Miracle-dz/UniFAR-FTUs huggingface dataset:
Underreporting, crash severity and fault assignment of minor crashes in China – a study based on self-reported surveys

ABSTRACT Unreported minor crashes have importance as a surrogate for more serious crashes that require infrastructure, education, and enforcement strategies; and they still inflict damages. To study factors that influence underreporting, cause, and severity of minor crashes; a survey was performed in Kunming and Beijing to collect self-reported personal characteristics and crash history data of the three major urban road users in China: automobile drivers, bicycle riders and electric bike (e-bike) riders. Underreporting rates of automobile to automobile, automobile to non-motorized vehicle, and non-motorized vehicle to non-motorized vehicle crashes are 56%, 77% and 94%, respectively. Minor crashes with higher reported injury severity levels are more likely to be reported. E-bike riders without a driver's license are more likely to cause crashes. Licensing and education could be an effective way to reduce their crashes. The party that is not at fault in a crash is more likely to sustain high level of injury.
A study on blind image restoration of blurred images using R-map

Image restoration which restores a clear image from a single blur image is a difficult problem of estimating two unknowns: a point spread function (PSF) and its ideal image. In this paper, we propose a novel blind deconvolution method to alternately estimate PSF and the latent image. And we incorporate the gradient reliability map (R-map) that enables edge selection appropriate for PSF estimation processing. This method improves restoration performance by excluding noise that adversely affects the estimation, and the experimental results show that robustness is improved in our proposed method.
Fiber Optic Micro-endoscopy for Detection of Bacteria in Early Stages of Infection

Fiber Optic Micro-endoscopy for Detection of Bacteria in Early Stages of Infection. (December 2010) Nooman Sadat Mufti, B.Tech, Aligarh Muslim University, India; M.S., National University of Singapore-MIT Alliance, Singapore Chair of Advisory Committee: Dr. Kristen Carlson Maitland Mycobacterium tuberculosis, the bacterium that causes tuberculosis, has an incubation period ranging from a few months to several years following infection via inhalation into the lungs. Whole body fluorescence scanners are used to image and monitor the growth of fluorescent protein expressing strains of M. tuberculosis in the lungs of animal models. Accurate quantitative analysis of bacterial growth during the early stages of infection inside lungs remains elusive, due to tissue absorption and scattering of photons emitted by the low numbers of bacteria deep in tissue. Fiber optic micro-endoscopy is uniquely suited to provide a novel solution to this problem by delivering light excitation directly to and collecting fluorescence from the infection site located in the lungs of an animal model, thereby enabling detection of fluorescent bacteria during the early stages of infection. In this thesis, I present a contact probe fiber bundle fluorescence micro-endoscope with a range of LED based excitation wavelengths, 4 μm resolution, a 750 μm field of view, and a 1 mm outer diameter. This system has detected tdTomato and GFP expressing Bacillus Calmette-Guérin (BCG)
CENNLP at SemEval-2018 Task 2: Enhanced Distributed Representation of Text using Target Classes for Emoji Prediction Representation

Emoji is one of the “fastest growing language ” in pop-culture, especially in social media and it is very unlikely for its usage to decrease. These are generally used to bring an extra level of meaning to the texts, posted on social media platforms. Providing such an added info, gives more insights to the plain text, arising to hidden interpretation within the text. This paper explains our analysis on Task 2, ” Multilingual Emoji Prediction” sharedtask conducted by Semeval-2018. In the task, a predicted emoji based on a piece of Twitter text are labelled under 20 different classes (most commonly used emojis) where these classes are learnt and further predicted are made for unseen Twitter text. In this work, we have experimented and analysed emojis predicted based on Twitter text, as a classification problem where the entailing emoji is considered as a label for every individual text data. We have implemented this using distributed representation of text through fastText. Also, we have made an effort to demonstrate how fastText framework can be useful in case of emoji prediction. This task is divide into two subtask, they are based on dataset presented in two different languages English and Spanish.
Activation of an Endogenous Retrotransposon Associated with Epigenetic Changes in Lotus japonicus: A Tool for Functional Genomics in Legumes

