A graph theoretical analysis of the energy landscape of model polymers

In systems characterized by a rough potential energy landscape, local energetic minima and saddles define a network of metastable states whose topology strongly influences the dynamics. Changes in temperature, causing the merging and splitting of metastable states, have non trivial effects on such networks and must be taken into account. We do this by means of a recently proposed renormalization procedure. This method is applied to analyze the topology of the network of metastable states for different polypeptidic sequences in a minimalistic polymer model. A smaller spectral dimension emerges as a hallmark of stability of the global energy minimum and highlights a non-obvious link between dynamic and thermodynamic properties.Comment: 15 pages, 15 figure

Sistema interferometrico per la misura della velocità e del verso di spostamento di un bersaglio

LAUREA MAGISTRAL

GrapHisto: A Robust Representation of Graph-Structured Data for Graph Convolutional Networks

Machine learning from graphs is an established branch of AI research motivated by the relevance of applications that involve graph-structured data. The most popular instance is the graph neural network (GNN). On the other hand, due to the promising results of deep learning models in the most diverse fields of application, several efforts have been made to replicate these successes when dealing with graphical data. A prominent specimen of the kind is the graph convolutional network (GCN). Along these lines, the paper propose a novel approach for processing graphs that exploits the capabilities of convolutional neural networks (CNNs) to learn from images. This is achieved by means of a new representation of graphs, called GrapHisto, that portrays graphs in the form of characteristic “pictures”. The GrapHisto is in the form of graph-specific, unique tensors encapsulating the graph topology and its features (i.e., the labels associated with vertexes and edges). This representation is fed to a CNN, and the resulting machine is termed GrapHisto-CNN. The paper provides some theoretical investigations of the properties of the approach, and proposes solutions to some practical issues. An experimental evaluation of the GrapHisto-CNN is reported, revolving around two setups: classification of synthetically-generated graphs, and molecule classification form the dataset QM9. The results show that the approach is effective and robust, and that it compares favorably with GNNs and GCNs

Partially fake it till you make it: mixing real and fake thermal images for improved object detection

In this paper we propose a novel data augmentation approach for visual content domains that have scarce training datasets, compositing synthetic 3D objects within real scenes. We show the performance of the proposed system in the context of object detection in thermal videos, a domain where 1) training datasets are very limited compared to visible spectrum datasets and 2) creating full realistic synthetic scenes is extremely cumbersome and expensive due to the difficulty in modeling the thermal properties of the materials of the scene. We compare different augmentation strategies, including state of the art approaches obtained through RL techniques, the injection of simulated data and the employment of a generative model, and study how to best combine our proposed augmentation with these other techniques.Experimental results demonstrate the effectiveness of our approach, and our single-modality detector achieves state-of-the-art results on the FLIR ADAS dataset

Matching Mechanics and Energetics of Muscle Contraction Suggests Unconventional Chemomechanical Coupling during the Actin-Myosin Interaction

The mechanical performances of the vertebrate skeletal muscle during isometric and isotonic contractions are interfaced with the corresponding energy consumptions to define the coupling between mechanical and biochemical steps in the myosin–actin energy transduction cycle. The analysis is extended to a simplified synthetic nanomachine in which eight HMM molecules purified from fast mammalian skeletal muscle are brought to interact with an actin filament in the presence of 2 mM ATP, to assess the emergent properties of a minimum number of motors working in ensemble without the effects of both the higher hierarchical levels of striated muscle organization and other sarcomeric, regulatory and cytoskeleton proteins. A three-state model of myosin–actin interaction is able to predict the known relationships between energetics and transient and steady-state mechanical properties of fast skeletal muscle either in vivo or in vitro only under the assumption that during shortening a myosin motor can interact with two actin sites during one ATP hydrolysis cycle. Implementation of the molecular details of the model should be achieved by exploiting kinetic and structural constraints present in the transients elicited by stepwise perturbations in length or force superimposed on the isometric contraction

Postrecurrence Survival After Liver Transplantation for Liver Metastases From Neuroendocrine Tumors

Background: Liver metastases from neuroendocrine tumors (NETs) is an accepted indication for liver transplantation (LT). Despite strict patient selection, post-LT recurrence is observed in 30-50% of cases. Postrecurrence survival is poorly investigated as well as factors influencing postrecurrence outcomes. Methods: Consecutive patients treated at a single Institution for post-LT recurrence of NET between Jan 1st, 2004 and Dec 31th, 2018 were included. Baseline patients' characteristics, data on the primary tumor, pretransplant therapies, posttransplant recurrence and treatments and long-term outcomes were prospectively collected and retrospectively analyzed. Results: Thirty-two patients presented with post-LT NET recurrence occurring 82.9 months (IQR 29.4-119.1) from LT, and the most common sites were abdominal lymph nodes (59.4%), peritoneum (6.3%) and lungs (6.3%). Fourteen patients (43.8%) underwent surgery with radical intent. Five- and 10-years survival after recurrence were 76.3% and 45.5%, respectively. Only time from LT to recurrence had a significant impact on post recurrence survival, being 5-years OS 89.5% versus 0% for patients recurring > 24 months after LT versus ≤ 24 months, respectively (p=.001). Moreover, for patients with Mib-1 > 2% at recurrence, 5-years OS was 87.5% versus 0% for those undergoing surgery versus loco-regional or systemic treatments (p=0.011). Conclusions: The presented results, although based on a retrospective and relatively small series, show that excellent long-term survival is observed after post-LT NET recurrence, particularly in those patients recurring long after LT (> 24 months). An aggressive surgical treatment might result in a new chance of cure for a selected subgroup of patients

Probabilistically grounded unsupervised training of neural networks

The chapter is a survey of probabilistic interpretations of artificial neural networks (ANN) along with the corresponding unsupervised learning algorithms. ANNs for estimating probability density functions (pdf) are reviewed first, including parametric estimation via constrained radial basis functions and nonparametric estimation via multilayer perceptrons. The approaches overcome the limitations of traditional statistical estimation methods, possibly leading to improved pdf models. The focus is then moved from pdf estimation to online neural clustering, relying on maximum-likelihood training. Finally, extension of the techniques to the unsupervised training of generative probabilistic hybrid paradigms for sequences of random observations is discusse

Towards a novel probabilistic graphical model of sequential data: a solution to the problem of structure learning and an empirical evaluation

This paper develops a maximum pseudo-likelihood algorithm for learning the structure of the dynamic extension of Hybrid Random Field introduced in the companion paper [5]. The technique turns out to be a viable method for capturing the statistical (in)dependencies among the random variables within a sequence of patterns. Complexity issues are tackled by means of adequate strategies from classic literature on probabilistic graphical models. A preliminary empirical evaluation is presented eventually