143,249 research outputs found
TCP throughput guarantee in the DiffServ Assured Forwarding service: what about the results?
Since the proposition of Quality of Service architectures by the IETF, the
interaction between TCP and the QoS services has been intensively studied. This
paper proposes to look forward to the results obtained in terms of TCP
throughput guarantee in the DiffServ Assured Forwarding (DiffServ/AF) service
and to present an overview of the different proposals to solve the problem. It
has been demonstrated that the standardized IETF DiffServ conditioners such as
the token bucket color marker and the time sliding window color maker were not
good TCP traffic descriptors. Starting with this point, several propositions
have been made and most of them presents new marking schemes in order to
replace or improve the traditional token bucket color marker. The main problem
is that TCP congestion control is not designed to work with the AF service.
Indeed, both mechanisms are antagonists. TCP has the property to share in a
fair manner the bottleneck bandwidth between flows while DiffServ network
provides a level of service controllable and predictable. In this paper, we
build a classification of all the propositions made during these last years and
compare them. As a result, we will see that these conditioning schemes can be
separated in three sets of action level and that the conditioning at the
network edge level is the most accepted one. We conclude that the problem is
still unsolved and that TCP, conditioned or not conditioned, remains
inappropriate to the DiffServ/AF service
Final report on the evaluation of RRM/CRRM algorithms
Deliverable public del projecte EVERESTThis deliverable provides a definition and a complete evaluation of the RRM/CRRM algorithms selected in D11 and D15, and evolved and refined on an iterative process. The evaluation will be carried out by means of simulations using the simulators provided at D07, and D14.Preprin
A cross-layer approach to enhance QoS for multimedia applications over satellite
The need for on-demand QoS support for communications over satellite is of primary importance for distributed multimedia applications. This is particularly true for the return link which is often a bottleneck due to the large set of end-users accessing a very limited uplink resource. Facing this need, Demand Assignment Multiple Access (DAMA) is a classical technique that allows satellite operators to offer various types of services, while managing the resources of the satellite system efficiently. Tackling the quality degradation and delay accumulation issues that can result from the use of these techniques, this paper proposes an instantiation of the Application Layer Framing (ALF) approach, using a cross-layer interpreter(xQoS-Interpreter). The information provided by this interpreter is used to manage the resource provided to a terminal by the satellite system in order to improve the quality of multimedia presentations from the end users point of view. Several experiments are carried out for different loads on the return link. Their impact on QoS is measured through different application as well as network level metrics
Exogenous WNT5A and WNT11 proteins rescue CITED2 dysfunction in mouse embryonic stem cells and zebrafish morphants
Mutations and inadequate methylation profiles of CITED2 are associated with human congenital heart disease (CHD). In mouse, Cited2 is necessary for embryogenesis, particularly for heart development, and its depletion in embryonic stem cells (ESC) impairs cardiac differentiation. We have now determined that Cited2 depletion in ESC affects the expression of transcription factors and cardiopoietic genes involved in early mesoderm and cardiac specification. Interestingly, the supplementation of the secretome prepared from ESC overexpressing CITED2, during the onset of differentiation, rescued the cardiogenic defects of Cited2-depleted ESC. In addition, we demonstrate that the proteins WNT5A and WNT11 held the potential for rescue. We also validated the zebrafish as a model to investigate cited2 function during development. Indeed, the microinjection of morpholinos targeting cited2 transcripts caused developmental defects recapitulating those of mice knockout models, including the increased propensity for cardiac defects and severe death rate. Importantly, the co-injection of anti-cited2 morpholinos with either CITED2 or WNT5A and WNT11 recombinant proteins corrected the developmental defects of Cited2-morphants. This study argues that defects caused by the dysfunction of Cited2 at early stages of development, including heart anomalies, may be remediable by supplementation of exogenous molecules, offering the opportunity to develop novel therapeutic strategies aiming to prevent CHD.AgĂȘncia financiadora:
Fundação para a CiĂȘncia e a Tecnologia (FCT)
Comissão de Coordenação e Desenvolvimento Regional do Algarve (CCDR Algarve)
ALG-01-0145-FEDER-28044; DFG 568/17-2 Algarve Biomedical Center (ABC)
Municipio de Louléinfo:eu-repo/semantics/publishedVersio
Small steps and giant leaps: Minimal Newton solvers for Deep Learning
We propose a fast second-order method that can be used as a drop-in
replacement for current deep learning solvers. Compared to stochastic gradient
descent (SGD), it only requires two additional forward-mode automatic
differentiation operations per iteration, which has a computational cost
comparable to two standard forward passes and is easy to implement. Our method
addresses long-standing issues with current second-order solvers, which invert
an approximate Hessian matrix every iteration exactly or by conjugate-gradient
methods, a procedure that is both costly and sensitive to noise. Instead, we
propose to keep a single estimate of the gradient projected by the inverse
Hessian matrix, and update it once per iteration. This estimate has the same
size and is similar to the momentum variable that is commonly used in SGD. No
estimate of the Hessian is maintained. We first validate our method, called
CurveBall, on small problems with known closed-form solutions (noisy Rosenbrock
function and degenerate 2-layer linear networks), where current deep learning
solvers seem to struggle. We then train several large models on CIFAR and
ImageNet, including ResNet and VGG-f networks, where we demonstrate faster
convergence with no hyperparameter tuning. Code is available
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