bluuebunny/crossref_metadata_2025 huggingface dataset:
Associations between high ambient temperatures and asthma exacerbation among children in Philadelphia, PA: a time series analysis
Authors: Schinasi Leah H , Kenyon Chen C , Hubbard Rebecca A , Zhao Yuzhe , Maltenfort Mitchell , Melly Steven J , Moore Kari , Forrest Christopher B , Diez Roux Ana V , de Roos Anneclaire J
Published: March 2022

Objectives

High ambient temperatures may contribute to acute asthma exacerbation, a leading cause of morbidity in children. We quantified associations between hot-season ambient temperatures and asthma exacerbation in children ages 0–18 years in Philadelphia, PA.

Methods

We created a time series of daily counts of clinical encounters for asthma exacerbation at the Children’s Hospital of Philadelphia linked with daily meteorological data, June–August of 2011–2016. We estimated associations between mean daily temperature (up to a 5-day lag) and asthma exacerbation using generalised quasi-Poisson distributed models, adjusted for seasonal and long-term trends, day of the week, mean relative humidity,and US holiday. In secondary analyses, we ran models with adjustment for aeroallergens, air pollutants and respiratory virus counts. We quantified overall associations, and estimates stratified by encounter location (outpatient, emergency department, inpatient), sociodemographics and comorbidities.

Results

The analysis included 7637 asthma exacerbation events. High mean daily temperatures that occurred 5 days before the index date were associated with higher rates of exacerbation (rate ratio (RR) comparing 33°C–13.1°C days: 1.37, 95% CI 1.04 to 1.82). Associations were most substantial for children ages 2 to <5 years and for Hispanic and non-Hispanic black children. Adjustment for air pollutants, aeroallergens and respiratory virus counts did not substantially change RR estimates.

Conclusions

This research contributes to evidence that ambient heat is associated with higher rates of asthma exacerbation in children. Further work is needed to explore the mechanisms underlying these associations.
Risk factors for placental human papillomavirus infection
Authors: Niyibizi Joseph , Mayrand Marie-Hélène , Audibert Francois , Monnier Patricia , Brassard Paul , Laporte Louise , Lacaille Julie , Zahreddine Monica , Bédard Marie-Josée , Girard Isabelle , Francoeur Diane , Carceller Ana Maria , Lacroix Jacques , Fraser William , Coutlée François , Trottier Helen
Published: March 2022

Objective

Human papillomavirus (HPV) has been associated with adverse pregnancy outcomes but placental HPV infection has been rarely studied. The objective was to determine the proportion of HPV-positive placentas and the associated risk factors among HPV-positive women during pregnancy.

Methods

We analysed data from pregnant women enrolled in HERITAGE cohort study between 2010 and 2016 with positive vaginal HPV infection during the first trimester of pregnancy (n=354). Placental swabs and biopsies were collected. HPV genotyping was performed using Linear Array. The predictors of placental HPV detection were identified by generalised estimating equations models.

Results

HPV was detected in 78 placentas (22.0%) (one among 96 caesarean sections and 77 among 258 vaginal deliveries). Overall, 91% of HPV-positive placentas were positive for a genotype that was detected in vaginal samples during pregnancy. Among women who delivered vaginally, abnormal cytology (adjusted OR (aOR) 1.78 (95% CI 1.02 to 3.10)), other genitourinary infection (aOR 2.41 (95% CI 1.31 to 4.44)), presence of multiple HPV genotypes in the first trimester (aOR 2.69 (95% CI 1.76 to 4.12)) and persistence of high-risk HPV infections during pregnancy (HPV-16/18: aOR 3.94 (95% CI 2.06 to 7.55) and other than HPV-16/18: aOR 2.06 (95% CI 1.05 to 4.02)) were independently associated with placental HPV.

Conclusions

HPV was frequently detected in the placenta of women who delivered vaginally and may be associated with host immune response characteristics.
Unlocking the secrets of long non-coding RNAs in asthma
Authors: Gysens Fien , Mestdagh Pieter , de Bony de Lavergne Eric , Maes Tania
Published: March 2022

Asthma is a very heterozygous disease, divided in subtypes, such as eosinophilic and neutrophilic asthma. Phenotyping and endotyping of patients, especially patients with severe asthma who are refractory to standard treatment, are crucial in asthma management and are based on a combination of clinical and biological features. Nevertheless, the quest remains to find better biomarkers that distinguish asthma subtypes in a more clear and objective manner and to find new therapeutic targets to treat people with therapy-resistant asthma. In the past, research to identify asthma subtypes mainly focused on expression profiles of protein-coding genes. However, advances in RNA-sequencing technologies and the discovery of non-coding RNAs as important post-transcriptional regulators have provided an entire new field of research opportunities in asthma. This review focusses on long non-coding RNAs (lncRNAs) in asthma; these are non-coding RNAs with a length of more than 200 nucleotides. Many lncRNAs are differentially expressed in asthma, and several have been associated with asthma severity or inflammatory phenotype. Moreover,in vivoandin vitrofunctional studies have identified the mechanisms of action of specific lncRNAs. Although lncRNAs remain not widely studied in asthma, the current studies show the potential of lncRNAs as biomarkers and therapeutic targets as well as the need for further research.
Comparative digestibility of energy, dry matter, and nutrients by gestating and lactating sows fed corn–soybean meal diets without or with full-fat or defatted rice bran
Authors: Casas Gloria A. , Oliveira Maryane S.F. , Espinosa Charmaine D. , Stein H.H.
Published: March 2022

