonstrate the feasibility of our approach through extensive evaluation of our proof-of-concept prototype implementation. We find that, when comparable policies are enforced, eZTrust incurs 2--5 times lower packet latency and 1.5--2.5 times lower CPU overhead than traditional perimeterization schemes.
A Hybrid Adaptive Large Neighborhood Heuristic for a Real-Life Dial-a-Ride Problem

The transportation of elderly and impaired people is commonly solved as a Dial-A-Ride Problem (DARP). The DARP aims to design pick-up and delivery vehicle routing schedules. Its main objective is to accommodate as many users as possible with a minimum operation cost. It adds realistic precedence and transit time constraints on the pairing of vehicles and customers. This paper tackles the DARP with time windows (DARPTW) from a new and innovative angle as it combines hybridization techniques with an adaptive large neighborhood search heuristic algorithm. The main objective is to improve the overall real-life performance of vehicle routing operations. Real-life data are refined and fed to a hybrid adaptive large neighborhood search (Hybrid-ALNS) algorithm which provides a near-optimal routing solution. The computational results on real-life instances, in the Canadian city of Vancouver and its region, and DARPTW benchmark instances show the potential improvements achieved by the proposed heuristic and its adaptability.
Predicting mobile application breakout using sentiment analysis of Facebook posts

Publishing mobile applications on the official stores is becoming a big business. Many developers are charmed by the billion-dollar success of breakout applications. Thus, in order to ensure success, mobile applications need to sustain top ranking. Previous work on the predictability of mobile applications success aimed to extract from app stores relevant features that influence high rating. In this article, we propose an automated approach to exploit data available on Facebook platform that predicts mobile applications breakout. We collect data from Facebook graph API, then determine sentiment polarity of user comments. We design statistical features to score users sentiment for each post. Then, we compose posts scores with Facebook statistical measures to form a mobile applications breakout dataset. Finally, we use machine learning techniques to build our breakout prediction model. We evaluate our approach with 199 mobile applications and obtain a prediction accuracy of 83.78%. We find that Likes count on a Facebook page is decisive for climbing mobile applications ranking. However, a high rate of negative opinions declines application ranking and deprives mobile application of achieving a breakout. Based on these findings, we provide evidence that user interactions on social networks can influence the success of mobile applications.
Transfer learning and U-Net for buildings segmentation

Monitoring urban changes has been considered a major concern for decision makers for decades because of the social and economic impact of the urbanization on societies. To achieve such goal, many technical approaches, combining Raster GIS and image processing techniques, have been proposed using remote sensing imageries in order to be able to identify buildings and urban changes in wider area. In other hands, we are recently witnessing a huge interest in deep learning architecture, and especially in their capacity to predict complex patterns, particularly those related to digital imageries. The aim of this paper is to explore a particular deep convolution network, U-Net, and its use for automatic semantic image building segmentation from remote sensing imageries. We show that the U-net deep learning architecture can achieve good results in buildings segmentation.