4,990 research outputs found
Modern control concepts in hydrology
Two approaches to an identification problem in hydrology are presented based upon concepts from modern control and estimation theory. The first approach treats the identification of unknown parameters in a hydrologic system subject to noisy inputs as an adaptive linear stochastic control problem; the second approach alters the model equation to account for the random part in the inputs, and then uses a nonlinear estimation scheme to estimate the unknown parameters. Both approaches use state-space concepts. The identification schemes are sequential and adaptive and can handle either time invariant or time dependent parameters. They are used to identify parameters in the Prasad model of rainfall-runoff. The results obtained are encouraging and conform with results from two previous studies; the first using numerical integration of the model equation along with a trial-and-error procedure, and the second, by using a quasi-linearization technique. The proposed approaches offer a systematic way of analyzing the rainfall-runoff process when the input data are imbedded in noise
Up-Down Unification, Neutrino Masses and Rare Lepton Decays
In a recent paper, we showed that tree level up-down unification of fermion
Yukawa couplings is a natural consequence of a large class of supersymmetric
models. They can lead to viable quark masses and mixings for moderately large
values of with interesting and testable predictions for CP
violation in the hadronic sector. In this letter, we extend our discussion to
the leptonic sector focusing on one particular class of these models, the
supersymmetric left-right model with the seesaw mechanism for neutrino masses.
We show that fitting the solar and the atmospheric neutrino data considerably
restricts the Majorana-Yukawa couplings of the leptons in this model and leads
to predictions for the decay , which is found to be
accessible to the next generation of rare decay searches. We also show that the
resulting parameter space of the model is consistent with the requirements of
generating adequate baryon asymmetry through lepton-number violating decays of
the right-handed neutrino.Comment: 16 pages, latex, 6 figures, typos correcte
Simulation of Leksell Gamma Knife-4C System with Different Phantoms Using PHITS and Geant4
This study used PHITS and Geant4 code packages to simulate a Leksell Gamma Knife system in order to determine radiation dose distribution in two types of phantoms. The results observed in the water phantom with configurations of single source and 201 sources are in good accord with the prior research, including both simulation and experiment. Several characteristics of Leksell Gamma Knife 4C, such as dose profiles, output factor, FWHM, and penumbra size, are calculated based on Monte Carlo simulations, which show the best consistency with other results. The output factors for collimators of 14 mm, 8 mm, and 4 mm are 0.984, 0.949, and 0.872, respectively. The simulation results with an adult mesh-type reference phantom reveal considerable similarities with the established radiosurgery plans. It indicates that the absorbed dose in brain tumors was highest when utilizing the 18 mm collimator and subsequently reduced with collimator size to 0.65, 0.25, and 0.5 with the 14 mm, 8 mm, and 4 mm collimators, respectively. The absorbed dose has a very low value for other essential organs and decreases with distance from the brain tumor. These findings may explain why the dose to organs decreases linearly as target distance, volume, and collimator size increase
Modeling and parameter estimation in a PO3G Polyether process with time delay
A new model is developed to describe batch polycondensation of bio-based 1,3-propanediol (PO3G) to produce a polyether commercialized by DuPont as Cerenol® (see Figure 1). Cerenol® is valued for its biodegradability and low toxicity, as well as high end-group reactivity, low viscosity, low melting point and superior oxidative stability[1]. The proposed model provides an improved fit to the available data compared to the previous model of Cui et al. [2] The main reason for this improvement is that the revised model equations and parameter estimation methodologies that were used account for time delay and accumulation of evaporated water, monomer and linear oligomers in the overhead condenser system. To handle this time delay, existing parameter ranking, subset selection and estimation methods[3-5] were extended to treat the delay associated with the overhead piping as an additional unknown model parameter. Although, the resulting parameter estimates lead to better predictions on average compared to the model of Cui et al., there is still noticeable mismatch between the data and the model predictions. For example, Figure 2 shows dynamic model predictions and data for linear dimer and tetramer concentrations in the reactor obtained using a super-acid catalyst. Further parameter estimation studies are underway to determine whether current estimates correspond only to a local minimum for the optimization problem. In future, the model will be extended to account for formation and evaporation of cyclic oligomers.
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A team effort: natural killer cells on the first leg of the tumor immunity relay race
Recent work by Böttcher and colleagues defines a new role for Natural Killer cells in the anti-tumor immune response, arriving early into the tumor microenvironment before passing the baton to DC1 dendritic cells. DC1 dendritic cells subsequently activate CD8+ T cells resulting in effective anti-tumor immunity. This work highlights the cooperative nature of anti-tumor immunity set in motion by Natural Killer cells, and immune evasion by tumors through their exclusion.National Cancer Institute (U.S.) (R00CA204595
Ist Fairer Handel Wirklich Fair?