Twenty-four gestating sows and 24 lactating sows were randomly allotted to three diets with eight replicate sows per treatment in a 3 × 2 factorial arrangement. Different sows were used in gestation and lactation periods. The hypothesis was that digestibility of gross energy (GE), dry matter (DM), and nutrients in lactating sows is not different from that in gestating sows. A corn–soybean-meal diet and two full-fat-rice-bran or defatted rice bran diets were used. Results indicated that regardless of diet, lactating sows had greater (P < 0.01) apparent total tract digestibility of GE, DM, neutral detergent fiber, organic matter, and phosphorus than gestating sows.
Constitutive relations of a chiral hadronic fluid
Authors: Mañes Juan L. , Megías Eugenio , Valle Manuel , Vázquez-Mozo Miguel Á.
Published: March 2022

We study the role of nonabelian anomalies in relativistic fluids. To this end, we compute the local functional that solves the anomaly equations, and obtain analytical expressions for the covariant currents and the Bardeen–Zumino terms. We particularize these results to a background with two flavors, and consider the cases of unbroken and broken chiral symmetry. Finally, we provide explicit results for the constitutive relations of chiral nuclear matter interacting with external electromagnetic fields and in presence of chiral imbalance. We emphasize the nondissipative nature of the chiral electric effect.
The ENUBET experiment
Authors: Delogu C. C. , Acerbi F. , Berra A. , Bonesini M. , Branca A. , Brizzolari C. , Brunetti G. , Calviani M. , Capelli S. , Carturan S. , Catanesi M. G. , Cecchini S. , Charitonidis N. , Cindolo F. , Collazuol G. , Conti E. , Corso F. Dal , De Rosa G. , Falcone A. , Gola A. , Iacob F. , Jollet C. , Kain V. , Klic̆ek B. , Kudenko Y. , Laveder M. , Longhin A. , Ludovici L. , Lutsenko E. , Magaletti L. , Mandrioli G. , Margotti A. , Mascagna V. , Mauri N. , Meazza L. , Meregaglia A. , Mezzetto M. , Nessi M. , Paoloni A. , Pari M. , Parozzi E. G. , Pasqualini L. , Paternoster G. , Patrizii L. , Pozzato M. , Prest M. , Pupilli F. , Radicioni E. , Riccio C. , Ruggeri A. C. , Scian C. , Sirri G. , Stipc̆ević M. , Tenti M. , Terranova F. , Torti M. , Vallazza E. , Velotti F. , Votano L.
Published: March 2022

The knowledge of the initial flux in conventional neutrino beams represents the main limitation for a precision (1%) measurement of [Formula: see text] and [Formula: see text] cross-sections. The ENUBET ERC project is studying a facility based on a narrow-band beam capable of constraining the neutrino fluxes normalization through the monitoring of the associated charged leptons in an instrumented decay tunnel. In particular, the identification of large-angle positrons from [Formula: see text] decays at single-particle level can reduce the [Formula: see text] flux uncertainty at the level of 1%. This setup would allow for an unprecedented measurement of the [Formula: see text] cross-section at the GeV scale. Such an experimental input would be highly beneficial to reduce the budget of systematic uncertainties in the next long baseline oscillation experiments. The ENUBET Collaboration presented at ICNFP 2020 the advances in the design and simulation of the hadron beamline, the optimization and performances of a 20 m long focusing transfer line, the design of an horn-based beamline, the results in terms of particle identification in the decay tunnel, and the final design of the ENUBET demonstrator for the instrumented decay tunnel.
Corrosion fatigue crack propagation of 7XXX series aluminum alloys from key components of high-speed train
Authors: Dian Liwen , Lu Wei , Chen Bing , Gou Guoqing , Che Xiaoli , Zhu Zhongyin , Gao Wei
Published: March 2022

The corrosion fatigue cracks propagation behavior of A7N01P-T4 and A7N01S-T5 aluminum alloys in 3.5 wt.% NaCl solution and air were studied by single side notch corrosion fatigue tests (SNET). The crack growth rate (da/dN) was measured and the crack propagation mechanism was analyzed by scanning electron microscopic analysis. The results showed that the corrosion fatigue cracks growth rate of A7N01S-T5 alloy in air and 3.5 wt.% NaCl solution is faster than that of A7N01P-T4 alloy. The crack propagation follows a mixed intergranular and transgranular mode. The anodic dissolution and hydrogen embrittlement accelerate the propagation of corrosion fatigue crack of 7-series Al alloy in 3.5 wt.% NaCl solution. The corrosion resistance and fatigue crack propagation are related to the strength of the alloys and the density of grain boundaries.
The fatigue fracture of mounting bracket: A microstructure characterization
Authors: Zhang Yu , Ye Fan , Qian Ting , Yan Jin , Chen Kaiyuan , Cheng Guang , Wang Yuxin
Published: March 2022

