UniHeap: Managing Persistent Objects Across Managed Runtimes for Non-Volatile Memory

Byte-addressable, non-volatile memory (NVM) is emerging as a promising technology. To facilitate its wide adoption, employing NVM in managed runtimes like JVM has proven to be an effective approach (i.e., managed NVM). However, such an approach is runtime specific, which lacks a generic abstraction across different managed languages. Similar to the well-known filesystem primitives that allow diverse programs to access same files via the block I/O interface, managed NVM deserves the same system-wide property for persistent objects across managed runtimes with low overhead. In this paper, we present UniHeap, a new NVM framework for managing persistent objects. It proposes a unified persistent object model that supports various managed languages, and manages NVM within a shared heap that enables cross-language persistent object sharing. UniHeap reduces the object persistence overhead by managing the shared heap in a log-structured manner and coalescing object updates during the garbage collection. We implement UniHeap as a generic framework and extend it to different managed runtimes that include HotSpot JVM, cPython, and JavaScript engine SpiderMonkey. We evaluate UniHeap with a variety of applications, such as key-value store and transactional database. Our evaluation shows that UniHeap significantly outperforms state-of-the-art object sharing approaches, while introducing negligible overhead to the managed runtimes.Comment: A 2 page extended abstract for NVMW 2022

You've Got Two Teachers: Co-evolutionary Image and Report Distillation for Semi-supervised Anatomical Abnormality Detection in Chest X-ray

Chest X-ray (CXR) anatomical abnormality detection aims at localizing and characterising cardiopulmonary radiological findings in the radiographs, which can expedite clinical workflow and reduce observational oversights. Most existing methods attempted this task in either fully supervised settings which demanded costly mass per-abnormality annotations, or weakly supervised settings which still lagged badly behind fully supervised methods in performance. In this work, we propose a co-evolutionary image and report distillation (CEIRD) framework, which approaches semi-supervised abnormality detection in CXR by grounding the visual detection results with text-classified abnormalities from paired radiology reports, and vice versa. Concretely, based on the classical teacher-student pseudo label distillation (TSD) paradigm, we additionally introduce an auxiliary report classification model, whose prediction is used for report-guided pseudo detection label refinement (RPDLR) in the primary vision detection task. Inversely, we also use the prediction of the vision detection model for abnormality-guided pseudo classification label refinement (APCLR) in the auxiliary report classification task, and propose a co-evolution strategy where the vision and report models mutually promote each other with RPDLR and APCLR performed alternatively. To this end, we effectively incorporate the weak supervision by reports into the semi-supervised TSD pipeline. Besides the cross-modal pseudo label refinement, we further propose an intra-image-modal self-adaptive non-maximum suppression, where the pseudo detection labels generated by the teacher vision model are dynamically rectified by high-confidence predictions by the student. Experimental results on the public MIMIC-CXR benchmark demonstrate CEIRD's superior performance to several up-to-date weakly and semi-supervised methods

An interpretable imbalanced semi-supervised deep learning framework for improving differential diagnosis of skin diseases

Dermatological diseases are among the most common disorders worldwide. This paper presents the first study of the interpretability and imbalanced semi-supervised learning of the multiclass intelligent skin diagnosis framework (ISDL) using 58,457 skin images with 10,857 unlabeled samples. Pseudo-labelled samples from minority classes have a higher probability at each iteration of class-rebalancing self-training, thereby promoting the utilization of unlabeled samples to solve the class imbalance problem. Our ISDL achieved a promising performance with an accuracy of 0.979, sensitivity of 0.975, specificity of 0.973, macro-F1 score of 0.974 and area under the receiver operating characteristic curve (AUC) of 0.999 for multi-label skin disease classification. The Shapley Additive explanation (SHAP) method is combined with our ISDL to explain how the deep learning model makes predictions. This finding is consistent with the clinical diagnosis. We also proposed a sampling distribution optimisation strategy to select pseudo-labelled samples in a more effective manner using ISDLplus. Furthermore, it has the potential to relieve the pressure placed on professional doctors, as well as help with practical issues associated with a shortage of such doctors in rural areas

Mining the candidate genes of rice panicle traits via a genome-wide association study

