bluuebunny/crossref_metadata_2025 huggingface dataset:
The RLK protein TaCRK10 activates wheat high‐temperature seedling‐plant resistance to stripe rust through interacting with TaH2A.1
Authors: Wang Jiahui , Wang Jingjing , Li Juan , Shang Hongsheng , Chen Xianming , Hu Xiaoping
Published: September 2021

Plants sense various pathogens and activate immunity responses through receptor‐like kinases (RLKs). Cysteine‐rich receptor‐like kinases (CRKs) are involved in massive transduction pathways upon perception of a pathogen. However, the roles of CRKs in response to stripe rust are unclear. In the present study, we identified a CRK gene (designated TaCRK10) from wheat variety Xiaoyan 6 (XY6) that harbors high‐temperature seedling‐plant (HTSP) resistance to stripe rust caused by fungal pathogen Puccinia striiformis f. sp. tritici (Pst). The expression level of TaCRK10 was induced by Pst inoculation and high temperature treatment. Knockdown of TaCRK10 by virus‐induced gene silencing resulted in attenuated wheat HTSP resistance to Pst, whereas there is no effect on Pst development and host responses under normal temperatures. Notably, overexpression of TaCRK10 in susceptible variety Fielder provided resistance only under normal temperatures at 14 days with reactive oxygen species accumulation and defense‐related gene expression of the salicylic acid pathway. Moreover, TaCRK10 physically interacted with and phosphorylated a histone variant TaH2A.1, which belongs to the H2A.W group. Silencing of TaH2A.1 suppressed wheat resistance to Pst, indicating that TaH2A.1 plays a positive role in wheat resistance to Pst. Thus, TaCRK10 serves as an important sensor of Pst infection and high temperatures, and it activates wheat resistance to Pst through regulating nuclear processes. This knowledge helps elucidate the molecular mechanism of wheat HTSP resistance to Pst and promotes efforts in developing wheat varieties with resistance to stripe rust.
Transcription factor OsbZIP49 controls tiller angle and plant architecture through the induction of indole‐3‐acetic acid‐amido synthetases in rice
Authors: Ding Chaohui , Lin Xianhui , Zuo Ying , Yu Zhilin , Baerson Scott R. , Pan Zhiqiang , Zeng Rensen , Song Yuanyuan
Published: September 2021

Tiller angle is an important determinant of plant architecture in rice (Oryza sativa L.). Auxins play a critical role in determining plant architecture; however, the underlying metabolic and signaling mechanisms are still largely unknown. In this study, we have identified a member of the bZIP family of TGA class transcription factors, OsbZIP49, that participates in the regulation of plant architecture and is specifically expressed in gravity‐sensing tissues, including the shoot base, nodes and lamina joints. Transgenic rice plants overexpressing OsbZIP49 displayed a tiller‐spreading phenotype with reduced plant height and internode lengths. In contrast, CRISPR/Cas9‐mediated knockout of OsbZIP49 resulted in a compact architecture. Follow‐up studies indicated that the effects of OsbZIP49 on tiller angles are mediated through changes in shoot gravitropic responses. Additionally, we provide evidence that OsbZIP49 activates the expression of indole‐3‐acetic acid‐amido synthetases OsGH3‐2 and OsGH3‐13 by directly binding to TGACG motifs located within the promoters of both genes. Increased GH3‐catalyzed conjugation of indole‐3‐acetic acid (IAA) in rice transformants overexpressing OsbZIP49 resulted in the increased accumulation of IAA‐Asp and IAA‐Glu, and a reduction in local free auxin, tryptamine and IAA‐Glc levels. Exogenous IAA or naphthylacetic acid (NAA) partially restored shoot gravitropic responses in OsbZIP49‐overexpressing plants. Knockout of OsbZIP49 led to reduced expression of both OsGH3‐2 and OsGH3‐13 within the shoot base, and increased accumulation of IAA and increased OsIAA20 expression levels were observed in transformants following gravistimulation. Taken together, the present results reveal the role transcription factor OsbZIP49 plays in determining plant architecture, primarily due to its influence on local auxin homeostasis.
Sequence variants in the proximal promoter and +5.8‐kb site of ABO in Koreans with weak B phenotypes
Authors: Yu HongBi , Kim Tae Yeul , Moon Sue Jin , Chung Yoo Na , Yoo Hwa Jong , Kim Jeong Hoon , Cho Duck
Published: September 2021

Background and Objectives

Several studies on Chinese and Japanese populations have revealed that a substantial proportion of weak B subgroups are caused by variants in the major regulatory regions of ABO, the proximal promoter, CCAAT‐binding factor/NF‐Y binding site and +5.8‐kb site. We performed molecular analyses of these regions in Koreans with weak B phenotypes.

Materials and Methods

This study included 16 samples with weak B phenotypes (4 B3, 1 Bw, 5 A1B3 and 6 A1Bw) harbouring no subgroup‐causing variants in ABO exons 6 and 7. These samples were subjected to sequencing analysis of exons 1–5 and the major regulatory regions of ABO.