Long terminal repeat retrotransposons occupy a large portion of genomes in flowering plants. In spite of their abundance, the majority are silenced and rarely transpose. One of the examples of a highly active retrotransposon is Lotus Retrotransposon 1 (LORE1), of the model legume Lotus japonicus (Regel) K. Larsen (syn. Lotus corniculatus L. var. japonicus Regel). LORE1 has several unusual characteristics that are interesting both for studying evolutional genomics and for the use of LORE1 as a genetic tool. In this review, we present the characteristics of LORE1 and discuss the biological significance of LORE1 as a member of chromovirus, a chromodomain containing clade of the Gypsy superfamily. Then we discuss possibilities and methodologies for using endogenous transposable elements as mutagens to generate gene tagging populations in plants.
Metabolic Responses to Low Temperature of Three Peach Fruit Cultivars Differently Sensitive to Cold Storage

Refrigerated storage is widely applied in order to maintain peach quality but it can also induce chilling injuries (CIs) such as flesh browning and bleeding, and mealiness. Peach fruit from three cultivars (‘Red Haven’, RH, ‘Regina di Londa’, RL, and ‘Flaminia’, FL) were stored for 4 weeks under low temperatures (0.5 and 5.5°C). GC-MS was employed to study changes in both metabolome and volatilome induced by cold storage in the mesocarp. CIs were assessed both at the end of each week of storage and after subsequent shelf-life (SL) at 20°C. Flesh browning and mealiness appeared to be more related to 5.5°C storage, while flesh bleeding revealed high incidence following 0.5°C storage. Compared to RL and FL, RH showed a marked lower incidence of CIs. Multivariate statistical analyses indicate that RH peaches indeed differ from RL and FL in particular when considering data from samples collected at the end of the cold storage. Common and divergent responses have been identified in terms of metabolic responses to the applied low temperatures. In all three cultivars raffinose, glucose-6P, fucose, xylose, sorbitol, GABA, epicatechin, catechin, and putrescine markedly increased during cold storage, while citramalic, glucuronic, mucic and shikimic acids decreased. Among volatile organic compounds (VOCs), aldehydes and alcohols generally accumulated more under low temperature conditions while esters and lactones evolved during subsequent SL. The main cultivar differences developed after cold storage during SL although some common responses (e.g., an increased production of ethyl acetate) were observed. The lower levels of flesh browning and bleeding displayed by RH peaches were related to compounds with antioxidant activity, or acting as osmotic protectants and membrane stabilizer. Indeed, RH showed higher levels of amino acids and urea, together with a marked increase in putrescine, sorbitol, maltitol, myoinositol and sucrose detected during storage and SL.
Multisensor Fault Diagnosis Modeling Based on the Evidence Theory

Fault diagnosis is a typical multisensor information fusion problem. The information obtained from different sensors, such as sound, pressure, vibration, and temperature, can be considered as a piece of evidence. From the viewpoint of the evidence theory, the problem of multisensor fault diagnosis can be viewed as the problem of evidence fusion and decision. However, the information obtained from different sensors may be inaccurate, uncertain, fuzzy, or even conflict, so how to set up the fault diagnosis architecture of a distributed multisensor system and combine the conflict evidence should be taken into consideration. In this paper, the classical Dempster–Shafer evidence theory is described and the disadvantage of a classical Dempster's combination rule is discussed. In order to solve the counter-intuitive result when using the classical Dempster's combination rule, the Euclidean distance is proposed to characterize the differences between different pieces of evidence, and then the support degree of each evidence is generated and the weighted pieces of evidence can be combined directly using the classical Dempster's combination rule. Numerical simulation examples indicate that the proposed method has a better performance of analyzing the conflict between different pieces of evidence, especially for high conflict evidence. Therefore, compared with the existing methods, it has better applicability. According to the requirement of the Dempster–Shafer evidence theory, the fault diagnosis architecture of a distributed multisensor system is analyzed in detail, and a fault case of a rotating machine is used to illustrate that the proposed model is effective and superior, which can be used in practice.
WM in Adolescence: What Is the Relationship With Emotional Regulation and Behavioral Outcomes?

