1,931 research outputs found
Escaping from nonhyperbolic chaotic attractors
We study the noise-induced escape process from chaotic attractors in
nonhyperbolic systems. We provide a general mechanism of escape in the low
noise limit, employing the theory of large fluctuations. Specifically, this is
achieved by solving the variational equations of the auxiliary Hamiltonian
system and by incorporating the initial conditions on the chaotic attractor
unambiguously. Our results are exemplified with the H{\'e}non and the Ikeda map
and can be implemented straightforwardly to experimental data.Comment: replaced with published versio
A Feasibility Study on Miniaturizing an Automatic Amino Acid Analyzer for Use on Apollo Mission and Mars Voyager Mission Progress Report, Jun. - Dec. 1966
Miniaturizing automatic amino acid analyzer for Apollo and Voyager mission
A Feasibility Study on Miniaturizing an Automatic Amino Acid Analyzer for Use on Apollo Mission and Mars Voyager Mission Progress Report, Jan. - Jun. 1967
Miniaturizing n automatic amino acid analyzer for use on Apollo mission and Mars Voyager missio
Stability Properties of Nonhyperbolic Chaotic Attractors under Noise
We study local and global stability of nonhyperbolic chaotic attractors
contaminated by noise. The former is given by the maximum distance of a noisy
trajectory from the noisefree attractor, while the latter is provided by the
minimal escape energy necessary to leave the basin of attraction, calculated
with the Hamiltonian theory of large fluctuations. We establish the important
and counterintuitive result that both concepts may be opposed to each other.
Even when one attractor is globally more stable than another one, it can be
locally less stable. Our results are exemplified with the Holmes map, for two
different sets of parameter, and with a juxtaposition of the Holmes and the
Ikeda maps. Finally, the experimental relevance of these findings is pointed
out.Comment: Phys.Rev. Lett., to be publishe
An Integrated Computational and Experimental Approach to Identifying Inhibitors for SARS-CoV-2 3CL Protease
The newly evolved SARS-CoV-2 has caused the COVID-19 pandemic, and the SARS-CoV-2 main protease 3CLpro is essential for the rapid replication of the virus. Inhibiting this protease may open an alternative avenue toward therapeutic intervention. In this work, a computational docking approach was developed to identify potential small-molecule inhibitors for SARS-CoV-2 3CLpro. Totally 288 potential hits were identified from a half-million bioactive chemicals via a protein-ligand docking protocol. To further evaluate the docking results, a quantitative structure activity relationship (QSAR) model of 3CLpro inhibitors was developed based on existing small molecule inhibitors of the 3CLproSARS– CoV– 1 and their corresponding IC50 data. The QSAR model assesses the physicochemical properties of identified compounds and estimates their inhibitory effects on 3CLproSARS– CoV– 2. Seventy-one potential inhibitors of 3CLpro were selected through these computational approaches and further evaluated via an enzyme activity assay. The results show that two chemicals, i.e., 5-((1-([1,1′-biphenyl]-4-yl)-2,5-dimethyl-1H-pyrrol-3-yl)methylene)pyrimidine-2,4,6(1H,3H,5H)-trione and N-(4-((3-(4-chlorophenylsulfonamido)quinoxalin-2-yl)amino)phenyl)acetamide, effectively inhibited 3CLpro SARS-CoV-2 with IC50’s of 19 ± 3 μM and 38 ± 3 μM, respectively. The compounds contain two basic structures, pyrimidinetrione and quinoxaline, which were newly found in 3CLpro inhibitor structures and are of high interest for lead optimization. The findings from this work, such as 3CLpro inhibitor candidates and the QSAR model, will be helpful to accelerate the discovery of inhibitors for related coronaviruses that may carry proteases with similar structures to SARS-CoV-2 3CLpro
All Who Wander: On the Prevalence and Characteristics of Multi-community Engagement
Although analyzing user behavior within individual communities is an active
and rich research domain, people usually interact with multiple communities
both on- and off-line. How do users act in such multi-community environments?
