Configurable memory systems for embedded many-core processors

The memory system of a modern embedded processor con- sumes a large fraction of total system energy. We explore a range of different configuration options and show that a reconfigurable design can make better use of the resources available to it than any fixed implementation, and provide large improvements in both performance and energy con- sumption. Reconfigurability becomes increasingly useful as resources become more constrained, so is particularly rele- vant in the embedded space. For an optimised architectural configuration, we show that a configurable cache system performs an average of 20% (maximum 70%) better than the best fixed implementation when two programs are competing for the same resources, and reduces cache miss rate by an average of 70% (maximum 90%). We then present a case study of AES encryption and decryption, and find that a custom memory configuration can almost double performance, with further benefits being achieved by specialising the task of each core when parallelising the program

Minitrack Introduction: Decision Analytics, Machine Learning, and Field Experimentation for Defense and Emergency Response

Proceedings of the 54th Hawaii International Conference on System Sciences 2021The article of record as published may be found at http://http://hdl.handle.net/10125/70746Defense and emergency first responders must make rapid, consequential decisions and machine learning can aid analytics to support these decisions. Machine learning offers enormous promise, yet well publicized struggles reveal the need for better datasets and for opportunities to learn in challenging settings. Field experimentation offers the potential to meet these needs through iterative interactions in complex scenarios. Field experimentation can provide live action to facilitate high fidelity datasets that can support machine learning and artificial/augmented intelligence applications. These experiments may incorporate participants from academia; government agencies; militaries; first responders at all levels; and global industry partners. This minitrack explores the interplay between machine learning, field experimentation, and optimization analytics, whether exploratory, theoretical, experimental, in such critical areas as Defense and Emergency Response

Machine Learning of Semi-Autonomous Intelligent Mesh Networks Operation Expertise

Operating networks in very dynamic environments makes network management both complex and difficult. It remains an open question how mesh or hastily formed networks with many nodes could be managed efficiently. Considering the various constraints such as limited communication channels on network management in dynamic environments, the need for semi-autonomous or autonomous networks is evident. Exploitation of machine learning techniques could be a way to solve this network management challenge. However, the need for large training datasets and the infrequency of network management events make it uncertain whether this approach is effective for highly dynamic networks and networks operating in unfriendly conditions, such as tactical military networks. This paper examines the feasibility of this approach by analyzing a recorded dataset of a mesh network experiment in a highly dynamic, austere military environment and derives conclusions for the design of future mesh networks and their network management systems

Machine Learning of Semi-Autonomous Intelligent Mesh Networks Operation Expertise

Proceedings of the 52nd Hawaii International Conference on System Sciences | 2019The article of record at published may be found at https://hdl.handle.net/10125/59562.Operating networks in very dynamic environments makes network management both complex and difficult. It remains an open question how mesh or hastily formed networks with many nodes could be managed efficiently. Considering the various constraints such as limited communication channels on network management in dynamic environments, the need for semi-autonomous or autonomous networks is evident. Exploitation of machine learning techniques could be a way to solve this network management challenge. However, the need for large training datasets and the infrequency of network management events make it uncertain whether this approach is effective for highly dynamic networks and networks operating in unfriendly conditions, such as tactical military networks. This paper examines the feasibility of this approach by analyzing a recorded dataset of a mesh network experiment in a highly dynamic, austere military environment and derives conclusions for the design of future mesh networks and their network management systems

Understanding the genomics and specialised metabolites of the biopesticidal bacterium Burkholderia ambifaria

Burkholderia ambifaria is a versatile bacterium frequently isolated from the environment in association with the rhizosphere of important crops species, and occasionally found as an opportunistic pathogen of cystic fibrosis patients. B. ambifaria strains were exploited successfully as biological pesticides during the 1990s, but declined in popularity following concerns over the pathogenicity of associated species in the Burkhlderia cepacia complex. A collection of environmentally and clinically sourced B. ambifaria strains were sequenced with the purpose of developing a deeper understanding of the biopesticide. Comparative genomics were combined with in vitro metabolite analyses, antagonism assays, and agriculturally relevant biological control assays to determine the contribution of antimicrobial metabolites to biocontrol. Genome mining for biosynthetic gene clusters (BGCs) revealed a considerable specialised metabolite potential, and multiple BGCs associated with characterised antimicrobials. Regulatory gene mining of quorum sensing associated luxR genes revealed an uncharacterised LuxRI system linked to an unknown BGC. Insertional mutagenesis and mass spectrometry confirmed the BGC as the biosynthetic origin of the historical Burkholderia polyyne metabolite cepacin. Comparison of the B. ambifaria BCC0191 wild-type with the cepacin-deficient mutant highlighted the importance of cepacin in the biological control of Pythium ultimum with a Pisum sativum crop model. The biocontrol phenotype was maintained following the deletion of the third replicon, and subsequent virulence testing in a murine respiratory inhalation model demonstrated a reduced persistence compared to the wild-type. This study systematically defined the specialised metabolite biosynthetic potential of B. ambifaria, and demonstrated the importance of the polyyne cepacin in biological control. Maintenance of biocontrol and loss of virulence following third-replicon deletion presents an opportunity to attenuate B. ambifaria and address the pathogenicity concerns that led to the decline of B. ambifaria as a biopesticide

