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The ultra high frequency emission of gravitational waves by binary systems of black holes has recently been investigated in details in the framework of new experimental ideas around resonant cavities. In this article, we consider the case of elliptic trajectories. At fixed masses and frequency, we conclude that the total amount of energy radiated by the system within the bandwidth of the detector can be significantly higher than for circular orbits. However, due to subtle experimental effects, the signal-to-noise ratio is, overall, a decreasing function of the eccentricity. Limits on the maximum distance at which a merging system of black holes can be detected derived for circular orbits are therefore not improved by considering elliptic trajectories. The article is written as pedagogically as possible so as to be accessible to the non-familiar reader.

In this work we present a new theoretical approach to interpreting and reproducing quantum mechanics using trajectory-guided wavelets. Inspired by the 1925 work of Louis de Broglie, we demonstrate that pulses composed of a difference between a retarded wave and an advanced wave (known as antisymmetric waves) are capable of following quantum trajectories predicted by de Broglie-Bohm theory (also known as Bohmian mechanics). Our theory reproduces the main results of orthodox quantum mechanics and, unlike Bohmian theory, is local in the Bell sense. We show that this is linked to the superdeterminism and past-future (anti)symmetry of our theory., Comment: submitted, comments welcome

Variational Quantum Imaginary Time Evolution (VQITE) is a leading technique for ground state preparation on quantum computers. A significant computational challenge of VQITE is the determination of the quantum geometric tensor. We show that requiring the imaginary-time evolution to be correct only when projected onto a chosen set of operators allows to achieve a twofold reduction in circuit depth by bypassing fidelity estimations, and reduces measurement complexity from quadratic to linear in the number of parameters. We demonstrate by a simulation of the transverse-field Ising model that our algorithm achieves a several orders of magnitude improvement in the number of measurements required for the same accuracy.

We leverage generative large language models for language learning applications, focusing on estimating the difficulty of foreign language texts and simplifying them to lower difficulty levels. We frame both tasks as prediction problems and develop a difficulty classification model using labeled examples, transfer learning, and large language models, demonstrating superior accuracy compared to previous approaches. For simplification, we evaluate the trade-off between simplification quality and meaning preservation, comparing zero-shot and fine-tuned performances of large language models. We show that meaningful text simplifications can be obtained with limited fine-tuning. Our experiments are conducted on French texts, but our methods are language-agnostic and directly applicable to other foreign languages., Comment: 10 pages, 4 figures

Transition metal dichalcogenides (TMD) like MoSe$_2$ exhibit remarkable optical properties such as intense photoluminescence (PL) in the monolayer form. To date, narrow-linewidth PL is only achieved in micrometer-sized exfoliated TMD flakes encapsulated in hexagonal boron nitride (hBN). In this work, we develop a growth strategy to prepare monolayer MoSe$_2$ on hBN flakes by molecular beam epitaxy in the van der Waals regime. It constitutes the first step towards the development of large area single crystalline TMDs encapsulated in hBN for potential integration in electronic or opto-electronic devices. For this purpose, we define a two-step growth strategy to achieve monolayer-thick MoSe$_2$ grains on hBN flakes. The high quality of MoSe$_2$ allows us to detect very narrow PL linewidth down to 5.5 meV at 13 K, comparable to the one of encapsulated exfoliated MoSe$_2$ flakes. Moreover, sizeable PL can be detected at room temperature as well as clear reflectivity signatures of A, B and charged excitons., Comment: 6 pages, 6 figures

We develop a quantum simulation-based approach for studying properties of strongly correlated magnetic materials at increasing scale. We consider a paradigmatic example of a quantum spin liquid (QSL) state hosted by the honeycomb Kitaev model, and use a trainable symmetry-guided ansatz for preparing its ground state. Applying the tools of quantum subspace expansion (QSE), Hamiltonian operator approximation, and overlap measurements, we simulate the QSL at zero temperature and finite magnetic field, thus moving outside of the symmetric subspace. Next, we implement a protocol for quantum subspace expansion-based measurement of spin-spin correlation functions. Finally, we perform QSE-based simulation of the dynamical structure factor obtained from Green's functions of the finite field Kitaev model. Our results show that quantum simulators offer an insight to quasiparticle properties of strongly correlated magnets and can become a valuable tool for studying material science., Comment: 8 pages, 6 figures