We report the fatigue fracture in a mounting bracket, which is attached to a front bumper. We conducted the macro- and micro-scale fracture morphology analyses and base materials characterization. We used a bracket made of QSTE 420 TM high-strength steel and did not find any metallurgical defects. Three cracks were formed on the bracket, and the cracks were initiated from the outer edges of the screws. From the scanning electron microscopy (SEM) images, we observed ratchet marks among the three cracks at their initiation positions with a similar distance between each other. We also observed fatigue striation in the propagation zones. We deduce from the macro- and micro-scale analyses that the three cracks were initiated and then expanded spontaneously with a similar growth rate. The stress concentration, assembly operation and joining effects cause the bidirectional bending fatigue fracture in the three locations. Optimized tightening forces and new gaskets are recommended to extend the fatigue life of the mounting bracket.
The corrosion resistance of porous Ni–Ti alloys in seawater
Authors: Wu Liang , Huang Jiajia , Liu Wen , Chen Jinwei , Li Xi , Zhang Qiankun , Li Chun
Published: March 2022

Porous Ni–Ti alloys were fabricated by reactive synthesis of Ni and Ti elemental powders. The corrosion behavior of porous Ni–Ti alloys was explored by electrochemical methods and weight change measurements at 298 K in seawater. The polarization curves and electrochemical impedance spectroscopy data show that porous Ni–Ti (6.5:3.5) alloys provide the best corrosion resistance. The values of corrosion current density and corrosion rate are 1.6944 × 10−4 mA/cm2 and 1.9929 mm/a, respectively. The corrosion behavior and corrosion mechanism of porous Ni–Ti alloys are also discussed.
Effect of micro-grooved texture on cutting performance of tool under dry condition
Authors: Pang Minghua , Yang Hui , Li Yongfeng , Wang Zhankui , Ma Lijie , Su Jianxiu , Hayat Muhammad
Published: March 2022

To clarify the anti-adhesive properties of the textured tool, two types of micro-grooved textures were fabricated on the tool rake face. The cutting tests were conducted on aluminum alloys under no lubrication condition. Experimental results showed that the cutting force, tool–chip friction coefficient, and surface wear of the P_1 textured tool decreased. However, noticeable disadvantages were observed in samples with other textures. Detailed research indicated that the chip material (Al alloy) was easy to squeeze into and clog in the micro-grooved texture, due to low hardness and high ductility. In contrast, the cutting force, tool–chip interfacial friction, and wear of steel samples with T_1, T_2, and P_2 textures were all greater than that of the nontextured tool. The difference observed in these various textures signifies the importance of the small geometric size of the surface texture in the P_1 texture. Thus, an improvement of tool anti-adhesion with aluminum alloy material was obtained under dry cutting conditions.
Impact of nanoparticles and radiation phenomenon on viscoelastic fluid
Authors: Khan W. A. , Ahmad A. , Anjum N. , Abbas S. Z. , Chammam W. , Riahi A. , Rebei H. , Zaway M.
Published: March 2022

In recent years, nanotechnologies have been widely used in several fields regarding their rapid developments which create a lot of prospects for researchers and engineers. More specifically, replacement of conventional liquids with nanoliquids is considered as an innovative solution to heat transfer problems. This research work reports mixed convective phenomenon of viscoelastic fluids with variable stretched surface. This study prospects the applications in biotechnology and irrigation systems. Characteristics of thermophoresis, nonlinear thermal radiation and Brownian moment are examined. Significant influence of no mass flux relation and variable thicked surface on second grade is studied in this work. Moreover, heat-mass transportation phenomenon is explored through thermophoresis, nonlinear thermal radiation and Brownian diffusion. Transformation procedure is adopted in order to achieve required ODEs. HAM procedure is adopted to obtain the series solution. Outcomes of physical parameters for velocity, temperature and concentration fields are discussed. Here velocity profile of second-grade fluid is detected to be a declining function K (local second-grade parameter). Higher [Formula: see text] (Brownian moment parameter) intensifies the temperature profile while concentration of viscoelastic liquid dwindles against [Formula: see text].
Influence of thickness on the focusing of terahertz metasurface
Authors: Wang Xufeng , Wang Zhenhua , Liu Hongyuan , Jiang Jiuxing , He Xunjun
Published: March 2022

Terahertz (THz) focusing metasurface plays a key role in micro-focusing and micro-imaging. Most of the existing focus sets only show the number and deflection of focuses. However, few researches are concerned about the adjustment of focus intensity. Herein we designed a complementary metasurface based on a C-ring and cross-shaped composite slot structures, and the effect of structural thickness on focusing intensity and focal length were studied. We find that the angle of anomalously transmitted beam can be scanned from [Formula: see text] to [Formula: see text] when the frequency changes from 1.2 THz to 1.8 THz, while the focal length of the focusing metasurface can be linearly changed from 1000 [Formula: see text] to 1650 [Formula: see text]. With increase in the thickness of the C-ring area, more interestingly, the focus intensity decreased linearly from 8.0 dB (V/m) to 4.5 dB (V/m). However, the focus intensity showed no obvious change when the thickness of the cross-shaped area was increased from 0.2 [Formula: see text] to 5 [Formula: see text]. Therefore, our work would enhance the application of THz in high-resolution imaging, new THz device, and flat lens with adjustable focus intensity.
The synthesis and characterization of α-Fe2O3 nanowires decorated with ZnO nanoparticles
Authors: Tooba Simin , Kimiagar Salimeh , Zare-Dehnavi Nasser
Published: March 2022

