Dif-MAML: Decentralized Multi-Agent Meta-Learning

The objective of meta-learning is to exploit the knowledge obtained from observed tasks to improve adaptation to unseen tasks. As such, meta-learners are able to generalize better when they are trained with a larger number of observed tasks and with a larger amount of data per task. Given the amount of resources that are needed, it is generally difficult to expect the tasks, their respective data, and the necessary computational capacity to be available at a single central location. It is more natural to encounter situations where these resources are spread across several agents connected by some graph topology. The formalism of meta-learning is actually well-suited to this decentralized setting, where the learner would be able to benefit from information and computational power spread across the agents. Motivated by this observation, in this work, we propose a cooperative fully-decentralized multi-agent meta-learning algorithm, referred to as Diffusion-based MAML or Dif-MAML. Decentralized optimization algorithms are superior to centralized implementations in terms of scalability, avoidance of communication bottlenecks, and privacy guarantees. The work provides a detailed theoretical analysis to show that the proposed strategy allows a collection of agents to attain agreement at a linear rate and to converge to a stationary point of the aggregate MAML objective even in non-convex environments. Simulation results illustrate the theoretical findings and the superior performance relative to the traditional non-cooperative setting

Discovering Influencers in Opinion Formation over Social Graphs

The adaptive social learning paradigm helps model how networked agents are able to form opinions on a state of nature and track its drifts in a changing environment. In this framework, the agents repeatedly update their beliefs based on private observations and exchange the beliefs with their neighbors. In this work, it is shown how the sequence of publicly exchanged beliefs over time allows users to discover rich information about the underlying network topology and about the flow of information over graph. In particular, it is shown that it is possible (i) to identify the influence of each individual agent to the objective of truth learning, (ii) to discover how well informed each agent is, (iii) to quantify the pairwise influences between agents, and (iv) to learn the underlying network topology. The algorithm derived herein is also able to work under non-stationary environments where either the true state of nature or the network topology are allowed to drift over time. We apply the proposed algorithm to different subnetworks of Twitter users, and identify the most influential and central agents merely by using their public tweets (posts)

Use of a Global Positioning System (GPS) to Manage Extensive Sheep Farming and Pasture Land

The terrestrial climate is not sufficient to produce enough food to meet the roughage needs of the animals benefiting from the pasture lands because of excessive and early grazing of those areas. Plant growth is adversely affected in pastures that are not uniformly grazed. Tracking animals using the Global Positioning System (GPS) is a very important factor in determining the uniform distribution of grazing animals in a pasture, increasing the utilization rate of the pasture, and saving costs and time. With GPS tracking systems, establishing more effective pasture-use systems by monitoring the feeding regimes of small animals, the status of feed in the pasture, and the grazing behavior of the animals would be possible. The present study aimed to investigate the use of GPS for pasture and herd management in Turkey in addition to using the traditional techniques.In the present study conducted in the village of Köseyusuflu in Yozgat Province in May 2017, 2018, and 2019, grazing benefits that were determined from the pasture containing two Akkaraman sheep herds were recorded using GPS tracking devices. The results suggested that the area covered with vegetation along the sheep’s spring grazing routes varied between 43.6 and 62.9%, the ratio of legumes in the pasture grass in the low grazing areas was between 0.50 and 4.10%, and the grass species were between 12.75 and 44.50%. We determined that the sheep in herd A traveled between 7.6 and 9.9 km, while the sheep in herd B traveled between 4.7 and 5.7 km daily, and the two herds grazed an average of between 122 and 254 daa

Factors influencing chloride deposition in a coastal hilly area and application to chloride deposition mapping

Chloride is commonly used as an environmental tracer for studying water flow and solute transport in the environment. It is especially useful for estimating groundwater recharge based on the commonly used chloride mass balance (CMB) method. Strong spatial variability in chloride deposition in coastal areas is one difficulty encountered in appropriately applying the method. A high-resolution bulk chloride deposition map in the coastal region is thus needed. The aim of this study is to construct a chloride deposition map in the Mount Lofty Ranges (MLR), a coastal hilly area of approximately 9000 km<sup>2</sup> spatial extent in South Australia. We examined geographic (related to coastal distance), orographic, and atmospheric factors that may influence chloride deposition, using partial correlation and regression analyses. The results indicate that coastal distance, elevation, as well as terrain aspect and slope, appear to be significant factors controlling chloride deposition in the study area. Coastal distance accounts for 70% of spatial variability in bulk chloride deposition, with elevation, terrain aspect and slope an additional 15%. The results are incorporated into a de-trended residual kriging model (ASOADeK) to produce a 1 km×1 km resolution bulk chloride deposition and concentration maps. The average uncertainty of the deposition map is about 20–30% in the western MLR, and 40–50% in the eastern MLR. The maps will form a useful basis for examining catchment chloride balance for the CMB application in the study area