Transition Metal Dichalcogenides (TMD) are layered materials obtained by stacking two-dimensional sheets weakly bonded by van der Waals interactions. In bulk TMD, band dispersions are observed in the direction normal to the sheet plane (z-direction) due to the hybridization of out-of-plane orbitals but no kz-dispersion is expected at the single-layer limit. Using angle-resolved photoemission spectroscopy, we precisely address the two-dimensional to three-dimensional crossover of the electronic band structure of epitaxial WSe$_2$ thin films. Increasing number of discrete electronic states appears in given kz-ranges while increasing the number of layers. The continuous bulk dispersion is nearly retrieved for 6-sheet films. These results are reproduced by calculations going from a relatively simple tight-binding model to a sophisticated KKR-Green's function calculation. This two-dimensional system is hence used as a benchmark to compare different theoretical approaches.

In the present work, we exhibit a class of self-descriptive sequences that can be explicitly computed and whose frequencies are known. In particular, as a corollary of our main result, we prove that the sequence introduced in \citeBJM23 has the expected frequencies of occurrences., Comment: In Proceedings GASCom 2024, arXiv:2406.14588

Two-dimensional (2D) materials like transition metal dichalcogenides (TMD) have proved to be serious candidates to replace silicon in several technologies with enhanced performances. In this respect, the two remaining challenges are the wafer scale growth of TMDs and their integration into operational devices using clean room compatible processes. In this work, two different CMOS-compatible protocols are developed for the fabrication of MoS$_2$-based memristors, and the resulting performances are compared. The quality of MoS$_2$ at each stage of the process is characterized by Raman spectroscopy and x-ray photoemission spectroscopy. In the first protocol, the structure of MoS$_2$ is preserved during transfer and patterning processes. However, a polymer layer with a minimum thickness of 3 nm remains at the surface of MoS$_2$ limiting the electrical switching performances. In the second protocol, the contamination layer is completely removed resulting in improved electrical switching performances and reproducibility. Based on physico-chemical and electrical results, the switching mechanism is discussed in terms of conduction through grain boundaries., Comment: 11 pages, 6 figures

Two-dimensional and van der Waals ferromagnets are ideal platform to study low dimensional magnetism and proximity effects in van der Waals heterostructures. Their ultimate two dimensional character offers also the opportunity to easily adjust their magnetic properties using strain or electric fields. Among 2D ferromagnets, the Cr$_{1+x}$Te$_2$ compounds with $x$=0-1 are very promising because their magnetic properties depend on the amount of self-intercalated Cr atoms between pure CrTe$_2$ layers and the Curie temperature (T$_C$) can reach room temperature for certain compositions. Here, we investigate the evolution of the composition, structural and magnetic properties of thin Cr$_{1.33}$Te$_2$ (Cr$_2$Te$_3$) films epitaxially grown on graphene upon annealing. We observe a transition above 450{\deg}C from the Cr$_{1.33}$Te$_2$ phase with perpendicular magnetic anisotropy and a T$_C$ of 180 K to a composition close to Cr$_{1.39}$Te$_2$ with in-plane magnetic anisotropy and a T$_C$ of 240-250 K. This phase remains stable up to 650{\deg}C above which a pure Cr film starts to form. This work demonstrates the complex interplay between intercalated Cr, lattice parameters and magnetic properties in Cr$_{1+x}$Te$_2$ compounds., Comment: 5 pages, 5 figures

To alleviate the performance and energy overheads of contemporary applications with large data footprints, we propose the Two Level Perceptron (TLP) predictor, a neural mechanism that effectively combines predicting whether an access will be off-chip with adaptive prefetch filtering at the first-level data cache (L1D). TLP is composed of two connected microarchitectural perceptron predictors, named First Level Predictor (FLP) and Second Level Predictor (SLP). FLP performs accurate off-chip prediction by using several program features based on virtual addresses and a novel selective delay component. The novelty of SLP relies on leveraging off-chip prediction to drive L1D prefetch filtering by using physical addresses and the FLP prediction as features. TLP constitutes the first hardware proposal targeting both off-chip prediction and prefetch filtering using a multi-level perceptron hardware approach. TLP only requires 7KB of storage. To demonstrate the benefits of TLP we compare its performance with state-of-the-art approaches using off-chip prediction and prefetch filtering on a wide range of single-core and multi-core workloads. Our experiments show that TLP reduces the average DRAM transactions by 30.7% and 17.7%, as compared to a baseline using state-of-the-art cache prefetchers but no off-chip prediction mechanism, across the single-core and multi-core workloads, respectively, while recent work significantly increases DRAM transactions. As a result, TLP achieves geometric mean performance speedups of 6.2% and 11.8% across single-core and multi-core workloads, respectively. In addition, our evaluation demonstrates that TLP is effective independently of the L1D prefetching logic., Comment: To appear in 30th International Symposium on High-Performance Computer Architecture (HPCA), 2024