Results

Of the 16 samples, 14 were found to carry a sequence variant either in the proximal promoter (g.4991_5008del [n = 3]) or the +5.8‐kb site (g.10893G>A [n = 4] and g.10925C>T [n = 7]). The remaining two samples were found to contain no subgroup‐causing variants.

Conclusion

Our study demonstrates that sequence variants in the proximal promoter and +5.8‐kb site account for a substantial proportion of weak B subgroups in Koreans, suggesting that molecular analysis of these regions is essential for the accurate determination of ABO genotypes in Koreans with weak B phenotypes.
Reaching New Heights
Authors: Jim Coaker P.E. , George W. Gibson
Published: September 2021

The history of the ASME A17 elevator safety code is intertwined with the ability to build ever-taller skyscrapers. One key landmark, the Empire State Building, may have been impossible without the contribution of ASME code committee members.
Electron beam deposition of cobalt on the silicon substrate: Experiment and simulation
Authors: L'vov Pavel E. , Bulyarskiy Sergey V. , Saurov Alexander N. , Svetukhin Vyacheslav V. , Terentyev Alexey I.
Published: September 2021

We explore the electron beam deposition of cobalt on a silicon substrate. The deposition has been studied in experiments at different electron beam powers. The temperature distribution over the metal surface has been calculated using the stationary heat conduction equation for a two-phase system. The obtained calculation results on the dependence of the film growth rate on electron beam power are in good agreement with our experimental data. We have shown that the film growth rate is limited by the flux of cobalt atoms on the film surface.
Supersonic jet noise source distributions
Authors: Breen Nicholas P. , Ahuja K. K.
Published: September 2021

The purpose of this work is to examine the noise source distributions of shock-containing supersonic jets at various pressure ratios corresponding to fully expanded Mach numbers ranging from 1.0 to 1.4 in intervals of 0.2 for various nozzle exit diameters. Source location measurements using a phased array (beamforming), farfield jet noise measurements, and schlieren flow visualization are presented. It is found that supersonic noise source distributions are more complex than those of subsonic jets. The source distributions for supersonic jets can be divided into three different Strouhal regions. At low Strouhal numbers (fD/U≤0.3), the noise source distributions appear very similar to those of a subsonic jet, as reported in open literature. This Strouhal region is dominated by jet-mixing noise associated with small-scale turbulence mixing. At high Strouhal numbers (fD/U≥1.0), the noise source distributions are comprised of several repetitive sources at various discrete downstream jet locations that produce noise at all frequencies. The locations of these sources roughly correspond to the shock cells in the jet, and thus, vary with jet Mach number. Another region exists at Strouhal numbers between these two regions (0.3<fD/U<1.0) for which the precise location of the sources as a function of Strouhal number was determined to be ambiguous due to a limitation of the phased array used. This region roughly corresponds to the frequencies of noise where jet-mixing noise and shock noise are of similar levels. The spacing of the shock sources in this region are smaller than the beam width of the array measuring them. Their locations can no longer can be separately recorded; and instead, they are averaged together and their centroid location is plotted.
The Burkholderia cenocepacia Type VI Secretion System Effector TecA Is a Virulence Factor in Mouse Models of Lung Infection
Authors: Loeven Nicole A. , Perault Andrew I. , Cotter Peggy A. , Hodges Craig A. , Schwartzman Joseph D. , Hampton Thomas H. , Bliska James B.
Published: September 2021

B. cenocepacia
is often considered the most virulent species in the Bcc because of its close association with cepacia syndrome in addition to its capacity to cause chronic lung infections in CF patients (1). Prior to the current study, virulence factors of
B. cenocepacia
important for causing lethal disease had not been identified in a CF animal model of lung infection.
Heterogeneous Flagellar Expression in Single Salmonella Cells Promotes Diversity in Antibiotic Tolerance
Authors: Lyu Zhihui , Yang Angela , Villanueva Patricia , Singh Abhyudai , Ling Jiqiang
Published: September 2021

Antibiotic resistance and tolerance pose a severe threat to human health. How bacterial pathogens acquire antibiotic tolerance is not clear.
Genetic Analysis of the Salmonella FliE Protein That Forms the Base of the Flagellar Axial Structure
Authors: Hendriksen Jordan J. , Lee Hee Jung , Bradshaw Alexander J. , Namba Keiichi , Chevance Fabienne F. V. , Minamino Tohru , Hughes Kelly T.
Published: September 2021

The FliE component of the bacterial flagellum is the first protein secreted through the flagellar type III secretion system (fT3SS) that is capable of self-assembly into the growing bacterial organelle. The FliE protein plays dual roles in the assembly of the
Salmonella
flagellum as the final component of the flagellar type III secretion system (fT3SS) and as an adaptor protein that anchors the rod (drive shaft) of the flagellar motor to the membrane-imbedded MS-ring structure.
Bacterial Quorum-Sensing Regulation Induces Morphological Change in a Key Host Tissue during the Euprymna scolopes-Vibrio fischeri Symbiosis
Authors: Essock-Burns T. , Bennett B. D. , Arencibia D. , Moriano-Gutierrez S. , Medeiros M. , McFall-Ngai M. J. , Ruby E. G.
Published: September 2021

