This is the second lecture of `RAGtime' series on electrodynamical effects near black holes. We will summarize the basic equations of relativistic electrodynamics in terms of spin-coefficient (Newman-Penrose) formalism. The aim of the lecture is to present important relations that hold for exact electro-vacuum solutions and to exhibit, in a pedagogical manner, some illustrative solutions and useful approximation approaches. First, we concentrate on weak electromagnetic fields and we illustrate their structure by constructing the magnetic and electric lines of force. Gravitational field of the black hole assumes axial symmetry, whereas the electromagnetic field may or may not share the same symmetry. With these solutions we can investigate the frame-dragging effects acting on electromagnetic fields near a rotating black hole. These fields develop magnetic null points and current sheets. Their structure suggests that magnetic reconnection takes place near the rotating black hole horizon. Finally, the last section will be devoted to the transition from test-field solution to exact solutions of coupled Einstein-Maxwell equations. New effects emerge within the framework of exact solutions: the expulsion of the magnetic flux out of the black-hole horizon depends on the intensity of the imposed magnetic field.

Arrays of optically trapped atoms excited to Rydberg states have recently emerged as a competitive physical platform for quantum simulation and computing, where high-fidelity state preparation and readout, quantum logic gates and controlled quantum dynamics of more than 100 qubits have all been demonstrated. These systems are now approaching the point where reliable quantum computations with hundreds of qubits and realistically thousands of multiqubit gates with low error rates should be within reach for the first time. In this article we give an overview of the Rydberg quantum toolbox, emphasizing the high degree of flexibility for encoding qubits, performing quantum operations and engineering quantum many-body Hamiltonians. We then review the state-of-the-art concerning high-fidelity quantum operations and logic gates as well as quantum simulations in many-body regimes. Finally, we discuss computing schemes that are particularly suited to the Rydberg platform and some of the remaining challenges on the road to general purpose quantum simulators and quantum computers.

By means of an inequality of the information and parametrization of family of distributions of the probabilities, supposing an effective estimation, introduction of the distributions containing time of the first achievement of a level as internal thermodynamic parameter ground.

We reply to the Comment made in arXiv:1107.4435v1 [quant-ph] (Phys. Lett. A \textbf{374} (2010) 1097) by noting some erroneous considerations therein resulting in a misleading view of the quantum key distribution protocol in question. We then correct the rates provided for the Intercept-and-Resend attack and we complete the analysis of Eve's attack based on a double CNOT gate.

Evaluating large language models at scale remains a practical bottleneck for many organizations. While existing evaluation frameworks work well for thousands of examples, they struggle when datasets grow to hundreds of thousands or millions of samples. This scale is common when assessing model behavior across diverse domains or conducting comprehensive regression testing. We present Spark-LLM-Eval, a distributed evaluation framework built natively on Apache Spark. The system treats evaluation as a data-parallel problem, partitioningexamplesacrossexecutorsandaggregatingresultswithproperstatistical accounting. Beyond raw throughput, we emphasize statistical rigor: every reported metric includes bootstrap confidence intervals, and model comparisons come with appropriate significance tests (paired t-tests, McNemar's test, or Wilcoxon signed-rank, depending on the metric type). The framework also addresses the cost problem inherent in LLM evaluation through content-addressable response caching backed by Delta Lake, which allows iterating on metric definitions without re-running inference. We describe the system architecture, the statistical methodology, and report benchmark results showing linear scaling with cluster size. The framework and all evaluation code are available as open source.

