Efficient Path Delay Test Generation with Boolean Satisfiability

This dissertation focuses on improving the accuracy and efficiency of path delay test generation using a Boolean satisfiability (SAT) solver. As part of this research, one of the most commonly used SAT solvers, MiniSat, was integrated into the path delay test generator CodGen. A mixed structural-functional approach was implemented in CodGen where longest paths were detected using the K Longest Path Per Gate (KLPG) algorithm and path justification and dynamic compaction were handled with the SAT solver. Advanced techniques were implemented in CodGen to further speed up the performance of SAT based path delay test generation using the knowledge of the circuit structure. SAT solvers are inherently circuit structure unaware, and significant speedup can be availed if structure information of the circuit is provided to the SAT solver. The advanced techniques explored include: Dynamic SAT Solving (DSS), Circuit Observability Don’t Care (Cir-ODC), SAT based static learning, dynamic learnt clause management and Approximate Observability Don’t Care (ACODC). Both ISCAS 89 and ITC 99 benchmarks as well as industrial circuits were used to demonstrate that the performance of CodGen was significantly improved with MiniSat and the use of circuit structure

Increasing Fairness in Compromise on Accuracy via Weighted Vote with Learning Guarantees

As the bias issue is being taken more and more seriously in widely applied machine learning systems, the decrease in accuracy in most cases deeply disturbs researchers when increasing fairness. To address this problem, we present a novel analysis of the expected fairness quality via weighted vote, suitable for both binary and multi-class classification. The analysis takes the correction of biased predictions by ensemble members into account and provides learning bounds that are amenable to efficient minimisation. We further propose a pruning method based on this analysis and the concepts of domination and Pareto optimality, which is able to increase fairness under a prerequisite of little or even no accuracy decline. The experimental results indicate that the proposed learning bounds are faithful and that the proposed pruning method can indeed increase ensemble fairness without much accuracy degradation.Comment: 18 pages, 15 figures, and 6 table

Lifelong Sequential Modeling with Personalized Memorization for User Response Prediction

User response prediction, which models the user preference w.r.t. the presented items, plays a key role in online services. With two-decade rapid development, nowadays the cumulated user behavior sequences on mature Internet service platforms have become extremely long since the user's first registration. Each user not only has intrinsic tastes, but also keeps changing her personal interests during lifetime. Hence, it is challenging to handle such lifelong sequential modeling for each individual user. Existing methodologies for sequential modeling are only capable of dealing with relatively recent user behaviors, which leaves huge space for modeling long-term especially lifelong sequential patterns to facilitate user modeling. Moreover, one user's behavior may be accounted for various previous behaviors within her whole online activity history, i.e., long-term dependency with multi-scale sequential patterns. In order to tackle these challenges, in this paper, we propose a Hierarchical Periodic Memory Network for lifelong sequential modeling with personalized memorization of sequential patterns for each user. The model also adopts a hierarchical and periodical updating mechanism to capture multi-scale sequential patterns of user interests while supporting the evolving user behavior logs. The experimental results over three large-scale real-world datasets have demonstrated the advantages of our proposed model with significant improvement in user response prediction performance against the state-of-the-arts.Comment: SIGIR 2019. Reproducible codes and datasets: https://github.com/alimamarankgroup/HPM

Saccharin induced liver inflammation in mice by altering the gut microbiota and its metabolic functions

Maintaining the balance of the gut microbiota and its metabolic functions is vital for human health, however, this balance can be disrupted by various external factors including food additives. A range of food and beverages are sweetened by saccharin, which is generally considered to be safe despite controversial debates. However, recent studies indicated that saccharin perturbed the gut microbiota. Inflammation is frequently associated with disruptions of the gut microbiota. The aim of this study is to investigate the relationship between host inflammation and perturbed gut microbiome by saccharin. C57BL/6J male mice were treated with saccharin in drinking water for six months. Q-PCR was used to detect inflammatory markers in mouse liver, while 16S rRNA gene sequencing and metabolomics were used to reveal changes of the gut microbiota and its metabolomic profiles. Elevated expression of pro-inflammatory iNOS and TNF-α in liver indicated that saccharin induced inflammation in mice. The altered gut bacterial genera, enriched orthologs of pathogen-associated molecular patterns, such as LPS and bacterial toxins, in concert with increased pro-inflammatory metabolites suggested that the saccharin-induced liver inflammation could be associated with the perturbation of the gut microbiota and its metabolic functions