Our aim is to analyze the relevance of the mentor-child paradigm with a robot for individuals with Autism Spectrum Disorders, and the adaptations required. This method could allow a more reliable evaluation of the socio-cognitive abilities of individuals with autism, which may have been underestimated due to pragmatic factors., Comment: 5 pages

We present a study by Scanning Tunneling Microscopy, supported by ab initio calculations, of the interaction between graphene and monolayer (semiconducting) PtSe$_2$ as a function of the twist angle ${\theta}$ between the two layers. We analyze the PtSe$_2$ contribution to the hybrid interface states that develop within the bandgap of the semiconductor to probe the interaction. The experimental data indicate that the interlayer coupling increases markedly with the value of ${\theta}$, which is confirmed by ab initio calculations. The moir\'e patterns observed within the gap are consistent with a momentum conservation rule between hybridized states, and the strength of the hybridization can be qualitatively described by a perturbative model., Comment: Article: 12 pages, 3 figures; Supporting Informations: 19 pages, 12 figures

There has been extensive activity exploring the doping of semiconducting two-dimensional (2D) transition metal dichalcogenides in order to tune their electronic and magnetic properties. The outcome of doping depends on various factors, including the intrinsic properties of the host material, the nature of the dopants used, their spatial distribution as well as their interactions with other types of defects. A thorough atomic-level analysis is essential to fully understand these mechanisms. In this work, vanadium doped WSe$_2$ monolayer grown by molecular beam epitaxy is investigated using four-dimensional scanning transmission electron microscopy (4D-STEM). Through center of mass-based reconstruction, atomic scale maps are produced, allowing the visualization of both the electric field and the electrostatic potential around individual V atoms. To provide quantitative insights, these results are successfully compared with multislice image simulations based on ab initio calculations, accounting for lens aberrations. Finally, a negative charge around the V dopants is detected as a drop in the electrostatic potential, unambiguously demonstrating that 4D-STEM can be used to detect and to accurately analyze single dopant charge states in semiconducting 2D materials., Comment: 17 pages, 4 figures and Supporting Information

The aim of this paper is to provide a stochastic version under location uncertainty of the compressible Navier-Stokes equations. The modelling under location uncertainty setting is used here to derive a physically consistent stochastic dynamical system for compressible flows. It relies on an extended stochastic version of the Reynolds transport theorem involving stochastic source terms. In a similar way as in the deterministic case, this conservation theorem is applied to density, momentum and total energy in order to obtain a transport equation of the primitive variables, i.e. density, velocity and temperature. For the modelling of ocean dynamics, the transport of mass fraction of species, such as salinity, is also considered. We show that performing low Mach and Boussinesq approximations to this more general set of equations allows us to recover previous versions of incompressible stochastic Navier-Stokes equations and the stochastic Boussinesq equations, respectively. Finally, we provide some research directions on the use of this general set of equations in the perspective of relaxing the Boussinesq and hydrostatic approximation for ocean modelling.

Non-Markovian noise can be a significant source of errors in superconducting qubits. We develop gate sequences utilising mirrored pseudoidentities that allow us to characterise and model the effects of non-Markovian noise on both idle and driven qubits. We compare three approaches to modelling the observed noise: (i) a Markovian noise model, (ii) a model including interactions with a two-level system (TLS), (iii) a model utilising the post Markovian master equation (PMME), which we show to be equivalent to the qubit-TLS model in certain regimes. When running our noise characterisation circuits on a superconducting qubit device we find that purely Markovian noise models cannot reproduce the experimental data. Our model based on a qubit-TLS interaction, on the other hand, is able to closely capture the observed experimental behaviour for both idle and driven qubits. We investigate the stability of the noise properties of the hardware over time, and find that the parameter governing the qubit-TLS interaction strength fluctuates significantly even over short time-scales of a few minutes. Finally, we evaluate the changes in the noise parameters when increasing the qubit drive pulse amplitude. We find that although the hardware noise parameters fluctuate significantly over different days, their drive pulse induced relative variation is rather well defined within computed uncertainties: both the phase error and the qubit-TLS interaction strength change significantly with the pulse strength, with the phase error changing quadratically with the amplitude of the applied pulse. Since our noise model can closely describe the behaviour of idle and driven qubits, it is ideally suited to be used in the development of quantum error mitigation and correction methods.

