19,007 research outputs found
Early Results from GLASS-JWST. XIX: A High Density of Bright Galaxies at in the Abell 2744 Region
We report the detection of a high density of redshift galaxies
behind the foreground cluster Abell 2744, selected from imaging data obtained
recently with NIRCam onboard {\it JWST} by three programs -- GLASS-JWST,
UNCOVER, and DDT\#2756. To ensure robust estimates of the lensing magnification
, we use an improved version of our model that exploits the first epoch of
NIRCam images and newly obtained MUSE spectra, and avoids regions with
where the uncertainty may be higher. We detect seven bright
galaxies with demagnified rest-frame mag,
over an area of sq. arcmin. Taking into account photometric
incompleteness and the effects of lensing on luminosity and cosmological
volume, we find that the density of galaxies in the field is
about () larger than the average at mag reported so far. The density is even higher when considering only
the GLASS-JWST data, which are the deepest and the least affected by
magnification and incompleteness. The GLASS-JWST field contains 5 out of 7
galaxies, distributed along an apparent filamentary structure of 2 Mpc in
projected length, and includes a close pair of candidates with mag having a projected separation of only 16 kpc. These findings suggest
the presence of a overdensity in the field. In addition to
providing excellent targets for efficient spectroscopic follow-up observations,
our study confirms the high density of bright galaxies observed in early {\it
JWST} observations, but calls for multiple surveys along independent lines of
sight to achieve an unbiased estimate of their average density and a first
estimate of their clustering.Comment: Accepted for publication in ApJL, 13 pages, 4 figure
Adaptive measurement filter: efficient strategy for optimal estimation of quantum Markov chains
Continuous-time measurements are instrumental for a multitude of tasks in
quantum engineering and quantum control, including the estimation of dynamical
parameters of open quantum systems monitored through the environment. However,
such measurements do not extract the maximum amount of information available in
the output state, so finding alternative optimal measurement strategies is a
major open problem.
In this paper we solve this problem in the setting of discrete-time
input-output quantum Markov chains. We present an efficient algorithm for
optimal estimation of one-dimensional dynamical parameters which consists of an
iterative procedure for updating a `measurement filter' operator and
determining successive measurement bases for the output units. A key ingredient
of the scheme is the use of a coherent quantum absorber as a way to
post-process the output after the interaction with the system. This is designed
adaptively such that the joint system and absorber stationary state is pure at
a reference parameter value. The scheme offers an exciting prospect for optimal
continuous-time adaptive measurements, but more work is needed to find
realistic practical implementations.Comment: 25 pages 7 figure
Vegetation responses to variations in climate: A combined ordinary differential equation and sequential Monte Carlo estimation approach
Vegetation responses to variation in climate are a current research priority in the context of accelerated shifts generated by climate change. However, the interactions between environmental and biological factors still represent one of the largest uncertainties in projections of future scenarios, since the relationship between drivers and ecosystem responses has a complex and nonlinear nature. We aimed to develop a model to study the vegetation’s primary productivity dynamic response to temporal variations in climatic conditions as measured by rainfall, temperature and radiation. Thus, we propose a new way to estimate the vegetation response to climate via a non-autonomous version of a classical growth curve, with a time-varying growth rate and carrying capacity parameters according to climate variables. With a Sequential Monte Carlo Estimation to account for complexities in the climate-vegetation relationship to minimize the number of parameters. The model was applied to six key sites identified in a previous study, consisting of different arid and semiarid rangelands from North Patagonia, Argentina. For each site, we selected the time series of MODIS NDVI, and climate data from ERA5 Copernicus hourly reanalysis from 2000 to 2021. After calculating the time series of the a posteriori distribution of parameters, we analyzed the explained capacity of the model in terms of the linear coefficient of determination and
