52 research outputs found
Biochar as Electron Acceptor for Microbial Extracellular Respiration
No full textBiochar is a charred carbonaceous material that has recently been identified to provide many potential environmental and agricultural applications. Biochar amendments are shown to effectively improve the quality of soil and increase soil microbial biomass. However, the interactions between biochar and microorganisms and the mechanisms through which biochar influences soil microbial growth and activities remain unclear. In this study, we investigated the potential for biochar to function as an electron acceptor for microbial extracellular respiration and growth. Anaerobic incubation of Geobacter sulfurreducens revealed that biochar was used as a sole terminal electron acceptor, as evidenced by a 31-fold increase of biomass and gradual increase in reducing equivalents of biochar and the consumption of acetate after 15 d. An electron stoichiometry analysis showed that 58.7% of the electrons released from acetate oxidation could be recovered in biochar, which was comparable to that of humic substances (44.8%). The finding that biochar participates in microbial extracellular respiration may have important environmental implications considering the widespread existence of both extracellular-respiring microorganisms and black carbon in the environment
Efficient Processing and Delivery of Multimedia Data
No full textThe explosion of multimedia data on the Internet in recent years has greatly enriched people's online experience. However, it also poses a great challenge to analyze and process, and then deliver such content to the worldwide audience. This dissertation presents novel approaches to improve the overall efficiency of the stack by tailoring software design to hardware properties, as well as optimize systems by exploiting workload characteristics using learning-based approaches.
First, to improve the caching performance of the flash-memory caches for content delivery network, this thesis proposes RIPQ, a framework for efficient and advanced caching with flash memory. Traditional implementations of these algorithms generate random writes that perform poorly on flash devices, decreasing the device's performance and lifespan. RIPQ overcomes this issue by aggregating small writes, colocating items with similar priorities, and perform lazy updates to achieve low over- head. By providing a priority queue interface, it allows a variety of caching algorithms to be easily implemented.
Second, this thesis proposes Chess, which uses popularity prediction for higher quality video streaming. Although better encodings improve video streaming, they are also compute-intensive, and it is infeasible to encode all videos uploaded to Face- book with the highest quality codec. However, because the accesses to videos are highly skewed, we may obtain most of the benefit by only running the compute- intensive encoding on a small portion of popular videos, and the challenge lies in how to accurately and scalably run popularity prediction to detect those videos before- hand. Chess meets this demand by designing an approximate but fast base predictor with the access history information, and using an online learning method to combine multiple such predictors as well as the social signals to boost accuracy.
Lastly, this thesis investigates how to accelerate deep learning models on many-core CPUs. Deep learning is now widely used for analyzing multimedia data, but it is compute-intensive, which constitutes its major bottleneck. The manycore CPU, combining both high FLOPS and a flexible computing model, is a promising solution to this problem. However, existing frameworks are still mainly optimized for GPU, and do not run efficiently on this architecture. To overcome this issue, this thesis proposes Graphi, the first attempt to accelerate the execution of computation graphs for deep learning models on this architecture. Graphi determines the optimal parallel settings with a profiling step, runs concurrent operations with low contention, and further reduces execution makespan with critical-path first scheduling. This thesis demonstrated that these techniques can achieve significant speedups over TensorFlow on manycore CPUs
Influence of Tuning Fork Resonance Properties on Quartz-Enhanced Photoacoustic Spectroscopy Performance
No full textA detailed investigation of the influence of quartz tuning forks (QTFs) resonance properties on the performance of quartz-enhanced photoacoustic spectroscopy (QEPAS) exploiting QTFs as acousto-electric transducers is reported. The performance of two commercial QTFs with the same resonance frequency (32.7 KHz) but different geometries and two custom QTFs with lower resonance frequencies (2.9 KHz and 7.2 KHz) were compared and discussed. The results demonstrated that the fundamental resonance frequency as well as the quality factor and the electrical resistance were strongly inter-dependent on the QTF prongs geometry. Even if the resonance frequency was reduced, the quality factor must be kept as high as possible and the electrical resistance as low as possible in order to guarantee high QEPAS performance
Towards a Robust Framework of Network Coordinate Systems
No full textPart 7: Network MappingInternational audienceNetwork Coordinate System (NCS) is an efficient and scalable mechanism to predict latency between any two network hosts based on historical measurements. Most NCS models, such as metric space embedding based, like Vivaldi, and matrix factorization based, like DMF and Phoenix, use squared error measure in training which suffers from the erroneous records, i.e. the records with large noise. To overcome this drawback, we introduce an elegant error measure, the Huber norm to network latency prediction. The Huber norm shows its robustness to the large data noise while remaining efficiency of optimization. Based on that, we upgrade the traditional NCS models into more robust versions, namely Robust Vivaldi model and Robust Matrix Factorization model. We conduct extensive experiments to compare the proposed models with traditional ones and the results show that our approaches significantly increase the accuracy of network latency prediction
