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Dense lattices of photonic crystals can serve as artificial materials, with light propagation in these structures described by effective material parameters that surpass the capabilities of natural materials. In this study, we introduce a metamaterial that supports quadrupole magnetization, a characteristic rarely observed in existing structures. We experimentally demonstrate a magnetic quadrupole metamaterial associated with Mie-resonance-excited stop bands below the Bragg band. Additionally, we develop a theoretical model that addresses both dispersion and boundary conditions within this framework. Using a Fabry-Perot resonator as a case study, we validate our model and reveal that the quadrupole metamaterial can exhibit a markedly different reflection/transmission spectrum, including zero reflection at normal incidence. Our findings underscore the practical potential for both experimental and theoretical investigations of metamaterials that extend beyond the dipole approximation.

The electronic band structure, describing the periodic dependence of electronic quantum states on lattice momentum in reciprocal space, is a fundamental concept in solid-state physics. However, it's only well-defined for static nuclei. To account for thermodynamic effects, this concept must be generalized by introducing the temperature-dependent spectral function, which characterizes the finite-width distributions of electronic quantum states at each reciprocal vector. Many-body perturbation theory can compute spectral functions and associated observables, but it approximates the dynamics of nuclei and its coupling to the electrons using the harmonic approximation and linear-order electron-phonon coupling elements, respectively. These approximations may fail at elevated temperatures or for mobile atoms. To avoid inaccuracies, the electronic spectral function can be obtained non-perturbatively, capturing higher-order couplings between electrons and vibrational degrees of freedom. This process involves recovering the representation of supercell bands in the first Brillouin zone of the primitive cell, a process known as unfolding. In this contribution, we describe the implementation of the band-structure unfolding technique in the electronic-structure theory package FHI-aims and the updates made since its original development., Comment: This is a contribution/chapter to the upcoming "Roadmap on Advancements of the FHI-aims Software Package"

Inspired by the recent success of machine-learned interatomic potentials for crystal structure prediction of the inorganic crystals, we present a methodology that exploits Moment Tensor Potentials and active learning (based on maxvol algorithm) to accelerate structure prediction of molecular crystals. Benzene and glycine are used as test systems. Interestingly, among obtained low energy structures of benzene we have found a peculiar polymeric benzene structure.

The recently emerged Mie resonant metaphotonics (or Mietronics) provides novel opportunities for subwavelength optics. Mietronics employs resonances in isolated nanoparticles and structured surfaces. We present a brief summary of the key concepts underpinning this rapidly developing area of research, using the examples of isolated high-index dielectric subwavelength particles. We also discuss recent advances and future trends in designs of high-Q elements for efficient resonant spatial and temporal control of light., Comment: 10 pages, 4 figures

In this article, we consider designs of simple analog artificial neural networks based on adiabatic Josephson cells with a sigmoid activation function. A new approach based on the gradient descent method is developed to adjust the circuit parameters, allowing efficient signal transmission between the network layers. The proposed solution is demonstrated on the example of the system implementing XOR and OR logical operations., Comment: 13 pages, 12 figures

Calculations of heat transport in crystalline materials have recently become mainstream, thanks to machine-learned interatomic potentials that allow for significant computational cost reductions while maintaining the accuracy of first-principles calculations. Moment tensor potentials (MTP) are among the most efficient and accurate models in this regard. In this study, we demonstrate the application of MTP to the calculation of the lattice thermal conductivity of alpha and beta Ga2O3. Although MTP is commonly employed for lattice thermal conductivity calculations, the advantages of applying the active learning methodology for potential generation is often overlooked. Here, we emphasize its importance and illustrate how it enables the generation of a robust and accurate interatomic potential while maintaining a moderate-sized training dataset.

Fluoride salts demonstrate significant potential for applications in next-generation nuclear reactors, necessitating a comprehensive understanding of their thermophysical properties for technological advancements. Experimental measurement of these properties poses challenges, due to factors such as high temperatures, impurity control, and corrosion. Consequently, precise computational modeling methods become essential for predicting the thermophysical properties of molten salts. In this work, we performed molecular dynamics (MD) simulations of several thermophysical properties of the eutectic salt mixture LiF-NaF-KF (FLiNaK) melt, including density, self-diffusion coefficients, viscosity, and thermal conductivity. We demonstrated the successful application of moment tensor potentials (MTP) as an accurate model for interatomic interactions in FLiNaK. Our results on thermophysical properties calculations exhibit strong agreement with experimental data. An important aspect of our methodology is the incorporation of an active learning scheme, which enables the generation of a robust and accurate potential, while maintaining a moderate-sized training dataset.