the parameters distribution variation. Results showed that most rangelands recorded changes in their sensitivity over time to climatic factors, but vegetation responses were heterogeneous and influenced by different drivers. Differences in this climate-vegetation relationship were recorded among different cases: (1) a marginal and decreasing sensitivity to temperature and radiation, respectively, but a high sensitivity to water availability; (2) high and increasing sensitivity to temperature and water availability, respectively; and (3) a case with an abrupt shift in vegetation dynamics driven by a progressively decreasing sensitivity to water availability, without any
changes in the sensitivity either to temperature or radiation. Finally, we also found that the time scale, in which the ecosystem integrated the rainfall phenomenon in terms of the width of the window function used to convolve the rainfall series into a water availability variable, was also variable in time. This approach allows us to estimate the connection degree between ecosystem productivity and climatic variables. The capacity of the model to identify changes over time in the vegetation-climate relationship might inform decision-makers about ecological transitions and the differential impact of climatic drivers on ecosystems.Estación Experimental Agropecuaria BarilocheFil: Bruzzone, Octavio Augusto. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bariloche; ArgentinaFil: Bruzzone, Octavio Augusto. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Instituto de Investigaciones Forestales y Agropecuarias Bariloche; ArgentinaFil: Perri, Daiana Vanesa. Instituto Nacional de Tecnologia Agropecuaria (INTA). Estación Experimental Agropecuaria Bariloche. Área de Recursos Naturales; ArgentinaFil: Perri, Daiana Vanesa. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Instituto de Investigaciones Forestales y Agropecuarias Bariloche; ArgentinaFil: Easdale, Marcos Horacio. Instituto Nacional de Tecnologia Agropecuaria (INTA). Estación Experimental Agropecuaria Bariloche. Área de Recursos Naturales; ArgentinaFil: Easdale, Marcos Horacio. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Instituto de Investigaciones Forestales y Agropecuarias Bariloche; Argentin
Examples of works to practice staccato technique in clarinet instrument
Klarnetin staccato tekniğini güçlendirme aşamaları eser çalışmalarıyla uygulanmıştır. Staccato
geçişlerini hızlandıracak ritim ve nüans çalışmalarına yer verilmiştir. Çalışmanın en önemli amacı
sadece staccato çalışması değil parmak-dilin eş zamanlı uyumunun hassasiyeti üzerinde de
durulmasıdır. Staccato çalışmalarını daha verimli hale getirmek için eser çalışmasının içinde etüt
çalışmasına da yer verilmiştir. Çalışmaların üzerinde titizlikle durulması staccato çalışmasının ilham
verici etkisi ile müzikal kimliğe yeni bir boyut kazandırmıştır. Sekiz özgün eser çalışmasının her
aşaması anlatılmıştır. Her aşamanın bir sonraki performans ve tekniği güçlendirmesi esas alınmıştır.
Bu çalışmada staccato tekniğinin hangi alanlarda kullanıldığı, nasıl sonuçlar elde edildiği bilgisine
yer verilmiştir. Notaların parmak ve dil uyumu ile nasıl şekilleneceği ve nasıl bir çalışma disiplini
içinde gerçekleşeceği planlanmıştır. Kamış-nota-diyafram-parmak-dil-nüans ve disiplin
kavramlarının staccato tekniğinde ayrılmaz bir bütün olduğu saptanmıştır. Araştırmada literatür
taraması yapılarak staccato ile ilgili çalışmalar taranmıştır. Tarama sonucunda klarnet tekniğin de
kullanılan staccato eser çalışmasının az olduğu tespit edilmiştir. Metot taramasında da etüt
çalışmasının daha çok olduğu saptanmıştır. Böylelikle klarnetin staccato tekniğini hızlandırma ve
güçlendirme çalışmaları sunulmuştur. Staccato etüt çalışmaları yapılırken, araya eser çalışmasının
girmesi beyni rahatlattığı ve istekliliği daha arttırdığı gözlemlenmiştir. Staccato çalışmasını yaparken
doğru bir kamış seçimi üzerinde de durulmuştur. Staccato tekniğini doğru çalışmak için doğru bir
kamışın dil hızını arttırdığı saptanmıştır. Doğru bir kamış seçimi kamıştan rahat ses çıkmasına
bağlıdır. Kamış, dil atma gücünü vermiyorsa daha doğru bir kamış seçiminin yapılması gerekliliği
vurgulanmıştır. Staccato çalışmalarında baştan sona bir eseri yorumlamak zor olabilir. Bu açıdan
çalışma, verilen müzikal nüanslara uymanın, dil atış performansını rahatlattığını ortaya koymuştur.
