Use of phytotherapics in dogs and cats.

Phytotherapy is one of the most utilized non conventional medicines (NCM) both in human and veterinary medicine. It can be used to mitigate and prevent slight diseases and to support conventional medicine using allopathic drugs. In this paper the Authors report the phytoterapeutics most utilized in both dogs and cats, in which the use of phytotherapics is increasing, despite the prejudices of the academic world and of the veterinary practitioners. Laws regarding the use of non conventional medicines in veterinary practises are lacking in Italy, despite many other countries in Europe; yet National Federation of Italian Veterinaries (F.N.O.V.I.) asserted that the use of NCM has to be considered a veterinary practise at all. At the end of this paper, the Authors provided many examples of phytotheapic prescriptions to control different illness in both dogs and cats

Optimal estimation for finite population parameters in two phase sampling.

In this paper we propose a general approh for estimating a finite population parameter in double sampling. When two dependent samples are drawn, several estimators were proposed to estimate the population me, ratio and variance. While there are few proposals in double sampling with independent samples. We treat both cases, i.e. dependent and independent samples, showing that all the proposed estimators can be obtained as particular cases of a unique general class. The minimum variance bound for any estimator in this class is provided (at the first order of approximation). Furthermore, a chain regression type estimator which reaches this minimum is found

Active Transfer Learning with Zero-Shot Priors: Reusing Past Datasets for Future Tasks

How can we reuse existing knowledge, in the form of available datasets, when solving a new and apparently unrelated target task from a set of unlabeled data? In this work we make a first contribution to answer this question in the context of image classification. We frame this quest as an active learning problem and use zero-shot classifiers to guide the learning process by linking the new task to the existing classifiers. By revisiting the dual formulation of adaptive SVM, we reveal two basic conditions to choose greedily only the most relevant samples to be annotated. On this basis we propose an effective active learning algorithm which learns the best possible target classification model with minimum human labeling effort. Extensive experiments on two challenging datasets show the value of our approach compared to the state-of-the-art active learning methodologies, as well as its potential to reuse past datasets with minimal effort for future tasks

Multi-objective optimization of RF circuit blocks via surrogate models and NBI and SPEA2 methods

Multi-objective optimization techniques can be categorized globally into deterministic and evolutionary methods. Examples of such methods are the Normal Boundary Intersection (NBI) method and the Strength Pareto Evolutionary Algorithm (SPEA2), respectively. With both methods one explores trade-offs between conflicting performances. Surrogate models can replace expensive circuit simulations so enabling faster computation of circuit performances. As surrogate models of behavioral parameters and performance outcomes, we consider look-up tables with interpolation and Neural Network models

Horvitz-Thompson Estimators in Center Sampling.

The problem of the estimation of the size of an immigrant population is very important in many countries as well as in Italy. Center sampling theory has been recently proposed and it is based on the hypothesis that all the individuals use to frequent centers of aggregation. Two approaches to center sampling are developed in literature. In the first people are drawn through simple random sampling without replacement from all the availables centers. In the second one centers are drawn through the same scheme and all the individuals in the sample centers are considered. In this paper the second approach is followed and generalized to a different sampling scheme according to the situation in which all centers has not the same importance: i.e. a big center could have a higher selection probability than a little center. So an unequal selection probablilities scheme is presented

High-J CO line emission from young stellar objects: from ISO to FIRST

we present the CO pure rotational spectrum at high J (Jup14) obtained with the Long Wavelength Spectrometer (LWS) on board of the ISO satellite towards molecular outflows exciting sources in nearby star formation regions. The physical conditions, derived using an LVG model for the line emission, indicate the presence of warm and dense gas, probably shock excited. The model fits show that often the bulk of this CO emission is expected in the spectral range that will be covered by FIRST, indicating the potentiality of this satellite to trace the warm component of gas emission in young stellar objects

ISO-LWS grating spectroscopy: the case of R CrA star forming region

We present the far infrared spectra of the R CrA star forming region obtained with ISO-LWS. We collected a pointed observation on the Herbig Ae star R CrA and a raster scan covering the surrounding region, where HH100 (with its exciting source) and the pre-Main Sequence star T CrA are located. The OI 63μm and the CII 158μm lines have been detected in all the pointed positions, with a ratio consistent with PDR excitation. CO rotational lines (between Jup=14 and Jup=19) are detected on R CrA; from their intensities we derived, using a LVG model, the density and temperature of the emitting region. Other molecular transitions (OH and H2O) have been detected on the investigated objects; the derived cooling of all the molecular species is in agreement with C-shock as the likely excitation mechanism. The continuum emission of R CrA peaks around 100μm (as expected for a Herbig star) while the other sources (T CrA, HH100) show increasing continua up to ~200μm, indicating that they are probably less evolved sources