Improved Answer-Set Programming Encodings for Abstract Argumentation

The design of efficient solutions for abstract argumentation problems is a crucial step towards advanced argumentation systems. One of the most prominent approaches in the literature is to use Answer-Set Programming (ASP) for this endeavor. In this paper, we present new encodings for three prominent argumentation semantics using the concept of conditional literals in disjunctions as provided by the ASP-system clingo. Our new encodings are not only more succinct than previous versions, but also outperform them on standard benchmarks.Comment: To appear in Theory and Practice of Logic Programming (TPLP), Proceedings of ICLP 201

On the expressivity of recurrent neural cascades with identity

Recurrent Neural Cascades (RNC) are the class of recurrent neural networks with no cyclic dependencies among recurrent neurons. Their subclass RNC+ with positive recurrent weights has been shown to be closely connected to the starfree regular languages, which are the expressivity of many well-established temporal logics. The existing expressivity results show that the regular languages captured by RNC+ are the star-free ones, and they leave open the possibility that RNC+ may capture languages beyond regular. We exclude this possibility for languages that include an identity element, i.e., an input that can occur an arbitrary number of times without affecting the output. Namely, in the presence of an identity element, we show that the languages captured by RNC+ are exactly the star-free regular languages. Identity elements are ubiquitous in temporal patterns, and hence our results apply to a large number of applications. The implications of our results go beyond expressivity. At their core, we establish a close structural correspondence between RNC+ and semiautomata cascades, showing that every neuron can be equivalently captured by a three-state semiautomaton. A notable consequence of this result is that RNC+ are no more succinct than cascades of three-state semiautomata

Cavity Quantum-Electrodynamical Chern Insulator: Route Towards Light-Induced Quantized Anomalous Hall Effect in Graphene

We show that an energy gap is induced in graphene by light-matter coupling to a circularly polarized photon mode in a cavity. Using many-body perturbation theory we compute the electronic spectra which exhibit photon-dressed sidebands akin to Floquet sidebands for laser-driven materials. In contrast with Floquet topological insulators, in which a strictly quantized Hall response is induced by light only for off-resonant driving in the high-frequency limit, the photon-dressed Dirac fermions in the cavity show a quantized Hall response characterized by an integer Chern number. Specifically for graphene we predict that a Hall conductance of $2 e^2/h$ can be induced in the low-temperature limit.Comment: 8 pages, 4 figures, incl. Supplementary Materia

Markov abstractions for PAC reinforcement learning in non-Markov decision processes

Our work aims at developing reinforcement learning algorithms that do not rely on the Markov assumption. We consider the class of Non-Markov Decision Processes where histories can be abstracted into a finite set of states while preserving the dynamics. We call it a Markov abstraction since it induces a Markov Decision Process over a set of states that encode the non-Markov dynamics. This phenomenon underlies the recently introduced Regular Decision Processes (as well as POMDPs where only a finite number of belief states is reachable). In all such kinds of decision process, an agent that uses a Markov abstraction can rely on the Markov property to achieve optimal behaviour. We show that Markov abstractions can be learned during reinforcement learning. Our approach combines automata learning and classic reinforcement learning. For these two tasks, standard algorithms can be employed. We show that our approach has PAC guarantees when the employed algorithms have PAC guarantees, and we also provide an experimental evaluation

A hybrid spatiotemporal model of PCa dynamics and insights into optimal therapeutic strategies

Using a hybrid cellular automaton with stochastic elements, we investigate the effectiveness of multiple drug therapies on prostate cancer (PCa) growth. The ability of Androgen Deprivation Therapy to reduce PCa growth represents a milestone in prostate cancer treatment, nonetheless most patients eventually become refractory and develop castration-resistant prostate cancer. In recent years, a “second generation” drug called enzalutamide has been used to treat advanced PCa, or patients already exposed to chemotherapy that stopped responding to it. However, tumour resistance to enzalutamide is not well understood, and in this context, preclinical models and in silico experiments (numerical simulations) are key to understanding the mechanisms of resistance and to assessing therapeutic settings that may delay or prevent the onset of resistance. In our mathematical system, we incorporate cell phenotype switching to model the development of increased drug resistance, and consider the effect of the micro-environment dynamics on necrosis and apoptosis of the tumour cells. The therapeutic strategies that we explore include using a single drug (enzalutamide), and drug combinations (enzalutamide and everolimus or cabazitaxel) with different treatment schedules. Our results highlight the effectiveness of alternating therapies, especially alternating enzalutamide and cabazitaxel over a year, and a comparison is made with data taken from TRAMP mice to verify our findings

Markov abstractions for PAC reinforcement learning in non-Markov decision processes

Our work aims at developing reinforcement learning algorithms that do not rely on the Markov assumption. We consider the class of Non-Markov Decision Processes where histories can be abstracted into a finite set of states while preserving the dynamics. We call it a Markov abstraction since it induces a Markov Decision Process over a set of states that encode the non-Markov dynamics. This phenomenon underlies the recently introduced Regular Decision Processes (as well as POMDPs where only a finite number of belief states is reachable). In all such kinds of decision process, an agent that uses a Markov abstraction can rely on the Markov property to achieve optimal behaviour. We show that Markov abstractions can be learned during reinforcement learning. Our approach combines automata learning and classic reinforcement learning. For these two tasks, standard algorithms can be employed. We show that our approach has PAC guarantees when the employed algorithms have PAC guarantees, and we also provide an experimental evaluation