Epitaxial growth has become a promising route to achieve highly crystalline continuous two-dimensional layers. However, high-quality layer production with expected electrical properties is still challenging due to the defects induced by the coalescence between imperfectly aligned domains. In order to control their intrinsic properties at the device scale, the synthesized materials should be described as a patchwork of coalesced domains. Here, we report multi-scale and multistructural analysis on highly oriented epitaxial WS$_2$ and WSe$_2$ monolayers using scanning transmission electron microscopy (STEM) techniques. Characteristic domain junctions are first identified and classified based on the detailed atomic structure analysis using aberration corrected STEM imaging. Mapping orientation, polar direction and phase at the micrometer scale using four-dimensional STEM enabled to access the density and the distribution of the specific domain junctions. Our results validate a readily applicable process for the study of highly oriented epitaxial transition metal dichalcogenides, providing an overview of synthesized materials from large scale down to atomic scale with multiple structural information., Comment: 22 pages, 6 figures and Supplementary Information

Matrix Product States (MPS) and Operators (MPO) have been proven to be a powerful tool to study quantum many-body systems but are restricted to moderately entangled states as the number of parameters scales exponentially with the entanglement entropy. While MPS can efficiently find ground states of 1D systems, their capacities are limited when simulating their dynamics, where the entanglement can increase ballistically with time. On the other hand, quantum devices appear as a natural platform to encode and perform the time evolution of correlated many-body states. However, accessing the regime of long-time dynamics is hampered by quantum noise. In this study we use the best of worlds: the short-time dynamics is efficiently performed by MPSs, compiled into short-depth quantum circuits, and is performed further in time on a quantum computer thanks to efficient MPO-optimized quantum circuits. We quantify the capacities of this hybrid classical-quantum scheme in terms of fidelities taking into account a noise model. We show that using classical knowledge in the form of tensor networks provides a way to better use limited quantum resources and lowers drastically the noise requirements to reach a practical quantum advantage. Finally we successfully demonstrate our approach with an experimental realization of the technique. Combined with efficient circuit transpilation we simulate a 10-qubit system on an actual quantum device over a longer time scale than low-bond-dimension MPSs and purely quantum Trotter evolution.

The first thermal machines steered the industrial revolution, but their quantum analogs have yet to prove useful. Here, we demonstrate a useful quantum absorption refrigerator formed from superconducting circuits. We use it to reset a transmon qubit to a temperature lower than that achievable with any one available bath. The process is driven by a thermal gradient and is autonomous -- requires no external control. The refrigerator exploits an engineered three-body interaction between the target qubit and two auxiliary qudits coupled to thermal environments. The environments consist of microwave waveguides populated with synthesized thermal photons. The target qubit, if initially fully excited, reaches a steady-state excited-level population of $5\times10^{-4} \pm 5\times10^{-4}$ (an effective temperature of 23.5~mK) in about 1.6~$\mu$s. Our results epitomize how quantum thermal machines can be leveraged for quantum information-processing tasks. They also initiate a path toward experimental studies of quantum thermodynamics with superconducting circuits coupled to propagating thermal microwave fields., Comment: 7 pages (4 figures) + supplementary information (6 pages)

The LHCb upgrade represents a major change of the experiment. The detectors have been almost completely renewed to allow running at an instantaneous luminosity five times larger than that of the previous running periods. Readout of all detectors into an all-software trigger is central to the new design, facilitating the reconstruction of events at the maximum LHC interaction rate, and their selection in real time. The experiment's tracking system has been completely upgraded with a new pixel vertex detector, a silicon tracker upstream of the dipole magnet and three scintillating fibre tracking stations downstream of the magnet. The whole photon detection system of the RICH detectors has been renewed and the readout electronics of the calorimeter and muon systems have been fully overhauled. The first stage of the all-software trigger is implemented on a GPU farm. The output of the trigger provides a combination of totally reconstructed physics objects, such as tracks and vertices, ready for final analysis, and of entire events which need further offline reprocessing. This scheme required a complete revision of the computing model and rewriting of the experiment's software., Comment: All figures and tables, along with any supplementary material and additional information, are available at http://lhcbproject.web.cern.ch/lhcbproject/Publications/LHCbProjectPublic/LHCb-DP-2022-002.html (LHCb public pages)