Manganese-induced sex-specific gut microbiome perturbations in C57BL/6 mice

Overexposure to manganese (Mn) leads to toxic effects, such as promoting the development of Parkinson’s-like neurological disorders. The gut microbiome is deeply involved in immune development, host metabolism, and xenobiotics biotransformation, and significantly influences central nervous system (CNS) via the gut-brain axis, i.e. the biochemical signaling between the gastrointestinal tract and the CNS. However, it remains unclear whether Mn can affect the gut microbiome and its metabolic functions, particularly those linked to neurotoxicity. In addition, sex-specific effects of Mn have been reported, with no mechanism being identified yet. Recently, we have shown that the gut microbiome is largely different between males and females, raising the possibility that differential gut microbiome responses may contribute to sex-selective toxicity of Mn. Here, we applied high-throughput sequencing and gas chromatography–mass spectrometry (GC-MS) metabolomics to explore how Mn2+ exposure affects the gut microbiome and its metabolism in C57BL/6 mice. Mn2+ exposure perturbed the gut bacterial compositions, functional genes and fecal metabolomes in a highly sex-specific manner. In particular, bacterial genes and/or key metabolites of neurotransmitter synthesis and pro-inflammatory mediators are significantly altered by Mn2+ exposure, which can potentially affect chemical signaling of gut-brain interactions. Likewise, functional genes involved in iron homeostasis, flagellar motility, quorum sensing, and Mn transportation/oxidation are also widely changed by Mn2+ exposure. Taken together, this study has demonstrated that Mn2+ exposure perturbs the gut microbiome and its metabolic functions, which highlights the potential role of the gut microbiome in Mn2+ toxicity, particularly its sex-specific toxic effects

Multi-Omics Reveals that Lead Exposure Disturbs Gut Microbiome Development, Key Metabolites, and Metabolic Pathways

Lead exposure remains a global public health issue, and the recent Flint water crisis has renewed public concern about lead toxicity. The toxicity of lead has been well established in a variety of systems and organs. The gut microbiome has been shown to be highly involved in many critical physiological processes, including food digestion, immune system development, and metabolic homeostasis. However, despite the key role of the gut microbiome in human health, the functional impact of lead exposure on the gut microbiome has not been studied. The aim of this study is to define gut microbiome toxicity induced by lead exposure in C57BL/6 mice using multiomics approaches, including 16S rRNA sequencing, whole genome metagenomics sequencing, and gas chromatography-mass spectrometry (GC-MS) metabolomics. 16S rRNA sequencing revealed that lead exposure altered the gut microbiome trajectory and phylogenetic diversity. Metagenomics sequencing and metabolomics profiling showed that numerous metabolic pathways, including vitamin E, bile acids, nitrogen metabolism, energy metabolism, oxidative stress, and the defense/detoxification mechanism, were significantly disturbed by lead exposure. These perturbed molecules and pathways may have important implications for lead toxicity in the host. Taken together, these results demonstrated that lead exposure not only altered the gut microbiome community structures/diversity but also greatly affected metabolic functions, leading to gut microbiome toxicity

Does metal pollution matter with C retention by rice soil?

Date of Acceptance: 17/07/2015 The research work was supported by the China Natural Science Foundation under a grant number of 40830528 and of 40671180. P.S. is a Royal Scoiety-Wolfson Research Merit Award holder and was supported by additional travel funds from a UK BBSRC China Partnership Award. P.S.’s contribution was supported by the UK-China Sustainable Agriculture Innovation Network (SAIN). D.C. was supported by an additional travel and collaboration funding from the China Ministry of Education under a “111” project.Peer reviewedPublisher PD