In this study, the [Formula: see text]-Fe2O3 nanowires were synthesized and decorated by ZnO nanoparticles using the hydrothermal method. The structural and morphological study of samples was carried out using X-ray diffraction (XRD), scanning electron microscopy (SEM), energy dispersive X-ray spectrum (EDS), High resolution transmission electron microscopy (HRTEM), and Fourier transform infrared spectroscopy (FTIR) techniques. FTIR was employed for [Formula: see text]-Fe2O3 and [Formula: see text]-Fe2O3 decorated with ZnO nanoparticles composites to verify the presence of ZnO in the composite after decorating. Furthermore, the optical properties was carried out using UV-vis spectrophotometer and photoluminescence spectra (PL) techniques. XRD analysis showed [Formula: see text]-Fe2O3 nanowires had a rhombohedral structure with a preferred crystal plane (104). In the [Formula: see text]-Fe2O3 nanowires decorated by ZnO nanoparticles, ZnO nanoparticles showed a hexagonal structure. Due to SEM images, the nanowires had an average diameter of 41 nm, and ZnO nanoparticles had a spherical shape with a maximum diameter of 70 nm. A uniform distribution was observed for both the [Formula: see text]-Fe2O3 nanowires and the [Formula: see text]-Fe2O3 nanowires decorated with ZnO nanoparticles. Furthermore, the result of bandgap energy calculation was 2.12 eV for [Formula: see text]-Fe2O3, while for [Formula: see text]-Fe2O3 nanowires decorated with ZnO nanoparticles was 3.17 eV which is near to the bandgap energy of ZnO nanoparticles. PL spectra revealed a few transitions in which the origin of these emissions would be associated to oxygen vacancies, iron vacancies, defects and band-to-band transition. The PL emission peaks of [Formula: see text]-Fe2O3 nanowires decorated with ZnO nanoparticles are similar to [Formula: see text]-Fe2O3 nanowires, but notably quenched with the ZnO nanoparticles. Based on the result of this study, it is possible to tune bandgap energy for various applications. In addition, this composite is promising candidates for solar cell photo electrodes and gas sensor applications.
Global Fixed-Priority Scheduling for Parallel Real-Time Tasks with Constrained Parallelism
Authors: Qiao Lei , Yang Maolin , Chen Zewei , Liao Yong , Lei Hang , Sang Nan
Published: March 2022

With the rapid development of parallel programming techniques and the widespread use of multiprocessors, scheduling and analysis techniques supporting parallel real-time tasks become a critical topic for multiprocessor real-time systems. Global scheduling that allows the vertices of a parallel task to execute on any processor is a promising scheduling approach with guaranteed theoretical bounds and wide use in practice. However, the complex internal structure of parallel tasks leads to extensive inner- and inter-task interference, which leads to significant pessimism in the worst-case timing analysis. In this paper, a global fixed-priority (G-FP) scheduling with constrained parallelism for parallel real-time tasks based on the sporadic directed acyclic graph (DAG) model is proposed. Each DAG task is assigned a parallel threshold, such that the number of processors occupied by the task is limited to the parallel threshold of the task at a time. We propose a heuristic algorithm to set the parallel threshold and present a response-time analysis (RTA) to exploit the feature of the constrained parallel scheduling. Experiments with randomly generated tasks show that the proposed approach improves the schedulability upon G-FP and federated scheduling in terms of acceptance ratio.
A Hybrid Method for Equivalence Checking Between System Level and RTL
Authors: Hu Jian , Hu Minhui , Zhao Kuang , Kang Yun , Yang Haitao , Cheng Jie
Published: March 2022

Deep State Sequence-based (DSS) equivalence checking and path-based equivalence checking have been successfully applied for verification of digital designs between System Level Model (SLM) and Register Transfer Level (RTL). The DSS-based equivalence checking method can validate designs without mapping information, but the query size for each DSS is large compared with path-based verification. The query size for path-based methods is small, but the number of comparisons is large. In this work, we combine the advantages of DSS-based methods and path-based methods. We use DSS-based methods to find the corresponding paths and use cut-points like in path-based methods to split the DSS to reduce the query size. Finite State Machine with Datapath (FSMD) is used to represent the SLM and RTL models. Experimental results demonstrate that our method can effectively validate the designs and reduce the query size for DSS-based equivalence checking method.
Damage Evolution Modeling for Steel Structures Subjected to Combined High Cycle Fatigue and High-Intensity Dynamic Loadings
Authors: Zhou Xuhong , Bai Yongtao , Nardi Deborah C. , Wang Yuqian , Wang Yuhang , Liu Zhanfang , Picón Ricardo A. , Flórez-López Julio
Published: March 2022

This paper presents a method for modeling the initiation and propagation of the damage in steel structures under combined fatigue loading. An existing model in the literature is used as the main key to the development of the new formulation herein proposed, where a significant computational improvement for the cracking incubation phase under high cycle fatigue (HCF) is achieved. The results show a satisfactory comparison with experimental data available in the literature and the carried parametric simulations demonstrate the effectiveness of the model once strength reduction could be estimated after 0, 20, 40, and 60 years of HCF. The reduction of the structural capacity was evaluated by the consideration of the final damage due to a high-intensity dynamic loading at the end of the HCF phase.
AGGRAVATING EFFECTS OF FOOD EXPORT RESTRICTIONS UNDER CLIMATE CHANGE ON FOOD SECURITY: AN ANALYSIS OF RICE ECONOMY BASED ON ALTERNATIVE INDICATORS
Authors: LIU YAWEN , YANG LINGYU , ZHANG JINZHU , CUI QI , LIU YU , NIE FENGYING , HU YUMENG
Published: March 2022