Nowadays, methods and techniques of Machine Learning and Deep Learning are being used in various scientific areas. They help to automatize calculations without losing in quality. In this paper the applying of convolutional neural network was considered in frame of problems from statistical physics and computer simulation of magnetic films. In a frame of the first task, CNN was used to determine critical Curie point for Ising model on 2D square lattice. Obtained results were compared with classical Monte-Carlo methods and exact solution. Systems of various lattice sizes and the influence of the size effect on the results' accuracy were considered. Also, authors considered the classical two-dimensional Heisenberg model, a spin system with direct short-range exchange, and studied of its competition with the Dzyaloshinskii-Moriya interaction. A neural network was applied to the recognition of Spiral (Sp), Spiral-skyrmion (SpSk) Skyrmion (Sk), Skyrmion-ferromagnetic (SkF) and Ferromagnetic (FM) phases of the Heisenberg spin system with magnetic skyrmions. The advantage of CNN's application over conventional methods for determination of skyrmion's phases was revealed., Comment: 6 pages, 3 figures

Exceptional points in an optical dimer of spheres, which have the same size and operate in the spectral region of the dipolar resonance, are considered. By choosing different materials of these spheres, we can offset the radiative loss and create a gain-loss contrast to achieve a parity-time (PT)-symmetric dimer. In this case, an exceptional point corresponds to the point where the PT symmetry is broken. At the same time, if we consider a symmetric dimer, where both spheres are made of the same material (which may have a purely real dielectric constant), exceptional points occurring due to the radiative loss non-Hermiticity can also be observed. We study the transition between the two regimes and demonstrate that the exceptional point emerges due to the retardative nature of the coupling between the spheres, which makes the equation for eigenfrequencies nonlinear and allows it to have nontrivial solutions even when there is no contrast between the spheres., Comment: 9 pages, 5 figures

Next-generation wireless communication systems impose much stricter requirements for transmission rate, latency, and reliability. The peak data rate of 6G networks should be no less than 1 Tb/s, which is comparable to existing long-haul optical transport networks. It is believed that using long error-correcting codes (ECC) with soft-decision decoding (SDD) is not feasible in this case due to the resulting high power consumption. On the other hand, ECC with hard-decision decoding (HDD) suffers from significant performance degradation. In this paper, we consider a concatenated solution consisting of an outer long HDD code and an inner short SDD code. The latter code is a crucial component of the system and the focus of our research. Due to its short length, the code cannot correct all errors, but it is designed to minimize the number of errors. Such codes are known as error-reducing codes. We investigate the error-reducing properties of superposition codes. Initially, we explore sparse regression codes (SPARCs) with Gaussian signals. This approach outperforms error-reducing binary LDPC codes optimized by Barakatain, et al. (2018) in terms of performance but faces limitations in practical applicability due to high implementation complexity. Subsequently, we propose an LDPC-based superposition code scheme with low-complexity soft successive interference cancellation (SIC) decoding. This scheme demonstrates comparable performance to SPARCs while maintaining manageable complexity. Numerical results were obtained for inner codes with an overhead (OH) of 8.24% within a concatenated scheme (15% OH) with an outer hard-decision decoded staircase code (6.25% OH)., Comment: This work has been submitted to the IEEE for possible publication

Deep neural network (DNN)-based channel decoding is widely considered in the literature. The existing solutions are investigated for the case of hard output, i.e. when the decoder returns the estimated information word. At the same time, soft-output decoding is of critical importance for iterative receivers and decoders. In this paper, we focus on the soft-output DNN-based decoding problem. We start with the syndrome-based approach proposed by Bennatan et al. (2018) and modify it to provide soft output in the AWGN channel. The new decoder can be considered as an approximation of the MAP decoder with smaller computation complexity. We discuss various regularization functions for joint DNN-MAP training and compare the resulting distributions for [64, 45] BCH code. Finally, to demonstrate the soft-output quality we consider the turbo-product code with [64, 45] BCH codes as row and column codes. We show that the resulting DNN-based scheme is very close to the MAP-based performance and significantly outperforms the solution based on the Chase decoder. We come to the conclusion that the new method is prospective for the challenging problem of DNN-based decoding of long codes consisting of short component codes., Comment: This work has been submitted to the IEEE for possible publication