Spindle: A Write-Optimized NVM Cache for Journaling File System
No full textPart 7: Memory and File SystemInternational audienceJournaling techniques are widely employed in modern file systems to guarantee crash consistency. However, journaling usually leads to system performance decrease due to the frequent storage accesses it entails. Architects can utilize emerging non-volatile memory (NVM) as a persistent cache or journaling device to reduce the storage accesses of journaling file systems. Yet problems such as double writes, metadata write amplification and heavy transaction ordering overhead still exist in current solutions. Therefore, we propose Spindle, a write-optimized NVM cache to address these challenges. Spindle decouples data and metadata accesses by processing data in DRAM while pinning metadata in NVM. With redesigned metadata log and state switch mechanism, Spindle eliminates double writes and relieves metadata write amplification. Moreover, Spindle adopts a lightweight transaction scheme to guarantee crash consistency and reduce transaction ordering overhead. Experimental results reveal that Spindle achieves up to throughput improvement compared with state-of-the-art design
Deep Exponential Families
No full textWe describe deep exponential families (DEFs), a class of latent variable models that are inspired by the hidden structures used in deep neural networks. DEFs capture a hierarchy of dependencies between latent variables, and are easily generalized to many settings through exponential families. We perform inference using recent “black box” variational inference techniques. We then evaluate various DEFs on text and combine multiple DEFs into a model for pairwise recommendation data. In an extensive study, we show going beyond one layer improves predictions for DEFs. We demonstrate that DEFs find interesting exploratory structure in large data sets, and give better predictive performance than state-of-the-art models.
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RIPQ: Advanced Photo Caching on Flash for Facebook
No full textFacebook uses flash devices extensively in its photo caching stack. The key design challenge for an efficient photo cache on flash at Facebook is its workload: many small random writes are generated by inserting cache-missed content, or updating cache-hit content for advanced caching algorithms. The Flash Translation Layer on flash devices performs poorly with such a workload, lowering throughput and decreasing device lifespan. Existing coping strategies under-utilize the space on flash devices, sacrificing cache capacity, or are limited to simple caching algorithms like FIFO, sacrificing hit ratios.
We overcome these limitations with the novel Restricted Insertion Priority Queue (RIPQ) framework that supports advanced caching algorithms with large cache sizes, high throughput, and long device lifespan. RIPQ aggregates small random writes, co-locates similarly prioritized content, and lazily moves updated content to further reduce device overhead. We show that two families of advanced caching algorithms, Segmented-LRU and Greedy-Dual-Size-Frequency, can be easily implemented with RIPQ. Our evaluation on Facebook’s photo trace shows that these algorithms running on RIPQ increase hit ratios up to ~20% over the current FIFO system, incur low overhead, and achieve high throughput
Biochar as an electron shuttle for reductive dechlorination of pentachlorophenol by Geobacter sulfurreducens
No full textThe reductive dechlorination of pentachlorophenol (PCP) by Geobacter sulfurreducens in the presence of different biochars was investigated to understand how biochars affect the bioreduction of environmental contaminants. The results indicated that biochars significantly accelerate electron transfer from cells to PCP, thus enhancing reductive dechlorination. The promotion effects of biochar (as high as 24-fold) in this process depend on its electron exchange capacity (EEC) and electrical conductivity (EC). A kinetic model revealed that the surface redox-active moieties (RAMs) and EC of biochar (900 degrees C) contributed to 56% and 41% of the biodegradation rate, respectively. This work demonstrates that biochars are efficient electron mediators for the dechlorination of PCP and that both the EC and RAMs of biochars play important roles in the electron transfer process
Next generation sequencing identified two novel mutations in NIPBL and a frame shift mutation in CREBBP in three Chinese children
No full textAbstract Background Cornelia de Lange syndrome (CdLS) and Rubinstein-Taybi syndrome (RSTS) are both rare congenital multiple malformation disorders caused by genes associated with transcription. They share a number of similar features clinically. In addition, it is difficult to make a molecular diagnosis rapidly and detect the mosaic mutation when only sanger sequencing is taken. This study aims to report three novel mutations in three Chinese children identified by next generation sequencing. Results We describe patient 1 and patient 2 presenting with characteristics of CdLS with mutations in NIPBL and patient 3 with a frame shift mutation in CREBBP who can be diagnosed as RSTS clinically and also have similar symptoms with CdLS to some extent. The splicing site c.4321-1G > A transversion in NIPBL is a mosaic mutation and produces an abnormal transcript bearing the loss of exon 20. The nonsense mutation c.218C > A in NIPBL and the frame shift c.1715delC mutation in CREBBP generate stop codon and yield the premature termination of proteins. Conclusions In general, we detect three novel heterozygous mutations including a splicing mutation and a nonsense mutation in NIPBL and a frame shift in CREBBP. And several similar features observed in patients indicate the clinical complexity and clinically overlapping of CdLS and RSTS termed “transcriptomopathies”, suggest the underlying molecular mechanism and emphasize the utilization of next generation sequencing technologies
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