Interbacterial signaling within a host-associated population can have profound effects on the behavior of the bacteria, for instance, in their production of virulence/colonization factors; in addition, such signaling can dictate the nature of the outcome for the host, in both pathogenic and beneficial associations. Using the monospecific squid-vibrio model of symbiosis, we examined how quorum-sensing regulation by the
Vibrio fischeri
population induces a biogeographic tissue phenotype that promotes the retention of this extracellular symbiont within the light organ of its host,
Euprymna scolopes
.
Preclinical Evaluation of Recombinant Microbial Glycoside Hydrolases in the Prevention of Experimental Invasive Aspergillosis
Authors: Ostapska Hanna , Raju Deepa , Lehoux Melanie , Lacdao Ira , Gilbert Stephanie , Sivarajah Piyanka , Bamford Natalie C. , Baker Perrin , Nguyen Thi Tuyet Mai , Zacharias Caitlin A. , Gravelat Fabrice N. , Howell P. Lynne , Sheppard Donald C.
Published: September 2021

The biofilm-forming mold
Aspergillus fumigatus
is a common causative agent of invasive fungal airway disease in patients with a compromised immune system or chronic airway disease. Treatment of
A. fumigatus
infection is limited by the few available antifungals to which fungal resistance is becoming increasingly common.
A Simple, Cost-Effective, and Automation-Friendly Direct PCR Approach for Bacterial Community Analysis
Authors: Song Fangchao , Kuehl Jennifer V. , Chandran Arjun , Arkin Adam P.
Published: September 2021

Understanding bacterial interactions and assembly in complex microbial communities using 16S rRNA sequencing normally requires a large experimental load. However, the current DNA extraction methods, including cell disruption and genomic DNA purification, are normally biased, costly, time-consuming, labor-intensive, and not amenable to miniaturization by droplets or 1,536-well plates due to the significant DNA loss during the purification step for tiny-volume and low-cell-density samples.
Getting the Message Out: the Many Modes of Host-Symbiont Communication during Early-Stage Establishment of the Squid-Vibrio Partnership
Authors: McFall-Ngai Margaret , Ruby Edward
Published: September 2021

Symbiosis, by its basic nature, depends on partner interactions that are mediated by cues and signals. This kind of critical reciprocal communication shapes the trajectory of host-microbe associations from their onset through their maturation and is typically mediated by both biochemical and biomechanical influences.
Evidence of past and current collaborations between traditional health practitioners and biomedical health practitioners: a scoping review protocol
Authors: Jama Ngcwalisa Amanda , Nyembezi Anam , Lehmann Uta
Published: September 2021

Introduction

Healthcare seekers around the globe use more than one healthcare system, with most using the traditional and the Western approaches concurrently. To date, little collaboration between the two systems has taken place within the mental health space compared with other areas of medicine. In order to inform integrating plans for traditional health practitioners and biomedical health practitioners in the South African mental health system, it is important to know which models of collaboration are used in other medical settings and contexts. This study aims to document global evidence on collaboration practices between traditional health practitioners and biomedical professionals when working with various health conditions.

Methods and analysis

This scoping review will be guided by an improved Arksey and O’Malley framework, the 2010 Levacet almethodological framework and the 2017 Joanna Briggs Institute guidelines. A systematic literature search will be carried out using seven different databases, EMBASE, PubMed, LILACS MEDLINE, APA PsycArticles, CINAHL Plus, Academic Search Complete and Scopus, in addition to the WHO repository, bibliographical search engines, and Open Access Theses and Dissertations. Moreover, the references of relevant publications between January 1978 and March 2020 will be scanned. Two reviewers will independently screen articles for eligibility based on the predetermined inclusion and exclusion criteria. Thematic analysis and descriptive numerical analysis will be performed using ATLAS.ti V.8 and Excel software, respectively. The results for this review will be presented using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis: Extension for Scoping Review.

Ethics and dissemination

This study will not require ethics approval because publicly available material will be used. Study findings will be published in an open-access journal and be presented to other key health system stakeholders and academic research gatherings.
Examining organisational responses to performance-based financial incentive systems: a case study using NHS staff influenza vaccination rates from 2012/2013 to 2019/2020
Authors: Liaqat Adiba , Gallier Suzy , Reeves Katharine , Crothers Hannah , Evison Felicity , Schmidtke Kelly , Bird Paul , Watson Samuel I , Khunti Kamlesh , Lilford Richard
Published: September 2021

Objective

Financial incentives are often applied to motivate desirable performance across organisations in healthcare systems. In the 2016/2017 financial year, the National Health Service (NHS) in England set a national performance-based incentive to increase uptake of the influenza vaccination among frontline staff. Since then, the threshold levels needed for hospital trusts to achieve the incentive (ie, the targets) have ranged from 70% to 80%. The present study examines the impact of this financial incentive across eight vaccination seasons.

