Optimization flow control with Newton-like algorithm

We proposed earlier an optimization approach to reactive flow control where the objective of the control is to maximize the aggregate utility of all sources over their transmission rates. The control mechanism is derived as a gradient projection algorithm to solve the dual problem. In this paper we extend the algorithm to a scaled gradient projection. The diagonal scaling matrix approximates the diagonal terms of the Hessian and can be computed at individual links using the same information required by the unscaled algorithm. We prove the convergence of the scaled algorithm and present simulation results that illustrate its superiority to the unscaled algorithm

Simulation comparison of RED and REM

We propose earlier an optimization based low control for the Internet called Random Exponential Marking (REM). REM consists of a link algorithm, that probabilistically marks packets inside the network, and a source algorithm, that adapts source rate to observed marking. The marking probability is exponential in a link congestion measure, so that the end-to-end marking probability is exponential in a path congestion measure. Because of the finer measure of congestion provided by REM, sources do not constantly probe the network for spare capacity, but settle around a globally optimal equilibrium, thus avoiding the perpetual cycle of sinking into and recovering from congestion. In this paper we compare the performance of REM with Reno over RED through simulation

An enhanced random early marking algorithm for Internet flow control

We propose earlier an optimization based flow control for the Internet called Random Early Marking (REM). In this paper we propose and evaluate an enhancement that attempts to speed up the convergence of REM in the face of large feedback delays. REM can be regarded as an implementation of an optimization algorithm in a distributed network. The basic idea is to treat the optimization algorithm as a discrete time system and apply linear control techniques to stabilize its transient. We show that the modified algorithm is stable globally and converges exponentially locally. This algorithm translates into an enhanced REM scheme and we illustrate the performance improvement through simulation

Prolonged N-acetylcysteine therapy in late acetaminophen poisoning associated with acute liver failure – a need to be more cautious?

Since the 1970s, N-acetylcysteine (NAC) has shown proven efficacy as an antidote for acetaminophen (APAP) poisoning and APAP-induced liver failure for early presenters. The current evidence of benefits of NAC for late presenters is controversial because of the poor understanding of the mechanism of late toxicity. In the previous issue of Critical Care, Yang and colleagues use a mouse model to demonstrate that NAC in doses similar to those used therapeutically to treat APAP poisoning in humans impairs liver regenerative capacity and that the effect is more pronounced when administered for a longer duration. Studies based on cell cultures support this evidence. Cytokine and growth factor signalling pathways are recognised to be involved in the process of liver regeneration and apoptosis. This research paper generates several issues related to the future management of APAP-induced liver failure and research into the mechanism of toxicity, especially of late toxicity

Multi-document Summarization: A Comparative Evaluation

This paper is aimed at evaluating state-of-the-art models for Multi-document Summarization (MDS) on different types of datasets in various domains and investigating the limitations of existing models to determine future research directions. To address this gap, we conducted an extensive literature review to identify state-of-the-art models and datasets. We analyzed the performance of PRIMERA and PEGASUS models on BigSurvey-MDS and MS$^2$ datasets, which posed unique challenges due to their varied domains. Our findings show that the General-Purpose Pre-trained Model LED outperforms PRIMERA and PEGASUS on the MS$^2$ dataset. We used the ROUGE score as a performance metric to evaluate the identified models on different datasets. Our study provides valuable insights into the models' strengths and weaknesses, as well as their applicability in different domains. This work serves as a reference for future MDS research and contributes to the development of accurate and robust models which can be utilized on demanding datasets with academically and/or scientifically complex data as well as generalized, relatively simple datasets

An empirical validation of a duality model of TCP and queue management algorithms

In this paper we validate through simulations a duality model of TCP and active queue management (AQM) proposed earlier. In this model, TCP and AQM are modeled as carrying out a distributed primal-dual algorithm over the Internet to maximize aggregate source utility. TCP congestion avoidance algorithms, such as Reno and Vegas, iterate on source rates, the primal variable. AQM algorithms, such as RED and REM, iterate on marking probability, the dual variable

Annex 17 : deep semantic segmentation for built-up area extraction and mapping from satellite imagery

Research focuses on generating more usable built-up area maps, as traditional methods (such as surveys and census) are infrequent and costly. The work proposes a modified Fully Convolutional Network (FCN) architecture that will improve semantic segmentation operation on satellite imagery for built-up area extraction and urban mapping. This method could bridge the gap between existing extraction techniques and actual land cover/built-up area maps used by practitioners. Applications are potentially to socio-economic classification and urban planning, where building density functions as a proxy measure for socio-economic level, and building distribution for urban area estimates and growth, respectively