The impact of climate change (CC) on food security has been widely discussed in the literature. However, the role of food export restrictions induced by CC is poorly understood. In this study, we take rice as an example and apply a global economic model to investigate the food security implications of CC and food export restrictions based on both self-sufficiency rate (SSR) and household rice consumption indicators. We find that for capturing the impacts of CC and export restrictions on food security, the household rice consumption indicator is much more suitable than the SSR indicator. The impacts of the export restrictions on food security vastly exceed CC effects in over half of the regions. Regarding household rice consumption indicators, the sign of the impact of rice export restriction for each country depends on its position in the global rice market and whether it enforces the restriction policy. The magnitude of impacts depends on the changes in consumer prices of rice and the affluence level of each region. Facing CC, household rice consumption in regions without export restrictions will be negatively affected. Interestingly, not all the regions enforcing export restrictions would benefit from the restrictive policy. Only the net rice exporters benefit slightly, while net importers are adversely affected. The export restriction policy would make the harmful effects of CC transmit from South Asia to Africa and the Middle East. For adaptation/mitigation policies, more attention should be paid to the leading exporters imposing export restrictions where the adverse effects are originated and the more impoverished regions with close trade connections with such regions.
PUBLIC SPENDING AS A NEW DETERMINATE OF SUSTAINABLE DEVELOPMENT GOAL AND GREEN ECONOMIC RECOVERY: POLICY PERSPECTIVE ANALYSIS IN THE POST-COVID ERA
Authors: LIU ZHEN , YIN TONG , SURYA PUTRA AHMAD ROMADHONI , SADIQ MUHAMMAD
Published: March 2022

Green economy talks about combing final mechanisms that have ecological and macroeconomic system gains. Likewise, this research piece examined the effects of increased spending on fiscal policies and tightening fiscal policies concerning greening the economic activity as the globe reclaims itself from the COVID-19 in China. Analysis was done applying the China longitudinal data for the period 2008–2018. We utilized the ordinary least square as well as the quantile regression equation to meticulously approximate the influences of increased fiscal spending policies in addition to tightening fiscal policies has on greening the economic system acts as the countries reclaim themselves from the pandemic via a formulated green performance indicator of China nations. The findings indicate a rather exciting pattern by saying a percentage growth in fiscal policy led to nearly 6.5% growth, that is, less than 0.5 growth in the minimum carbon dioxide polluting vaporous from energy suppliers, such as natural gas, and a 0.2% less than 0.01 cuts in the midway carbon dioxide polluting liquefied energy suppliers, that is, hydrocarbon byproducts and a nonsignificant expansion of 0.2%, more significant than 0.5 in the entire case scenario coming from polluting dense energy suppliers, that is, from coal byproduct sources. At the same time, a 1% expansion in fiscal policy reduces cumulative carbon dioxide pollution to 0.2%, less than 0.05%. On this score, the presence of the environmental hypothesis was authenticated in all scenarios analyzed. Furthermore, the causality test indicated a dual movement causative correlation between fiscal policy and carbon dioxide pollution and one-way movement concerning the fiscal policy to energy use. The findings demonstrated that China witnessed a rising switch to green advancement in China; their Green Economic Efficiency increased steadily.
What Can Be Seen from India’s Policy Toward China Under the Narendra Modi’s Administration?
Authors: NGA LE THI HANG , OANH NGUYEN THI
Published: March 2022

Under the Narendra Modi’s administration, India has been implementing a pragmatic foreign policy following realistic principles toward China. By using methods of policy analysis, in combination with content analysis and discourse research, the paper demonstrates that India’s policy toward China under the Prime Minister (PM) Modi’s leadership is a set of strategic responses of a rising power to an assertive neighbor who is rising at global level. This set of responses includes a policy of cooperation when it sees China as a partner, a policy of containment when it sees China as a rival, a policy of conflict management when it sees China as a neighbor who has direct territorial disputes; and a policy of balance of power when it sees China as an opponent. The authors of the paper are of the view that one of the key elements in India’s policies toward China is that it is a combination of both cooperation and competition aspects. In addition, it is to be seen that India has simultaneously used various channels to implement these policies toward China to secure its national goals.
Can Computing Be Diversified on “Principles” Alone? Exploring the Role of AP Computer Science Courses in Students’ Major and Career Intentions
Authors: Sax Linda J. , Newhouse Kaitlin N. S. , Goode Joanna , Nakajima Tomoko M. , Skorodinsky Max , Sendowski Michelle
Published: March 2022