Design

A retrospective observational study examining routinely recorded rates of influenza vaccination among staff in all acute NHS hospital trusts across eight vaccination seasons (2012/2013–2019/2020). The number of trusts included varied per year, from 127 to 137, due to organisational changes. McCrary’s density test is conducted to determine if the number of hospital trusts narrowly achieving the target by the end of each season is higher than would be expected in the absence of any responsiveness to the target. We refer to this bunching above the target threshold as a ‘threshold effect’.

Results

In the years before a national incentive was set, 9%–31% of NHS Trusts reported achieving the target, compared with 43%–74% in the 4 years after. Threshold effects did not emerge before the national incentive for payment was set; however, since then, threshold effects have appeared every year. Some trusts report narrowly achieving the target each year, both as the target rises and falls. Threshold effects were not apparent at targets for partial payments.

Conclusions

We provide compelling evidence that performance-based financial incentives produced threshold effects. Policymakers who set such incentives are encouraged to track threshold effects since they contain information on how organisations are responding to an incentive, what enquiries they may wish to make, how the incentive may be improved and what unintended effects it may be having.
Pediatric brain arteriovenous malformation recurrence: a cohort study, systematic review and meta-analysis
Authors: Hak Jean-Francois , Boulouis Gregoire , Kerleroux Basile , Benichi Sandro , Stricker Sarah , Gariel Florent , Garzelli Lorenzo , Meyer Philippe , Kossorotoff Manoelle , Boddaert Nathalie , Vidal Vincent , Girard Nadine , Dangouloff-Ros Volodia , Brunelle Francis , Fullerton Heather , Hetts Steven W , Blauwblomme Thomas , Naggara Olivier
Published: September 2021

Background

Recurrence following obliteration of brain arteriovenous malformations (AVMs) is common in children surgically treated, but recurrences following endovascular (EVT) and radiosurgical approaches are scantily reported.

Objective

To analyze the rates and risk factors for AVM recurrence after obliteration in a single-center cohort of children with ruptured AVMs treated with multimodal approaches, and to carry out a comprehensive review and meta-analysis of current data.

Methods

Children with ruptured AVMs between 2000 and 2019 enrolled in a prospective registry were retrospectively screened and included after angiographically determined obliteration to differentiate children with/without recurrence. A complementary systematic review and meta-analysis of studies investigating AVM recurrence in children between 2000 and 2020 was aggregated to explore the overall recurrence rates across treatment modalities by analyzing surgery versus other treatments.

Results

Seventy children with obliterated AVMs were included. AVM recurrences (n=10) were more commonly treated with EVT as final treatment (60% in the recurrence vs 13.3% in the no-recurrence group, p=0.018). Infratentorial locations were associated with earlier and more frequent recurrences (adjusted relative risk=4.62, 95% CI 1.08 to 19.04; p=0.04).

In the aggregate analysis, the pooled rate of AVM recurrence was 10.9% (95% CI 8.7% to 13.5%). Younger age at presentation was associated with more frequent recurrences (RR per year increase, 0.97, 95% CI 0.93 to 0.99; p=0.046).

Conclusion

Location of infratentorial AVMs and younger age at presentation may be associated with earlier and more frequent recurrences. The higher rates of recurrence in patients with AVMs obliterated with EVT questions its role in an intent-to-cure approach and reinforces its position as an adjunct to surgery and/or radiosurgery.
Gender Differences in Access to Health Care among the Elderly: Evidence from Southeast Asia
Authors: RODGERS YANA VAN DER MEULEN , ZVEGLICH JOSEPH E.
Published: September 2021

Populations become increasingly feminized with age. Since older women are more vulnerable to poverty, they may find it more difficult than men to access health care. This study examines factors that may constrain older persons in Southeast Asia from meeting their health-care needs when sick. Our analysis of household survey data from Cambodia, the Philippines, and Viet Nam shows that women are more likely to have reported sickness or injury than men, a difference that is meaningful and statistically significant. While women in Cambodia and the Philippines are more likely to seek treatment than men, the gender difference is reversed in Viet Nam where the stigma and discrimination associated with some diseases may more strongly deter women. The probability of seeking treatment rises with age more sharply for women than men in all countries. However, for the subsample of elders, the gender difference is not significant.
Discouraged Worker Effect and Labor Market Behavior of Urban Married Women
Authors: TAYAL DEEKSHA , PAUL SOURABH
Published: September 2021

The study investigates the importance of poor local labor market conditions in explaining the labor market behavior of married women in urban India. Using nationally representative employment data, we empirically test for the existence of a discouraged worker effect arising from either of two mechanisms: (i) unexplained gender wage gap, or (ii) degree of underemployment. A three-stage, district-level analysis of female labor market behavior was undertaken, and selectivity bias was controlled for by using censored probit in the second stage and trivariate probit with Geweke–Hajivassiliou–Keane smooth recursive simulator technique in the third stage of this multilevel framework. We find evidence that the wage gap discourages women from participating in the labor market and the prevalence of underemployment, in terms of overqualification by occupation, discourages them from exploring better job opportunities by making on-job search efforts.
Workload Balancing Among Heathcare Workers Under Uncertain Service Time Using Distributionally Robust Optimization
Authors: Anh Nguyen Duy
Published: September 2021

