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Book ; Online: Happiness, Well-being and Sustainability

Musikanski, Laura / Phillips, Rhonda / Bradbury, James / de Graaf, John / Bliss, Clinton L

A Course in Systems Change

2021

Keywords	Applied ecology ; Environmental policy & protocols ; Psychology ; Assertiveness, motivation & self-esteem ; community well-being;ecological sustainability;personal happiness;positive change theory
Language	English
Size	1 electronic resource (216 pages)
Publisher	Taylor and Francis
Document type	Book ; Online
Note	English
HBZ-ID	HT030382807
ISBN	9780367488703 ; 0367488701
Database	ZB MED Catalogue: Medicine, Health, Nutrition, Environment, Agriculture

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Article ; Online: Fibre Bragg Grating Based Interface Pressure Sensor for Compression Therapy.

Bradbury, James A / Zhang, Qimei / Hernandez Ledezma, Francisco U / Correia, Ricardo / Korposh, Serhiy / Hayes-Gill, Barrie R / Tamoué, Ferdinand / Parnham, Alison / McMaster, Simon A / Morgan, Stephen P

Sensors (Basel, Switzerland)

2022 Volume 22, Issue 5

Abstract: Compression therapy is widely used as the gold standard for management of chronic venous insufficiency and venous leg ulcers, and the amount of pressure applied during the compression therapy is crucial in supporting healing. A fibre optic pressure ... ...

Abstract	Compression therapy is widely used as the gold standard for management of chronic venous insufficiency and venous leg ulcers, and the amount of pressure applied during the compression therapy is crucial in supporting healing. A fibre optic pressure sensor using Fibre Bragg Gratings (FBGs) is developed in this paper to measure sub-bandage pressure whilst removing cross-sensitivity due to strain in the fibre and temperature. The interface pressure is measured by an FBG encapsulated in a polymer and housed in a textile to minimise discomfort for the patient. The repeatability of a manual fabrication process is investigated by fabricating and calibrating ten sensors. A customized calibration setup consisting of a programmable translation stage and a weighing scale gives sensitivities in the range 0.4-1.5 pm/mmHg (2.6-11.3 pm/kPa). An alternative calibration method using a rigid plastic cylinder and a blood pressure cuff is also demonstrated. Investigations are performed with the sensor under a compression bandage on a phantom leg to test the response of the sensor to changing pressures in static situations. Measurements are taken on a human subject to demonstrate changes in interface pressure under a compression bandage during motion to mimic a clinical application. These results are compared to the current gold standard medical sensor using a Bland-Altman analysis, with a median bias ranging from -4.6 to -20.4 mmHg, upper limit of agreement (LOA) from -13.5 to 2.7 mmHg and lower LOA from -32.4 to -7.7 mmHg. The sensor has the potential to be used as a training tool for nurses and can be left in situ to monitor bandage pressure during compression therapy.
MeSH term(s)	Calibration ; Compression Bandages ; Humans ; Temperature ; Varicose Ulcer/therapy ; Wound Healing
Language	English
Publishing date	2022-02-24
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2052857-7
ISSN	1424-8220 ; 1424-8220
ISSN (online)	1424-8220
ISSN	1424-8220
DOI	10.3390/s22051798
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Plutonium 238 pacemaker failure secondary to lead fracture.

Moran, Lynn N / Matsumura, Martin E / Bradbury, James J / Martinez, Matthew W

Heart rhythm

2011 Volume 8, Issue 2, Page(s) 322

MeSH term(s)	Aged, 80 and over ; Device Removal ; Equipment Design ; Equipment Failure ; Follow-Up Studies ; Humans ; Male ; Pacemaker, Artificial/adverse effects ; Plutonium ; Retreatment
Chemical Substances	Plutonium (53023GN24M)
Language	English
Publishing date	2011-02
Publishing country	United States
Document type	Journal Article
ZDB-ID	2229357-7
ISSN	1556-3871 ; 1547-5271
ISSN (online)	1556-3871
ISSN	1547-5271
DOI	10.1016/j.hrthm.2010.10.036
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ab Jg. 2022: Lesesaal (EG)

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Article ; Online: MUSCLE: automated multi-objective evolutionary optimization of targeted LC-MS/MS analysis.

