Rotational dynamics of CO solvated in small He clusters: a quantum Monte Carlo study

The rotational dynamics of CO single molecules solvated in small He clusters (CO@He_N) has been studied using Reptation Quantum Monte Carlo for cluster sizes up to N=30. Our results are in good agreement with the roto-vibrational features of the infrared spectrum recently determined for this system, and provide a deep insight into the relation between the structure of the cluster and its dynamics. Simulations for large N also provide a prediction of the effective moment of inertia of CO in the He nano-droplet regime, which has not been measured so far

Rotational dynamics of molecular impurities solvated in 4He clusters. A computational study based on reptation quantum Monte Carlo

The thesis is organized as follows: in the first chapter we provide a short description of the scenario which frames our work. Some significant experiments are presented, together with the questions raised by them, the theoretical investigations they stimulated, and the open issues. The second chapter describes the reptation quantum Monte Carlo method and its theoretical foundations. Technical aspects (the choice of the trial functions, the procedure to calculate the cluster rotational energies) are discussed in third chapter. We also report some studies on the reptation algorithm. In the fourth chapter we apply RQMC for the interpretation of the infrared spectra of CO@HeN [5]. The fifth chapter addresses the problem of the evolution of the rotational dinamics of He solvated rotors toward the nanodroplet regime; two paradigmatic cases, OCS@HeN and HCN@HeN , are studied. Our conclusions are drawn in the last chapter

The Information Complexity of Learning Tasks, their Structure and their Distance

We introduce an asymmetric distance in the space of learning tasks, and a framework to compute their complexity. These concepts are foundational for the practice of transfer learning, whereby a parametric model is pre-trained for a task, and then fine-tuned for another. The framework we develop is non-asymptotic, captures the finite nature of the training dataset, and allows distinguishing learning from memorization. It encompasses, as special cases, classical notions from Kolmogorov complexity, Shannon, and Fisher Information. However, unlike some of those frameworks, it can be applied to large-scale models and real-world datasets. Our framework is the first to measure complexity in a way that accounts for the effect of the optimization scheme, which is critical in Deep Learning

Biophysical and biological contributions of polyamine-coated carbon nanotubes and bidimensional buckypapers in the delivery of miRNAs to human cells

Recent findings in nanomedicine have revealed that carbon nanotubes (CNTs) can be used as potential drug carriers, therapeutic agents and diagnostics tools. Moreover, due to their ability to cross cellular membranes, their nanosize dimension, high surface area and relatively good biocompatibility, CNTs have also been employed as a novel gene delivery vector system. In our previous work, we functionalized CNTs with two polyamine polymers, polyethyleneimine (PEI) and polyamidoamine dendrimer (PAMAM). These compounds have low cytotoxicity, ability to conjugate microRNAs (such as miR-503) and, at the same time, transfect efficiently endothelial cells. The parameters contributing to the good efficiency of transfection that we observed were not investigated in detail. In fact, the diameter and length of CNTs are important parameters to be taken into account when evaluating the effects on drug delivery efficiency. In order to investigate the biophysical and biological contributions of polymer-coated CNTs in delivery of miRNAs to human cells, we decided to investigate three different preparations, characterized by different dimensions and aspect ratios. In particular, we took into account very small CNTs, a suspension of CNTs starting from the commercial product and a 2D material based on CNTs (ie, buckypapers [BPs]) to examine the transfection efficiency of a rigid scaffold. In conclusion, we extensively investigated the biophysical and biological contributions of polyamine-coated CNTs and bidimensional BPs in the delivery of miRNAs to human cells, in order to optimize the transfection efficiency of these compounds to be employed as efficient drug delivery vectors in biomedical applications

Short report: autistic gastrointestinal and eating symptoms treated with secretin: a subtype of autism

Pervasive Developmental Disorders (PDD) are chronic, lifelong disorders for which there is as yet no effective cure, and medical management remains a challenge for clinicians. The current report describes two patients affected by autistic disorder with associated gastrointestinal symptoms. They received multiple doses of intravenous secretin for a six-month period and were assessed with several specific outcome measures to evaluate drug effect. The administration of secretin led to some significant and lasting improvement in only one case. Gastroesophageal reflux may contribute to some of the behavioural problems and explain the effect of secretin since its suppressive effect on gastric secretion is well known. It is also true that autistic children with gastroesophageal reflux and a higher IQ could constitute a subtype which responds to secretin administration and that could be labelled as a "gastrointestinal subtype"

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\`A-la-carte Prompt Tuning (APT): Combining Distinct Data Via Composable Prompting

We introduce \`A-la-carte Prompt Tuning (APT), a transformer-based scheme to tune prompts on distinct data so that they can be arbitrarily composed at inference time. The individual prompts can be trained in isolation, possibly on different devices, at different times, and on different distributions or domains. Furthermore each prompt only contains information about the subset of data it was exposed to during training. During inference, models can be assembled based on arbitrary selections of data sources, which we call "\`a-la-carte learning". \`A-la-carte learning enables constructing bespoke models specific to each user's individual access rights and preferences. We can add or remove information from the model by simply adding or removing the corresponding prompts without retraining from scratch. We demonstrate that \`a-la-carte built models achieve accuracy within $5\%$ of models trained on the union of the respective sources, with comparable cost in terms of training and inference time. For the continual learning benchmarks Split CIFAR-100 and CORe50, we achieve state-of-the-art performance.Comment: 13 pages, 4 figures, 8 table