Gauge-invariant formulation of axion-electrodynamics

In this paper, we propose a simple generalization of axion-electrodynamics (AED) for the general case in which Dirac fermion fields and scalar/pseudoscalar axion-like fields are present in the local [Formula: see text]([Formula: see text])[Formula: see text] gauge-invariant Lagrangian of the system. Our primary goal (which is not explored here) is to understand and predict novel phenomena that have no counterpart in standard (pseudoscalar) AED. With this end in view, we discuss on very general grounds, possible processes in which a Dirac field is coupled to axionic fields via the electromagnetic (EM) field.

A simple regulatory architecture allows learning the statistical structure of a changing environment

Bacteria live in environments that are continuously fluctuating and changing. Exploiting any predictability of such fluctuations can lead to an increased fitness. On longer timescales, bacteria can ‘learn’ the structure of these fluctuations through evolution. However, on shorter timescales, inferring the statistics of the environment and acting upon this information would need to be accomplished by physiological mechanisms. Here, we use a model of metabolism to show that a simple generalization of a common regulatory motif (end-product inhibition) is sufficient both for learning continuous-valued features of the statistical structure of the environment and for translating this information into predictive behavior; moreover, it accomplishes these tasks near-optimally. We discuss plausible genetic circuits that could instantiate the mechanism we describe, including one similar to the architecture of two-component signaling, and argue that the key ingredients required for such predictive behavior are readily accessible to bacteria.

Holographic phonons by gauge-axion coupling

In this paper, we show that a simple generalization of the holographic axion model can realize spontaneous breaking of translational symmetry by considering a special gauge-axion higher derivative term. The finite real part and imaginary part of the stress tensor imply that the dual boundary system is a viscoelastic solid. By calculating quasi-normal modes and making a comparison with predictions from the elasticity theory, we verify the existence of phonons and pseudo-phonons, where the latter is realized by introducing a weak explicit breaking of translational symmetry, in the transverse channel. Finally, we discuss how the phonon dynamics affects the charge transport.

On automorphisms of monotone transformation posemigroups

In this paper, we provide a simple generalization of results of Sullivan for [Formula: see text] the full transformation monotone pomonoid and for [Formula: see text] the partial transformation monotone pomonoid by showing that every automorphism of [Formula: see text] and [Formula: see text] is inner induced by the elements of [Formula: see text] the pogroup of all ordered bijections on [Formula: see text]. We also show that [Formula: see text] is isomorphic to [Formula: see text]. Finally, we apply these results to get some more results in this direction.

Remarks on axion-electrodynamics

We propose a simple generalization of axion-electrodynamics (A-ED) for the general case in which both scalar and pseudoscalar axion-like fields are present in the (scalar) Lagrangian of the system. We make some remarks on the problem of finding solutions to the differential equations of motion characterizing the propagation of coupled axion fields and electromagnetic (EM) waves. Our primary goal (which is not explored here) is to understand and predict novel phenomena that have no counterpart in pseudoscalar A-ED. With this end in view, we discuss on very general grounds possible processes related to scalar (and pseudoscalar) axions, e.g., the Primakoff effect; the Compton scattering; and, notably, the EM two-photon axion decay.

An Extremum Principle for Smooth Problems

We derive an extremum principle. It can be treated as an intermediate result between the celebrated smooth-convex extremum principle due to Ioffe and Tikhomirov and the Dubovitskii–Milyutin theorem. The proof of this principle is based on a simple generalization of the Fermat’s theorem, the smooth-convex extremum principle and the local implicit function theorem. An integro-differential example illustrating the new principle is presented.

Trapping of null geodesics in slowly rotating spacetimes

Extremely compact objects containing a region of trapped null geodesics could be of astrophysical relevance due to trapping of neutrinos with consequent impact on cooling processes or trapping of gravitational waves. These objects have previously been studied under the assumption of spherical symmetry. In the present paper, we consider a simple generalization by studying trapping of null geodesics in the framework of the Hartle–Thorne slow-rotation approximation taken to first order in the angular velocity, and considering a uniform-density object with uniform emissivity for the null geodesics. We calculate effective potentials and escape cones for the null geodesics and how they depend on the parameters of the spacetimes, and also calculate the “local” and “global” coefficients of efficiency for the trapping. We demonstrate that due to the rotation the trapping efficiency is different for co-rotating and retrograde null geodesics, and that trapping can occur even for $$R>3GM/c^2$$ R > 3 G M / c 2 , contrary to what happens in the absence of rotation.

KMD clustering: Robust generic clustering of biological data

The challenges of clustering noisy high-dimensional biological data have spawned advanced clustering algorithms that are tailored for specific subtypes of biological datatypes. However, the performance of such methods varies greatly between datasets, they require post hoc tuning of cryptic hyperparameters, and they are often not transferable to other types of data. Here we present a novel generic clustering approach called k minimal distances (KMD) clustering, based on a simple generalization of single and average linkage hierarchical clustering. We show how a generalized silhouette-like function is predictive of clustering accuracy and exploit this property to eliminate the main hyperparameter k. We evaluated KMD clustering on standard simulated datasets, simulated datasets with high noise added, mass cytometry datasets and scRNA-seq datasets. When compared to standard generic and state-of-the-art specialized algorithms, KMD clustering’s performance was consistently better or comparable to that of the best algorithm on each of the tested datasets.