Bradbury, James / Genta-Jouve, Grégory / Allwood, J William / Dunn, Warwick B / Goodacre, Royston / Knowles, Joshua D / He, Shan / Viant, Mark R

Bioinformatics (Oxford, England)

2015 Volume 31, Issue 6, Page(s) 975–977

Abstract: Developing liquid chromatography tandem mass spectrometry (LC-MS/MS) analyses of (bio)chemicals is both time consuming and challenging, largely because of the large number of LC and MS instrument parameters that need to be optimized. This bottleneck ... ...

Abstract	Developing liquid chromatography tandem mass spectrometry (LC-MS/MS) analyses of (bio)chemicals is both time consuming and challenging, largely because of the large number of LC and MS instrument parameters that need to be optimized. This bottleneck significantly impedes our ability to establish new (bio)analytical methods in fields such as pharmacology, metabolomics and pesticide research. We report the development of a multi-platform, user-friendly software tool MUSCLE (multi-platform unbiased optimization of spectrometry via closed-loop experimentation) for the robust and fully automated multi-objective optimization of targeted LC-MS/MS analysis. MUSCLE shortened the analysis times and increased the analytical sensitivities of targeted metabolite analysis, which was demonstrated on two different manufacturer's LC-MS/MS instruments.
MeSH term(s)	Automation ; Chromatography, Liquid/instrumentation ; Chromatography, Liquid/methods ; Software ; Steroids/analysis ; Tandem Mass Spectrometry/instrumentation ; Tandem Mass Spectrometry/methods
Chemical Substances	Steroids
Language	English
Publishing date	2015-03-15
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	1422668-6
ISSN	1367-4811 ; 1367-4803
ISSN (online)	1367-4811
ISSN	1367-4803
DOI	10.1093/bioinformatics/btu740
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Zs.A 2374: Show issues

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Jg. 1995 - 2021: Lesesall (2.OG)
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Book ; Online: Exploring the limits of Concurrency in ML Training on Google TPUs

Kumar, Sameer / Bradbury, James / Young, Cliff / Wang, Yu Emma / Levskaya, Anselm / Hechtman, Blake / Chen, Dehao / Lee, HyoukJoong / Deveci, Mehmet / Kumar, Naveen / Kanwar, Pankaj / Wang, Shibo / Wanderman-Milne, Skye / Lacy, Steve / Wang, Tao / Oguntebi, Tayo / Zu, Yazhou / Xu, Yuanzhong / Swing, Andy

2020

Abstract: Recent results in language understanding using neural networks have required training hardware of unprecedentedscale, with thousands of chips cooperating on a single training run. This paper presents techniques to scaleML models on the Google TPU ... ...

Abstract	Recent results in language understanding using neural networks have required training hardware of unprecedentedscale, with thousands of chips cooperating on a single training run. This paper presents techniques to scaleML models on the Google TPU Multipod, a mesh with 4096 TPU-v3 chips. We discuss model parallelism toovercome scaling limitations from the fixed batch size in data parallelism, communication/collective optimizations,distributed evaluation of training metrics, and host input processing scaling optimizations. These techniques aredemonstrated in both the TensorFlow and JAX programming frameworks. We also present performance resultsfrom the recent Google submission to the MLPerf-v0.7 benchmark contest, achieving record training times from16 to 28 seconds in four MLPerf models on the Google TPU-v3 Multipod machine.
Keywords	Computer Science - Machine Learning ; Computer Science - Distributed ; Parallel ; and Cluster Computing
Subject code	006
Publishing date	2020-11-06
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: PaLI

Chen, Xi / Wang, Xiao / Changpinyo, Soravit / Piergiovanni, AJ / Padlewski, Piotr / Salz, Daniel / Goodman, Sebastian / Grycner, Adam / Mustafa, Basil / Beyer, Lucas / Kolesnikov, Alexander / Puigcerver, Joan / Ding, Nan / Rong, Keran / Akbari, Hassan / Mishra, Gaurav / Xue, Linting / Thapliyal, Ashish / Bradbury, James /

Kuo, Weicheng / Seyedhosseini, Mojtaba / Jia, Chao / Ayan, Burcu Karagol / Riquelme, Carlos / Steiner, Andreas / Angelova, Anelia / Zhai, Xiaohua / Houlsby, Neil / Soricut, Radu

A Jointly-Scaled Multilingual Language-Image Model

2022

Abstract: Effective scaling and a flexible task interface enable large language models to excel at many tasks. We present PaLI (Pathways Language and Image model), a model that extends this approach to the joint modeling of language and vision. PaLI generates text ...