We investigate the fundamental limits of the unsourced random access over the binary-input Gaussian channel. By fundamental limits, we mean the minimal energy per bit required to achieve the target per-user probability of error. The original method proposed by Y. Polyanskiy (2017) and based on Gallager's trick does not work well for binary signaling. We utilize Fano's method, which is based on the choice of the so-called ``good'' region. We apply this method for the cases of Gaussian and binary codebooks and obtain two achievability bounds. The first bound is very close to Polyanskiy's bound but does not lead to any improvement. At the same time, the numerical results show that the bound for the binary case practically coincides with the bound for the Gaussian codebook. Thus, we conclude that binary modulation does not lead to performance degradation, and energy-efficient schemes with binary modulation do exist., Comment: This work has been submitted to the IEEE for possible publication

In this study, we delve into an emerging optimization challenge involving a black-box objective function that can only be gauged via a ranking oracle-a situation frequently encountered in real-world scenarios, especially when the function is evaluated by human judges. Such challenge is inspired from Reinforcement Learning with Human Feedback (RLHF), an approach recently employed to enhance the performance of Large Language Models (LLMs) using human guidance. We introduce ZO-RankSGD, an innovative zeroth-order optimization algorithm designed to tackle this optimization problem, accompanied by theoretical assurances. Our algorithm utilizes a novel rank-based random estimator to determine the descent direction and guarantees convergence to a stationary point. Moreover, ZO-RankSGD is readily applicable to policy optimization problems in Reinforcement Learning (RL), particularly when only ranking oracles for the episode reward are available. Last but not least, we demonstrate the effectiveness of ZO-RankSGD in a novel application: improving the quality of images generated by a diffusion generative model with human ranking feedback. Throughout experiments, we found that ZO-RankSGD can significantly enhance the detail of generated images with only a few rounds of human feedback. Overall, our work advances the field of zeroth-order optimization by addressing the problem of optimizing functions with only ranking feedback, and offers a new and effective approach for aligning Artificial Intelligence (AI) with human intentions., Comment: ICLR 2024

Neural networks that satisfy invariance with respect to input permutations have been widely studied in machine learning literature. However, in many applications, only a subset of all input permutations is of interest. For heterogeneous graph data, one can focus on permutations that preserve node types. We fully characterize linear layers invariant to such permutations. We verify experimentally that implementing these layers in graph neural network architectures allows learning important node interactions more effectively than existing techniques. We show that the dimension of space of these layers is given by a generalization of Bell numbers, extending the work (Maron et al., 2019). We further narrow the invariant network design space by addressing a question about the sizes of tensor layers necessary for function approximation on graph data. Our findings suggest that function approximation on a graph with $n$ nodes can be done with tensors of sizes $\leq n$, which is tighter than the best-known bound $\leq n(n-1)/2$. For $d \times d$ image data with translation symmetry, our methods give a tight upper bound $2d - 1$ (instead of $d^{4}$) on sizes of invariant tensor generators via a surprising connection to Davenport constants.

Periodicity is usually assumed to be the necessary and sufficient condition for the formation of band gaps, i.e., energy bands with a suppressed density of states. Here, we check this premise by analyzing the band gap properties of three structures that differ in the degree of periodicity and ordering. We consider a photonic crystal, disordered lattice, and ordered but nonperiodic quasicrystalline structure. A real-space metric allows us to compare the degree of periodicity of these different structures. Using this metric, we reveal that the disordered lattice and the ordered quasicrystal can be attributed to the same group of material structures. We examine the density of their photonic states both theoretically and experimentally. The analysis reveals that despite their dramatically different degrees of periodicity, the photonic crystal and the quasicrystalline structure demonstrate an almost similar suppression of the density of states. Our results give new insight into the physical mechanisms resulting in the formation of band gaps., Comment: 6 figures

This paper describes the International Disruptive Information Solutions hackathon and one the winning solutions. The purpose of the hackathon was to promote the use of disruptive ICT technologies (e.g. IoT, Big data, AI, blockchain) in urban infrastructures to create innovative waste management solutions in a smart city context. 29 students enrolled into this hackathon and in the end 4 teams submitted their solutions to the challenges. The winning proposal EcoQ, an approach for plogging collecting trashes while jogging, answered more than well to the presented challenge on waste management and engagement. The original idea was extended and partly refocused during an internship. As the outcome of the internship a mobile application for organizing and holding waste collection events was developed. This mobile application was shortly tested in a real environment and it provides a working citizen-centric platform, which enables anyone to arrange waste management events, and motivates other residents to participate in these activities.

