874 research outputs found
Active noise control in finite length ducts
No full textA simple technique for the active control of sound in ducts, initially suggested by Olson and May [1], is investigated in detail. A simple, "virtual earth" principle, feedback loop is used to drive the sound pressure to a minimum at a microphone placed close to a loudspeaker in the duct wall. This produces a reflection of downstream travelling plane waves. A detailed investigation of the loudspeaker near field has enabled the optimum position of the microphone to be identified. The system is shown to be especially effective at the frequencies of the longitudinal duct resonances, where the acoustic response of the duct produces a high loop gain. Results are presented which show a reduction of up to 20 dB in the amplitude of low frequency broadband noise at a position downstream of the cancelling source.</p
A study of zinc transporter 1 and its role in Type 3 Haemochromatosis
Get PDFHereditary haemochromatosis is an autosomal recessive disorder of iron metabolism, characterised by increased iron absorption and progressive iron accumulation particularly in the liver. It has been shown that hepatocytes can acquire iron in two forms; transferrin-bound iron (TBI) and non-transferrin-bound iron (NTBI)1. Known transporters of NTBI into the cell include DMT1 and ZIP14, and FPN is the only transporter known to export iron.
The aims of this study were to characterize the zinc transporter, ZnT-1 and determine whether it is involved in iron transport, specifically as an exporter.
There was a decrease in mRNA expression of the short untranslated isoform of ZnT-1 in double mutant (Hfe -/- and TfR2 Y245X) mouse liver, a trend also seen in TfR1 and Hamp. The long untranslated isoform, however, was significantly higher in the iron-deficient mice as was expression of TfR1 and Ferroportin. Iron and zinc efflux was measured in cells over-expressing ZnT-1 and control cells. There was no difference between over-expressed and control cells in iron efflux. However, at 60 min, over-expressed cells had significantly more zinc efflux than control cells. More zinc than iron was released from the cell.
The results of this study do not support the hypotheses that (i) ZnT-1 reduces intracellular cytoplasmic iron concentration by promoting efflux and 2 ZnT-1 is down-regulated in iron-loaded cells
Improving the network scalability of Erlang
Get PDFAs the number of cores grows in commodity architectures so does the like- lihood of failures. A distributed actor model potentially facilitates the de- velopment of reliable and scalable software on these architectures. Key com- ponents include lightweight processes which ‘share nothing’ and hence can fail independently. Erlang is not only increasingly widely used, but the un- derlying actor model has been a beacon for programming language design, influencing for example Scala, Clojure and Cloud Haskell.
While the Erlang distributed actor model is inherently scalable, we demon- strate that it is limited by some pragmatic factors. We address two network scalability issues here: globally registered process names must be updated on every node (virtual machine) in the system, and any Erlang nodes that com- municate maintain an active connection. That is, there is a fully connected O(n^2) network of n nodes.
We present the design, implementation, and initial evaluation of a con- servative extension of Erlang – Scalable Distributed (SD) Erlang. SD Erlang partitions the global namespace and connection network using s groups. An s group is a set of nodes with its own process namespace and with a fully connected network within the s group, but only individual connections out- side it. As a node may belong to more than one s group it is possible to construct arbitrary connection topologies like trees or rings
Source code of the PRSS and CWSS applications
No full textSource code accompanying the paper:
M. Lubbers, P. Koopman, A. Ramsingh, J. Singer, and P. Trinder, ‘Tiered versus Tierless IoT Stacks: Comparing Smart Campus Software Architectures’, presented at the 10th International Conference on the Internet of Things, Malmö, 2020, doi: 10.1145/3410992.3411002.
Abstract:
Internet of Things (IoT) software stacks are notoriously complex, conventionally comprising multiple tiers/components and requiring that the developer not only uses multiple programming languages, but also correctly interoperate the components. A novel alternative is to use a single tierless language with a compiler that generates the code for each component, and for their correct interoperation.
We report the first ever systematic comparison of tiered and tierless IoT software architectures. The comparison is based on two implementations of a non-trivial smart campus application. PRSS has a conventional tiered Python-based architecture, and Clean Wemos Super Sensors (CWSS) has a novel tierless architecture based on Clean and the iTask and mTask embedded DSLs. An operational comparison of CWSS and PRSS demonstrates that they have equivalent functionality, and that both meet the University of Glasgow (UoG) smart campus requirements.
Crucially, the tierless CWSS stack requires 70% less code than the tiered PRSS stack. We analyse the impact of the following three main factors. (1) Tierless developers need to manage less interoperation: CWSS uses two DSLs in a single paradigm where PRSS uses five languages and three paradigms. (2) Tierless developers benefit from automatically generated, and hence correct, communication. (3) Tierless developers can exploit the powerful high-level abstractions such as Task Oriented Programming (TOP) in CWSS.
