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| 1 | Information-theoretic differential geometry of quantum phase transitions 显示文摘 | ZANARDI P GIORDA P COZZINI M | 2007 | Phys Rev Lett2007，99，10： | 1 |
| 2 | Free energy of ligand binding to protein: Evaluation of the contribution of water molecules by computational methods显示文摘 | COZZINI P FORNABAIO M MARABOTTI A | 2004 | Current Medicinal Chemistry2004，11，： | 1 |
| 3 | Textured silicon calorimetric light detector显示文摘 | Frank T Angloher G Cozzini C | 2003 | Journal of Applied Physics2003，94，11： | 1 |
| 4 | The consequences of scoring docked ligand conformations using free energy correlations显示文摘 | Spyrakis F Amadasi A Fornabaio M Abraham D.J Mozzarelli A Kellogg G.E Cozzini P | | 0，，07： | 1 |
| 5 | Mycotoxin Detection Plays 'Cops and Robbers' : Cycledextrin Chemosansors a Specialized Police显示文摘 | Cozzini P Ingletto G Singh R | 2008 | Intematianal Journal of Molecular Sciences2008，9，12： | 1 |
| 6 | Textured silicon calorimetric light detector显示文摘 | Frank T Angloher G Cozzini C | 2003 | Journal of Applied Physics2003，94，10： | 1 |
| 7 | Detection of the Natural Alpha Decay of Tungsten 显示文摘 | Cozzini C Anglaher G Bucci C | 2004 | Physical Rev/ew C2004，70，： | 1 |
| 8 | CRESST Cryogenic Dark Matter Search 显示文摘 | Cozzini C Angloher G Bucci C | 2005 | New Astronomy Reviews2005，49，： | 1 |
| 9 | Vortex signatures in annular Bose-Einstein condensates显示文摘 | Cozzini M Jackson B Stringari S | 2006 | Phys Rev A2006，73，01： | 1 |
| 10 | Towards the FAIRification of Scanning Tunneling Microscopy Images显示文摘In this paper,we describe the data management practices and services developed for making FAIR compliant a scientific archive of Scanning Tunneling Microscopy(STM)images.As a first step,we extracted the instrument metadata of each image of the dataset to create a structured database.We then enriched these metadata with information on the structure and composition of the surface by means of a pipeline that leverages human annotation,machine learning techniques,and instrument metadata filtering.To visually explore both images and metadata,as well as to improve the accessibility and usability of the dataset,we developed'STM explorer'as a web service integrated within the Trieste Advanced Data services(TriDAS)website.On top of these data services and tools,we propose an implementation of the W3C PROV standard to describe provenance metadata of STM images. | Tommaso Rodani Elda Osmenaj Alberto Cazzaniga Mirco Panighel Cristina Africh Stefano Cozzini | 2023 | Data Intelligence2023，5，1： | 0 |
| 11 | Deep Learning,Feature Learning,and Clustering Analysis for SEM Image Classification显示文摘In this paper,we report upon our recent work aimed at improving and adapting machine learning algorithms to automatically classify nanoscience images acquired by the Scanning Electron Microscope(SEM).This is done by coupling supervised and unsupervised learning approaches.We first investigate supervised learning on a ten-category data set of images and compare the performance of the different models in terms of training accuracy.Then,we reduce the dimensionality of the features through autoencoders to perform unsupervised learning on a subset of images in a selected range of scales(from 1μm to 2μm).Finally,we compare different clustering methods to uncover intrinsic structures in the images. | Rossella Aversa Piero Coronica Cristiano De Nobili Stefano Cozzini | 2020 | Data Intelligence2020，2，4： | 0 |
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