Rheological Characteristics of Municipal Thickened Excess Activated Sludge (TEAS): Impacts of pH, Temperature, Solid Concentration and Polymer Dose

Rheological characterization of sludge is known to be an essential tool to optimize flow, mixing and other process parameters in wastewater treatment plants. This study deals with the characterization of thickened excess activated sludge in comparison to raw primary sludge and excess activated sludge. The effects of key parameters (total solid concentration, temperature, and pH) on the rheology and flow behavior of thickened excess activated sludge were studied. The rheological investigations were carried out for total solid concentration range of 0.9–3.7 %w/w, temperature range of 23–55 °C, and pH range of 3.6–10.0. Different rheological model equations were fitted to the experimental data. The model equations with better fitting were used to calculate the yield stress, apparent, zero-rate, infinite-rate viscosities, flow consistency index, and flow index. The decrease in concentration from 3.7 to 3.1 %w/w resulted in a drastic reduction of yield stress from 27.6 to 11.0 Pa, while a further reduction of yield stress to 1.3 Pa was observed as solid concentration was reduced to 1.3 %w/w. The viscosity at higher shear rate (>600 s−1) decreased from 0.05 Pa·s down to 0.008 Pa·s when the total solid concentration was reduced from 3.7 to 0.9 %. Yield stress decreased from 20.1 Pa down to 8.3 Pa for the Bingham plastic model when the temperature was raised from 25 to 55 °C. Activation energy and viscosity also showed decreasing trends with increasing temperature. Yield stress of thickened excess activated sludge increased from a value of 6.0 Pa to 8.3 Pa when the pH was increased from 3.6 to 10.0. The effect of polymer dose on the rheological behavior of the thickening of excess activated sludge was also investigated, and the optimum polymer dosage for enhanced thickener performance was determined to be 1.3 kg/ton DS

Phenylketonuria in Portugal: Genotype-Phenotype Correlations Using Molecular, Biochemical, and Haplotypic Analyses

The impairment of the hepatic enzyme phenylalanine hydroxylase (PAH) causes elevation of phenylalanine levels in blood and other body fluids resulting in the most common inborn error of amino acid metabolism (phenylketonuria). Persistently high levels of phenylalanine lead to irreversible damage to the nervous system. Therefore, early diagnosis of the affected individuals is important, as it can prevent clinical manifestations of the disease.info:eu-repo/semantics/publishedVersio

Imaging in assessing hepatic and peritoneal metastases of gastric cancer: a systematic review

<p>Abstract</p> <p>Background</p> <p>Hepatic and peritoneal metastases of gastric cancer are operation contraindications. Systematic review to provide an overview of imaging in predicting the status of liver and peritoneum pre-therapeuticly is essential.</p> <p>Methods</p> <p>A systematic review of relevant literatures was performed in Pubmed/Medline, Embase, The Cochrane Library and the China Biological Medicine Databases. QUADAS was used for assessing the methodological quality of included studies and the bivariate model was used for this meta-analysis.</p> <p>Results</p> <p>Totally 33 studies were included (8 US studies, 5 EUS studies, 22 CT studies, 2 MRI studies and 5 18F-FDG PET studies) and the methodological quality of included studies was moderate. The result of meta-analysis showed that CT is the most sensitive imaging method [0.74 (95% CI: 0.59-0.85)] with a high rate of specificity [0.99 (95% CI: 0.97-1.00)] in detecting hepatic metastasis, and EUS is the most sensitive imaging modality [0.34 (95% CI: 0.10-0.69) ] with a specificity of 0.96 (95% CI: 0.87-0.99) in detecting peritoneal metastasis. Only two eligible MRI studies were identified and the data were not combined. The two studies found that MRI had both high sensitivity and specificity in detecting liver metastasis.</p> <p>Conclusion</p> <p>US, EUS, CT and ¹⁸F-FDG PET did not obtain consistently high sensitivity and specificity in assessing liver and peritoneal metastases of gastric cancer. The value of laparoscopy, PET/CT, DW-MRI, and new PET tracers such as ¹⁸F-FLT needs to be studied in future.</p