Use of the far infrared spectroscopy for NaCl and KCl minerals characterization : a case study of halides from Kłodawa in Poland

The paper presents research on chloride minerals of natural origin from Kłodawa (Poland), i.e., colorless, blue and purple halite as well as colorless sylvite. Selected samples of minerals were studied by chemical analysis (ICP-OES, ICP-MS, titration methods) and crystallographic measurements. Then, for the tested halides, research was carried out using far-infrared spectroscopy. Spectroscopic studies confirmed the simple way of distinguishing NaCl and KCl minerals using far-infrared spectroscopy, known in the literature. The novelty is that the article presents for the first time the experimental far infrared spectra of natural blue and purple halite. It was observed that the blue (178 cm−1) and purple (176 cm−1) halites have the strongest infrared band slightly shifted towards higher wavenumbers compared to colorless halite (174 cm−1). As part of the work, the infrared spectra of the crystal structure models of sodium and potassium chloride were calculated for the first time using the density functional theory (with the B3LYP functional and the 6-31G* basis set, 125-atom model). The proposed approach can be used not only as a powerful method differentiating NaCl and KCl minerals, but it can also help with understanding of different defects in crystal lattices for naturally occurring halides and crystals of other minerals

Robustness Verification of Support Vector Machines

We study the problem of formally verifying the robustness to adversarial examples of support vector machines (SVMs), a major machine learning model for classification and regression tasks. Following a recent stream of works on formal robustness verification of (deep) neural networks, our approach relies on a sound abstract version of a given SVM classifier to be used for checking its robustness. This methodology is parametric on a given numerical abstraction of real values and, analogously to the case of neural networks, needs neither abstract least upper bounds nor widening operators on this abstraction. The standard interval domain provides a simple instantiation of our abstraction technique, which is enhanced with the domain of reduced affine forms, which is an efficient abstraction of the zonotope abstract domain. This robustness verification technique has been fully implemented and experimentally evaluated on SVMs based on linear and nonlinear (polynomial and radial basis function) kernels, which have been trained on the popular MNIST dataset of images and on the recent and more challenging Fashion-MNIST dataset. The experimental results of our prototype SVM robustness verifier appear to be encouraging: this automated verification is fast, scalable and shows significantly high percentages of provable robustness on the test set of MNIST, in particular compared to the analogous provable robustness of neural networks

The Effects of Eccentric Cadence on Power and Velocity of the Bar during the Concentric Phase of the Bench Press Movement

Training at a specific movement tempo is a relatively new concept in resistance training. It is based on manipulation of the duration of particular phases of a movement. General studies have demonstrated that faster movement tempo in resistance training leads to an increase in muscle power, whereas lower movement speed is beneficial in the development of muscle strength and hypertrophy. However, the studies in this area are inconclusive and do not relate precisely to various tempos and movement speeds. The aim of the study was to determine the effect of duration of the eccentric cadence ECCREG (2/0/X/0) and ECCSLO (6/0/X/0) on muscular power generated in the concentric phase of the movement expressed in maximal PMAX, VMAX and average values PAVE, VAVE. For the ECCSLO (6/0/X/0) cadence, a significantly lower value of P (401.95 ± 65.42 W) was observed compared to the ECCREG 2/0/X/0 tempo (467.65 ± 79.18 W), at p = 0.007. The same was true for power evaluated in maximal values (PMAX), as significantly higher values were recorded for the regular ECCREG (2/0/X/0) (671.55 ± 115.79 W) compared to the slow tempo ECCSLO (6/0/X/0) (565,70 ± 117,37 W), at the level of significance of p = 0.007. The velocity evaluated for ECCREG (2/0/X/0) tempo expressed in average values (VAVE) 0.60±0.09 m/s was significantly higher compared to the ECCSLO (6/0/X/0) tempo (0.52 ± 0.08 m/s), with p=0.004. When maximal velocity (VMAX), was considered higher values for ECCREG (2/0/X/0) tempo was registered (0.79 ± 0.10 m/s) compared to the ECCSLO (6/0/X/0) tempo (0.69 ± 0.13 m/s), at significance of p = 0.001. The main finding of the study indicates that the duration of the eccentric phase of the movement has a significant impact on muscular power and velocity during the concentric phase of the movement

Robustness Verification of Support Vector Machines

We study the problem of formally verifying the robustness to adversarial examples of support vector machines (SVMs), a major machine learning model for classification and regression tasks. Following a recent stream of works on formal robustness verification of (deep) neural networks, our approach relies on a sound abstract version of a given SVM classifier to be used for checking its robustness. This methodology is parametric on a given numerical abstraction of real values and, analogously to the case of neural networks, needs neither abstract least upper bounds nor widening operators on this abstraction. The standard interval domain provides a simple instantiation of our abstraction technique, which is enhanced with the domain of reduced affine forms, an efficient abstraction of the zonotope abstract domain. This robustness verification technique has been fully implemented and experimentally evaluated on SVMs based on linear and nonlinear (polynomial and radial basis function) kernels, which have been trained on the popular MNIST dataset of images and on the recent and more challenging Fashion-MNIST dataset. The experimental results of our prototype SVM robustness verifier appear to be encouraging: this automated verification is fast, scalable and shows significantly high percentages of provable robustness on the test set of MNIST, in particular compared to the analogous provable robustness of neural networks