Panicle traits are important for improving the panicle architecture and grain yield of rice. Therefore, we performed a genome-wide association study (GWAS) to analyze and determine the genetic determinants of five panicle traits. A total of 1.29 million single nucleotide polymorphism (SNP) loci were detected in 162 rice materials. We carried out a GWAS of panicle length (PL), total grain number per panicle (TGP), filled grain number per panicle (FGP), seed setting rate (SSR) and grain weight per panicle (GWP) in 2019, 2020 and 2021. Four quantitative trait loci (QTLs) for PL were detected on chromosomes 1, 6, and 9; one QTL for TGP, FGP, and GWP was detected on chromosome 4; two QTLs for FGP were detected on chromosomes 4 and 7; and one QTL for SSR was detected on chromosome 1. These QTLs were detected via a general linear model (GLM) and mixed linear model (MLM) in both years of the study period. In this study, the genomic best linear unbiased prediction (BLUP) method was used to verify the accuracy of the GWAS results. There are nine QTLs were both detected by the multi-environment GWAS method and the BLUP method. Moreover, further analysis revealed that three candidate genes, LOC_Os01g43700, LOC_Os09g25784, and LOC_Os04g47890, may be significantly related to panicle traits of rice. Haplotype analysis indicated that LOC_Os01g43700 and LOC_Os09g25784 are highly associated with PL and that LOC_Os04g47890 is highly associated with TGP, FGP, and GWP. Our results offer essential genetic information for the molecular improvement of panicle traits. The identified candidate genes and elite haplotypes could be used in marker-assisted selection to improve rice yield through pyramid breeding

Genetically predicted high IGF-1 levels showed protective effects on COVID-19 susceptibility and hospitalization:a Mendelian randomisation study with data from 60 studies across 25 countries

Background: Epidemiological studies observed gender differences in COVID-19 outcomes, however, whether sex hormone plays a causal in COVID-19 risk remains unclear. This study aimed to examine associations of sex hormone, sex hormones-binding globulin (SHBG), insulin-like growth factor-1 (IGF-1), and COVID-19 risk. Methods: Two-sample Mendelian randomization (TSMR) study was performed to explore the causal associations between testosterone, estrogen, SHBG, IGF-1, and the risk of COVID-19 (susceptibility, hospitalization, and severity) using genome-wide association study (GWAS) summary level data from the COVID-19 Host Genetics Initiative (N=1,348,701). Random-effects inverse variance weighted (IVW) MR approach was used as the primary MR method and the weighted median, MR-Egger, and MR Pleiotropy RESidual Sum and Outlier (MR-PRESSO) test were conducted as sensitivity analyses. Results: Higher genetically predicted IGF-1 levels have nominally significant association with reduced risk of COVID-19 susceptibility and hospitalization. For one standard deviation increase in genetically predicted IGF-1 levels, the odds ratio was 0.77 (95% confidence interval [CI], 0.61-0.97, p=0.027) for COVID-19 susceptibility, 0.62 (95% CI: 0.25-0.51, p=0.018) for COVID-19 hospitalization, and 0.85 (95% CI: 0.52-1.38, p=0.513) for COVID-19 severity. There was no evidence that testosterone, estrogen, and SHBG are associated with the risk of COVID-19 susceptibility, hospitalization, and severity in either overall or sex-stratified TSMR analysis. Conclusions: Our study indicated that genetically predicted high IGF-1 levels were associated with decrease the risk of COVID-19 susceptibility and hospitalization, but these associations did not survive the Bonferroni correction of multiple testing. Further studies are needed to validate the findings and explore whether IGF-1 could be a potential intervention target to reduce COVID-19 risk

Boosting with an aerosolized Ad5-nCoV elicited robust immune responses in inactivated COVID-19 vaccines recipients

IntroductionThe SARS-CoV-2 Omicron variant has become the dominant SARS-CoV-2 variant and exhibits immune escape to current COVID-19 vaccines, the further boosting strategies are required.MethodsWe have conducted a non-randomized, open-label and parallel-controlled phase 4 trial to evaluate the magnitude and longevity of immune responses to booster vaccination with intramuscular adenovirus vectored vaccine (Ad5-nCoV), aerosolized Ad5-nCoV, a recombinant protein subunit vaccine (ZF2001) or homologous inactivated vaccine (CoronaVac) in those who received two doses of inactivated COVID-19 vaccines. ResultsThe aerosolized Ad5-nCoV induced the most robust and long-lasting neutralizing activity against Omicron variant and IFNg T-cell response among all the boosters, with a distinct mucosal immune response. SARS-CoV-2-specific mucosal IgA response was substantially generated in subjects boosted with the aerosolized Ad5-nCoV at day 14 post-vaccination. At month 6, participants boosted with the aerosolized Ad5-nCoV had remarkably higher median titer and seroconversion of the Omicron BA.4/5-specific neutralizing antibody than those who received other boosters. DiscussionOur findings suggest that aerosolized Ad5-nCoV may provide an efficient alternative in response to the spread of the Omicron BA.4/5 variant.Clinical trial registrationhttps://www.chictr.org.cn/showproj.html?proj=152729, identifier ChiCTR2200057278

