Unstable and elusive superconductors

We briefly review earlier and report original experimental results in the context of metastable or possible superconducting materials. We show that applied electric field induces conducting state in Copper Chloride (CuCl) whose characteristics resemble behavior of sliding charge-density-wave(s) (CDW). We discuss whether the sliding CDW or collective transport of similar ordered charge phase(s) may account for the problem of "high-temperature superconductivity" observed in this and other materials, including Cadmium Sulfide (CdS), metal-ammonia solutions, polymers, amorphous carbon and tungsten oxides. We also discuss a local superconductivity that occurs at the surface of graphite and amorphous carbon under deposition of foreign atoms/molecules.Comment: Invited review article published in a special edition on Superconducting Materials in honor of the 95th birthday year of Ted Geballe, edited by M. B. Maple, J. Hirsch, and F. Marsigli

The Electromagnetic Form Factor of the Kaon in the Light-Front Approach

The kaon electromagnetic form factor is calculated within a light-front constituent quark model (LFCQM). The electromagnetic components of the current are extracted from the Feynman triangle diagram within the light-front approach. We also obtain the electroweak decay constant and the charge radius for the kaon in the light-front approach. In this work, the kaon observables are calculated and a fairly good agreement is obtained with a very higher accuracy when compared with the experimental data.Comment: Paper with 4 pages, 1 figure, reference: XII HADRON PHYSICS Conference - to appear in AIP Conference Proceeding

Quantifying the Impact of Non-Stationarity in Reinforcement Learning-Based Traffic Signal Control

In reinforcement learning (RL), dealing with non-stationarity is a challenging issue. However, some domains such as traffic optimization are inherently non-stationary. Causes for and effects of this are manifold. In particular, when dealing with traffic signal controls, addressing non-stationarity is key since traffic conditions change over time and as a function of traffic control decisions taken in other parts of a network. In this paper we analyze the effects that different sources of non-stationarity have in a network of traffic signals, in which each signal is modeled as a learning agent. More precisely, we study both the effects of changing the \textit{context} in which an agent learns (e.g., a change in flow rates experienced by it), as well as the effects of reducing agent observability of the true environment state. Partial observability may cause distinct states (in which distinct actions are optimal) to be seen as the same by the traffic signal agents. This, in turn, may lead to sub-optimal performance. We show that the lack of suitable sensors to provide a representative observation of the real state seems to affect the performance more drastically than the changes to the underlying traffic patterns.Comment: 13 page

Electromagnetic Structure of the Pion

In this work, we analyze the electromagnetic structure of the pion. We calculate its electromagnetic radius and electromagnetic form factor in low and intermediate momentum range. Such observables are determined by means of a theoretical model that takes into account the constituent quark and antiquark of the pion within the formalism of light-front field theory. In particular, we consider a nonsymmetrical vertex in this model, with which we calculate the electromagnetic form factor of the pion in an optimized way, so that we obtain a value closer to the experimental charge radius of the pion. The theoretical calculations are also compared with the most recent experimental data involving the pion electromagnetic form factor and the results show very good agreement.Comment: Paper with 4 pages, 1 figure, presented in XII HADRON PHYSICS Conference - to appear in AIP Conference Proceeding

Sample-Efficient Multi-Objective Learning via Generalized Policy Improvement Prioritization

Multi-objective reinforcement learning (MORL) algorithms tackle sequential decision problems where agents may have different preferences over (possibly conflicting) reward functions. Such algorithms often learn a set of policies (each optimized for a particular agent preference) that can later be used to solve problems with novel preferences. We introduce a novel algorithm that uses Generalized Policy Improvement (GPI) to define principled, formally-derived prioritization schemes that improve sample-efficient learning. They implement active-learning strategies by which the agent can (i) identify the most promising preferences/objectives to train on at each moment, to more rapidly solve a given MORL problem; and (ii) identify which previous experiences are most relevant when learning a policy for a particular agent preference, via a novel Dyna-style MORL method. We prove our algorithm is guaranteed to always converge to an optimal solution in a finite number of steps, or an $\epsilon$-optimal solution (for a bounded $\epsilon$) if the agent is limited and can only identify possibly sub-optimal policies. We also prove that our method monotonically improves the quality of its partial solutions while learning. Finally, we introduce a bound that characterizes the maximum utility loss (with respect to the optimal solution) incurred by the partial solutions computed by our method throughout learning. We empirically show that our method outperforms state-of-the-art MORL algorithms in challenging multi-objective tasks, both with discrete and continuous state and action spaces.Comment: Accepted to AAMAS 202

