Sapienza University of Rome

About: Sapienza University of Rome is a education organization based out in Rome, Lazio, Italy. It is known for research contribution in the topics: Population & Large Hadron Collider. The organization has 62002 authors who have published 155468 publications receiving 4397244 citations. The organization is also known as: La Sapienza & Università La Sapienza di Roma.

Gelation of particles with short-range attraction

Abstract: Nanoscale or colloidal particles change the properties of materials, imparting solid-like behaviour to a wide variety of complex fluids. This behaviour arises when particles aggregate to form mesoscopic clusters and networks. Numerous scenarios for gelation have been proposed, but no consensus has emerged. Lu et al. report experiments showing that gelation of spherical particles with isotropic, short-range attractions is initiated by spinodal decomposition; this thermodynamic instability triggers the formation of density fluctuations, leading to spanning clusters that dynamically arrest to create a gel. This simple picture of gelation should apply to any particle system with short-range attractions. Solid-like behaviour arises in a wide variety of complex fluids upon gelation — aggregation of particles to form mesoscopic clusters and networks. The authors show that gelation of spherical particles with isotropic, short-range attractions is initiated by spinodal decomposition. Nanoscale or colloidal particles are important in many realms of science and technology. They can dramatically change the properties of materials, imparting solid-like behaviour to a wide variety of complex fluids1,2. This behaviour arises when particles aggregate to form mesoscopic clusters and networks. The essential component leading to aggregation is an interparticle attraction, which can be generated by many physical and chemical mechanisms. In the limit of irreversible aggregation, infinitely strong interparticle bonds lead to diffusion-limited cluster aggregation3 (DLCA). This is understood as a purely kinetic phenomenon that can form solid-like gels at arbitrarily low particle volume fraction4,5. Far more important technologically are systems with weaker attractions, where gel formation requires higher volume fractions. Numerous scenarios for gelation have been proposed, including DLCA6, kinetic or dynamic arrest4,7,8,9,10, phase separation5,6,11,12,13,14,15,16, percolation4,12,17,18 and jamming8. No consensus has emerged and, despite its ubiquity and significance, gelation is far from understood—even the location of the gelation phase boundary is not agreed on5. Here we report experiments showing that gelation of spherical particles with isotropic, short-range attractions is initiated by spinodal decomposition; this thermodynamic instability triggers the formation of density fluctuations, leading to spanning clusters that dynamically arrest to create a gel. This simple picture of gelation does not depend on microscopic system-specific details, and should thus apply broadly to any particle system with short-range attractions. Our results suggest that gelation—often considered a purely kinetic phenomenon4,8,9,10—is in fact a direct consequence of equilibrium liquid–gas phase separation5,13,14,15. Without exception, we observe gelation in all of our samples predicted by theory and simulation to phase-separate; this suggests that it is phase separation, not percolation12, that corresponds to gelation in models for attractive spheres.

Tolerization of dendritic cells by T(S) cells: the crucial role of inhibitory receptors ILT3 and ILT4.

Abstract: Immunoglobulin-like transcript 3 (ILT3) and ILT4 belong to a family of inhibitory receptors expressed by human monocytes and dendritic cells. We show here that CD8+CD28− alloantigen-specific T suppressor (TS) cells induce the up-regulation of ILT3 and ILT4 on monocytes and dendritic cells, rendering these antigen-presenting cells (APCs) tolerogenic. Tolerogenic APCs show reduced expression of costimulatory molecules and induce antigen-specific unresponsiveness in CD4+ T helper cells. Studies of human heart transplant recipients showed that rejection-free patients have circulating TS cells, which induce the up-regulation of ILT3 and ILT4 in donor APCs. These findings demonstrate an important mechanism of immune regulation.

Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning

Abstract: Deep Learning has recently become hugely popular in machine learning for its ability to solve end-to-end learning systems, in which the features and the classifiers are learned simultaneously, providing significant improvements in classification accuracy in the presence of highly-structured and large databases. Its success is due to a combination of recent algorithmic breakthroughs, increasingly powerful computers, and access to significant amounts of data. Researchers have also considered privacy implications of deep learning. Models are typically trained in a centralized manner with all the data being processed by the same training algorithm. If the data is a collection of users' private data, including habits, personal pictures, geographical positions, interests, and more, the centralized server will have access to sensitive information that could potentially be mishandled. To tackle this problem, collaborative deep learning models have recently been proposed where parties locally train their deep learning structures and only share a subset of the parameters in the attempt to keep their respective training sets private. Parameters can also be obfuscated via differential privacy (DP) to make information extraction even more challenging, as proposed by Shokri and Shmatikov at CCS'15. Unfortunately, we show that any privacy-preserving collaborative deep learning is susceptible to a powerful attack that we devise in this paper. In particular, we show that a distributed, federated, or decentralized deep learning approach is fundamentally broken and does not protect the training sets of honest participants. The attack we developed exploits the real-time nature of the learning process that allows the adversary to train a Generative Adversarial Network (GAN) that generates prototypical samples of the targeted training set that was meant to be private (the samples generated by the GAN are intended to come from the same distribution as the training data). Interestingly, we show that record-level differential privacy applied to the shared parameters of the model, as suggested in previous work, is ineffective (i.e., record-level DP is not designed to address our attack).