Fathers’ Involvement at Home and Children’s Achievement: Evidence from Rural China, Gansu Province

Based on the Gansu Survey of Children and Families (GSCF, 2007), this thesis investigates the hierarchical effects of teacher personal characteristics and teaching job attributes as determinants of wages and sources of variations from the perspective of Hedonic Wage Theory. Based on the Hedonic Wage Theory, this study has made use of a scientific sampled micro data set to analyze teacher wage disparities in rural Gansu, which is a typical less-developed northwestern remote province in Mainland China. Hierarchical Linear Modeling(HLM) is employed to study the regional effects. Major foci of this thesis consist of: (1)The nature and strength of economic values of teacher personal characteristics and teaching job attributes. (2)The substitution between pecuniary rewards from wages and non-pecuniary benefit derived from working conditions and living amenities, and its implications for teacher personnel costs. (3)How regional policies are related to teacher wage variations and what can government do to narrow the consequential gap in education service. The conclusions of the study include: Both teacher personal characteristics and teaching job attributes are major determining factors of wages. Human capital components proxying higher teacher quality are positively compensated, while better daily working and living conditions are paid in the form of lower wages. In other words, hardships are associated with compensating wage differentials. Working conditions in schools and living conditions in community where the teaching position is located are substitutable with wages. The substitution between wages and job conditions varies from -0.03 to 0.05. Negative values mean that teachers are willing to accept lower wages to work in a better-off county. It costs more for hard-to-staff regions to recruit a comparable teacher. In consideration of wage compensations, the Helping the Poor policy can give a better indication than the Subsidy to Remote and Difficult Districts Scheme . Dis-utilities from uncomfortable working and living environment in poor counties cost 15% extra wage expenditures. Accounting for teacher utility preference, disadvantageous counties classified by economic-geographic features should be financially aided based on teacher cost index(TCI) to recruit and retain quality teachers. Simulation implies that counties labeled as poor should be provided 10% more marginal personnel budget in order to hire an average teacher who meets the basic education requirements. However, fiscal assistances based on degree of remoteness do not show consistent patterns. The most remote counties can hire a comparable teacher at a cost of only 74% of the average, while those second most remote ones pay 3.6-11.8% more. There are two major policy implications from the results of the study: (1)The Subsidy to Remote and Difficult Districts Scheme and the Helping the Poor policy have different focuses. Though the former scheme may have public-goods considerations, the latter can give a clear and differentiative policy implication for education finance. (2)It would be an equitable and efficient way to incorporate uncontrollable external factors into a teacher wage index(TCI), and to use it to adjust education financial strategies to these difficult areas

AI-based engaging video generation

With the flourishment of e-commerce in recent years, there is a prevailing trend for online retailers to promote their products via engaging videos. The production of such videos usually involves human experts to do footage selection, sequencing and editing, which can be demanding and time-consuming. In this project, a web-based application was designed and developed which enables e-commerce sellers to easily generate promotional videos with the help of Artificial Intelligence (AI) techniques. The system integrates AI algorithms to automatically generate a persuasive visual storyline from a given set of images and videos and it supports users’ further customization on the sequence order, duration and speed. Besides, the web application also features draft saving and editing, as well as post sharing and commenting. Various software technologies were adopted in the implementation of the web application, such as Django, Bootstrap, Ajax and etc. This report presents details of the project with six sections: Introduction, System Design and Architecture, Technology Considerations, Implementation, Testing and Evaluation, and Conclusion.Bachelor of Engineering (Computer Science

Detecting and understanding crash-consistency bugs across the parallel I/O stack

Parallel file systems (PFS’es) and parallel I/O libraries have been the backbone of high- performance computing (HPC) infrastructures for decades. However, their crash consistency bugs have not been extensively studied, and bug-finding or testing tools for identifying them are lacking. In this dissertation, we developed a generic testing system PFSCheck to study crash consistency bugs on popular PFS’es, such as BeeGFS and OrangeFS, with a cross-stack approach that covers HPC I/O library, PFS, and their interactions with local file systems. We evaluated the crash vulnerabilities of common HPC workloads and the evaluation results show that PFS’es and I/O libraries suffer from more crash consistency bugs than regular file systems due to the scale and complexity of the I/O stack. Local file system configurations such as journaling mode and the choice of consistency checker also influence the discovered vulnerability pattern.U of I OnlyAuthor requested U of Illinois access only (OA after 2yrs) in Vireo ETD syste