Abstract	Effective scaling and a flexible task interface enable large language models to excel at many tasks. We present PaLI (Pathways Language and Image model), a model that extends this approach to the joint modeling of language and vision. PaLI generates text based on visual and textual inputs, and with this interface performs many vision, language, and multimodal tasks, in many languages. To train PaLI, we make use of large pre-trained encoder-decoder language models and Vision Transformers (ViTs). This allows us to capitalize on their existing capabilities and leverage the substantial cost of training them. We find that joint scaling of the vision and language components is important. Since existing Transformers for language are much larger than their vision counterparts, we train a large, 4-billion parameter ViT (ViT-e) to quantify the benefits from even larger-capacity vision models. To train PaLI, we create a large multilingual mix of pretraining tasks, based on a new image-text training set containing 10B images and texts in over 100 languages. PaLI achieves state-of-the-art in multiple vision and language tasks (such as captioning, visual question-answering, scene-text understanding), while retaining a simple, modular, and scalable design. Comment: ICLR 2023 (Notable-top-5%)
Keywords	Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Computation and Language
Subject code	004
Publishing date	2022-09-14
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Use of the wetting method on cassava flour in three konzo villages in Mozambique reduces cyanide intake and may prevent konzo in future droughts.

Nhassico, Dulce / Bradbury, James Howard / Cliff, Julie / Majonda, Rita / Cuambe, Constantino / Denton, Ian C / Foster, Matthew P / Martins, Arlinda / Cumbane, Adelaide / Sitoe, Luis / Pedro, Joao / Muquingue, Humberto

Food science & nutrition

2015 Volume 4, Issue 4, Page(s) 555–561

Abstract: Konzo is an irreversible paralysis of the legs that occurs mainly in children and young women associated with large cyanide intake from bitter cassava coupled with malnutrition. In East Africa outbreaks occur during drought, when cassava plants produce ... ...

Abstract	Konzo is an irreversible paralysis of the legs that occurs mainly in children and young women associated with large cyanide intake from bitter cassava coupled with malnutrition. In East Africa outbreaks occur during drought, when cassava plants produce much more cyanogens than normal. A wetting method that removes cyanogens from cassava flour was taught to the women of three konzo villages in Mozambique, to prevent sporadic konzo and konzo outbreaks in the next drought. The intervention was in three villages with 72 konzo cases and mean konzo prevalence of 1.2%. The percentage of children with high (>350 μmol/L) urinary thiocyanate content and at risk of contracting konzo in Cava, Acordos de Lusaka, and Mujocojo reduced from 52, 10, and 6 at baseline to 17, 0, and 4 at conclusion of the intervention. Cassava flour showed large reductions in total cyanide over the intervention. The percentage of households using the wetting method was 30-40% in Acordos de Lusaka and Mujocojo and less in Cava. If the wetting method is used extensively by households during drought it should prevent konzo outbreaks and chronic cyanide intoxication. We recommend that the wetting method be taught in all konzo areas in East Africa.
Language	English
Publishing date	2015-11-20
Publishing country	United States
Document type	Journal Article
ZDB-ID	2703010-6
ISSN	2048-7177
ISSN	2048-7177
DOI	10.1002/fsn3.317
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Book ; Online: OpenSpiel

Lanctot, Marc / Lockhart, Edward / Lespiau, Jean-Baptiste / Zambaldi, Vinicius / Upadhyay, Satyaki / Pérolat, Julien / Srinivasan, Sriram / Timbers, Finbarr / Tuyls, Karl / Omidshafiei, Shayegan / Hennes, Daniel / Morrill, Dustin / Muller, Paul / Ewalds, Timo / Faulkner, Ryan / Kramár, János / De Vylder, Bart / Saeta, Brennan / Bradbury, James /

Ding, David / Borgeaud, Sebastian / Lai, Matthew / Schrittwieser, Julian / Anthony, Thomas / Hughes, Edward / Danihelka, Ivo / Ryan-Davis, Jonah

A Framework for Reinforcement Learning in Games

2019

Abstract: OpenSpiel is a collection of environments and algorithms for research in general reinforcement learning and search/planning in games. OpenSpiel supports n-player (single- and multi- agent) zero-sum, cooperative and general-sum, one-shot and sequential, ... ...

