A Semi-Supervised Self-Organizing Map for Clustering and Classification

There has been an increasing interest in semi-supervised learning in the recent years because of the great number of datasets with a large number of unlabeled data but only a few labeled samples. Semi-supervised learning algorithms can work with both types of data, combining them to obtain better performance for both clustering and classification. Also, these datasets commonly have a high number of dimensions. This article presents a new semi-supervised method based on self-organizing maps (SOMs) for clustering and classification, called Semi-Supervised Self-Organizing Map (SS-SOM). The method can dynamically switch between supervised and unsupervised learning during the training according to the availability of the class labels for each pattern. Our results show that the SS-SOM outperforms other semi-supervised methods in conditions in which there is a low amount of labeled samples, also achieving good results when all samples are labeled

A Semi-Supervised Self-Organizing Map with Adaptive Local Thresholds

In the recent years, there is a growing interest in semi-supervised learning, since, in many learning tasks, there is a plentiful supply of unlabeled data, but insufficient labeled ones. Hence, Semi-Supervised learning models can benefit from both types of data to improve the obtained performance. Also, it is important to develop methods that are easy to parameterize in a way that is robust to the different characteristics of the data at hand. This article presents a new method based on Self-Organizing Map (SOM) for clustering and classification, called Adaptive Local Thresholds Semi-Supervised Self-Organizing Map (ALTSS-SOM). It can dynamically switch between two forms of learning at training time, according to the availability of labels, as in previous models, and can automatically adjust itself to the local variance observed in each data cluster. The results show that the ALTSS-SOM surpass the performance of other semi-supervised methods in terms of classification, and other pure clustering methods when there are no labels available, being also less sensitive than previous methods to the parameters values

Bridging the Gap Between Offline and Online Reinforcement Learning Evaluation Methodologies

Reinforcement learning (RL) has shown great promise with algorithms learning in environments with large state and action spaces purely from scalar reward signals. A crucial challenge for current deep RL algorithms is that they require a tremendous amount of environment interactions for learning. This can be infeasible in situations where such interactions are expensive; such as in robotics. Offline RL algorithms try to address this issue by bootstrapping the learning process from existing logged data without needing to interact with the environment from the very beginning. While online RL algorithms are typically evaluated as a function of the number of environment interactions, there exists no single established protocol for evaluating offline RL methods.In this paper, we propose a sequential approach to evaluate offline RL algorithms as a function of the training set size and thus by their data efficiency. Sequential evaluation provides valuable insights into the data efficiency of the learning process and the robustness of algorithms to distribution changes in the dataset while also harmonizing the visualization of the offline and online learning phases. Our approach is generally applicable and easy to implement. We compare several existing offline RL algorithms using this approach and present insights from a variety of tasks and offline datasets.Comment: TMLR 202

MOEA/D with Uniformly Randomly Adaptive Weights

When working with decomposition-based algorithms, an appropriate set of weights might improve quality of the final solution. A set of uniformly distributed weights usually leads to well-distributed solutions on a Pareto front. However, there are two main difficulties with this approach. Firstly, it may fail depending on the problem geometry. Secondly, the population size becomes not flexible as the number of objectives increases. In this paper, we propose the MOEA/D with Uniformly Randomly Adaptive Weights (MOEA/DURAW) which uses the Uniformly Randomly method as an approach to subproblems generation, allowing a flexible population size even when working with many objective problems. During the evolutionary process, MOEA/D-URAW adds and removes subproblems as a function of the sparsity level of the population. Moreover, instead of requiring assumptions about the Pareto front shape, our method adapts its weights to the shape of the problem during the evolutionary process. Experimental results using WFG41-48 problem classes, with different Pareto front shapes, shows that the present method presents better or equal results in 77.5% of the problems evaluated from 2 to 6 objectives when compared with state-of-the-art methods in the literature

Adenine interaction with and adsorption on Fe-ZSM-5 zeolites: A prebiotic chemistry study using different techniques

Most adsorption experiments are performed under conditions that did not exist on Earth before the life arose on it. Because adsorption is the first step for all other processes (protection against degradation and polymerization), it is important that it is performed under conditions that existed on prebiotic Earth. In this paper, we use an artificial seawater (seawater 4.0 Ga), which contains major cations and anions that could present on the oceans of the prebiotic Earth. In addition, zeolites, with substituted Fe in the framework, and adenine were probably common substances on the prebiotic Earth. Thus, study the interaction between them is an important issue in prebiotic chemistry. There are two main findings described in this paper. Firstly, zeolites with different Si/Fe ratios adsorbed adenine differently. Secondly, XAFS showed that, after treatments with seawater 4.0 Ga and adenine, an increase in the complexity of the system occurred. In general, salts of seawater 4.0 Ga did not affect the adsorption of adenine onto zeolites and adenine adsorbed less onto zeolites with iron isomorphically substituted. The C=C and NH2 groups of adenine interacted with the zeolites. Gypsum, formed from aqueous species dissolved in seawater 4.0 Ga, precipitated onto zeolites. EPR spectra of zeolites showed lines caused by Fe framework and Fe3+ species. TG curves of zeolites showed events caused by loss of water weakly bound to zeolite (in the 30-140 °C range), water bounded to iron species or cations from seawater 4.0 Ga or located in the cavities of zeolites (157-268 °C) and degradation of adenine adsorbed onto zeolites (360-600 °C). Mass loss follows almost the same order as the amount of adenine adsorbed onto zeolites. The XAFS spectrum showed that Fe3+ could be substituted into the framework of the Fe7-ZSM-5 zeolite

The Newly Synthesized Pyrazole Derivative 5-(1-(3 Fluorophenyl)-1H-Pyrazol-4-yl)-2H-Tetrazole Reduces Blood Pressure of Spontaneously Hypertensive Rats via NO/cGMO Pathway

The search for new antihypertensive drugs has grown in recent years because of high rate of morbidity among hypertensive patients and several side effects that are associated with the first-line medications. The current study sought to investigate the antihypertensive effect of a newly synthesized pyrazole derivative known as 5-(1-(3 fluorophenyl)-1H-pyrazol-4-yl)-2H-tetrazole (LQFM-21). Spontaneously hypertensive rats (SHR) were used to evaluate the effect of LQFM-21 on mean arterial pressure (MAP), heart rate (HR), renal vascular conductance (RVC), arterial vascular conductance (AVC), baroreflex sensitivity (BRS) index, and vascular reactivity. Acute intravenous (iv) administration of LQFM-21 (0.05, 0.1, 0.2, and 0.4 mg kg-1) reduced MAP and HR, and increased RVC and AVC. Chronic oral administration of LQFM-21 (15 mg kg-1) for 15 days reduced MAP without altering BRS. The blockade of muscarinic receptors and nitric oxide synthase by intravenous infusion of atropine and L-NAME, respectively, attenuated cardiovascular effects of LQFM-21. In addition, ex vivo experiments showed that LQFM-21 induced an endothelium-dependent relaxation in isolated aortic rings from SHR. This effect was blocked by guanylyl cyclase inhibitor (ODQ) and L-NAME. These findings suggest the involvement of muscarinic receptor and NO/cGMP pathway in the antihypertensive and vasodilator effects of LQFM-21