Healthcare systems are facing serious challenges in balancing their human resources to cope with volatile service demand, while at the same time providing necessary job satisfaction to the healthcare workers. In this paper, we propose a distributionally robust optimization formulation to generate a task assignment plan that promotes the fairness in allocation, attained by reducing the difference in the total working time among workers, under uncertain service time. The proposed joint chance constraint model is conservatively approximated by a worst-case Conditional Value-at-Risk, and we devise a sequential algorithm to solve the finite-dimensional reformulations which are linear (mixed-binary) optimization problems. We also provide explicit formula in the situation where the support set of the random vectors is a hyperrectangle. The experiment with both synthetic and real data indicates promising results for our distributionally robust optimization approach.
Fresh look at the effects of gravitational tidal forces on a freely-falling quantum particle
Authors: Hammad F. , Sadeghi P. , Fleury N. , Leblanc A.
Published: September 2021

In this paper, we take a closer and new look at the effects of tidal forces on the free fall of a quantum particle inside a spherically symmetric gravitational field. We derive the corresponding Schrödinger equation for the particle by starting from the fully relativistic Klein–Gordon equation in order (i) to briefly discuss the issue of the equivalence principle and (ii) to be able to compare the relativistic terms in the equation to the tidal-force terms. To the second order of the nonrelativistic approximation, the resulting Schrödinger equation is that of a simple harmonic oscillator in the horizontal direction and that of an inverted harmonic oscillator in the vertical direction. Two methods are used for solving the equation in the vertical direction. The first method is based on a fixed boundary condition, and yields a discrete-energy spectrum with a wavefunction that is asymptotic to that of a particle in a linear gravitational field. The second method is based on time-varying boundary conditions and yields a quantized-energy spectrum that is decaying in time. Moving on to a freely-falling reference frame, we derive the corresponding time-dependent energy spectrum. The effects of tidal forces yield an expectation value for the Hamiltonian and a relative change in time of a wavepacket’s width that are mass-independent. The equivalence principle, which we understand here as the empirical equivalence between gravitation and inertia, is discussed based on these various results. For completeness, we briefly discuss the consequences expected to be obtained for a Bose–Einstein condensate or a superfluid in free fall using the nonlinear Gross–Pitaevskii equation.
Proposed experimental test of Randall–Sundrum models
Authors: Pourhassan Behnam , Bhat Anha , Patel Hrishikesh , Faizal Mir , Mantella Nicholas
Published: September 2021

The Randall–Sundrum models are expected to modify the short distance behaviour of general relativity. In this paper, we will propose an experimental test for this short distance modification due to Randall–Sundrum models. This will be done by the analysing motion of a particle which is moving in spherical gravitational field with a drag force. The position at which the particle stops will be different in general relativity and Randall–Sundrum model. This difference in the distance moved by the particle before stopping can be measured using a nanoelectromechanical setup. Thus, it is possible to experimentally test Randall–Sundrum models using currently available technology.
A Graph-Theoretic Approach to Evaluate the Intensity of Barriers in the Implementation of Industry 4.0
Authors: Kumar Veepan , Vrat Prem , Shankar Ravi
Published: September 2021

Industry 4.0 is seen as a relatively new concept with regard to developing nations like India. It is, however, recognized as an innovative approach for ensuring competitiveness and sustainability in manufacturing according to the Triple Bottom Line (TBL) perspective. But the practitioners are not endorsing the concept with much enthusiasm due to numerous barriers across the different segments of the manufacturing sector. Therefore, determining the essence and impact of these barriers is essential so that practitioners can devise appropriate mitigation strategies. In this direction, an attempt has been made to evaluate the intensity of barriers in the implementation of Industry 4.0 by using a graph-theoretic approach (GTA). Based on this methodology, a single numerical index value (i.e. I4.0BII) (Industry 4.0 Barrier Intensity Index) has been calculated to evaluate the intensity (or overall impact) of the barriers with respect to the considered case company. The GTA results revealed that the case company is most significantly impacted by organization-related barriers, strategic barriers, and technology-oriented barriers. In contrast, it is least affected by ethical and regulatory barriers and socio-cultural barriers. Furthermore, the scenario analysis was conducted to develop a scale that would assist practitioners in evaluating and benchmarking the implementation of Industry 4.0. It was observed from the developed scale that the case company was placed under the category “partially ready to meet the expectations of Industry 4.0” with a normalized I4.0BII value proportional to 0.78. The case company practitioners need to concentrate on those barriers which have a high I4.0BII value, and accordingly, strategies can be developed to mitigate their hindering impacts.
Semi-Direct Monocular Visual-Inertial Odometry Using Point and Line Features for IoV
Authors: Jiang Nan , Huang Debin , Chen Jing , Wen Jie , Zhang Heng , Chen Honglong
Published: September 2021