Adolescence is a fundamental transition phase, marked by physical, social, cognitive and emotional changes. At this stage in development two contrasting phenomena take place: brain changes cause a sensitivity to emotional aspects (Dahl, 2004); while also control processes register as well impressive improvements (e.g., Hooper et al., 2004; Best and Miller, 2010). The study is aimed to investigate the relationship between a core cognitive feature such as working memory (WM) (Diamond, 2013) and complex abilities such as emotion regulation (ER) and behavioral self-reported outcomes using a structural equation model approach. A sample of 227 typically developed adolescents between 14 and19 years of age (148 females; mean age in months 202.8, SD 18.57) participated in this study. The following tasks and self-reports were administered in a 45-min test session at school: Symmetry Span task (Kane et al., 2004). Reading Span task (Daneman and Carpenter, 1980), Mr. Cucumber (Case, 1985); Youth Self-Report (YSR, 11–18 years, Achenbach and Rescorla, 2001); Difficulties ER Scale (DERS, Gratz and Roemer, 2004; Italian version by Giromini et al., 2012). Results showed that difficulties in ER correlated with WM: high levels of ER difficulties are associated with low WM efficiency while no significant contributions of these predictors was observed on externalizing or internalizing symptoms. This study showed a significant relationship between self-reported difficulties in ER and WM, while no significant contribution of the considered predictors was showed on the outcomes, adding knowledge about how behavioral and emotional self-reported outcomes may relate to these processes.
Privacy-Preserving DDoS Attack Detection Using Cross-Domain Traffic in Software Defined Networks

Existing distributed denial-of-service attack detection in software defined networks (SDNs) typically perform detection in a single domain. In reality, abnormal traffic usually affects multiple network domains. Thus, a cross-domain attack detection has been proposed to improve detection performance. However, when participating in detection, the domain of each SDN needs to provide a large amount of real traffic data, from which private information may be leaked. Existing multiparty privacy protection schemes often achieve privacy guarantees by sacrificing accuracy or increasing the time cost. Achieving both high accuracy and reasonable time consumption is a challenging task. In this paper, we propose Predis, which is a privacy-preserving cross-domain attack detection scheme for SDNs. Predis combines perturbation encryption and data encryption to protect privacy and employs a computationally simple and efficient algorithm k-Nearest Neighbors (kNN) as its detection algorithm. We also improve kNN to achieve better efficiency. Via theoretical analysis and extensive simulations, we demonstrate that Predis is capable of achieving efficient and accurate attack detection while securing sensitive information of each domain.
Response to marine cloud brightening in a multi-model ensemble

Abstract. Here we show results from Earth system model simulations from the marine cloud brightening experiment G4cdnc of the Geoengineering Model Intercomparison Project (GeoMIP). The nine contributing models prescribe a 50 % increase in the cloud droplet number concentration (CDNC) of low clouds over the global oceans in an experiment dubbed G4cdnc, with the purpose of counteracting the radiative forcing due to anthropogenic greenhouse gases under the RCP4.5 scenario. The model ensemble median effective radiative forcing (ERF) amounts to −1.9 W m−2, with a substantial inter-model spread of −0.6 to −2.5 W m−2. The large spread is partly related to the considerable differences in clouds and their representation between the models, with an underestimation of low clouds in several of the models. All models predict a statistically significant temperature decrease with a median of (for years 2020–2069) −0.96 [−0.17 to −1.21] K relative to the RCP4.5 scenario, with particularly strong cooling over low-latitude continents. Globally averaged there is a weak but significant precipitation decrease of −2.35 [−0.57 to −2.96] % due to a colder climate, but at low latitudes there is a 1.19 % increase over land. This increase is part of a circulation change where a strong negative top-of-atmosphere (TOA) shortwave forcing over subtropical oceans, caused by increased albedo associated with the increasing CDNC, is compensated for by rising motion and positive TOA longwave signals over adjacent land regions.
An effective analysis on various scheduling algorithms in cloud computing