Ship Routing with Pickup and Delivery for a Maritime Oil Transportation System: MIP Modeland Heuristics

This paper examines a ship routing problem with pickup and delivery and time windows for maritime oil transportation, motivated by the production and logistics activities of an oil company operating in the Brazilian coast. The transportation costs from offshore platforms to coastal terminals are an important issue in the search for operational excellence in the oil industry, involving operations that demand agile and effective decision support systems. This paper presents an optimization approach to address this problem, based on a mixed integer programming (MIP) model and a novel and exploratory application of two tailor-made MIP heuristics, based on relax-and-fix and time decomposition procedures. The model minimizes fuel costs of a heterogeneous fleet of oil tankers and costs related to freighting contracts. The model also considers company-specific constraints for offshore oil transportation. Computational experiments based on the mathematical models and the related MIP heuristics are presented for a set of real data provided by the company, which confirm the potential of optimization-based methods to find good solutions for problems of moderate sizes

Phosphorus efficiency in brazilian soybean cultivars.

The primary goal of this study was to evaluate the phosphorus uptake and use efficiency in Brazilian soybean cultivars, besides root morphology and architecture characteristics related to phosphorus uptake, carrying out two greenhouse experiments. The experiment 1 was completely randomised, with 56 treatments (soybean cultivars) and 3 replicates. Experiment 2 was completely randomised design with three replicates, and the ten cultivars (greater and lower efficiency) were selected for this assay according to their ability to absorb phosphorus less available determined in experiment 1. The study was carried out at Center of Nuclear Energy in Agriculture, University of São Paulo, Brazil, between February 2011 and August 2012. The isotopic dilution technique was used in the first experiment to assess the phosphorus availability in the soil and to determine the ability of plants to access labile phosphorus by measuring the specific activity of plants grown in soil labeled with radioactive phosphorus. Nine cultivars showed greater phosphorus uptake and used efficiency. The second experiment evaluated the root morphology and architecture. The cultivars with greater uptake efficiency have root morphology and architecture characteristics that favour acquisition of phosphorus from soil compartments that are inaccessible to other cultivars. Phosphorus uptake by plants was not affected by soybean seeds phosphorus content. Identification of these cultivars is very important because it could enable soybean farming in low fertility soils, reducing fertiliser dependence

A task-and-technique centered survey on visual analytics for deep learning model engineering

Although deep neural networks have achieved state-of-the-art performance in several artificial intelligence applications in the past decade, they are still hard to understand. In particular, the features learned by deep networks when determining whether a given input belongs to a specific class are only implicitly described concerning a considerable number of internal model parameters. This makes it harder to construct interpretable hypotheses of what the network is learning and how it is learning both of which are essential when designing and improving a deep model to tackle a particular learning task. This challenge can be addressed by the use of visualization tools that allow machine learning experts to explore which components of a network are learning useful features for a pattern recognition task, and also to identify characteristics of the network that can be changed to improve its performance. We present a review of modern approaches aiming to use visual analytics and information visualization techniques to understand, interpret, and fine-tune deep learning models. For this, we propose a taxonomy of such approaches based on whether they provide tools for visualizing a network's architecture, to facilitate the interpretation and analysis of the training process, or to allow for feature understanding. Next, we detail how these approaches tackle the tasks above for three common deep architectures: deep feedforward networks, convolutional neural networks, and recurrent neural networks. Additionally, we discuss the challenges faced by each network architecture and outline promising topics for future research in visualization techniques for deep learning models. (C) 2018 Elsevier Ltd. All rights reserved.</p