Facilitated diffusion on confined DNA

In living cells, proteins combine 3D bulk diffusion and 1D sliding along the DNA to reach a target faster. This process is known as facilitated diffusion, and we investigate its dynamics in the physiologically relevant case of confined DNA. The confining geometry and DNA elasticity are key parameters: we find that facilitated diffusion is most efficient inside an isotropic volume, and on a flexible polymer. By considering the typical copy numbers of proteins in vivo, we show that the speedup due to sliding becomes insensitive to fine tuning of parameters, rendering facilitated diffusion a robust mechanism to speed up intracellular diffusion-limited reactions. The parameter range we focus on is relevant for in vitro systems and for facilitated diffusion on yeast chromatin

Conformal Off-Policy Evaluation in Markov Decision Processes

Reinforcement Learning aims at identifying and evaluating efficient control policies from data. In many real-world applications, the learner is not allowed to experiment and cannot gather data in an online manner (this is the case when experimenting is expensive, risky or unethical). For such applications, the reward of a given policy (the target policy) must be estimated using historical data gathered under a different policy (the behavior policy). Most methods for this learning task, referred to as Off-Policy Evaluation (OPE), do not come with accuracy and certainty guarantees. We present a novel OPE method based on Conformal Prediction that outputs an interval containing the true reward of the target policy with a prescribed level of certainty. The main challenge in OPE stems from the distribution shift due to the discrepancies between the target and the behavior policies. We propose and empirically evaluate different ways to deal with this shift. Some of these methods yield conformalized intervals with reduced length compared to existing approaches, while maintaining the same certainty level

Colloids in active fluids: Anomalous micro-rheology and negative drag

We simulate an experiment in which a colloidal probe is pulled through an active nematic fluid. We find that the drag on the particle is non-Stokesian (not proportional to its radius). Strikingly, a large enough particle in contractile fluid (such as an actomyosin gel) can show negative viscous drag in steady state: the particle moves in the opposite direction to the externally applied force. We explain this, and the qualitative trends seen in our simulations, in terms of the disruption of orientational order around the probe particle and the resulting modifications to the active stress.Comment: 5 pages, 3 figure

You Can't See Me: Anonymizing Graphs Using the Szemerédi Regularity Lemma.

Complex networks gathered from our online interactions provide a rich source of information that can be used to try to model and predict our behavior. While this has very tangible benefits that we have all grown accustomed to, there is a concrete privacy risk in sharing potentially sensitive data about ourselves and the people we interact with, especially when this data is publicly available online and unprotected from malicious attacks. k-anonymity is a technique aimed at reducing this risk by obfuscating the topological information of a graph that can be used to infer the nodes' identity. In this paper we propose a novel algorithm to enforce k-anonymity based on a well-known result in extremal graph theory, the Szemerédi regularity lemma. Given a graph, we start by computing a regular partition of its nodes. The Szemerédi regularity lemma ensures that such a partition exists and that the edges between the sets of nodes behave almost randomly. With this partition, we anonymize the graph by randomizing the edges within each set, obtaining a graph that is structurally similar to the original one yet the nodes within each set are structurally indistinguishable. We test the proposed approach on real-world networks extracted from Facebook. Our experimental results show that the proposed approach is able to anonymize a graph while retaining most of its structural information

Bulk rheology and microrheology of active fluids

We simulate macroscopic shear experiments in active nematics and compare them with microrheology simulations where a spherical probe particle is dragged through an active fluid. In both cases we define an effective viscosity: in the case of bulk shear simulations this is the ratio between shear stress and shear rate, whereas in the microrheology case it involves the ratio between the friction coefficient and the particle size. We show that this effective viscosity, rather than being solely a property of the active fluid, is affected by the way chosen to measure it, and strongly depends on details such as the anchoring conditions at the probe surface and on both the system size and the size of the probe particle.Comment: 12 pages, 10 figure