The hidden genomic diversity, specialised metabolite capacity, and revised taxonomy of Burkholderia sensu lato

Burkholderia sensu lato is a collection of closely related genera within the family Burkholderiaceae that includes species of environmental, industrial, biotechnological, and clinical importance. Multiple species within the complex are the source of diverse specialized metabolites, many of which have been identified through genome mining of their biosynthetic gene clusters (BGCs). However, the full, true genomic diversity of these species and genera, and their biosynthetic capacity have not been investigated. This study sought to cluster and classify over 4000 Burkholderia sensu lato genome assemblies into distinct genomic taxa representing named and uncharacterized species. We delineated 235 species groups by average nucleotide identity analyses that formed seven distinct phylogenomic clades, representing the genera of Burkholderia sensu lato: Burkholderia, Paraburkholderia, Trinickia, Caballeronia, Mycetohabitans, Robbsia, and Pararobbisa. A total of 137 genomic taxa aligned with named species possessing a sequenced type strain, while 93 uncharacterized species groups were demarcated. The 95% ANI threshold proved capable of delineating most genomic species and was only increased to resolve several closely related species. These analyses enabled the assessment of species classifications of over 4000 genomes, and the correction of over 400 genome taxonomic assignments in public databases into existing and uncharacterized genomic species groups. These species groups were genome mined for BGCs, their specialized metabolite capacity calculated per species and genus, and the number of distinct BGCs per species estimated through kmer-based de-replication. Mycetohabitans species dedicated a larger proportion of their relatively small genomes to specialized metabolite biosynthesis, while Burkholderia species harbored more BGCs on average per genome and possessed the most distinct BGCs per species compared to the remaining genera. Exploring the hidden genomic diversity of this important multi-genus complex contributes to our understanding of their taxonomy and evolutionary relationships, and supports future efforts toward natural product discovery

Consuming Lines of Difference: The Politics of Wealth and Poverty along the Color Line

Commentators on African American life have often focused on poverty, evaded African American wealth, and ignored the ways genteel affluence and impoverishment were constructed along turn-of-the-century color lines. Documentary research and archaeology at the Madam CJ Walker home in Indianapolis, Indiana illuminates how the continuum of wealth and poverty was defined and negotiated by one of African America’s wealthiest early 20th century entrepreneurs. The project provides an opportunity to compare the ways in which wealth was defined and experienced along the color line in the early 20th century and how such notions of Black affluence shaped racialized definitions of poverty and materialit

A rapid screening method for the detection of specialised metabolites from bacteria: induction and suppression of metabolites from Burkholderia species

Screening microbial cultures for specialised metabolites is essential for the discovery of new biologically active compounds. A novel, cost-effective and rapid screening method is described for extracting specialised metabolites from bacteria grown on agar plates, coupled with HPLC for basic identification of known and potentially novel metabolites. The method allows the screening of culture collections to identify optimal production strains and metabolite induction conditions. The protocol was optimised on two Burkholderia species known to produce the antibiotics, enacyloxin IIa (B. ambifaria) and gladiolin (B. gladioli), respectively; it was then applied to strains of each species to identify high antibiotic producers. B. ambifaria AMMD and B. gladioli BCC0238 produced the highest concentrations of the respective antibiotic under the conditions tested. To induce expression of silent biosynthetic gene clusters, the addition of low concentrations of antibiotics to growth media was evaluated as known elicitors of Burkholderia specialised metabolites. Subinhibitory concentrations of trimethoprim and other clinically therapeutic antibiotics were evaluated and screened against a panel of B. gladioli and B. ambifaria. To enhance rapid strain screening with more antibiotic elicitors, antimicrobial susceptibility testing discs were included within the induction medium. Low concentrations of trimethoprim suppressed the production of specialised metabolites in B. gladioli, including the toxins, toxoflavin and bongkrekic acid. However, the addition of trimethoprim significantly improved enacylocin IIa concentrations in B. ambifaria AMMD. Rifampicin and ceftazidime significantly improved the yield of gladiolin and caryoynencin by B. gladioli BCC0238, respectively, and cepacin increased 2-fold with tobramycin in B. ambifaria BCC0191. Potentially novel metabolites were also induced by subinhibitory concentrations of tobramycin and chloramphenicol in B. ambifaria. In contrast to previous findings that low concentrations of antibiotic elicit Burkholderia metabolite production, we found they acted as both inducers or suppressors dependent on the metabolite and the strains producing them. In conclusion, the screening protocol enabled rapid characterization of Burkholderia metabolites, the identification of suitable producer strains, potentially novel natural products and an understanding of metabolite regulation in the presence of inducing or suppressing conditions