2,330 research outputs found
The Dreaming Variational Autoencoder for Reinforcement Learning Environments
Reinforcement learning has shown great potential in generalizing over raw
sensory data using only a single neural network for value optimization. There
are several challenges in the current state-of-the-art reinforcement learning
algorithms that prevent them from converging towards the global optima. It is
likely that the solution to these problems lies in short- and long-term
planning, exploration and memory management for reinforcement learning
algorithms. Games are often used to benchmark reinforcement learning algorithms
as they provide a flexible, reproducible, and easy to control environment.
Regardless, few games feature a state-space where results in exploration,
memory, and planning are easily perceived. This paper presents The Dreaming
Variational Autoencoder (DVAE), a neural network based generative modeling
architecture for exploration in environments with sparse feedback. We further
present Deep Maze, a novel and flexible maze engine that challenges DVAE in
partial and fully-observable state-spaces, long-horizon tasks, and
deterministic and stochastic problems. We show initial findings and encourage
further work in reinforcement learning driven by generative exploration.Comment: Best Student Paper Award, Proceedings of the 38th SGAI International
Conference on Artificial Intelligence, Cambridge, UK, 2018, Artificial
Intelligence XXXV, 201
Discrimination of Optical Coherent States using a Photon Number Resolving Detector
The discrimination of non-orthogonal quantum states with reduced or without
errors is a fundamental task in quantum measurement theory. In this work, we
investigate a quantum measurement strategy capable of discriminating two
coherent states probabilistically with significantly smaller error
probabilities than can be obtained using non-probabilistic state
discrimination. We find that appropriate postselection of the measurement data
of a photon number resolving detector can be used to discriminate two coherent
states with small error probability. We compare our new receiver to an optimal
intermediate measurement between minimum error discrimination and unambiguous
state discrimination.Comment: 5 pages, 4 figure
Ovarian cancer symptom awareness and anticipated delayed presentation in a population sample
Background: While ovarian cancer is recognised as having identifiable early symptoms, understanding of the key determinants of symptom awareness and early presentation is limited. A population-based survey of ovarian cancer awareness and anticipated delayed presentation with symptoms was conducted as part of the International Cancer Benchmarking Partnership (ICBP). Methods: Women aged over 50 years were recruited using random probability sampling (n = 1043). Computer-assisted telephone interviews were used to administer measures including ovarian cancer symptom recognition, anticipated time to presentation with ovarian symptoms, health beliefs (perceived risk, perceived benefits/barriers to early presentation, confidence in symptom detection, ovarian cancer worry), and demographic variables. Logistic regression analysis was used to identify the contribution of independent variables to anticipated presentation (categorised as < 3 weeks or ≥ 3 weeks). Results: The most well-recognised symptoms of ovarian cancer were post-menopausal bleeding (87.4%), and persistent pelvic (79.0%) and abdominal (85.0%) pain. Symptoms associated with eating difficulties and changes in bladder/bowel habits were recognised by less than half the sample. Lower symptom awareness was significantly associated with older age (p ≤ 0.001), being single (p ≤ 0.001), lower education (p ≤ 0.01), and lack of personal experience of ovarian cancer (p ≤ 0.01). The odds of anticipating a delay in time to presentation of ≥ 3 weeks were significantly increased in women educated to degree level (OR = 2.64, 95% CI 1.61 – 4.33, p ≤ 0.001), women who reported more practical barriers (OR = 1.60, 95% CI 1.34 – 1.91, p ≤ 0.001) and more emotional barriers (OR = 1.21, 95% CI 1.06 – 1.40, p ≤ 0.01), and those less confident in symptom detection (OR = 0.56, 95% CI 0.42 – 0.73, p ≤ 0.001), but not in those who reported lower symptom awareness (OR = 0.99, 95% CI 0.91 – 1.07, p = 0.74). Conclusions: Many symptoms of ovarian cancer are not well-recognised by women in the general population. Evidence-based interventions are needed not only to improve public awareness but also to overcome the barriers to recognising and acting on ovarian symptoms, if delays in presentation are to be minimised
Mechanical Metamaterials with Negative Compressibility Transitions
When tensioned, ordinary materials expand along the direction of the applied
force. Here, we explore network concepts to design metamaterials exhibiting
negative compressibility transitions, during which a material undergoes
