A Proposal for Semantic Map Representation and Evaluation

Semantic mapping is the incremental process of “mapping” relevant information of the world (i.e., spatial information, temporal events, agents and actions) to a formal description supported by a reasoning engine. Current research focuses on learning the semantic of environments based on their spatial location, geometry and appearance. Many methods to tackle this problem have been proposed, but the lack of a uniform representation, as well as standard benchmarking suites, prevents their direct comparison. In this paper, we propose a standardization in the representation of semantic maps, by defining an easily extensible formalism to be used on top of metric maps of the environments. Based on this, we describe the procedure to build a dataset (based on real sensor data) for benchmarking semantic mapping techniques, also hypothesizing some possible evaluation metrics. Nevertheless, by providing a tool for the construction of a semantic map ground truth, we aim at the contribution of the scientific community in acquiring data for populating the dataset

Standard Flaws for Eddy Current Probe Characterization

Calibration procedures for eddy current inspections often involve the use of artifact standards containing manufactured flaws. The manufactured flaw is assumed to be a good approximation of the type of flaw being sought during the inspection. These manufactured flaws are most often produced by electrical discharge machining (EDM), milling, or the controlled growth of fatigue cracks. With simple amplitude display inspection equipment this type of artifact is usually sufficient, but as more sophisticated inspection equipment is developed some drawbacks to the commonly accepted practice are becoming evident. Instruments that are sensitive to eddy current signal phase as well as amplitude can show considerable differences in phase between a relatively wide EDM notch or milled slot and a real fatigue crack [1]. The use of controlled growth fatigue cracks can also cause problems when forces at the crack’s tip drive the crack faces together, making electrical contact [2], In addition, estimates of crack depth will always be estimates until the crack is broken apart. We describe here a technique for consistently producing well characterized discontinuities in aluminum which are not subject to these problems

Break-junction tunneling measurements of the high-\u3ci\u3eT\u3c/i\u3e\u3csub\u3e\u3ci\u3ec\u3c/i\u3e\u3c/sub\u3e superconductor Y\u3csub\u3e1\u3c/sub\u3eBa\u3csub\u3e2\u3c/sub\u3eCu\u3csub\u3e3\u3c/sub\u3eO\u3csub\u3e9- δ \u3c/sub\u3e

Current-voltage tunneling characteristics in a high-critical-temperature superconducting material containing predominately Y1Ba2Cu3O9- δ have been measured using the break-junction technique. Sharp gap structure was observed, with the largest superconductive energy gap measured to be Δ=19.5±1 meV, assuming a superconductor-insulator-superconductor junction. This energy gap corresponds to 2Δ/kBTc=4.8 at T=4 K, for a critical temperature of 93 K (midpoint of the resistive transition)

Radial Velocity Studies of Close Binary Stars.VIII

Radial-velocity measurements and sine-curve fits to the orbital velocity variations are presented for the seventh set of ten close binary systems: V410 Aur, V523 Cas, QW Gem, V921 Her, V2357 Oph, V1130 Tau, HN UMa, HX UMa, HD 93917, NSV 223. All systems, but three (V523 Cas, HD 93917, NSV 223), were discovered photometrically by the Hipparcos mission. All systems are double-lined (SB2) binaries and all, but the detached, very close system V1130 Tau, are contact binaries. The broadening-function permitted improvement of the orbital elements for V523 Cas, which was the only system observed before for radial velocity variations. Spectroscopic/visual companions were detected for V410 Aur and HX UMa.Comment: AASTeX5, 4 figures, 3 tables, to appear AJ, June 200

Agent-Based Markov Modeling for Improved COVID-19 Mitigation Policies

The year 2020 saw the covid-19 virus lead to one of the worst global pandemics in history. As a result, governments around the world have been faced with the challenge of protecting public health while keeping the economy running to the greatest extent possible. Epidemiological models provide insight into the spread of these types of diseases and predict the e_ects of possible intervention policies. However, to date, even the most data-driven intervention policies rely on heuristics. In this paper, we study how reinforcement learning (RL) and Bayesian inference can be used to optimize mitigation policies that minimize economic impact without overwhelming hospital capacity. Our main contributions are (1) a novel agent-based pandemic simulator which, unlike traditional models, is able to model _ne-grained interactions among people at speci_c locations in a community; (2) an RL- based methodology for optimizing _ne-grained mitigation policies within this simulator; and (3) a Hidden Markov Model for predicting infected individuals based on partial observations regarding test results, presence of symptoms, and past physical contacts

Probiotics improve the neurometabolic profile of rats with chronic cholestatic liver disease.