Very high-energy constraints on the infrared extragalactic background light

Context. Measurements of the Extragalactic Background Light (EBL) are a fundamental source of information on the collective emission of cosmic sources. Aims. At infrared wavelengths, however, these measurements are precluded by the overwhelming dominance from Interplanetary Dust emission and the Galactic infrared foreground. Only at $\lambda > 300 \ \mu$m, where the foregrounds are minimal, has the Infrared EBL (IR EBL) been inferred from analysis of the COBE maps. The present paper aims to assess the possibility of evaluating the IR EBL from a few $\mu$m up to the peak of the emission at >100 $\mu$m using an indirect method that avoids the foreground problem. Methods. To this purpose we exploit the effect of pair-production from gamma-gamma interaction by considering the highest energy photons emitted by extragalactic sources and their interaction with the IR EBL photons. We simulate observations of a variety of low redshift emitters with the forthcoming Imaging Atmospheric Cherenkov Telescope (IACT) arrays (CTA in particular) and water Cherenkov observatories (LHAASO, HAWC, SWGO) to assess their suitability to constrain the EBL at such long wavelengths. Results. We find that, even under the most extremely favorable conditions of huge emission flares, extremely high-energy emitting blazars are not very useful for our purpose because they are much too distant (>100 Mpc the nearest ones, MKN 501 and MKN 421). Observations of more local Very High Energy (VHE) emitting AGNs, like low-redshift radio galaxies (M87, IC 310, Centaurus A), are better suited and will potentially allow us to constrain the EBL up to $\lambda \simeq 100\ \mu$m

Colloidal dispersions in active and passive liquid crystalline fluids: a simulation study

In this thesis we study the physics of colloidal dispersions in active and passive liquid crystals by computer simulations. Liquid crystals are materials that exhibit long-range orientational order, with characteristics intermediate between the ones of simple, isotropic fluids and the ones of crystalline solids. Active fluids are suspensions of particles that continuously stir their ambient fluid. Like liquid crystals, active fluids undergo phase transitions to orientationally ordered phases. The framework that we apply here to describe them extends hydrodynamic equations for liquid crystals to the active case, in which their constituent particles exert local stresses on the simple fluid in which they are embedded. Studying systems of colloids embedded in these materials can be done with multiple aims. Here we use colloids as probe particles to investigate the rheological properties of active nematics. To do so we apply a constant force to a spherical particle embedded therein and define an effective viscosity, which we determine by measuring the velocity in steady state. We find an important dependence of the effective viscosity on the size of the particle, and a regime characterised by a steady state of negative drag. We also consider collective properties for systems of many colloids and analyse how they are affected by activity. We find that spontaneous flow can either hinder or favour colloidal aggregation, depending mainly on whether a fixed orientation of the liquid crystal is imposed close to the colloidal surface. This remains true independently of the initial condition chosen for the liquid crystal, which only affects the transition to spontaneous flow

European strategy on AI: Are we truly fostering social good?

Artificial intelligence (AI) is already part of our daily lives and is playing a key role in defining the economic and social shape of the future. In 2018, the European Commission introduced its AI strategy able to compete in the next years with world powers such as China and US, but relying on the respect of European values and fundamental rights. As a result, most of the Member States have published their own National Strategy with the aim to work on a coordinated plan for Europe. In this paper, we present an ongoing study on how European countries are approaching the field of Artificial Intelligence, with its promises and risks, through the lens of their national AI strategies. In particular, we aim to investigate how European countries are investing in AI and to what extent the stated plans can contribute to the benefit of the whole society. This paper reports the main findings of a qualitative analysis of the investment plans reported in 15 European National Strategie

Investing in AI for social good: an analysis of European national strategies

Artificial Intelligence (AI) has become a driving force in modern research, industry and public administration and the European Union (EU) is embracing this technology with a view to creating societal, as well as economic, value. This effort has been shared by EU Member States which were all encouraged to develop their own national AI strategies outlining policies and investment levels. This study focuses on how EU Member States are approaching the promise to develop and use AI for the good of society through the lens of their national AI strategies. In particular, we aim to investigate how European countries are investing in AI and to what extent the stated plans contribute to the good of people and society as a whole. Our contribution consists of three parts: (i) a conceptualization of AI for social good highlighting the role of AI policy, in particular, the one put forward by the European Commission (EC); (ii) a qualitative analysis of 15 European national strategies mapping investment plans and suggesting their relation to the social good (iii) a reflection on the current status of investments in socially good AI and possible steps to move forward. Our study suggests that while European national strategies incorporate money allocations in the sphere of AI for social good (e.g. education), there is a broader variety of underestimated actions (e.g. multidisciplinary approach in STEM curricula and dialogue among stakeholders) that can boost the European commitment to sustainable and responsible AI innovation.The authors are supported by the project A European AI On Demand Platform and Ecosystem (AI4EU) H2020-ICT-26 #825619. The views expressed in this paper are not necessarily those of the consortium AI4EU. The authors would also thank Sinem Aslan and Chiara Bissolo for their support in the quantitative overview and qualitative analysis respectively.Peer ReviewedPostprint (published version