The Advanced Placement Computer Science Principles (APCSP) course was introduced in 2016 to address long-standing gender and racial/ethnic disparities in the United States among students taking Advanced Placement Computer Science (APCS) in high school, as well as among those who pursued computing majors in college. Although APCSP has drawn a more diverse population of course takers than the traditional Advanced Placement Computer Science A (APCSA) course, questions remain about whether the new course portends students’ longer-term interest in computing and technology. This study used data from more than 120,000 first-year college students in the United States to understand the relationship between taking APCSA, APCSP, or both APCS courses and students' aspirations to major in computing or to pursue a computing or technology career. Our results indicated that taking APCSA or taking both APCS courses positively predicted students’ major or career aspirations in computing and technology, but that taking only APCSP did not predict long-term computing interest (although positive associations were identified for women). The findings identify a possible tension in the function of APCSP, as it appears to broaden access to computing knowledge, but may not necessarily encourage longer-term computing aspirations for all groups of underrepresented students.
Interactive Re-ranking via Object Entropy-Guided Question Answering for Cross-Modal Image Retrieval
Authors: Yanagi Rintaro , Togo Ren , Ogawa Takahiro , Haseyama Miki
Published: March 2022

Cross-modal image-retrieval methods retrieve desired images from a query text by learning relationships between texts and images. Such a retrieval approach is one of the most effective ways of achieving the easiness of query preparation. Recent cross-modal image-retrieval methods are convenient and accurate when users input a query text that can be used to uniquely identify the desired image. However, in reality, users frequently input ambiguous query texts, and these ambiguous queries make it difficult to obtain desired images. To overcome these difficulties, in this study, we propose a novel interactive cross-modal image-retrieval method based on question answering. The proposed method analyzes candidate images and asks users questions to obtain information that can narrow down retrieval candidates. By only answering questions generated by the proposed method, users can reach their desired images, even when using an ambiguous query text. Experimental results show the proposed method’s effectiveness.
A Systematic Literature Review on the Use of Deep Learning in Software Engineering Research
Authors: Watson Cody , Cooper Nathan , Palacio David Nader , Moran Kevin , Poshyvanyk Denys
Published: March 2022

An increasingly popular set of techniques adopted by software engineering (SE) researchers to automate development tasks are those rooted in the concept of Deep Learning (DL). The popularity of such techniques largely stems from their automated feature engineering capabilities, which aid in modeling software artifacts. However, due to the rapid pace at which DL techniques have been adopted, it is difficult to distill the current successes, failures, and opportunities of the current research landscape. In an effort to bring clarity to this cross-cutting area of work, from its modern inception to the present, this article presents a systematic literature review of research at the intersection of SE & DL. The review canvasses work appearing in the most prominent SE and DL conferences and journals and spans 128 papers across 23 unique SE tasks. We center our analysis around the
components of learning
, a set of principles that governs the application of machine learning techniques (ML) to a given problem domain, discussing several aspects of the surveyed work at a granular level. The end result of our analysis is a
research roadmap
that both delineates the foundations of DL techniques applied to SE research and highlights likely areas of fertile exploration for the future.
A Safe Computational Framework for Integer Programming Applied to Chvátal’s Conjecture
Authors: Eifler Leon , Gleixner Ambros , Pulaj Jonad
Published: March 2022

We describe a general and safe computational framework that provides integer programming results with the degree of certainty that is required for machine-assisted proofs of mathematical theorems. At its core, the framework relies on a rational branch-and-bound certificate produced by an exact integer programming solver, SCIP, in order to circumvent floating-point round-off errors present in most state-of-the-art solvers for mixed-integer programs. The resulting certificates are self-contained and checker software exists that can verify their correctness independently of the integer programming solver used to produce the certificate. This acts as a safeguard against programming errors that may be present in complex solver software. The viability of this approach is tested by applying it to finite cases of Chvátal’s conjecture, a long-standing open question in extremal combinatorics. We take particular care to verify also the correctness of the input for this specific problem, using the Coq formal proof assistant. As a result, we are able to provide the first machine-assisted proof that Chvátal’s conjecture holds for all downsets whose union of sets contains seven elements or less.
OSS Effort Estimation Using Software Features Similarity and Developer Activity-Based Metrics
Authors: Kapur Ritu , Sodhi Balwinder
Published: March 2022

Software development effort estimation (SDEE) generally involves leveraging the information about the effort spent in developing similar software in the past. Most organizations do not have access to sufficient and reliable forms of such data from past projects. As such, the existing SDEE methods suffer from low usage and accuracy.

We propose an efficient SDEE method for open source software, which provides accurate and fast effort estimates. The significant contributions of our article are (i) novel
SDEE software metrics
derived from developer activity information of various software repositories, (ii) an
SDEE dataset
comprising the SDEE metrics’ values derived from approximately 13,000 GitHub repositories from 150 different software categories, and (iii) an effort estimation tool based on SDEE metrics and a
software description similarity model
. Our software description similarity model is basically a machine learning model trained using the PVA on the software product descriptions of GitHub repositories. Given the software description of a newly envisioned software, our tool yields an effort estimate for developing it.