Organisationen des Fairen Handels verfolgen das Ziel benachteiligten Produzenten in Entwicklungsländern faire Preise für deren Erzeugnisse zu zahlen. Dennoch ist es aus makro- und mikroökonomischer Sicht fraglich, ob die Zahlung eines Preisaufschlags wirklich effektiv für die betroffenen Produzenten ist. Am Beispiel des Bananenmarktes soll anhand einer Kosten-Nutzen-Analyse und der Analyse möglicher negativer Externalitäten des Fairen Handels auf nichtteilnehmende Produzenten die Frage erörtert werden, inwieweit dieses alternative Handelsmodell als „fair“ bezeichnet werden kann.Fairer Handel, Bananenmarkt, Kosten-Nutzen-Analyse, Verteilungsaspekte, negative Externalitäten
Navya3DSeg -- Navya 3D Semantic Segmentation Dataset & split generation for autonomous vehicles
Autonomous driving (AD) perception today relies heavily on deep learning
based architectures requiring large scale annotated datasets with their
associated costs for curation and annotation. The 3D semantic data are useful
for core perception tasks such as obstacle detection and ego-vehicle
localization. We propose a new dataset, Navya 3D Segmentation (Navya3DSeg),
with a diverse label space corresponding to a large scale production grade
operational domain, including rural, urban, industrial sites and universities
from 13 countries. It contains 23 labeled sequences and 25 supplementary
sequences without labels, designed to explore self-supervised and
semi-supervised semantic segmentation benchmarks on point clouds. We also
propose a novel method for sequential dataset split generation based on
iterative multi-label stratification, and demonstrated to achieve a +1.2% mIoU
improvement over the original split proposed by SemanticKITTI dataset. A
complete benchmark for semantic segmentation task was performed, with state of
the art methods. Finally, we demonstrate an active learning (AL) based dataset
distillation framework. We introduce a novel heuristic-free sampling method
called distance sampling in the context of AL. A detailed presentation on the
dataset is available at https://www.youtube.com/watch?v=5m6ALIs-s20 .Comment: Submitted to RA-L. Version with supplementary material
Particle distribution and nuclear stopping in Au-Au collisions at =200 GeV
The transverse momentum distribution of produced charged particles is
investigated for gold-gold collisions at GeV. A simple
parameterization is suggested for the particle distribution based on the
nuclear stopping effect. The model can fit very well both the transverse
momentum distributions at different pseudo-rapidities and the pseudo-rapidity
distributions at different centralities. The ratio of rapidity distributions
for peripheral and central collisions is calculated and compared with the data.Comment: 5 pages in RevTeX, 3 eps figure
Harnessing Neuron Stability to Improve DNN Verification
Deep Neural Networks (DNN) have emerged as an effective approach to tackling
real-world problems. However, like human-written software, DNNs are susceptible
to bugs and attacks. This has generated significant interests in developing
effective and scalable DNN verification techniques and tools. In this paper, we
present VeriStable, a novel extension of recently proposed DPLL-based
constraint DNN verification approach. VeriStable leverages the insight that
while neuron behavior may be non-linear across the entire DNN input space, at
intermediate states computed during verification many neurons may be
constrained to have linear behavior - these neurons are stable. Efficiently
detecting stable neurons reduces combinatorial complexity without compromising
the precision of abstractions. Moreover, the structure of clauses arising in
DNN verification problems shares important characteristics with industrial SAT
benchmarks. We adapt and incorporate multi-threading and restart optimizations
targeting those characteristics to further optimize DPLL-based DNN
verification. We evaluate the effectiveness of VeriStable across a range of
challenging benchmarks including fully-connected feedforward networks (FNNs),
convolutional neural networks (CNNs) and residual networks (ResNets) applied to
the standard MNIST and CIFAR datasets. Preliminary results show that VeriStable
is competitive and outperforms state-of-the-art DNN verification tools,
including --CROWN and MN-BaB, the first and second performers of
the VNN-COMP, respectively.Comment: VeriStable and experimental data are available at:
https://github.com/veristable/veristabl
Brownian Motion in an N-scale periodic Potential
We study the problem of Brownian motion in a multiscale potential. The potential is assumed to have N+1 scales (i.e. N small scales and one macroscale) and to depend periodically on all the small scales. We show that for nonseparable potentials, i.e. potentials in which the microscales and the macroscale are fully coupled, the homogenized equation is an overdamped Langevin equation with multiplicative noise driven by the free energy, for which the detailed balance condition still holds. This means, in particular, that homogenized dynamics is reversible and that the coarse-grained Fokker–Planck equation is still a Wasserstein gradient flow with respect to the coarse-grained free energy. The calculation of the effective diffusion tensor requires the solution of a system of N coupled Poisson equations
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