The precise measuring of vehicle location has been a critical task in enhancing the autonomous driving in terms of intelligent decision making and safe transportation.
Internet of Vehicles
(
IoV
) is an important infrastructure in support of autonomous driving, allowing real-time road information exchanging and sharing for localizing vehicles.
Global positioning System
(
GPS
) is widely used in the traditional IoV system. GPS is unable to meet the key application requirements of autonomous driving due to meter level error and signal deterioration. In this article, we propose a novel solution, named
Semi-Direct Monocular Visual-Inertial Odometry using Point and Line Features
(
SDMPL-VIO
) for precise vehicle localization. Our SDMPL-VIO model takes advantage of a low-cost
Inertial Measurement Unit
(
IMU
) and monocular camera, using them as the sensor to acquire the surrounding environmental information.
Visual-Inertial Odometry
(
VIO
), taking into account both point and line features, is proposed, which is able to deal with both weak texture and dynamic environment. We use a semi-direct method to deal with keyframes and non-keyframes, respectively. Dual sliding window mechanisms can effectively fuse point-line and IMU information. To evaluate our SDMPL-VIO system model, we conduct extensive experiments on both an indoor dataset (i.e., EuRoC) and an outdoor dataset (i.e., KITTI) from the real-world applications, respectively. The experimental results show that the accuracy of SDMPL-VIO proposed by us is better than the mainstream VIO system at present. Especially in the weak texture of the datasets, fast-moving datasets, and other challenging datasets, SDMPL-VIO has a relatively high robustness.
Remark on Algorithm 992: An OpenGL- and C++-based Function Library for Curve and Surface Modeling in a Large Class of Extended Chebyshev Spaces
Authors: Róth Ágoston
Published: September 2021

We provide a number of corrections to the software component that accompanied this Algorithm submission [3]. An updated version of the code is available from the ACM Collected Algorithms site [1].
HyperSoRec: Exploiting Hyperbolic User and Item Representations with Multiple Aspects for Social-aware Recommendation
Authors: Wang Hao , Lian Defu , Tong Hanghang , Liu Qi , Huang Zhenya , Chen Enhong
Published: September 2021

Social recommendation has achieved great success in many domains including e-commerce and location-based social networks. Existing methods usually explore the user-item interactions or user-user connections to predict users’ preference behaviors. However, they usually learn both user and item representations in Euclidean space, which has large limitations for exploring the latent hierarchical property in the data. In this article, we study a novel problem of hyperbolic social recommendation, where we aim to learn the compact but strong representations for both users and items. Meanwhile, this work also addresses two critical domain-issues, which are under-explored. First, users often make trade-offs with multiple underlying aspect factors to make decisions during their interactions with items. Second, users generally build connections with others in terms of different aspects, which produces different influences with aspects in social network. To this end, we propose a novel graph neural network (GNN) framework with multiple aspect learning, namely, HyperSoRec. Specifically, we first embed all users, items, and aspects into hyperbolic space with superior representations to ensure their hierarchical properties. Then, we adapt a GNN with novel multi-aspect message-passing-receiving mechanism to capture different influences among users. Next, to characterize the multi-aspect interactions of users on items, we propose an adaptive hyperbolic metric learning method by introducing learnable interactive relations among different aspects. Finally, we utilize the hyperbolic translational distance to measure the plausibility in each user-item pair for recommendation. Experimental results on two public datasets clearly demonstrate that our HyperSoRec not only achieves significant improvement for recommendation performance but also shows better representation ability in hyperbolic space with strong robustness and reliability.
Learning from Substitutable and Complementary Relations for Graph-based Sequential Product Recommendation
Authors: Zhang Wei , Chen Zeyuan , Zha Hongyuan , Wang Jianyong
Published: September 2021

Sequential product recommendation, aiming at predicting the products that a target user will interact with soon, has become a hotspot topic. Most of the sequential recommendation models focus on learning from users’ interacted product sequences in a purely data-driven manner. However, they largely overlook the knowledgeable substitutable and complementary relations between products. To address this issue, we propose a novel Substitutable and Complementary Graph-based Sequential Product Recommendation model, namely, SCG-SPRe. The innovations of SCG-SPRe lie in its two main modules: (1) The module of interactive graph neural networks jointly encodes the high-order product correlations in the substitutable graph and the complementary graph into two types of relation-specific product representations. (2) The module of kernel-enhanced transformer networks adaptively fuses multiple temporal kernels to characterize the unique temporal patterns between a candidate product to be recommended and any interacted product in a target behavior sequence. Thanks to the seamless integration of the two modules, SCG-SPRe obtains candidate-dependent user representations for different candidate products to compute the corresponding ranking scores. We conduct extensive experiments on three public datasets, demonstrating SCG-SPRe is superior to competitive sequential recommendation baselines and validating the benefits of explicitly modeling the product-product relations.
Adaptive Hypermutation for Search-Based System Test Generation: A Study on REST APIs with EvoMaster
Authors: Zhang Man , Arcuri Andrea
Published: September 2021