Gelecek nesillere edinilen bilgi ve birikimlerin aktarılması ve geliştirici olması teşvik edilmiştir.
Çıkacak eserlerin nasıl çözüleceği, staccato tekniğinin nasıl üstesinden gelinebileceği anlatılmıştır.
Staccato tekniğinin daha kısa sürede çözüme kavuşturulması amaç edinilmiştir. Parmakların
yerlerini öğrettiğimiz kadar belleğimize de çalışmaların kaydedilmesi önemlidir. Gösterilen azmin ve
sabrın sonucu olarak ortaya çıkan yapıt başarıyı daha da yukarı seviyelere çıkaracaktır
Path integrals and stochastic calculus
Path integrals are a ubiquitous tool in theoretical physics. However, their
use is sometimes hindered by the lack of control on various manipulations --
such as performing a change of the integration path -- one would like to carry
out in the light-hearted fashion that physicists enjoy. Similar issues arise in
the field of stochastic calculus, which we review to prepare the ground for a
proper construction of path integrals. At the level of path integration, and in
arbitrary space dimension, we not only report on existing Riemannian
geometry-based approaches that render path integrals amenable to the standard
rules of calculus, but also bring forth new routes, based on a fully
time-discretized approach, that achieve the same goal. We illustrate these
various definitions of path integration on simple examples such as the
diffusion of a particle on a sphere.Comment: 96 pages, 4 figures. New title, expanded introduction and additional
references. Version accepted in Advandes in Physic
Merge of two oppositely biased Wiener processes
We introduce a technique to merge two biased Brownian motions into a single
regular process. The outcome follows a stochastic differential equation with a
constant diffusion coefficient and a non-linear drift. The emerging stochastic
process has outstanding properties, such as spatial and temporal translational
invariance of its mean squared displacement, and can be efficiently simulated
via a random walk with site-dependent one-step transition probabilities.Comment: 6 pages, no figure
Dynamic Subspace Estimation with Grassmannian Geodesics
Dynamic subspace estimation, or subspace tracking, is a fundamental problem
in statistical signal processing and machine learning. This paper considers a
geodesic model for time-varying subspaces. The natural objective function for
this model is non-convex. We propose a novel algorithm for minimizing this
objective and estimating the parameters of the model from data with
Grassmannian-constrained optimization. We show that with this algorithm, the
objective is monotonically non-increasing. We demonstrate the performance of
this model and our algorithm on synthetic data, video data, and dynamic fMRI
data
Deep Transfer Learning Applications in Intrusion Detection Systems: A Comprehensive Review
Globally, the external Internet is increasingly being connected to the
contemporary industrial control system. As a result, there is an immediate need
to protect the network from several threats. The key infrastructure of
industrial activity may be protected from harm by using an intrusion detection
system (IDS), a preventive measure mechanism, to recognize new kinds of
dangerous threats and hostile activities. The most recent artificial
intelligence (AI) techniques used to create IDS in many kinds of industrial
control networks are examined in this study, with a particular emphasis on
IDS-based deep transfer learning (DTL). This latter can be seen as a type of
information fusion that merge, and/or adapt knowledge from multiple domains to
enhance the performance of the target task, particularly when the labeled data
in the target domain is scarce. Publications issued after 2015 were taken into
account. These selected publications were divided into three categories:
DTL-only and IDS-only are involved in the introduction and background, and
DTL-based IDS papers are involved in the core papers of this review.
