1,602 research outputs found
Cut-rose production in response to planting density in two contrasting cultivars
Growing in lower planting density, rose plants produce more assimilates, which can be used to produce more and/or heavier flowering shoots. The effect of planting density was investigated during a period including the first five flowering flushes of a young crop. In a heated greenhouse two cut-rose cultivars were grown under bent canopy management. ‘Akito’ on own-roots and ‘Ilios’ on ‘Natal Briar’ rootstock were planted with densities of 8 and 4 plants per m2. Starting at the end of June 2007, flowering shoots were harvested over a time span of eight months. Based on ‘flowering flushes’, times of high harvest rate, the harvesting time span could be divided into five consecutive periods, each including one flush. The cultivars showed contrasting responses to planting density. In the first three periods the response in ‘Ilios’ was extraordinary, because at low density plants did not produce more flowering shoots, as would be expected. However, the response in shoot fresh weight was larger for ‘Ilios’ than for ‘Akito’, 35% compared to 21% over the entire study period. The results imply that there was a genetic difference in the effect of assimilate availability and/or local light environment. During the first three periods, these factors can not have influenced shoot number in ‘Ilios’, while they did in ‘Akito’. It is suggested that decreases of assimilate availability in winter caused the shoot number response to emerge for ‘Ilios’ later on
Light Scattering From Polaritons And Plasmaritons In Cds Near Resonance
We have investigated light scattering from polaritons and plasmaritons in CdS near resonance. These results are compared with computer-calculated dispersion curves for these excitations. We find a good agreement between theory and experiments. Particular attention is given to interesting effects arising from birefringence and resonance. © 1971 The American Physical Society.3124238424
What can a cook in Italy teach a mechanic in India? Action Recognition Generalisation Over Scenarios and Locations
Patterning of ultrathin YBCO nanowires using a new focused-ion-beam process
Manufacturing superconducting circuits out of ultrathin films is a
challenging task when it comes to patterning complex compounds, which are
likely to be deteriorated by the patterning process. With the purpose of
developing high-T superconducting photon detectors, we designed a novel
route to pattern ultrathin YBCO films down to the nanometric scale. We believe
that our method, based on a specific use of a focused-ion beam, consists in
locally implanting Ga^{3+} ions and/or defects instead of etching the film.
This protocol could be of interest to engineer high-T superconducting
devices (SQUIDS, SIS/SIN junctions and Josephson junctions), as well as to
treat other sensitive compounds.Comment: 13 pages, 7 figure
Supporting smoking cessation in older patients: a continuing challenge for community nurses
Tobacco smoking continues to pose negative health consequences for smokers and their families, and is the single greatest cause of health inequalities in the UK. Older people are particularly vulnerable to the negative health impacts of smoking and therefore, supporting older smokers to quit remains an important public health goal. Community nurses are required to help patients to lead healthier lifestyles and have ideal opportunities to encourage smoking cessation in older people who are affected by smoking-related health conditions, or whose existing conditions may be exacerbated by continued smoking. This paper discusses how community nurses can support their older patients to quit smoking by fostering a patient-centred partnership through good communication and empathy. The newly developed ‘Very Brief Advice on Smoking’ (VBA) interventions can provide a useful tool for community nurses who experience time constraints to advise older people that psychosocial support with treatment is the most effective method of smoking cessation, while respecting the health decisions of patients
Different Perspectives on Diagnosis and Prognosis of Hip and Knee Osteoarthritis in Primary Care
With the aging of the population, osteoarthritis is an increasing challenge for health care worldwide. Although osteoarthritis is the most frequently diagnosed joint disorder in primary care, no clear diagnostic set of criteria are available for primary care.
The overall aim of the work in this thesis was to identify early OA criteria for epidemiological research in primary care, and to establish the usefulness of radiographic signs widely used in epidemiological research and clinical practice
TIM: a time interval machine for audio-visual action recognition
Diverse actions give rise to rich audio-visual signals in
long videos. Recent works showcase that the two modalities of audio and video exhibit different temporal extents of
events and distinct labels. We address the interplay between
the two modalities in long videos by explicitly modelling the
temporal extents of audio and visual events. We propose
the Time Interval Machine (TIM) where a modality-specific
time interval poses as a query to a transformer encoder that
ingests a long video input. The encoder then attends to the
specified interval, as well as the surrounding context in both
modalities, in order to recognise the ongoing action.
We test TIM on three long audio-visual video datasets:
EPIC-KITCHENS, Perception Test, and AVE, reporting state-of-the-art (SOTA) for recognition. On EPICKITCHENS, we beat previous SOTA that utilises LLMs and
significantly larger pre-training by 2.9% top-1 action recognition accuracy. Additionally, we show that TIM can be
adapted for action detection, using dense multi-scale interval queries, outperforming SOTA on EPIC-KITCHENS-100
for most metrics, and showing strong performance on the
Perception Test. Our ablations show the critical role of integrating the two modalities and modelling their time intervals in achieving this performance. Code and models at:
https://github.com/JacobChalk/TIM
Meta-Learning with Context-Agnostic Initialisations
Meta-learning approaches have addressed few-shot problems by finding
initialisations suited for fine-tuning to target tasks. Often there are
additional properties within training data (which we refer to as context), not
relevant to the target task, which act as a distractor to meta-learning,
particularly when the target task contains examples from a novel context not
seen during training. We address this oversight by incorporating a
context-adversarial component into the meta-learning process. This produces an
initialisation for fine-tuning to target which is both context-agnostic and
task-generalised. We evaluate our approach on three commonly used meta-learning
algorithms and two problems. We demonstrate our context-agnostic meta-learning
improves results in each case. First, we report on Omniglot few-shot character
classification, using alphabets as context. An average improvement of 4.3% is
observed across methods and tasks when classifying characters from an unseen
alphabet. Second, we evaluate on a dataset for personalised energy expenditure
predictions from video, using participant knowledge as context. We demonstrate
that context-agnostic meta-learning decreases the average mean square error by
30%
Temporal-Relational CrossTransformers for Few-Shot Action Recognition
We propose a novel approach to few-shot action recognition, finding
temporally-corresponding frame tuples between the query and videos in the
support set. Distinct from previous few-shot works, we construct class
prototypes using the CrossTransformer attention mechanism to observe relevant
sub-sequences of all support videos, rather than using class averages or single
best matches. Video representations are formed from ordered tuples of varying
numbers of frames, which allows sub-sequences of actions at different speeds
and temporal offsets to be compared.
Our proposed Temporal-Relational CrossTransformers (TRX) achieve
state-of-the-art results on few-shot splits of Kinetics, Something-Something V2
(SSv2), HMDB51 and UCF101. Importantly, our method outperforms prior work on
SSv2 by a wide margin (12%) due to the its ability to model temporal relations.
A detailed ablation showcases the importance of matching to multiple support
set videos and learning higher-order relational CrossTransformers.Comment: Accepted in CVPR 202
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