REST web services are widely popular in industry, and search techniques have been successfully used to automatically generate system-level test cases for those systems. In this article, we propose a novel mutation operator which is designed specifically for test generation at system-level, with a particular focus on REST APIs. In REST API testing, and often in system testing in general, an individual can have a long and complex chromosome. Furthermore, there are two specific issues: (1) fitness evaluation in system testing is highly costly compared with the number of objectives (e.g., testing targets) to optimize for; and (2) a large part of the genotype might have no impact on the phenotype of the individuals (e.g., input data that has no impact on the execution flow in the tested program). Due to these issues, it might be not suitable to apply a typical low mutation rate like 1/
n
(where
n
is the number of genes in an individual), which would lead to mutating only one gene on average. Therefore, in this article, we propose an adaptive weight-based hypermutation, which is aware of the different characteristics of the mutated genes. We developed adaptive strategies that enable the selection and mutation of genes adaptively based on their fitness impact and mutation history throughout the search. To assess our novel proposed mutation operator, we implemented it in the
EvoMaster
tool, integrated in the MIO algorithm, and further conducted an empirical study with three artificial REST APIs and four real-world REST APIs. Results show that our novel mutation operator demonstrates noticeable improvements over the default MIO. It provides a significant improvement in performance for six out of the seven case studies, where the relative improvement is up to +12.09% for target coverage, +12.69% for line coverage, and +32.51% for branch coverage.
Stream Data Cleaning under Speed and Acceleration Constraints
Authors: Song Shaoxu , Gao Fei , Zhang Aoqian , Wang Jianmin , Yu Philip S.
Published: September 2021

Stream data are often dirty, for example, owing to unreliable sensor reading or erroneous extraction of stock prices. Most stream data cleaning approaches employ a smoothing filter, which may seriously alter the data without preserving the original information. We argue that the cleaning should avoid changing those originally correct/clean data, a.k.a. the
minimum modification rule
in data cleaning. To capture the knowledge about
what is clean
, we consider the (widely existing) constraints on the speed and acceleration of data changes, such as fuel consumption per hour, daily limit of stock prices, or the top speed and acceleration of a car. Guided by these semantic constraints, in this article, we propose the constraint-based approach for cleaning stream data. It is notable that existing data repair techniques clean (a sequence of) data
as a whole
and fail to support stream computation. To this end, we have to relax the global optimum over the entire sequence to the local optimum in a window. Rather than the commonly observed NP-hardness of general data repairing problems, our major contributions include (1) polynomial time algorithm for global optimum, (2) linear time algorithm towards local optimum under an efficient
median-based solution
, and (3) experiments on real datasets demonstrate that our method can show significantly lower L1 error than the existing approaches such as smoother.
The Impact of Dormant Defects on Defect Prediction: A Study of 19 Apache Projects
Authors: Falessi Davide , Ahluwalia Aalok , Penta Massimiliano DI
Published: September 2021

Defect prediction models can be beneficial to prioritize testing, analysis, or code review activities, and has been the subject of a substantial effort in academia, and some applications in industrial contexts. A necessary precondition when creating a defect prediction model is the availability of defect data from the history of projects. If this data is noisy, the resulting defect prediction model could result to be unreliable. One of the causes of noise for defect datasets is the presence of “dormant defects,” i.e., of defects discovered several releases after their introduction. This can cause a class to be labeled as defect-free while it is not, and is, therefore “snoring.” In this article, we investigate the impact of snoring on classifiers' accuracy and the effectiveness of a possible countermeasure, i.e., dropping too recent data from a training set. We analyze the accuracy of 15 machine learning defect prediction classifiers, on data from more than 4,000 defects and 600 releases of 19 open source projects from the Apache ecosystem. Our results show that on average across projects (i) the presence of dormant defects decreases the recall of defect prediction classifiers, and (ii) removing from the training set the classes that in the last release are labeled as not defective significantly improves the accuracy of the classifiers. In summary, this article provides insights on how to create defects datasets by mitigating the negative effect of dormant defects on defect prediction.
Propagating Geometry Information to Finite Element Computations
Authors: Heltai Luca , Bangerth Wolfgang , Kronbichler Martin , Mola Andrea
Published: September 2021

The traditional workflow in continuum mechanics simulations is that a geometry description —for example obtained using Constructive Solid Geometry (CSG) or Computer Aided Design (CAD) tools—forms the input for a mesh generator. The mesh is then used as the sole input for the finite element, finite volume, and finite difference solver, which at this point no longer has access to the original, “underlying” geometry. However, many modern techniques—for example, adaptive mesh refinement and the use of higher order geometry approximation methods—really do need information about the underlying geometry to realize their full potential. We have undertaken an exhaustive study of where typical finite element codes use geometry information, with the goal of determining what information geometry tools would have to provide. Our study shows that nearly all geometry-related needs inside the simulators can be satisfied by just two “primitives”: elementary queries posed by the simulation software to the geometry description. We then show that it is possible to provide these primitives in all of the frequently used ways in which geometries are described in common industrial workflows, and illustrate our solutions using a number of examples.
Automatic Fault Detection for Deep Learning Programs Using Graph Transformations
Authors: Nikanjam Amin , Braiek Houssem Ben , Morovati Mohammad Mehdi , Khomh Foutse
Published: September 2021