Researchers will be able to have a better grasp of the current state of DTL
approaches used in IDS in many different types of networks by reading this
review paper. Other useful information, such as the datasets used, the sort of
DTL employed, the pre-trained network, IDS techniques, the evaluation metrics
including accuracy/F-score and false alarm rate (FAR), and the improvement
gained, were also covered. The algorithms, and methods used in several studies,
or illustrate deeply and clearly the principle in any DTL-based IDS subcategory
are presented to the reader
Reduction of Petri net maintenance modeling complexity via Approximate Bayesian Computation
This paper is part of the ENHAnCE ITN project (https://www.h2020-enhanceitn.eu/) funded by the European Union's Horizon 2020 research and innovation programme under the Marie SklodowskaCurie grant agreement No. 859957. The authors would like to thank the Lloyd's Register Foundation (LRF), a charitable foundation in the U.K. helping to protect life and property by supporting engineeringrelated education, public engagement, and the application of research. The authors gratefully acknowledge the support of these organizations which have enabled the research reported in this paper.The accurate modeling of engineering systems and processes using Petri nets often results in complex graph
representations that are computationally intensive, limiting the potential of this modeling tool in real life
applications. This paper presents a methodology to properly define the optimal structure and properties of
a reduced Petri net that mimic the output of a reference Petri net model. The methodology is based on
Approximate Bayesian Computation to infer the plausible values of the model parameters of the reduced model
in a rigorous probabilistic way. Also, the method provides a numerical measure of the level of approximation
of the reduced model structure, thus allowing the selection of the optimal reduced structure among a set
of potential candidates. The suitability of the proposed methodology is illustrated using a simple illustrative
example and a system reliability engineering case study, showing satisfactory results. The results also show
that the method allows flexible reduction of the structure of the complex Petri net model taken as reference,
and provides numerical justification for the choice of the reduced model structure.European Commission 859957Lloyd's Register Foundation (LRF), a charitable foundation in the U.K
Mathematical models to evaluate the impact of increasing serotype coverage in pneumococcal conjugate vaccines
Of over 100 serotypes of Streptococcus pneumoniae, only 7 were included in the first pneumo- coccal conjugate vaccine (PCV). While PCV reduced the disease incidence, in part because of a herd immunity effect, a replacement effect was observed whereby disease was increasingly caused by serotypes not included in the vaccine. Dynamic transmission models can account for these effects to describe post-vaccination scenarios, whereas economic evaluations can enable decision-makers to compare vaccines of increasing valency for implementation. This thesis has four aims. First, to explore the limitations and assumptions of published pneu- mococcal models and the implications for future vaccine formulation and policy. Second, to conduct a trend analysis assembling all the available evidence for serotype replacement in Europe, North America and Australia to characterise invasive pneumococcal disease (IPD) caused by vaccine-type (VT) and non-vaccine-types (NVT) serotypes. The motivation behind this is to assess the patterns of relative abundance in IPD cases pre- and post-vaccination, to examine country-level differences in relation to the vaccines employed over time since introduction, and to assess the growth of the replacement serotypes in comparison with the serotypes targeted by the vaccine. The third aim is to use a Bayesian framework to estimate serotype-specific invasiveness, i.e. the rate of invasive disease given carriage. This is useful for dynamic transmission modelling, as transmission is through carriage but a majority of serotype-specific pneumococcal data lies in active disease surveillance. This is also helpful to address whether serotype replacement reflects serotypes that are more invasive or whether serotypes in a specific location are equally more invasive than in other locations. Finally, the last aim of this thesis is to estimate the epidemiological and economic impact of increas- ing serotype coverage in PCVs using a dynamic transmission model. Together, the results highlight that though there are key parameter uncertainties that merit further exploration, divergence in serotype replacement and inconsistencies in invasiveness on a country-level may make a universal PCV suboptimal.Open Acces
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