contraction when tensioned (or expansion when pressured). Continuous
contraction of a material in the same direction of an applied tension, and in
response to this tension, is inherently unstable. The conceptually similar
effect we demonstrate can be achieved, however, through destabilisations of
(meta)stable equilibria of the constituents. These destabilisations give rise
to a stress-induced solid-solid phase transition associated with a twisted
hysteresis curve for the stress-strain relationship. The strain-driven
counterpart of negative compressibility transitions is a force amplification
phenomenon, where an increase in deformation induces a discontinuous increase
in response force. We suggest that the proposed materials could be useful for
the design of actuators, force amplifiers, micro-mechanical controls, and
protective devices.Comment: Supplementary information available at
http://www.nature.com/nmat/journal/v11/n7/abs/nmat3331.htm
Accurate masses and radii of normal stars: modern results and applications
This paper presents and discusses a critical compilation of accurate,
fundamental determinations of stellar masses and radii. We have identified 95
detached binary systems containing 190 stars (94 eclipsing systems, and alpha
Centauri) that satisfy our criterion that the mass and radius of both stars be
known to 3% or better. To these we add interstellar reddening, effective
temperature, metal abundance, rotational velocity and apsidal motion
determinations when available, and we compute a number of other physical
parameters, notably luminosity and distance. We discuss the use of this
information for testing models of stellar evolution. The amount and quality of
the data also allow us to analyse the tidal evolution of the systems in
considerable depth, testing prescriptions of rotational synchronisation and
orbital circularisation in greater detail than possible before. The new data
also enable us to derive empirical calibrations of M and R for single (post-)
main-sequence stars above 0.6 M(Sun). Simple, polynomial functions of T(eff),
log g and [Fe/H] yield M and R with errors of 6% and 3%, respectively.
Excellent agreement is found with independent determinations for host stars of
transiting extrasolar planets, and good agreement with determinations of M and
R from stellar models as constrained by trigonometric parallaxes and
spectroscopic values of T(eff) and [Fe/H]. Finally, we list a set of 23
interferometric binaries with masses known to better than 3%, but without
fundamental radius determinations (except alpha Aur). We discuss the prospects
for improving these and other stellar parameters in the near future.Comment: 56 pages including figures and tables. To appear in The Astronomy and
Astrophysics Review. Ascii versions of the tables will appear in the online
version of the articl
On the way to specifically targeting minimal residual disease?
The target of all adjuvant systemic therapies after surgery in breast cancer is the eradication of a minimal subclinical residual disease. Although it is well known that tumor cell dissemination takes place already at an early stage of the disease, little is known about the tumorbiological parameters of these residual cells. Selection of patients eligible for adjuvant endocrine therapies is based on the analysis of receptor expression in the primary tumor – although the analysis is directed against disseminated tumor cells, these cells may vary in receptor expression in comparison with the primary tumor
Time intervals from first symptom to treatment of cancer: a cohort study of 2,212 newly diagnosed cancer patients
<p>Abstract</p> <p>Background</p> <p>Delay in diagnosis of cancer may worsen prognosis. The aim of this study is to explore patient-, general practitioner (GP)- and system-related delay in the interval from first cancer symptom to diagnosis and treatment, and to analyse the extent to which delays differ by cancer type.</p> <p>Methods</p> <p>Population-based cohort study conducted in 2004-05 in the County of Aarhus, Denmark (640,000 inhabitants). Data were collected from administrative registries and questionnaires completed by GPs on 2,212 cancer patients newly diagnosed during a 1-year period. Median delay (in days) with interquartile interval (IQI) was the main outcome measure.</p> <p>Results</p> <p>Median total delay was 98 days (IQI 57-168). Most of the total delay stemmed from patient (median 21 days (7-56)) and system delay (median 55 days (32-93)). Median GP delay was 0 (0-2) days. Total delay was shortest among patients with ovarian (median 60 days (45-112)) and breast cancer (median 65 days (39-106)) and longest among patients with prostate (median 130 days (89-254)) and bladder cancer (median 134 days (93-181)).</p> <p>Conclusion</p> <p>System delay accounted for a substantial part of the total delay experienced by cancer patients. This points to a need for shortening clinical pathways if possible. A long patient delay calls for research into patient awareness of cancer. For all delay components, special focus should be given to the 4<sup>th </sup>quartile of patients with the longest time intervals and we need research into the quality of the diagnostic work-up process. We found large variations in delay for different types of cancer. Improvements should therefore target both the population at large and the specific needs associated with individual cancer types and their symptoms.</p