Although there are a host of intriguing aspects to this question, it has
received much less attention in the research community in comparison to the
intra-community case. In this paper, we examine three aspects of
multi-community engagement: the sequence of communities that users post to, the
language that users employ in those communities, and the feedback that users
receive, using longitudinal posting behavior on Reddit as our main data source,
and DBLP for auxiliary experiments. We also demonstrate the effectiveness of
features drawn from these aspects in predicting users' future level of
activity.
One might expect that a user's trajectory mimics the "settling-down" process
in real life: an initial exploration of sub-communities before settling down
into a few niches. However, we find that the users in our data continually post
in new communities; moreover, as time goes on, they post increasingly evenly
among a more diverse set of smaller communities. Interestingly, it seems that
users that eventually leave the community are "destined" to do so from the very
beginning, in the sense of showing significantly different "wandering" patterns
very early on in their trajectories; this finding has potentially important
design implications for community maintainers. Our multi-community perspective
also allows us to investigate the "situation vs. personality" debate from
language usage across different communities.Comment: 11 pages, data available at
https://chenhaot.com/pages/multi-community.html, Proceedings of WWW 2015
(updated references
Géométries, invariances et interprétations par le rapport signal à bruit de détecteurs en sous-espaces adaptés ou adaptatifs
Matched subspace detectors generalize the matched filter by accommodating signals that are only constrained to lie in a multidimensional subspace. There are four of these detectors, depending upon knowledge of signal phase and noise power. The adaptive subspace detectors generalize the matched subspace detectors by accommodating problems where the noise covariance matrix is unknow, and must be estimated from training data. In this paper we review the geometries and invariances of the matched and adaptive subspace detectors. We also establish that every version of a matched or adaptative subspace detectors can be interpreted as an estimator of output signal-to-noise ratio (SNR), in disquise
The importance of plume rise on the concentrations and atmospheric impacts of biomass burning aerosol
We quantified the effects of the plume rise of biomass burning aerosol and gases for the forest fires that occurred in Saskatchewan, Canada, in July 2010. For this purpose, simulations with different assumptions regarding the plume rise and the vertical distribution of the emissions were conducted. Based on comparisons with observations, applying a one-dimensional plume rise model to predict the injection layer in combination with a parametrization of the vertical distribution of the emissions outperforms approaches in which the plume heights are initially predefined. Approximately 30 % of the fires exceed the height of 2 km with a maximum height of 8.6 km. Using this plume rise model, comparisons with satellite images in the visible spectral range show a very good agreement between the simulated and observed spatial distributions of the biomass burning plume. The simulated aerosol optical depth (AOD) with data of an AERONET station is in good agreement with respect to the absolute values and the timing of the maximum. Comparison of the vertical distribution of the biomass burning aerosol with CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) retrievals also showed the best agreement when the plume rise model was applied. We found that downwelling surface short-wave radiation below the forest fire plume is reduced by up to 50 % and that the 2 m temperature is decreased by up to 6 K. In addition, we simulated a strong change in atmospheric stability within the biomass burning plume
Cross-sectional survey of users of internet depression communities
Background: Internet-based depression communities provide a forum for individuals to
communicate and share information and ideas. There has been little research into the health status
and other characteristics of users of these communities.
Methods: Online cross-sectional survey of Internet depression communities to identify depressive
morbidity among users of Internet depression communities in six European countries; to
investigate whether users were in contact with health services and receiving treatment; and to
identify user perceived effects of the communities.
Results: Major depression was highly prevalent among respondents (varying by country from 40%
to 64%). Forty-nine percent of users meeting criteria for major depression were not receiving
treatment, and 35% had no consultation with health services in the previous year. Thirty-six
percent of repeat community users who had consulted a health professional in the previous year
felt that the Internet community had been an important factor in deciding to seek professional help.
Conclusions: There are high levels of untreated and undiagnosed depression in users of Internet
depression communities. This group represents a target for intervention. Internet communities can
provide information and support for stigmatizing conditions that inhibit more traditional modes of
information seeking
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