Cloud Computing focuses on demand services to the customers as pay-as-you go model with different Service level agreements at different levels. Most of the cloud services run with virtual machines. In this scenarios task scheduling plays an important role to schedule the tasks in an effective manner. The existing scheduling algorithms have the limitations like less memory utilization, high execution time, and high response time. These limitations in existing algorithms lead to inadequate load balance in dynamic situations, and provide the resource inefficient manner. In this paper, we have studied wide range of existing scheduling algorithms in cloud computing with respect to new metrics such as average migration time and electricity price per unit cost which are missing in existing studies, also studied conventional performance metrics like Response time, makespan, QOS, Memory utilization and Response time. Eventually, we have done the comparison study on existing scheduling algorithms and based on our study we identify the new research direction in the cloud scheduling algorithms.
Association of nad7a Gene with Cytoplasmic Male Sterility in Pigeonpea

Cytoplasmic male sterility (CMS) has been exploited in the commercial pigeonpea [Cajanus cajan (L.) Millsp. ] hybrid breeding system; however, the molecular mechanism behind this system is unknown. To understand the underlying molecular mechanism involved in A4 CMS system derived from C. cajanifolius (Haines) Maesen, 34 mitochondrial genes were analyzed for expression profiling and structural variation analysis between CMS line (ICRISAT Pigeonpea A line, ICPA 2039) and its cognate maintainer (ICPB 2039). Expression profiling of 34 mitochondrial genes revealed nine genes with significant fold differential gene expression at P ≤ 0.01, including one gene, nad4L, with 1366‐fold higher expression in CMS line as compared with the maintainer. Structural variation analysis of these mitochondrial genes identified length variation between ICPA 2039 and ICPB 2039 for nad7a (subunit of nad7 gene). Sanger sequencing of nad4L and nad7a genes in the CMS and the maintainer lines identified two single nucleotide polymorphisms (SNPs) in upstream region of nad4L and a deletion of 10 bp in nad7a in the CMS line. Protein structure evaluation showed conformational changes in predicted protein structures for nad7a between ICPA 2039 and ICPB 2039 lines. All above analyses indicate association of nad7a gene with the CMS for A4 cytoplasm in pigeonpea. Additionally, one polymerase chain reaction (PCR) based Indel marker (nad7a_del) has been developed and validated for testing genetic purity of A4 derived CMS lines to strengthen the commercial hybrid breeding program in pigeonpea.
A ug 2 01 2 DBI Inflation in N = 1 Supergravity

It was recently demonstrated that, when coupled to N = 1 supergravity, the Dirac-Born-Infeld (DBI) action constructed from a single chiral superfield has the property that when the higher-derivative terms become important, the potential becomes negative. Thus, DBI inflation cannot occur in its most interesting, relativistic regime. In this paper, it is shown how to overcome this problem by coupling the model to one or more additional chiral supermultiplets. In this way, one obtains effective single real scalar field DBI models with arbitrary positive potentials, as well as multiple real scalar field DBI inflation models with hybrid potentials. michael.koehn@aei.mpg. de, jlehners@aei.mpg. de, ovrut@elcapitan.hep. upenn.edu
Robust Tracing and Visualization of Heterogeneous Microvascular Networks

Advances in high-throughput imaging allow researchers to collect three-dimensional images of whole organ microvascular networks. These extremely large images contain networks that are highly complex, time consuming to segment, and difficult to visualize. In this paper, we present a framework for segmenting and visualizing vascular networks from terabyte-sized three-dimensional images collected using high-throughput microscopy. While these images require terabytes of storage, the volume devoted to the fiber network is <inline-formula><tex-math notation="LaTeX">$\approx 4$</tex-math><alternatives> <inline-graphic xlink:href="govyadinov-ieq1-2818701.gif"/></alternatives></inline-formula> percent of the total volume size. While the networks themselves are sparse, they are tremendously complex, interconnected, and vary widely in diameter. We describe a parallel GPU-based predictor-corrector method for tracing filaments that is robust to noise and sampling errors common in these data sets. We also propose a number of visualization techniques designed to convey the complex statistical descriptions of fibers across large tissue sections—including commonly studied microvascular characteristics, such as orientation and volume.
Sociophonetics of phonotactic phenomena in French

