Solving HPP and SAT by P Systems with Active Membranes and Separation Rules

The P systems (or membrane systems) are a class of distributed parallel computing devices of a biochemical type, where membrane division is the frequently investigated way for obtaining an exponential working space in a linear time, and on this basis solving hard problems, typically NP-complete problems, in polynomial (often, linear) time. In this paper, using another way to obtain exponential working space – membrane separation, it was shown that Satisfiability Problem and Hamiltonian Path Problem can be deterministically solved in linear or polynomial time by a uniform family of P systems with separation rules, where separation rules are not changing labels, but polarizations are used. Some related open problems are mentioned.     

Characterizing the computational power of energy-based P systems

We investigate the computational power of energy-based P systems, a model of membrane systems, where a fixed amount of energy is associated with each object and the rules transform single objects by adding or removing energy from them. We answer the recently proposed open questions about the power of such systems without priorities associated with the rules, for both sequential and maximally parallel modes. We also conjecture that deterministic energy-based P systems are not computationally complete.     

An Integrated Multifunctional Nanoplatform for Deep‐Tissue Dual‐Mode Imaging

The combination of biocompatible superparamagnetic and photoluminescent nanoparticles (NPs) is intensively studied as highly promising multifunctional (magnetic confinement and targeting, imaging, etc.) tools in biomedical applications. However, most of these hybrid NPs exhibit low signal contrast and shallow tissue penetration for optical imaging due to tissue‐induced optical extinction and autofluorescence, since in many cases, their photoluminescent components emit in the visible spectral range. Yet, the search for multifunctional NPs suitable for high photoluminescence signal‐to‐noise ratio, deep‐tissue imaging is still ongoing. Herein, a biocompatible core/shell/shell sandwich structured Fe₃O₄@SiO₂@NaYF₄:Nd³⁺ nanoplatform possessing excellent superparamagnetic and near‐infrared (excitation) to near‐infrared (emission), i.e., NIR‐to‐NIR photoluminescence properties is developed. They can be rapidly magnetically confined, allowing the NIR photoluminescence signal to be detected through a tissue as thick as 13 mm, accompanied by high T₂ relaxivity in magnetic resonance imaging. The fact that both the excitation and emission wavelengths of these NPs are in the optically transparent biological windows, along with excellent photostability, fast magnetic response, significant T₂‐contrast enhancement, and negligible cytotoxicity, makes them extremely promising for use in high‐resolution, deep‐tissue dual‐mode (optical and magnetic resonance) in vivo imaging and magnetic‐driven applications.     

Decoupling Theranostics with Rare Earth Doped Nanoparticles

Theranostic nanoagents targeted for personalized medicine provide a unified platform for therapeutics and diagnostics. To be able to discretely control each individually, allows for safer, more precise, and truly multifunctional theranostics. Rare earth doped nanoparticles can be rationally tailored to best match this condition with the aid of core/shell engineering. In such nanoparticles, the light‐mediated theranostic approach is functionally decoupled—therapeutics or diagnostics are prompted on‐demand, by wavelength‐specific excitation. These decoupled rare earth nanoparticles (dNPs) operate entirely under near‐infrared (NIR) excitation, for minimized light interference with the target and extended tissue depth action. Under heating‐free 806 nm irradiation, dNPs behave solely as high‐contrast NIR‐to‐NIR optical markers and nanothermometers, visualizing and probing the area of interest without prompting the therapeutic effect beforehand. On the contrary, 980 nm NIR irradiation is upconverted by the dNPs to UV/visible light, which triggers secondary photochemical processes, e.g., generation of reactive oxygen species by photosensitizers coupled to the dNPs, causing damage to cancer cells. Additionally, integration of NIR nanothermometry helps to control the temperature in the vicinity of the dNPs avoiding possible overheating and quenching of upconversion (UC) emission, harnessed for photodynamic therapy. Overall, a new direction is outlined in the development of state‐of‐the‐art rare earth based theranostic nanoplatforms.     

Beyond Independence: An Extension of the A Contrario Decision Procedure

The a contrario approach is a principled method for making algorithmic decisions that has been applied successfully to many tasks in image analysis. The method is based on a background model (or null hypothesis) for the image. This model relies on independence assumptions and characterizes images in which no detection should be made. It is often image dependent, relying on statistics gathered from the image, and therefore adaptive. In this paper we propose a generalization for background models which relaxes the independence assumption and instead uses image dependent second order properties. The second order properties are accounted for thanks to graphical models. The modified a contrario technique is applied to two tasks: line segment detection and part-based object detection, and its advantages are demonstrated. In particular, we show that the proposed method enables reasonably accurate prediction of the false detection rate with no need for training data.     

Sequential and maximally parallel multiset rewriting: reversibility and determinism

We study reversibility and determinism aspects and the strong versions of these properties of sequential multiset processing systems and of maximally parallel systems, from the computability point of view. In the sequential case, syntactic criteria are established for both strong determinism and strong reversibility. In the parallel case, a criterion is established for strong determinism, whereas strong reversibility is shown to be decidable. In the sequential case, without control all four classes—deterministic, strongly deterministic, reversible, strongly reversible—are not universal, whereas in the parallel case deterministic systems are universal. When allowing inhibitors, the first and the third class become universal in both models, whereas with priorities all of them are universal. In the maximally parallel case, strongly deterministic systems with both promoters and inhibitors are universal. We also present a few more specific results and conjectures.     

P systems without multiplicities of symbol-objects

In this paper we investigate P systems whose compartments contain sets of symbol-objects rather than multisets of objects, as it is common in membrane computing. If the number of membranes cannot grow, then in this framework we can characterize exactly the regular languages. If membrane creation or membrane division is allowed, then the Parikh sets of recursively enumerable languages can be generated. The last result also implies the universality of P systems with active membranes (with multisets of symbol-objects) without polarizations.     

Hyperdimensional Computing for Blind and One-Shot Classification of EEG Error-Related Potentials

Mobile Networks and Applications - The mathematical properties of high-dimensional (HD) spaces show remarkable agreement with behaviors controlled by the brain. Computing with HD vectors, referred...