The Dreaming Variational Autoencoder for Reinforcement Learning Environments
Reinforcement learning has shown great potential in generalizing over raw sensory data using only a single neural network for value optimization. There are several challenges in the current state-of-the-art reinforcement learning algorithms that prevent them from converging towards the global optima. It is likely that the solution to these problems lies in short- and long-term planning, exploration and memory management for reinforcement learning algorithms. Games are often used to benchmark reinforcement learning algorithms as they provide a flexible, reproducible, and easy to control environment. Regardless, few games feature a state-space where results in exploration, memory, and planning are easily perceived. This paper presents The Dreaming Variational Autoencoder (DVAE), a neural network based generative modeling architecture for exploration in environments with sparse feedback. We further present Deep Maze, a novel and flexible maze engine that challenges DVAE in partial and fully-observable state-spaces, long-horizon tasks, and deterministic and stochastic problems. We show initial findings and encourage further work in reinforcement learning driven by generative exploration.The Dreaming Variational Autoencoder for Reinforcement Learning EnvironmentsacceptedVersionNivå
Acceptability of novel lifelogging technology to determine context of sedentary behaviour in older adults
<strong>Objective:</strong> Lifelogging, using body worn sensors (activity monitors and time lapse photography) has the potential to shed light on the context of sedentary behaviour. The objectives of this study were to examine the acceptability, to older adults, of using lifelogging technology and indicate its usefulness for understanding behaviour.<strong> </strong><strong>Method:</strong> 6 older adults (4 males, mean age: 68yrs) wore the equipment (ActivPAL<sup>TM</sup> and Vicon Revue<sup>TM</sup>/SenseCam<sup>TM</sup>) for 7 consecutive days during free-living activity. The older adults’ perception of the lifelogging technology was assessed through semi-structured interviews, including a brief questionnaire (Likert scale), and reference to the researcher's diary. <strong>Results:</strong> Older adults in this study found the equipment acceptable to wear and it did not interfere with privacy, safety or create reactivity, but they reported problems with the actual technical functioning of the camera. <strong>Conclusion:</strong> This combination of sensors has good potential to provide lifelogging information on the context of sedentary behaviour
The Distribution of GYR- and YLP-Like Motifs in Drosophila Suggests a General Role in Cuticle Assembly and Other Protein-Protein Interactions
Background: Arthropod cuticle is composed predominantly of a self-assembling matrix of chitin and protein. Genes encoding structural cuticular proteins are remarkably abundant in arthropod genomes, yet there has been no systematic survey of conserved motifs across cuticular protein families. Methodology/Principal Findings: Two short sequence motifs with conserved tyrosines were identified in Drosophila cuticular proteins that were similar to the GYR and YLP Interpro domains. These motifs were found in members of the CPR, Tweedle, CPF/CPFL, and (in Anopheles gambiae) CPLCG cuticular protein families, and the Dusky/Miniature family of cuticleassociated proteins. Tweedle proteins have a characteristic motif architecture that is shared with the Drosophila protein GCR1 and its orthologs in other species, suggesting that GCR1 is also cuticular. A resilin repeat, which has been shown to confer elasticity, matched one of the motifs; a number of other Drosophila proteins of unknown function exhibit a motif architecture similar to that of resilin. The motifs were also present in some proteins of the peritrophic matrix and the eggshell, suggesting molecular convergence among distinct extracellular matrices. More surprisingly, gene regulation, development, and proteolysis were statistically over-represented ontology terms for all non-cuticular matches in Drosophila. Searches against other arthropod genomes indicate that the motifs are taxonomically widespread. Conclusions: This survey suggests a more general definition for GYR and YLP motifs and reveals their contribution to severa
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