Nowadays, we are witnessing an increasing demand in both corporates and academia for exploiting
Deep Learning
(
DL
) to solve complex real-world problems. A DL program encodes the network structure of a desirable DL model and the process by which the model learns from the training dataset. Like any software, a DL program can be faulty, which implies substantial challenges of software quality assurance, especially in safety-critical domains. It is therefore crucial to equip DL development teams with efficient fault detection techniques and tools. In this article, we propose
NeuraLint
, a model-based fault detection approach for DL programs, using meta-modeling and graph transformations. First, we design a meta-model for DL programs that includes their base skeleton and fundamental properties. Then, we construct a graph-based verification process that covers 23 rules defined on top of the meta-model and implemented as graph transformations to detect faults and design inefficiencies in the generated models (i.e., instances of the meta-model). First, the proposed approach is evaluated by finding faults and design inefficiencies in 28 synthesized examples built from common problems reported in the literature. Then
NeuraLint
successfully finds 64 faults and design inefficiencies in 34 real-world DL programs extracted from Stack Overflow posts and GitHub repositories. The results show that
NeuraLint
effectively detects faults and design issues in both synthesized and real-world examples with a recall of 70.5% and a precision of 100%. Although the proposed meta-model is designed for feedforward neural networks, it can be extended to support other neural network architectures such as recurrent neural networks. Researchers can also expand our set of verification rules to cover more types of issues in DL programs.
The Case for Adaptive Security Interventions
Authors: Rauf Irum , Petre Marian , Tun Thein , Lopez Tamara , Lunn Paul , Van Der Linden Dirk , Towse John , Sharp Helen , Levine Mark , Rashid Awais , Nuseibeh Bashar
Published: September 2021

Despite the availability of various methods and tools to facilitate secure coding, developers continue to write code that contains common vulnerabilities. It is important to understand why technological advances do not sufficiently facilitate developers in writing secure code. To widen our understanding of developers' behaviour, we considered the complexity of the security decision space of developers using theory from cognitive and social psychology. Our interdisciplinary study reported in this article (1) draws on the psychology literature to provide conceptual underpinnings for three categories of impediments to achieving security goals, (2) reports on an in-depth meta-analysis of existing software security literature that identified a catalogue of factors that influence developers' security decisions, and (3) characterises the landscape of existing security interventions that are available to the developer during coding and identifies gaps. Collectively, these show that different forms of impediments to achieving security goals arise from different contributing factors. Interventions will be more effective where they reflect psychological factors more sensitively and marry technical sophistication, psychological frameworks, and usability. Our analysis suggests “adaptive security interventions” as a solution that responds to the changing security needs of individual developers and a present a proof-of-concept tool to substantiate our suggestion.
Bag Query Containment and Information Theory
Authors: Khamis Mahmoud Abo , Kolaitis Phokion G. , Ngo Hung Q. , Suciu Dan
Published: September 2021

The query containment problem is a fundamental algorithmic problem in data management. While this problem is well understood under set semantics, it is by far less understood under bag semantics. In particular, it is a long-standing open question whether or not the conjunctive query containment problem under bag semantics is decidable. We unveil tight connections between information theory and the conjunctive query containment under bag semantics. These connections are established using information inequalities, which are considered to be the laws of information theory. Our first main result asserts that deciding the validity of a generalization of information inequalities is many-one equivalent to the restricted case of conjunctive query containment in which the containing query is acyclic; thus, either both these problems are decidable or both are undecidable. Our second main result identifies a new decidable case of the conjunctive query containment problem under bag semantics. Specifically, we give an exponential-time algorithm for conjunctive query containment under bag semantics, provided the containing query is chordal and admits a simple junction tree.
Learning a Hierarchical Intent Model for Next-Item Recommendation
Authors: Zhu Nengjun , Cao Jian , Lu Xinjiang , Xiong Hui
Published: September 2021

A session-based recommender system (SBRS) captures users’ evolving behaviors and recommends the next item by profiling users in terms of items in a session. User intent and user preference are two factors affecting his (her) decisions. Specifically, the former narrows the selection scope to some item types, while the latter helps to compare items of the same type. Most SBRSs assume one arbitrary user intent dominates a session when making a recommendation. However, this oversimplifies the reality that a session may involve multiple types of items conforming to different intents. In current SBRSs, items conforming to different user intents have cross-interference in profiling users for whom only one user intent is considered. Explicitly identifying and differentiating items conforming to various user intents can address this issue and model rich contextual information of a session. To this end, we design a framework modeling user intent and preference explicitly, which empowers the two factors to play their distinctive roles. Accordingly, we propose a key-array memory network (KA-MemNN) with a hierarchical intent tree to model coarse-to-fine user intents. The two-layer weighting unit (TLWU) in KA-MemNN detects user intents and generates intent-specific user profiles. Furthermore, the hierarchical semantic component (HSC) integrates multiple sets of intent-specific user profiles along with different user intent distributions to model a multi-intent user profile. The experimental results on real-world datasets demonstrate the superiority of KA-MemNN over selected state-of-the-art methods.