Abstract	OpenSpiel is a collection of environments and algorithms for research in general reinforcement learning and search/planning in games. OpenSpiel supports n-player (single- and multi- agent) zero-sum, cooperative and general-sum, one-shot and sequential, strictly turn-taking and simultaneous-move, perfect and imperfect information games, as well as traditional multiagent environments such as (partially- and fully- observable) grid worlds and social dilemmas. OpenSpiel also includes tools to analyze learning dynamics and other common evaluation metrics. This document serves both as an overview of the code base and an introduction to the terminology, core concepts, and algorithms across the fields of reinforcement learning, computational game theory, and search.
Keywords	Computer Science - Machine Learning ; Computer Science - Artificial Intelligence ; Computer Science - Computer Science and Game Theory ; Computer Science - Multiagent Systems
Publishing date	2019-08-25
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: PyTorch

An Imperative Style, High-Performance Deep Learning Library

2019

Abstract: Deep learning frameworks have often focused on either usability or speed, but not both. PyTorch is a machine learning library that shows that these two goals are in fact compatible: it provides an imperative and Pythonic programming style that supports ... ...

Abstract	Deep learning frameworks have often focused on either usability or speed, but not both. PyTorch is a machine learning library that shows that these two goals are in fact compatible: it provides an imperative and Pythonic programming style that supports code as a model, makes debugging easy and is consistent with other popular scientific computing libraries, while remaining efficient and supporting hardware accelerators such as GPUs. In this paper, we detail the principles that drove the implementation of PyTorch and how they are reflected in its architecture. We emphasize that every aspect of PyTorch is a regular Python program under the full control of its user. We also explain how the careful and pragmatic implementation of the key components of its runtime enables them to work together to achieve compelling performance. We demonstrate the efficiency of individual subsystems, as well as the overall speed of PyTorch on several common benchmarks. Comment: 12 pages, 3 figures, NeurIPS 2019
Keywords	Computer Science - Machine Learning ; Computer Science - Mathematical Software ; Statistics - Machine Learning
Subject code	005
Publishing date	2019-12-03
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Scaling Up Models and Data with $\texttt{t5x}$ and $\texttt{seqio}$

Roberts, Adam / Chung, Hyung Won / Levskaya, Anselm / Mishra, Gaurav / Bradbury, James / Andor, Daniel / Narang, Sharan / Lester, Brian / Gaffney, Colin / Mohiuddin, Afroz / Hawthorne, Curtis / Lewkowycz, Aitor / Salcianu, Alex / van Zee, Marc / Austin, Jacob / Goodman, Sebastian / Soares, Livio Baldini / Hu, Haitang / Tsvyashchenko, Sasha /

2022

Abstract: Recent neural network-based language models have benefited greatly from scaling up the size of training datasets and the number of parameters in the models themselves. Scaling can be complicated due to various factors including the need to distribute ... ...

Abstract	Recent neural network-based language models have benefited greatly from scaling up the size of training datasets and the number of parameters in the models themselves. Scaling can be complicated due to various factors including the need to distribute computation on supercomputer clusters (e.g., TPUs), prevent bottlenecks when infeeding data, and ensure reproducible results. In this work, we present two software libraries that ease these issues: $\texttt{t5x}$ simplifies the process of building and training large language models at scale while maintaining ease of use, and $\texttt{seqio}$ provides a task-based API for simple creation of fast and reproducible training data and evaluation pipelines. These open-source libraries have been used to train models with hundreds of billions of parameters on datasets with multiple terabytes of training data. Along with the libraries, we release configurations and instructions for T5-like encoder-decoder models as well as GPT-like decoder-only architectures. $\texttt{t5x}$ and $\texttt{seqio}$ are open source and available at https://github.com/google-research/t5x and https://github.com/google/seqio, respectively.
Keywords	Computer Science - Machine Learning ; Computer Science - Computation and Language
Subject code	006
Publishing date	2022-03-31
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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