Terahertz (THz) Spintronic emitters based on ferromagnetic/metal junctions have become an important technology for the THz range, offering powerful and ultra-large spectral bandwidths. These developments have driven recent investigations of two-dimensional (2D) materials for new THz spintronic concepts. 2D materials, such as transition metal dichalcogenides (TMDs), are ideal platforms for SCC as they possess strong spin-orbit coupling (SOC) and reduced crystal symmetries. Moreover, SCC and the resulting THz emission can be tuned with the number of layers, electric field or strain. Here, epitaxially grown 1T-PtSe$_2$ and sputtered Ferromagnet (FM) heterostructures are presented as a novel THz emitter where the 1T crystal symmetry and strong SOC favor SCC. High quality of as-grown PtSe$_2$ layers is demonstrated and further FM deposition leaves the PtSe$_2$ unaffected, as evidenced with extensive characterization. Through this atomic growth control, the unique thickness dependent electronic structure of PtSe$_2$ allows the control of the THz emission by SCC. Indeed, we demonstrate the transition from the inverse Rashba-Edelstein effect in one monolayer to the inverse spin Hall effect in multilayers. This band structure flexibility makes PtSe$_2$ an ideal candidate as a THz spintronic 2D material and to explore the underlying mechanisms and engineering of the SCC for THz emission., Comment: 26 pages, 6 figures

In recent years, graph-processing has become an essential class of workloads with applications in a rapidly growing number of fields. Graph-processing typically uses large input sets, often in multi-gigabyte scale, and data-dependent graph traversal methods exhibiting irregular memory access patterns. Recent work demonstrates that, due to the highly irregular memory access patterns of data-dependent graph traversals, state-of-the-art graph-processing workloads spend up to 80 % of the total execution time waiting for memory accesses to be served by the DRAM. The vast disparity between the Last Level Cache (LLC) and main memory latencies is a problem that has been addressed for years in computer architecture. One of the prevailing approaches when it comes to mitigating this performance gap between modern CPUs and DRAM is cache replacement policies. In this work, we characterize the challenges drawn by graph-processing workloads and evaluate the most relevant cache replacement policies., Comment: Extended abstract submitted to the 10th BSC Doctoral Symposium

Solving the Anderson impurity model typically involves a two-step process, where one first calculates the ground state of the Hamiltonian, and then computes its dynamical properties to obtain the Green's function. Here we propose a hybrid classical/quantum algorithm where the first step is performed using a classical computer to obtain the tensor network ground state as well as its quantum circuit representation, and the second step is executed on the quantum computer to obtain the Green's function. Our algorithm exploits the efficiency of tensor networks for preparing ground states on classical computers, and takes advantage of quantum processors for the evaluation of the time evolution, which can become intractable on classical computers. We demonstrate the algorithm using 24 qubits on a quantum computing emulator for SrVO$_3$ with a multi-orbital Anderson impurity model within the dynamical mean field theory. The tensor network based ground state quantum circuit preparation algorithm can also be performed for up to 60 qubits with our available computing resources, while the state vector emulation of the quantum algorithm for time evolution is beyond what is accessible with such resources. We show that, provided the tensor network calculation is able to accurately obtain the ground state energy, this scheme does not require a perfect reproduction of the ground state wave function on the quantum circuit to give an accurate Green's function. This hybrid approach may lead to quantum advantage in materials simulations where the ground state can be computed classically, but where the dynamical properties cannot.

We propose a variational quantum eigensolver (VQE) algorithm that uses a fault-tolerant gate-set, and is hence suitable for implementation on a future error-corrected quantum computer. VQE quantum circuits are typically designed for near-term, noisy quantum devices and have continuously parameterized rotation gates as the central building block. On the other hand, a fault-tolerant quantum computer can only implement a discrete set of logical gates, such as the so-called Clifford+T gates. We show that the energy minimization of VQE can be performed with such a fault-tolerant discrete gate-set, where we use the Ross-Selinger algorithm to transpile the continuous rotation gates to the error-correctable Clifford+T gate-set. We find that there is no loss of convergence when compared to the one of parameterized circuits if an adaptive accuracy of the transpilation is used in the VQE optimization. State preparation with VQE requires only a moderate number of T-gates, depending on the system size and transpilation accuracy. We demonstrate these properties on emulators for two prototypical spin models with up to 16 qubits. This is a promising result for the integration of VQE and more generally variational algorithms in the emerging fault-tolerant setting, where they can form building blocks of the general quantum algorithms that will become accessible in a fault-tolerant quantum computer., Comment: 10 pages, 9 figures