Our method achieves the highest standardized accuracy score of 87.26% (with Cliff’s δ = 0.88 at 99.999% confidence level) and 42.7% with the automatically transformed linear baseline model. Our software artifacts are available at https://doi.org/10.5281/zenodo.5095723.
Enable Deep Learning on Mobile Devices: Methods, Systems, and Applications
Authors: Cai Han , Lin Ji , Lin Yujun , Liu Zhijian , Tang Haotian , Wang Hanrui , Zhu Ligeng , Han Song
Published: March 2022

Deep neural networks (DNNs) have achieved unprecedented success in the field of artificial intelligence (AI), including computer vision, natural language processing, and speech recognition. However, their superior performance comes at the considerable cost of computational complexity, which greatly hinders their applications in many resource-constrained devices, such as mobile phones and Internet of Things (IoT) devices. Therefore, methods and techniques that are able to lift the efficiency bottleneck while preserving the high accuracy of DNNs are in great demand to enable numerous edge AI applications. This article provides an overview of efficient deep learning methods, systems, and applications. We start from introducing popular model compression methods, including pruning, factorization, quantization, as well as compact model design. To reduce the large design cost of these manual solutions, we discuss the AutoML framework for each of them, such as neural architecture search (NAS) and automated pruning and quantization. We then cover efficient on-device training to enable user customization based on the local data on mobile devices. Apart from general acceleration techniques, we also showcase several task-specific accelerations for point cloud, video, and natural language processing by exploiting their spatial sparsity and temporal/token redundancy. Finally, to support all these algorithmic advancements, we introduce the efficient deep learning system design from both software and hardware perspectives.
INC-Hg: An Intelligent Collaborative Haptic-Gripper Virtual Reality System
Authors: Zhao Huan , Zaini Amat Ashwaq , Migovich Miroslava , Swanson Amy , Weitlauf Amy S. , Warren Zachary , Sarkar Nilanjan
Published: March 2022

Collaborative Virtual Environments (CVE) have shown potential to be an effective social skill training platform for children with Autism Spectrum Disorders (ASD) to learn and practice collaborative and communication skills through peer interactions. However, most existing CVE systems require that appropriately matched partners be available at the same time to promote interaction, which limits their applicability to some community settings due to scheduling constraints. A second shortcoming of these more naturalistic peer-based designs is the intensive resources required to manually code the unrestricted conversations that occurred during the peer-based interactions. To preserve the benefits of CVE-based platforms and mitigate some of the resource limitations related to peer availability, we developed an Intelligent Collaborative Haptic-Gripper System (INC-Hg). This system provides an intelligent agent partner who can understand, communicate, and haptically interact with the user, without requiring the presence of another human peer. The INC-Hg operates in real time and thus is able to perform collaborative training tasks at any time and at the user's pace. INC-Hg can also record the real-time data regarding spoken language and task performance, thereby greatly reducing the resource burden of communication and interaction performance analysis. A preliminary usability study with 10 participants with ASD (ages 8–12 years) indicated that the system could classify the participant's utterances into five classes with an accuracy of 70.34%, which suggested the potential of INC-Hg to automatically recognize and analyze conversational content. The results also indicated high accuracies of the agent to initiate a conversation (97.56%) and respond to the participants (86.52%), suggesting the capability of the agent to conduct proper conversations with the participants. Compared to the results of human-to-human collaborative tasks, the human-to-agent mode achieved higher average collaborative operation ratio (61% compared to 40%) and comparable average frequencies for Initiations and Responses among the participants with ASD. These results offer preliminary support as well as areas of improvement regarding the agent's ability to respond to participants, work with participants to complete tasks, engage in back-and-forth conversations, and support the potential of the agent to be a useful partner for individuals with ASD completing CVE tasks.
Inner Knowledge-based Img2Doc Scheme for Visual Question Answering
Authors: Li Qun , Xiao Fu , Bhanu Bir , Sheng Biyun , Hong Richang
Published: March 2022

Visual Question Answering (VQA) is a research topic of significant interest at the intersection of computer vision and natural language understanding. Recent research indicates that attributes and knowledge can effectively improve performance for both image captioning and VQA. In this article, an inner knowledge-based Img2Doc algorithm for VQA is presented. The inner knowledge is characterized as the inner attribute relationship in visual images. In addition to using an attribute network for inner knowledge-based image representation, VQA scheme is associated with a question-guided Doc2Vec method for question–answering. The attribute network generates inner knowledge-based features for visual images, while a novel question-guided Doc2Vec method aims at converting natural language text to vector features. After the vector features are extracted, they are combined with visual image features into a classifier to provide an answer. Based on our model, the VQA problem is resolved by textual question answering. The experimental results demonstrate that the proposed method achieves superior performance on multiple benchmark datasets.
Matching Faces and Attributes Between the Artistic and the Real Domain: the PersonArt Approach
Authors: Cornia Marcella , Tomei Matteo , Baraldi Lorenzo , Cucchiara Rita
Published: March 2022

In this article, we present an approach for retrieving similar faces between the artistic and the real domain. The application we refer to is an interactive exhibition inside a museum, in which a visitor can take a photo of himself and search for a lookalike in the collection of paintings. The task requires not only to identify faces but also to extract discriminative features from artistic and photo-realistic images, tackling a significant domain shift. Our method integrates feature extraction networks which account for the aesthetic similarity of two faces and their correspondences in terms of semantic attributes. Also, it addresses the domain shift between realistic images and paintings by translating photo-realistic images into the artistic domain. Noticeably, by exploiting the same technique, our model does not need to rely on annotated data in the artistic domain. Experimental results are conducted on different paired datasets to show the effectiveness of the proposed solution in terms of identity and attribute preservation. The approach is also evaluated on unpaired settings and in combination with an interactive relevance feedback strategy. Finally, we show how the proposed algorithm has been implemented in a real showcase at the Gallerie Estensi museum in Italy, with the participation of more than 1,100 visitors in just three days.
Enhanced 3D Shape Reconstruction With Knowledge Graph of Category Concept
Authors: Sun Guofei , Wong Yongkang , Kankanhalli Mohan S. , Li Xiangdong , Geng Weidong
Published: March 2022