Three-Dimensional Bioprinting for Cartilage Tissue Engineering: Insights into Naturally-Derived Bioinks from Land and Marine Sources

In regenerative medicine and tissue engineering, the possibility to: (I) customize the shape and size of scaffolds, (II) develop highly mimicked tissues with a precise digital control, (III) manufacture complex structures and (IV) reduce the wastes related to the production process, are the main advantages of additive manufacturing technologies such as three-dimensional (3D) bioprinting. Specifically, this technique, which uses suitable hydrogel-based bioinks, enriched with cells and/or growth factors, has received significant consideration, especially in cartilage tissue engineering (CTE). In this field of interest, it may allow mimicking the complex native zonal hyaline cartilage organization by further enhancing its biological cues. However, there are still some limitations that need to be overcome before 3D bioprinting may be globally used for scaffolds' development and their clinical translation. One of them is represented by the poor availability of appropriate, biocompatible and eco-friendly biomaterials, which should present a series of specific requirements to be used and transformed into a proper bioink for CTE. In this scenario, considering that, nowadays, the environmental decline is of the highest concerns worldwide, exploring naturally-derived hydrogels has attracted outstanding attention throughout the scientific community. For this reason, a comprehensive review of the naturally-derived hydrogels, commonly employed as bioinks in CTE, was carried out. In particular, the current state of art regarding eco-friendly and natural bioinks' development for CTE was explored. Overall, this paper gives an overview of 3D bioprinting for CTE to guide future research towards the development of more reliable, customized, eco-friendly and innovative strategies for this field of interest

Spectral Functions of the Uniform Electron Gas via Coupled-Cluster Theory and Comparison to the GWGW and Related Approximations

We use, for the first time, ab initio coupled-cluster theory to compute the spectral function of the uniform electron gas at a Wigner-Seitz radius of $r_\mathrm{s}=4$. The coupled-cluster approximations we employ go significantly beyond the diagrammatic content of state-of-the-art $GW$ theory. We compare our calculations extensively to $GW$ and $GW$-plus-cumulant theory, illustrating the strengths and weaknesses of these methods in capturing the quasiparticle and satellite features of the electron gas. Our accurate calculations further allow us to address the long-standing debate over the occupied bandwidth of metallic sodium. Our findings indicate that the future application of coupled-cluster theory to condensed phase material spectra is highly promising.Comment: 6 pages, 2 figure

Synthesis and characterization of divinyl-fumarate Poly-ε-caprolactone for scaffolds with controlled architectures

A vinyl-terminated Polycaprolactone has been developed for tissue engineering applications using a one-step synthesis and functionalization method based on Ring Opening Polymerization (ROP) of ԑ-caprolactone, with Hydroxyl Ethyl Vinyl Ether (HEVE) acting both as the initiator of ROP and as photo-curable functional group. The proposed method employs a catalyst based on Al, instead of the most popular Tin(II) 2-ethylhexanoate, to reduce the cytotoxicity. Following the synthesis of the vinyl-terminated polycaprolactone, its reaction with Fumaryl Chloride (FuCl) results in a divinyl-fumarate polycaprolactone (VPCLF). The obtained polymers were thoroughly characterized using Fourier Transform Infrared Spectroscopy (FTIR) and gel permeation chromatography (GPC) techniques. The polymer has been successfully employed, in combination with N-vinyl Pyrrolidone (NVP), to fabricate films and computer-designed porous scaffolds by micro-stereolithography (μ-SL) with Gyroid and Diamond architectures. Characterization of the networks indicated the influence of NVP content on the network properties. Human Mesenchymal Stem Cells (hMSCs) adhered and spread onto VPCLF/NVP networks showing good biological properties and no cytotoxic effect

Shining Light on the Microscopic Resonant Mechanism Responsible for Cavity-Mediated Chemical Reactivity

Strong light-matter interaction in cavity environments has emerged as a promising and general approach to control chemical reactions in a non-intrusive manner. The underlying mechanism that distinguishes between steering, accelerating, or decelerating a chemical reaction has, however, remained thus far largely unclear, hampering progress in this frontier area of research. In this work, we leverage a combination of first-principles techniques, foremost quantum-electrodynamical density functional theory, applied to the recent experimental realization by Thomas et al. [1] to unveil the microscopic mechanism behind the experimentally observed reduced reaction-rate under resonant vibrational strong light-matter coupling. We find that the cavity mode functions as a mediator between different vibrational eigenmodes, transferring vibrational excitation and anharmonicity, correlating vibrations, and ultimately strengthening the chemical bond of interest. Importantly, the resonant feature observed in experiment, theoretically elusive so far, naturally arises in our investigations. Our theoretical predictions in polaritonic chemistry shine new light on cavity induced mechanisms, providing a crucial control strategy in state-of-the-art photocatalysis and energy conversion, pointing the way towards generalized quantum optical control of chemical systems