All-dielectric nanophotonics opens a venue for a variety of novel phenomena and scattering regimes driven by unique optical effects in semiconductor and dielectric nanoresonators. Their peculiar optical signatures enabled by simultaneous electric and magnetic responses in the visible range pave a way for a plenty of new applications in nano-optics, biology, sensing, etc. In this work, we investigate fabrication-friendly truncated cone resonators and achieve several important scattering regimes due to the inherent property of cones - broken symmetry along the main axis without involving complex geometries or structured beams. We show this symmetry breaking to deliver various kinds of Kerker effects (Generalized and Transverse Kerker effects), non-scattering hybrid anapole regime (simultaneous anapole conditions for all the multipoles in a particle leading to the nearly full scattering suppression) and, vice versa, superscattering regime. Being governed by the same straightforward geometrical paradigm, discussed effects could greatly simplify the manufacturing process of photonic devices with different functionalities. Moreover, the additional degrees of freedom driven by the conicity open new horizons to tailor light-matter interactions at the nanoscale., Comment: 35 pages, total 12 figures (2 figures in the supplementary)

Contactless acoustic methods were used to determine electrical parameters - electrical conductivity, carrier mobility and their concentration - in single-layer graphene deposited on the surface of lithium niobate., Comment: 7 pages, 9 figures

We propose a design of interlaced wire medium with quasicrystalline lattice based on five-fold rotation symmetry Penrose tiling. The transport properties of this structure are studied. We distinguish two transport regimes, namely, propagation regime related to the low-frequency interval and localization regime in the high-frequency interval. While the former is observed in structures both with and without translation symmetry property, the latter is exclusive for aperiodic structures only. We show that the localization regime is promising for many applications including engineering of effective multi-channel devices for telecommunication and imaging systems.

We consider a coded compressed sensing approach for the unsourced random access and replace the outer tree code proposed by Amalladinne et al. with the list recoverable code capable of correcting t errors. A finite-length random coding bound for such codes is derived. The numerical experiments in the single antenna quasi-static Rayleigh fading MAC show that transition to list recoverable codes correcting t errors improves the performance of coded compressed sensing scheme by 7-10 dB compared to the tree code-based scheme. We propose two practical constructions of outer codes. The first is a modification of the tree code. It utilizes the same code structure, and a key difference is a decoder capable of correcting up to t errors. The second is based on the Reed-Solomon codes and Guruswami-Sudan list decoding algorithm. The first scheme provides an energy efficiency very close to the random coding bound when the decoding complexity is unbounded. But for the practical parameters, the second scheme is better and improves the performance of a tree code-based scheme when the number of active users is less than 200.

Detectors of terahertz radiation based on field-effect transistors (FETs) are among most promising candidates for low-noise passive signal rectification both in imaging systems and wireless communications. However, it was not realised so far that geometric asymmetry of common FET with respect to source-drain interchange is a strong objective to photovoltage harvesting. Here, we break the traditional scheme and reveal the optimally-asymmetric FET structure providing the maximization of THz responsivity. We fabricate a series of graphene transistors with variable top gate position with respect to mid-channel, and compare their sub-THz responsivities in a wide range of carrier densities. We show that responsivity is maximized for input gate electrode shifted toward the source contact. Theoretical simulations show that for large channel resistance, exceeding the gate impedance, such recipe for responsivity maximisation is universal, and holds for both resistive self-mixing and photo-thermoelectric detection pathways. In the limiting case of small channel resistance, the thermoelectric and self-mixing voltages react differently upon changing the asymmetry, which may serve to disentangle the origin of nonlinearities in novel materials., Comment: 6 pages, 4 figures

We consider "odd symplectic Lie algebras" defined in terms of maximal rank skew-symmetric forms. We provide FFLV polytopes for these algebras and prove their standard properties. In particular, we obtain a new graded character formula and PBW bases for an analogue of irreducible representations in terms of lattice points of FFLV polytopes., Comment: 14 pages, reference added