Chronic liver disease leads to neuropsychiatric complications called hepatic encephalopathy (HE). Current treatments have some limitations in their efficacy and tolerability, emphasizing the need for alternative therapies. Modulation of gut bacterial flora using probiotics is emerging as a therapeutic alternative. However, knowledge about how probiotics influence brain metabolite changes during HE is missing. In the present study, we combined the advantages of ultra-high field in vivo <sup>1</sup> H MRS with behavioural tests to analyse whether a long-term treatment with a multistrain probiotic mixture (VIVOMIXX) in a rat model of type C HE had a positive effect on behaviour and neurometabolic changes. We showed that the prophylactic administration of this probiotic formulation led to an increase in gut Bifidobacteria and attenuated changes in locomotor activity and neurometabolic profile in a rat model of type C HE. Both the performance in behavioural tests and the neurometabolic profile of BDL + probiotic rats were improved compared to the BDL group at week 8 post-BDL. They displayed a significantly lesser increase in brain Gln, a milder decrease in brain mIns and a smaller decrease in neurotransmitter Glu than untreated animals. The clinical implications of these findings are potentially far-reaching given that probiotics are generally safe and well-tolerated by patients

Luminosity- and morphology-dependent clustering of galaxies

How does the clustering of galaxies depend on their inner properties like morphological type and luminosity? We address this question in the mathematical framework of marked point processes and clarify the notion of luminosity and morphological segregation. A number of test quantities such as conditional mark-weighted two-point correlation functions are introduced. These descriptors allow for a scale-dependent analysis of luminosity and morphology segregation. Moreover, they break the degeneracy between an inhomogeneous fractal point set and actual present luminosity segregation. Using the Southern Sky Redshift Survey~2 (da Costa et al. 1998, SSRS2) we find both luminosity and morphological segregation at a high level of significance, confirming claims by previous works using these data (Benoist et al. 1996, Willmer et al. 1998). Specifically, the average luminosity and the fluctuations in the luminosity of pairs of galaxies are enhanced out to separations of 15Mpc/h. On scales smaller than 3Mpc/h the luminosities on galaxy pairs show a tight correlation. A comparison with the random-field model indicates that galaxy luminosities depend on the spatial distribution and galaxy-galaxy interactions. Early-type galaxies are also more strongly correlated, indicating morphological segregation. The galaxies in the PSCz catalog (Saunders et al. 2000) do not show significant luminosity segregation. This again illustrates that mainly early-type galaxies contribute to luminosity segregation. However, based on several independent investigations we show that the observed luminosity segregation can not be explained by the morphology-density relation alone.Comment: aastex, emulateapj5, 20 pages, 13 figures, several clarifying comments added, ApJ accepte

Spatial modeling for low pathogenicity avian influenza virus at the interface of wild birds and backyard poultry

Low pathogenicity avian influenza virus (LPAIV) is endemic in wild birds and poultry in Argentina, and active surveillance has been in place to prevent any eventual virus mutation into a highly pathogenic avian influenza virus (HPAIV), which is exotic in this country. Risk mapping can contribute effectively to disease surveillance and control systems, but it has proven a very challenging task in the absence of disease data. We used a combination of expert opinion elicitation, multicriteria decision analysis (MCDA), and ecological niche modeling (ENM) to identify the most suitable areas for the occurrence of LPAIV at the interface between backyard domestic poultry and wild birds in Argentina. This was achieved by calculating a spatially‐explicit risk index. As evidenced by the validation and sensitivity analyses, our model was successful in identifying high‐risk areas for LPAIV occurrence. Also, we show that the risk for virus occurrence is significantly higher in areas closer to commercial poultry farms. Although the active surveillance systems have been successful in detecting LPAIV‐positive backyard farms and wild birds in Argentina, our predictions suggest that surveillance efforts in those compartments could be improved by including high‐risk areas identified by our model. Our research provides a tool to guide surveillance activities in the future, and presents a mixed methodological approach which could be implemented in areas where the disease is exotic or rare and a knowledge‐driven modeling method is necessary