Achieving large-scale growth of two-dimensional (2D) ferromagnetic materials with high Curie temperature (TC) and perpendicular magnetic anisotropy (PMA) is highly desirable for the development of ultra-compact magnetic sensors and magnetic memories. In this context, van der Waals (vdW) Cr2Te3 appears as a promising candidate. Bulk Cr2Te3 exhibits strong PMA and a TC of 180 K. Moreover, both PMA and TC might be adjusted in ultrathin films by engineering composition, strain, or applying an electric field. In this work, we demonstrate the molecular beam epitaxy (MBE) growth of vdW heterostructures of five-monolayer quasi-freestanding Cr2Te3 on three classes of 2D materials: graphene (semimetal), WSe2 (semiconductor) and Bi2Te3 (topological insulator). By combining structural and chemical analysis down to the atomic level with ab initio calculations, we confirm the single crystalline character of Cr2Te3 films on the 2D materials with sharp vdW interfaces. They all exhibit PMA and TC close to the bulk Cr2Te3 value of 180 K. Ab initio calculations confirm this PMA and show how its strength depends on strain. Finally, Hall measurements reveal a strong anomalous Hall effect, which changes sign at a given temperature. We theoretically explain this effect by a sign change of the Berry phase close to the Fermi level. This transition temperature depends on the 2D material in proximity, notably as a consequence of charge transfer. MBE-grown Cr2Te3/2D material bilayers constitute model systems for the further development of spintronic devices combining PMA, large spin-orbit coupling and sharp vdW interface., Comment: 18 pages, 13 figures and Supplemental Material

Topological insulators (TIs) are a promising class of materials for manipulating the magnetization of an adjacent ferromagnet (FM) through the spin-orbit torque (SOT) mechanism. However, current studies combining TIs with conventional FMs present large device-to-device variations, resulting in a broad distribution of SOT magnitudes. It has been identified that the interfacial quality between the TI and the FM is of utmost importance in determining the nature and efficiency of the SOT. To optimize the SOT magnitude and enable ultra-low-power magnetization switching, an atomically smooth interface is necessary. To this end, we have developed the growth of a full van der Waals FM/TI heterostructure by molecular beam epitaxy. The compensated TI (Bi0.4Sb0.6)2Te3 and ferromagnetic Fe3GeTe2 (FGT) were chosen because of their exceptional crystalline quality, low carrier concentration in BST and relatively large Curie temperature and perpendicular magnetic anisotropy in FGT. We characterized the magnitude of the SOTs by using thorough harmonic magnetotransport measurements and showed that the magnetization of an ultrathin FGT film could be switched with a current density Jc < 10^10 A/m^2. In comparison to previous studies utilizing traditional FMs, our findings are highly reliable, displaying little to no variation between devices.

Toxic comment detection on social media has proven to be essential for content moderation. This paper compares a wide set of different models on a highly skewed multi-label hate speech dataset. We consider inference time and several metrics to measure performance and bias in our comparison. We show that all BERTs have similar performance regardless of the size, optimizations or language used to pre-train the models. RNNs are much faster at inference than any of the BERT. BiLSTM remains a good compromise between performance and inference time. RoBERTa with Focal Loss offers the best performance on biases and AUROC. However, DistilBERT combines both good AUROC and a low inference time. All models are affected by the bias of associating identities. BERT, RNN, and XLNet are less sensitive than the CNN and Compact Convolutional Transformers.

The Oldenburger-Kolakoski sequence is the only infinite sequence over the alphabet $\{1,2\}$ that starts with $1$ and is its own run-length encoding. In the present work, we take a step back from this largely known and studied sequence by introducing some randomness in the choice of the letters written. This enables us to provide some results on the convergence of the density of $1$'s in the resulting sequence. When the choice of the letters is given by an infinite sequence of i.i.d. random variables or by a Markov chain, the average densities of letters converge. Moreover, in the case of i.i.d. random variables, we are able to prove that the densities even almost surely converge.