The aim of this paper is to shed new light on the behaviour of 5 phonotactic phenomena in French, which have been shown to be rather sensitive to regional and/or stylistic variation: schwa deletion at the initial of polysyllabic words and in monosyllabic grammatical words, liaison, deletion of final postobstruent /l/ and /R/ and of /l/ in the 3 personal clitic subject pronoun ‘il’. The analysis is performed on a 13-hour speech corpus, labelled at different levels and including productions of 120 speakers originated from 3 French-speaking countries (Belgium, France and Switzerland) and recorded in two different tasks (reading and conversation). Beside the regional and stylistic variables, parameters such as speakers’ age and socioeconomic status are also taken into account. The results of the analysis show that the 5 phenomena are mostly dependent on the speaking style, but that the effects of the other variables is less relevant than expected.
Subjective Knowledge Base Construction Powered By Crowdsourcing and Knowledge Base

Knowledge base construction (KBC) has become a hot and in-time topic recently with the increasing application need of large-scale knowledge bases (KBs), such as semantic search, QA systems, the Google Knowledge Graph and IBM Watson QA System. Existing KBs mainly focus on encoding the factual facts of the world, e.g., city area and company product, which are regarded as the objective knowledge, whereas the subjective knowledge, which is frequently mentioned in Web queries, has been neglected. The subjective knowledge has no documented ground truth, instead, the truth relies on people's dominant opinion, which can be solicited from online crowd workers. In our work, we propose a KBC framework for subjective knowledge base construction taking advantage of the knowledge from the crowd and existing KBs. We develop a two-staged framework for subjective KB construction which consists of core subjective KB construction and subjective KB enrichment. Firstly, we try to build a core subjective KB mined from existing KBs, where every instance has rich objective properties. Then, we populate the core subjective KB with instances extracted from existing KBs, in which the crowd is leverage to annotate the subjective property of the instances. In order to optimize the crowd annotation process, we formulate the problem of subjective KB enrichment procedure as a cost-aware instance annotation problem and propose two instance annotation algorithms, i.e., adaptive instance annotation and batch-mode instance annotation algorithms. We develop a two-stage system for subjective KB construction which consists of core subjective KB construction and subjective knowledge enrichment. We evaluate our framework on real knowledge bases and a real crowdsourcing platform, the experimental results show that we can derive high quality subjective knowledge facts from existing KBs and crowdsourcing techniques through our proposed framework.
Temporal Associations Between Social Activity and Mood, Fatigue, and Pain in Older Adults With HIV: An Ecological Momentary Assessment Study

Background Social isolation is associated with an increased risk for mental and physical health problems, especially among older persons living with HIV (PLWH). Thus, there is a need to better understand real-time temporal associations between social activity and mood- and health-related factors in this population to inform possible future interventions. Objective This study aims to examine real-time relationships between social activity and mood, fatigue, and pain in a sample of older PLWH. Methods A total of 20 older PLWH, recruited from the University of California, San Diego HIV Neurobehavioral Research Program in 2016, completed smartphone-based ecological momentary assessment (EMA) surveys 5 times per day for 1 week. Participants reported their current social activity (alone vs not alone and number of social interactions) and levels of mood (sadness, happiness, and stress), fatigue, and pain. Mixed-effects regression models were used to analyze concurrent and lagged associations among social activity, mood, fatigue, and pain. Results Participants (mean age 58.8, SD 4.3 years) reported being alone 63% of the time, on average, (SD 31.5%) during waking hours. Being alone was related to lower concurrent happiness (beta=−.300; 95% CI −.525 to −.079; P=.008). In lagged analyses, social activity predicted higher levels of fatigue later in the day (beta=−1.089; 95% CI −1.780 to −0.396; P=.002), and higher pain levels predicted being alone in the morning with a reduced likelihood of being alone as the day progressed (odds ratio 0.945, 95% CI 0.901-0.992; P=.02). Conclusions The use of EMA elucidated a high rate of time spent alone among older PLWH. Promoting social activity despite the presence of pain or fatigue may improve happiness and psychological well-being in this population.