The mentioned article was written by Ettore Majorana, in a partially educational way, for a journal of Sociology; but he gave up publishing it (and threw it away). It appeared posthumous, thanks to Giovanni Gentile Jr. (the inventor of "parastatistics") in "Scientia" 36 (1942) 58-66. It has not been re-published, in Italian, till the beginning of 2006, when we made known some abridgements of it by Italian newspapers and by the journal "Fisica in Medicina". We don't know when was it written: perhaps in 1930. However, its central theme was still alive in Majorana's mind in 1934: in fact, on July 27, 1934, he will write to G.Gentile Jr. to expect that <<soon it will be generally understood that science ceased to be a justification for the vulgar materialism>>. Here, in Part I, we present a suitable reduction, edited by us, of Majorana's article; while in Part II we add a complete transcription of it. [Since the paper which appeared in "Scientia" contains some errors in the interpretation of Majorana's handwriting, the present versions have been very slightly "corrected" by us]. For the translations into English of Majorana's paper, see Refs.[5,6] below. A more extended Summary (in English, besides in Italian) can be found at the beginning of the present e-print. The interested reader can found all the known biographical documents --apart from the ones discovered during the last two years-- in the book by E.Recami, "Il Caso Majorana: Epistolario, Testimonianze, Documenti" (Mondadori, Milan, 1987 and 1991; Di Renzo Editore, Rome, 2000 and 2002); and in the e-prints arXiv:physics/9810023v4 [physics.hist-ph]; arXiv:0708.2855v1 [physics.hist-ph]; and arXiv:0709.1183 [physics.hist-ph].

A critical task in systems biology is the identification of genes that interact to control cellular processes by transcriptional activation of a set of target genes. Many methods have been developed to use statistical correlations in high-throughput datasets to infer such interactions. However, cellular pathways are highly cooperative, often requiring the joint effect of many molecules, and few methods have been proposed to explicitly identify such higher-order interactions, partially due to the fact that the notion of multivariate statistical dependency itself remains imprecisely defined. We define the concept of dependence among multiple variables using maximum entropy techniques and introduce computational tests for their identification. Synthetic network results reveal that this procedure uncovers dependencies even in undersampled regimes, when the joint probability distribution cannot be reliably estimated. Analysis of microarray data from human B cells reveals that third-order statistics, but not second-order ones, uncover relationships between genes that interact in a pathway to cooperatively regulate a common set of targets.

A nucleotides sequence is identified, in the two (four) letters alphabet, by the the labels of a vector state of an irreducible representation of U_q(sl(2)) (U_q(sl(2) + sl(2))), in the limit q -> 0. A master equation for the distribution function is written, where the intensity of the one-spin flip is assumed to depend from the variation of the labels of the state. In the two letters approximation, the numerically computed equilibrium distribution for short sequences is nicely fitted by a Yule distribution, which is the observed distribution of the ranked short oligonucleotides frequency in DNA. The four letter alphabet description, applied to the codons, is able to reproduce the form of the fitted rank ordered usage frequencies distribution.

We develop a theory of aggregation using statistical mechanical methods. An example of a complicated aggregation system with several levels of structures is peptide/protein self-assembly. The problem of protein aggregation is important for the understanding and treatment of neurodegenerative diseases and also for the development of bio-macromolecules as new materials. We write the effective Hamiltonian in terms of interaction energies between protein monomers, protein and solvent, as well as between protein filaments. The grand partition function can be expressed in terms of a Zimm-Bragg-like transfer matrix, which is calculated exactly and all thermodynamic properties can be obtained. We start with two-state and three-state descriptions of protein monomers using Potts models that can be generalized to include q-states, for which the exactly solvable feature of the model remains. We focus on n X N lattice systems, corresponding to the ordered structures observed in some real fibrils. We have obtained results on nucleation processes and phase diagrams, in which a protein property such as the sheet content of aggregates is expressed as a function of the number of proteins on the lattice and inter-protein or interfacial interaction energies. We have applied our methods to Aβ(1-40) and Curli fibrils and obtained results in good agreement with experiments.

The evolution of the user's content still remains a problem for an accurate recommendation.This is why the current research aims to design Recommender Systems (RS) able to continually adapt information that matches the user's interests. This paper aims to explain this problematic point in outlining the proposals that have been made in research with their advantages and disadvantages.