663 research outputs found
Electromagnetics from a quasistatic perspective
Quasistatics is introduced so that it fits smoothly into the standard
textbook presentation of electrodynamics. The usual path from statics to
general electrodynamics is rather short and surprisingly simple. A closer look
reveals however that it is not without confusing issues as has been illustrated
by many contributions to this Journal. Quasistatic theory is conceptually
useful by providing an intermediate level in between statics and the full set
of Maxwell's equations. Quasistatics is easier than general electrodynamics and
in some ways more similar to statics. It is however, in terms of interesting
physics and important applications, far richer than statics. Quasistatics is
much used in electromagnetic modeling, an activity that today is possible on a
PC and which also has great pedagogical potential. The use of electromagnetic
simulations in teaching gives additional support for the importance of
quasistatics. This activity may also motivate some change of focus in the
presentation of basic electrodynamics
Analytical reasoning task reveals limits of social learning in networks
Social learning -by observing and copying others- is a highly successful
cultural mechanism for adaptation, outperforming individual information
acquisition and experience. Here, we investigate social learning in the context
of the uniquely human capacity for reflective, analytical reasoning. A hallmark
of the human mind is our ability to engage analytical reasoning, and suppress
false associative intuitions. Through a set of lab-based network experiments,
we find that social learning fails to propagate this cognitive strategy. When
people make false intuitive conclusions, and are exposed to the analytic output
of their peers, they recognize and adopt this correct output. But they fail to
engage analytical reasoning in similar subsequent tasks. Thus, humans exhibit
an 'unreflective copying bias,' which limits their social learning to the
output, rather than the process, of their peers' reasoning -even when doing so
requires minimal effort and no technical skill. In contrast to much recent work
on observation-based social learning, which emphasizes the propagation of
successful behavior through copying, our findings identify a limit on the power
of social networks in situations that require analytical reasoning
An efficient and principled method for detecting communities in networks
A fundamental problem in the analysis of network data is the detection of
network communities, groups of densely interconnected nodes, which may be
overlapping or disjoint. Here we describe a method for finding overlapping
communities based on a principled statistical approach using generative network
models. We show how the method can be implemented using a fast, closed-form
expectation-maximization algorithm that allows us to analyze networks of
millions of nodes in reasonable running times. We test the method both on
real-world networks and on synthetic benchmarks and find that it gives results
competitive with previous methods. We also show that the same approach can be
used to extract nonoverlapping community divisions via a relaxation method, and
demonstrate that the algorithm is competitively fast and accurate for the
nonoverlapping problem.Comment: 14 pages, 5 figures, 1 tabl
Meta-Reinforcement Learning for the Tuning of PI Controllers: An Offline Approach
Meta-learning is a branch of machine learning which trains neural network
models to synthesize a wide variety of data in order to rapidly solve new
problems. In process control, many systems have similar and well-understood
dynamics, which suggests it is feasible to create a generalizable controller
through meta-learning. In this work, we formulate a meta reinforcement learning
(meta-RL) control strategy that can be used to tune proportional--integral
controllers. Our meta-RL agent has a recurrent structure that accumulates
"context" to learn a system's dynamics through a hidden state variable in
closed-loop. This architecture enables the agent to automatically adapt to
changes in the process dynamics. In tests reported here, the meta-RL agent was
trained entirely offline on first order plus time delay systems, and produced
excellent results on novel systems drawn from the same distribution of process
dynamics used for training. A key design element is the ability to leverage
model-based information offline during training in simulated environments while
maintaining a model-free policy structure for interacting with novel processes
where there is uncertainty regarding the true process dynamics. Meta-learning
is a promising approach for constructing sample-efficient intelligent
controllers.Comment: 23 pages; postprin
Mesoscopic structure and social aspects of human mobility
The individual movements of large numbers of people are important in many
contexts, from urban planning to disease spreading. Datasets that capture human
mobility are now available and many interesting features have been discovered,
including the ultra-slow spatial growth of individual mobility. However, the
detailed substructures and spatiotemporal flows of mobility - the sets and
sequences of visited locations - have not been well studied. We show that
individual mobility is dominated by small groups of frequently visited,
dynamically close locations, forming primary "habitats" capturing typical daily
activity, along with subsidiary habitats representing additional travel. These
habitats do not correspond to typical contexts such as home or work. The
temporal evolution of mobility within habitats, which constitutes most motion,
is universal across habitats and exhibits scaling patterns both distinct from
all previous observations and unpredicted by current models. The delay to enter
subsidiary habitats is a primary factor in the spatiotemporal growth of human
travel. Interestingly, habitats correlate with non-mobility dynamics such as
communication activity, implying that habitats may influence processes such as
information spreading and revealing new connections between human mobility and
social networks.Comment: 7 pages, 5 figures (main text); 11 pages, 9 figures, 1 table
(supporting information
The effects of continued azacitidine treatment cycles on response in higher risk patients with myelodysplastic syndromes: an update
The international, phase III, multi-centre AZA-001 trial demonstrated azacitidine (AZA) is the first treatment to significantly extend overall survival (OS) in higher risk myelodysplastic syndromes (MDS) patients (Fenaux (2007) Blood 110 817). The current treatment paradigm, which is based on a relationship between complete remission (CR) and survival, is increasingly being questioned (Cheson (2006) Blood 108 419). Results of AZA-001 show CR is sufficient but not necessary to prolong OS (List (2008) Clin Oncol 26 7006). Indeed, the AZA CR rate in AZA-001 was modest (17%), while partial remission (PR, 12%) and haematological improvement (HI, 49%) were also predictive of prolonged survival. This analysis was conducted to assess the median number of AZA treatment cycles associated with achievement of first response, as measured by IWG 2000-defined CR, PR or HI (major + minor). The number of treatment cycles from first response to best response was also measured
Azacitidine prolongs overall survival and reduces infections and hospitalizations in patients with WHO-defined acute myeloid leukaemia compared with conventional care regimens: an update
Azacitidine (AZA), as demonstrated in the phase III trial (AZA-001), is the first MDS treatment to significantly prolong overall survival (OS) in higher risk MDS pts ((2007) Blood 110 817). Approximately, one-third of the patients (pts) enrolled in AZA-001 were FAB RAEB-T (≥20–30% blasts) and now meet the WHO criteria for acute myeloid leukaemia (AML) ((1999) Blood 17 3835). Considering the poor prognosis (median survival <1 year) and the poor response to chemotherapy in these pts, this sub-group analysis evaluated the effects of AZA versus conventional care regimens (CCR) on OS and on response rates in pts with WHO AML
Circadian hormone secretory profiles in women with severe premenstrual tension syndrome.
The circadian secretory profiles of serum prolactin, growth hormone and cortisol were measured in two women suffering from severe premenstrual tension syndrome and in two asymptomatic control subjects. Subjects and controls were screened and included after a rigorous selection process. Blood samples were obtained every 30 min over a period of 24 h in each woman both on day 9 (follicular phase) and day 26 (luteal phase) of the menstrual cycle. There was no relationship between the hormonal secretory profiles and the premenstrual tension syndrome.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/75119/1/j.1471-0528.1984.tb04785.x.pd
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