Contents:
- README.md: this readme containing information about both applications
- cwss.tgz: source code for the mTask based smart campus applications dubbed CWSS.
It consists of:
* A snapshot of the mTask git repository: https://gitlab.science.ru.nl/mlubbers/mTask
* A Clean programming language compiler.
* The source code for the CWSS application.
- prss.zip: source code for the anyscale sensor based smart campus application dubbed PRSS.
It consists of:
* A snapshot of the anyscale-sensors git repository: https://bitbucket.org/jsinger/anyscale-sensor
mCAP: Memory-Centric Partitioning for Large-Scale Pipeline-Parallel DNN Training
Get PDFMemory usage is becoming an increasingly pressing bottleneck in the training process of Deep Neural Networks (DNNs), especially when training on Graphics Processing Units (GPUs). Existing solutions for multi-GPU training setups partition the neural network over the GPUs in a way that favors training throughput over memory usage, and thus maximum trainable network size. We propose mCAP, a partitioning solution for pipeline-parallel DNN training that focuses specifically on memory usage. It evenly distributes Deep Learning models over the available resources with respect to per-device peak memory usage. Our partitioning approach uses a novel incremental profiling strategy to extract per-layer memory usage statistics. A model-based predictor uses the profiling data to recommend a partitioning that balances peak memory usage. Our approach is DL-framework agnostic and orthogonal to existing memory optimizations found in large-scale DNN training systems. Our results show that our approach enables training of neural networks that are 1.55 times larger than existing partitioning solutions in terms of the number of parameters.</p
An essay concerning the outward and salutary application of oils on the human body: By the Rev. William Martin Trinder, MD.
No full text31,[1]p. ; 8⁰.Reproduction of original from the British Library.English Short Title Catalog, ESTCT120465.Electronic data. Farmington Hills, Mich. : Thomson Gale, 2003. Page image (PNG). Digitized image of the microfilm version produced in Woodbridge, CT by Research Publications, 1982-2002 (later known as Primary Source Microfilm, an imprint of the Gale Group)
Generic Graphical User Interfaces
Get PDFContains fulltext :
60466.pdf (author's version ) (Open Access)It is important to be able to program GUI applications in a fast and easy manner.
Current GUI tools for creating visually attractive applications offer limited functionality.
In this paper we introduce a new, easy to use method to program GUI applications in a pure functional language such as Clean or Generic Haskell. The method we use is a refined version of the model-view paradigm.
The basic component in our approach is the Graphical Editor Component (GEC(t)) that can contain any value of any flat data type t and that can be freely used to display
and edit its value. GEC(t)s can depend on others, but also on themselves.
They can even be mutually dependent. With these components we can construct a flexible, reusable and customizable editor. For the realization of the components we had to invent a new generic implementation technique for interactive applications.IFL 200
Source code of the PRSS and CWSS applications
No full textSource code accompanying the paper:
M. Lubbers, P. Koopman, A. Ramsingh, J. Singer, and P. Trinder, ‘Tiered versus Tierless IoT Stacks: Comparing Smart Campus Software Architectures’, presented at the 10th International Conference on the Internet of Things, Malmö, 2020, doi: 10.1145/3410992.3411002.
Abstract:
Internet of Things (IoT) software stacks are notoriously complex, conventionally comprising multiple tiers/components and requiring that the developer not only uses multiple programming languages, but also correctly interoperate the components. A novel alternative is to use a single tierless language with a compiler that generates the code for each component, and for their correct interoperation.
We report the first ever systematic comparison of tiered and tierless IoT software architectures. The comparison is based on two implementations of a non-trivial smart campus application. PRSS has a conventional tiered Python-based architecture, and Clean Wemos Super Sensors (CWSS) has a novel tierless architecture based on Clean and the iTask and mTask embedded DSLs. An operational comparison of CWSS and PRSS demonstrates that they have equivalent functionality, and that both meet the University of Glasgow (UoG) smart campus requirements.
Crucially, the tierless CWSS stack requires 70% less code than the tiered PRSS stack. We analyse the impact of the following three main factors. (1) Tierless developers need to manage less interoperation: CWSS uses two DSLs in a single paradigm where PRSS uses five languages and three paradigms. (2) Tierless developers benefit from automatically generated, and hence correct, communication. (3) Tierless developers can exploit the powerful high-level abstractions such as Task Oriented Programming (TOP) in CWSS.
Contents:
- README.md: this readme containing information about both applications
- cwss.tgz: source code for the mTask based smart campus applications dubbed CWSS.
It consists of:
* A snapshot of the mTask git repository: https://gitlab.science.ru.nl/mlubbers/mTask
* A Clean programming language compiler.
* The source code for the CWSS application.
- prss.zip: source code for the anyscale sensor based smart campus application dubbed PRSS.
It consists of:
* A snapshot of the anyscale-sensors git repository: https://bitbucket.org/jsinger/anyscale-sensor
- …
