1,726,135 research outputs found
Dataset for: Hollow Core Optical Fibres with Comparable Attenuation 1 to Silica Fibres 2 between 600 and 1100 nm
No full textDataset supports: Sakr, H (2020). Hollow Core Optical Fibres with Comparable Attenuation 1 to Silica Fibres 2 between 600 and 1100 nm. Nature Communications.</span
Low-latency WDM intensity-modulation and direct-detection transmission over >100km distances in a hollow core fiber
No full textDataset to support article by Yang Hong, Kyle R. H. Bottrill, Thomas D. Bradley, Hesham Sakr, Gregory T. Jasion, Kerrianne Harrington, Francesco Poletti, Periklis Petropoulos, and David J. Richardson. Low-latency WDM intensity-modulation and direct-detection transmission over >100km distances in a hollow core fiber. Laser & Photonics Reviews. https://doi.org/10.1002/lpor.202100102</span
Jahresbericht 2006 der Schweizerischen Arbeitsgruppe für Kardiale Rehabilitation (SAKR)
No full textIm Bereiche der Fortbildung führte die SAKR 2006 erstmals zusammen mit den Herztherapeuten (VSHT) eine gemeinsame Herbsttagung durch, die schwergewichtig dem Thema der Motivation gewidmet war und von den Teilnehmern eine interaktive Mitarbeit verlangte [...
Selecting Third-party Libraries: The Data Scientist’s Perspective
No full textArtifacts for EMSE publication "Selecting Third-party Libraries: The Data Scientist’s Perspective" by Sarah Nadi and Nourhan Sakr. </p
An organisational approach to supporting pedagogical leadership: reporting on a case study with London Early Years Foundation
Get PDFDecision confidence-based multi-level support vector machines
No full textSupport vector machines (SVM) have been showing high accuracy of prediction in many applications. However, as any statistical learning algorithm, SVM's accuracy drops if some of the training points are contaminated by an unknown source of noise. The choice of clean training points is critical to avoid the overfitting problem which occurs generally when the model is excessively complex, which is reflected by a high accuracy over the training set and a low accuracy over the testing set (unseen points). In this paper we present a new multi-level SVM architecture that splits the training set into points that are labeled as 'easily classifiable' which do not cause an increase in the model complexity and 'non-easily classifiable' which are responsible for increasing the complexity. This method is used to create an SVM architecture that yields on average a higher accuracy than a traditional soft margin SVM trained with the same training set. The architecture is tested on the well known US postal handwritten digit recognition problem, the Wisconsin breast cancer dataset and on the agitation detection dataset. The results show an increase in the overall accuracy for the three datasets. Throughout this paper the word confidence is used to denote the confidence over the decision as commonly used in the literature. © 2013 Elsevier Ltd. All rights reserved.Aronszajn N., 1950, INTRO THEORY HILBERT; Burges CJC, 1998, DATA MIN KNOWL DISC, V2, P121, DOI 10.1023-A:1009715923555; CORTES C, 1995, MACH LEARN, V20, P273, DOI 10.1023-A:1022627411411; Deniz O, 2003, PATTERN RECOGN LETT, V24, P2153, DOI 10.1016-S0167-8655(03)00081-3; Devijver P., 1982, PATTERN RECOGNITION; Dumais S., 1998, Proceedings of the 1998 ACM CIKM International Conference on Information and Knowledge Management, DOI 10.1145-288627.288651; Gammerman A., 1998, Uncertainty in Artificial Intelligence. Proceedings of the Fourteenth Conference (1998); Joachims T., 1998, MACH LEARN ECML 98, P137, DOI DOI 10.1007-BFB0026683; Kecman V., 2001, LEARNING SOFT COMPUT; KOLMOGOR.AN, 1968, INT J COMPUT MATH, V2, P157, DOI 10.1080-00207166808803030; Le Cun BB, 1990, ADV NEURAL INFORM PR; Li L, 2006, LECT NOTES COMPUT SC, V4282, P437; Mitra P, 2004, IEEE T PATTERN ANAL, V26, P413, DOI 10.1109-TPAMI.2004.1262340; Platt J. C., 1999, ADV LARGE MARGIN CLA, P61; Sakr GE, 2010, IEEE T AFFECT COMPUT, V1, P98, DOI 10.1109-T-AFFC.2010.2; Saunders C., 1999, INT JOINT C ART INT, V2, P722; Scholkopf B., 2002, LEARNING KERNELS; Street N., 1992, NUCL FEATURE EXTRACT; Vapnik V., 1998, STAT LEARNING THEORY; Vapnik V.N., 1995, NATURE STAT LEARNING; Wu YC, 2008, PATTERN RECOGN, V41, P2874, DOI 10.1016-j.patcog.2008.02.0101
Major victory for reasonable and proper liquidator remuneration: NSW Court of Appeal delivers judgment on liquidator remuneration in Sakr Nominees
No full textA summary and analysis of the NSW Court of Appeal judgment in the case of 'Sanderson as Liquidator of Sakr Nominees Pty Ltd (in liq) v Sakr' [2017] NSWCA 38. The case and judgment relate to the principles governing the determination of liquidator remuneration, including the concept of 'proportionality'
Multi level SVM for subject independent agitation detection
No full textThe need to automate the detection of agitation for dementia patients is a major requirement for caregivers. This research aims at detecting the agitation status of the subjects using soft computing techniques that does not require supervision beyond the training phase. An autonomous multi-sensory device has been developed to achieve automatic assessment of agitation and to control stimulation that will reduce the agitation level automatically. The focus of this paper is the agitation detection algorithm. Three vital signs are monitored for agitation detection: the Heart Rate (HR) the Galvanic Skin Response (GSR) and Skin Temperature (ST). These measures are fed into a new SVM architecture: The Multi level SVM learning machine. Results show very high detection accuracy of agitation, quick adaptation to the subject and a strong correlation between the physiological signals monitored and the emotional states of the subjects. The result is a learning algorithm that is Subject-Independent. ©2009 IEEE.American Psychiatric Association, 1994, DIAGN STAT MAN MENT; CULTER NR, 1996, UNDERSTANDING ALZHEI, P65; FOOK VFS, 2007, E HLTH NETW APPL SER, P68; Kecman V., 2001, LEARNING SOFT COMPUT; Kistler A, 1998, INT J PSYCHOPHYSIOL, V29, P35, DOI 10.1016-S0167-8760(97)00087-1; LIAO WH, 2005, IEEE COMP SOC C, V3, P70; Murray DR, 2003, CHEST, V123, P664, DOI 10.1378-chest.123.3.664; ROD K, 2000, INT J PSYCHOPHYSIOL, V37, P121; Rosenblatt Adam, 2005, Cleve Clin J Med, V72 Suppl 3, pS3; SAKR GE, 2008, ADV INT MECH IEEE AS; Tamura T., 1997, P 19 ANN INT C IEEE, V3, P999, DOI 10.1109-IEMBS.1997.756513; TULEN JHM, 1989, PHARMACOL BIOCHEM BE, V32, P9, DOI 10.1016-0091-3057(89)90204-9; *US BUR CENS, 2006, 65 US; Vapnik V.N., STAT LEARNING THEORY; Zhai J., 2006, FLAIRS C, P39532
Supporting quality in the baby room: what the global evidence says
Get PDFA new research project is looking into the quality of provision in English baby rooms following the UK government's expansion of subsidised education and care to infants as young as nine months. In the second of two articles, Dr Mona Sakr, discusses the structural factors affecting baby rooms, staff qualifications and the need for a vision for the baby room
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