Reconstructing three-dimensional (3D) objects from images has attracted increasing attention due to its wide applications in computer vision and robotic tasks. Despite the promising progress of recent deep learning–based approaches, which directly reconstruct the full 3D shape without considering the conceptual knowledge of the object categories, existing models have limited usage and usually create unrealistic shapes. 3D objects have multiple forms of representation, such as 3D volume, conceptual knowledge, and so on. In this work, we show that the conceptual knowledge for a category of objects, which represents objects as prototype volumes and is structured by graph, can enhance the 3D reconstruction pipeline. We propose a novel multimodal framework that explicitly combines graph-based conceptual knowledge with deep neural networks for 3D shape reconstruction from a single RGB image. Our approach represents conceptual knowledge of a specific category as a structure-based knowledge graph. Specifically, conceptual knowledge acts as visual priors and spatial relationships to assist the 3D reconstruction framework to create realistic 3D shapes with enhanced details. Our 3D reconstruction framework takes an image as input. It first predicts the conceptual knowledge of the object in the image, then generates a 3D object based on the input image and the predicted conceptual knowledge. The generated 3D object satisfies the following requirements: (1) it is consistent with the predicted graph in concept, and (2) consistent with the input image in geometry. Extensive experiments on public datasets (i.e.,  ShapeNet, Pix3D, and Pascal3D+) with 13 object categories show that (1) our method outperforms the state-of-the-art methods, (2) our prototype volume-based conceptual knowledge representation is more effective, and (3) our pipeline-agnostic approach can enhance the reconstruction quality of various 3D shape reconstruction pipelines.
When Pairs Meet Triplets: Improving Low-Resource Captioning via Multi-Objective Optimization
Authors: Wu Yike , Zhao Shiwan , Zhang Ying , Yuan Xiaojie , Su Zhong
Published: March 2022

Image captioning for low-resource languages has attracted much attention recently. Researchers propose to augment the low-resource caption dataset into (image, rich-resource language, and low-resource language) triplets and develop the dual attention mechanism to exploit the existence of triplets in training to improve the performance. However, datasets in triplet form are usually small due to their high collecting cost. On the other hand, there are already many large-scale datasets, which contain one pair from the triplet, such as caption datasets in the rich-resource language and translation datasets from the rich-resource language to the low-resource language. In this article, we revisit the caption-translation pipeline of the translation-based approach to utilize not only the triplet dataset but also large-scale paired datasets in training. The caption-translation pipeline is composed of two models, one caption model of the rich-resource language and one translation model from the rich-resource language to the low-resource language. Unfortunately, it is not trivial to fully benefit from incorporating both the triplet dataset and paired datasets into the pipeline, due to the gap between the training and testing phases and the instability in the training process. We propose to jointly optimize the two models of the pipeline in an end-to-end manner to bridge the training and testing gap, and introduce two auxiliary training objectives to stabilize the training process. Experimental results show that the proposed method improves significantly over the state-of-the-art methods.
Approximate Degree, Weight, and Indistinguishability
Authors: Huang Xuangui , Viola Emanuele
Published: March 2022

We prove that the OR function on {-1,1\}

n

can be pointwise approximated with error ε by a polynomial of degree
O
(
k
) and weight 2

O
(
n
log (1/ε)/k)

, for any
k
≥ √
n
log (1/ε). This result is tight for any
k
≤ (1-Ω (1))
n
. Previous results were either not tight or had ε = Ω (1). In general, we obtain a tight approximate degree-weight result for any symmetric function. Building on this, we also obtain an approximate degree-weight result for bounded-width CNF. For these two classes no such result was known.

We prove that the

\( \mathsf {OR} \)

function on

\( \lbrace -1,1\rbrace ^n \)

can be pointwise approximated with error

\( \epsilon \)

by a polynomial of degree

\( O(k) \)

and weight

\( 2^{O(n \log (1/\epsilon) /k)} \)

, for any

\( k \ge \sqrt {n \log (1/\epsilon)} \)

. This result is tight for any

\( k \le (1-\Omega (1))n \)

. Previous results were either not tight or had

\( \epsilon = \Omega (1) \)

. In general, we obtain a tight approximate degree-weight result for any symmetric function. Building on this, we also obtain an approximate degree-weight result for bounded-width

\( \mathsf {CNF} \)

. For these two classes no such result was known.

One motivation for such results comes from the study of indistinguishability. Two distributions

\( P \)

,

\( Q \)

over

\( n \)

-bit strings are

\( (k,\delta) \)

-indistinguishable if their projections on any

\( k \)

bits have statistical distance at most

\( \delta \)

. The above approximations give values of

\( (k,\delta) \)

that suffice to fool

\( \mathsf {OR} \)

, symmetric functions, and bounded-width

\( \mathsf {CNF} \)

, and the first result is tight for all

\( k \)

while the second result is tight for

\( k \le (1-\Omega (1))n \)

. We also show that any two

\( (k, \delta) \)

-indistinguishable distributions are

\( O(n^{k/2}\delta) \)

-close to two distributions that are

\( (k,0) \)

-indistinguishable, improving the previous bound of

\( O(n)^k \delta \)

. Finally, we present proofs of some known approximate degree lower bounds in the language of indistinguishability, which we find more intuitive.