Fitting Jump Models

We describe a new framework for fitting jump models to a sequence of data. The key idea is to alternate between minimizing a loss function to fit multiple model parameters, and minimizing a discrete loss function to determine which set of model parameters is active at each data point. The framework is quite general and encompasses popular classes of models, such as hidden Markov models and piecewise affine models. The shape of the chosen loss functions to minimize determine the shape of the resulting jump model.Comment: Accepted for publication in Automatic

Fixed-order FIR approximation of linear systems from quantized input and output data

Abstract The problem of identifying a fixed-order FIR approximation of linear systems with unknown structure, assuming that both input and output measurements are subjected to quantization, is dealt with in this paper. A fixed-order FIR model providing the best approximation of the input-output relationship is sought by minimizing the worst-case distance between the output of the true system and the modeled output, for all possible values of the input and output data consistent with their quantized measurements. The considered problem is firstly formulated in terms of robust optimization. Then, two different algorithms to compute the optimum of the formulated problem by means of linear programming techniques are presented. The effectiveness of the proposed approach is illustrated by means of a simulation example

Differentiating present-day from ancient bones by vibrational spectroscopy upon acetic acid treatment

Acetic acid treatment for an accurate differentiation between ancient and recent human bones was assessed using Raman and FTIR-ATR spectroscopies. Each set of skeletal samples was analysed by these techniques, prior and after chemical washing, in order to determine the variations in bone´s chemical composition and crystallinity. Bone samples were collected from several independent sources: recent bones burned under controlled experimental conditions or cremated, and archaeological (XVII century and Iron Age). The effect of acetic acid, expected to impact mostly on carbonates, was clearly evidenced in the spectra of all samples, particularly in FTIR-ATR, mainly through the bands typical of A- and B-carbonates. Furthermore, as seen for crematoria and archaeological samples, acetic acid was found to remove contaminants such as calcium hydroxide. Overall, acetic acid treatment can be an effective method for removing carbonates (exogenous but possibly also endogenous) and external contaminants from bone. However, these effects are dependent on the skeletal conditions (e.g. post-mortem interval and burning settings). In addition, this chemical washing was shown to be insufficient for an unequivocal discrimination between recent and archaeological skeletal remains. Based on the measured IR indexes, only cremated bones could be clearly distinguished.info:eu-repo/semantics/publishedVersio

Minimal LPV state-space realization driven set-membership identification

Abstract-Set-membership identification algorithms have been recently proposed to derive linear parameter-varying (LPV) models in input-output form, under the assumption that both measurements of the output and the scheduling signals are affected by bounded noise. In order to use the identified models for controller synthesis, linear time-invariant (LTI) realization theory is usually applied to derive a statespace model whose matrices depend statically on the scheduling signals, as required by most of the LPV control synthesis techniques. Unfortunately, application of the LTI realization theory leads to an approximate state-space description of the original LPV input-output model. In order to limit the effect of the realization error, a new set-membership algorithm for identification of input/output LPV models is proposed in the paper. A suitable nonconvex optimization problem is formulated to select the model in the feasible set which minimizes a suitable measure of the state-space realization error. The solution of the identification problem is then derived by means of convex relaxation techniques

Sim2Real Bilevel Adaptation for Object Surface Classification using Vision-Based Tactile Sensors

In this paper, we address the Sim2Real gap in the field of vision-based tactile sensors for classifying object surfaces. We train a Diffusion Model to bridge this gap using a relatively small dataset of real-world images randomly collected from unlabeled everyday objects via the DIGIT sensor. Subsequently, we employ a simulator to generate images by uniformly sampling the surface of objects from the YCB Model Set. These simulated images are then translated into the real domain using the Diffusion Model and automatically labeled to train a classifier. During this training, we further align features of the two domains using an adversarial procedure. Our evaluation is conducted on a dataset of tactile images obtained from a set of ten 3D printed YCB objects. The results reveal a total accuracy of 81.9%, a significant improvement compared to the 34.7% achieved by the classifier trained solely on simulated images. This demonstrates the effectiveness of our approach. We further validate our approach using the classifier on a 6D object pose estimation task from tactile data.Comment: 6 pages, submitted to ICRA 202

Mitochondrial dysfunction in Parkinsonian mesenchymal stem cells impairs differentiation

Sporadic cases account for 90-95% of all patients with Parkinson's Disease (PD). Atypical Parkinsonism comprises approximately 20% of all patients with parkinsonism. Progressive Supranuclear Palsy (PSP) belongs to the atypical parkinsonian diseases and is histopathologically classified as a tauopathy. Here, we report that mesenchymal stem cells (MSCs) derived from the bone marrow of patients with PSP exhibit mitochondrial dysfunction in the form of decreased membrane potential and inhibited NADH-dependent respiration. Furthermore, mitochondrial dysfunction in PSP-MSCs led to a significant increase in mitochondrial ROS generation and oxidative stress, which resulted in decrease of major cellular antioxidant GSH. Additionally, higher basal rate of mitochondrial degradation and lower levels of biogenesis were found in PSP-MSCs, together leading to a reduction in mitochondrial mass. This phenotype was biologically relevant to MSC stemness properties, as it heavily impaired their differentiation into adipocytes, which mostly rely on mitochondrial metabolism for their bioenergetic demand. The defect in adipogenic differentiation was detected as a significant impairment of intracellular lipid droplet formation in PSP-MSCs. This result was corroborated at the transcriptional level by a significant reduction of PPARγ and FABP4 expression, two key genes involved in the adipogenic molecular network. Our findings in PSP-MSCs provide new insights into the etiology of 'idiopathic' parkinsonism, and confirm that mitochondrial dysfunction is important to the development of parkinsonism, independent of the type of the cell

Econometric notes

Lecture notes for a course of Introductory Econometrics (linear regression model and ordinary least squares, including concepts of Linear Algebra and Inferential Statistics), and for a second course of Econometrics (simultaneous equations, instrumental variables, limited and full information estimation methods, maximum likelihood)

Bioactive potential of minor italian olive genotypes from apulia, sardinia and abruzzo

This research focuses on the exploration, recovery and valorization of some minor Italian olive cultivars, about which little information is currently available. Autochthonous and unexplored germplasm has the potential to face unforeseen changes and thus to improve the sustainability of the whole olive system. A pattern of nine minor genotypes cultivated in three Italian regions has been molecularly fingerprinted with 12 nuclear microsatellites (SSRs), that were able to unequivocally identify all genotypes. Moreover, some of the principal phenolic compounds were determined and quantified in monovarietal oils and the expression levels of related genes were also investigated at different fruit developmental stages. Genotypes differed to the greatest extent in the content of oleacein (3,4-DHPEA-EDA) and total phenols. Thereby, minor local genotypes, characterized by stable production and resilience in a low-input agro-system, can provide a remarkable contribution to the improvement of the Italian olive production chain and can become very profitable from a socio-economic point of view