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Spin-photon interfaces based on group-IV colour centres in diamond offer a promising platform for quantum networks. A key challenge in the field is realizing precise single-defect positioning and activation, which is crucial for scalable device fabrication. Here we address this problem by demonstrating a two-step fabrication method for tin vacancy (SnV-) centres that uses site-controlled ion implantation followed by local femtosecond laser annealing with in-situ spectral monitoring. The ion implantation is performed with sub-50 nm resolution and a dosage that is controlled from hundreds of ions down to single ions per site, limited by Poissonian statistics. Using this approach, we successfully demonstrate site-selective creation and modification of single SnV- centres. The technique opens a window onto materials tuning at the single defect level, and provides new insight into defect structures and dynamics during the annealing process. While demonstrated for SnV- centres, this versatile approach can be readily generalised to other implanted colour centres in diamond and wide-bandgap materials., Comment: 10 pages, 4 figures, methods, and supplementary information

This study utilises linear-scaling density functional theory (DFT) and develops a new machine-learning potential for carbon and nitrogen (GAP-CN), based on the carbon potential (GAP20), to investigate the interaction between carbon self-interstitials and nitrogen-vacancy (NV) centers in diamond, focusing on their excited states and diffusion behaviour. From the simulated excited states, 'Bright', 'Spike', and 'Dark' defect configurations are classified based on their absorption spectrum features. Furthermore, machine learning molecular dynamics simulation provides insight into the possible diffusion mechanism of Ci and NV, showing that Ci can diffuse away or recombine with NV. The study yields new insight into the formation of NV defects in diamond for quantum technology applications.

Shape measurement of rod-shaped particles in fluids is an outstanding challenge with applications in characterising synthetic functional nanoparticles and in early warning detection of rod-shaped pathogens in water supplies. However, it is challenging to achieve accurate and real-time measurements at a single particle scale in solution with existing methods. Here we introduce a novel technique to measure the aspect ratio of rod-shaped particles by analysing changes in the polarisation state of a laser beam transmitted through an optical microcavity through which the particle diffuses. The resolution in aspect ratio measurement is found to be around 1%. Our work opens the new possibility of in-situ and single-particle shape measurements, which have promising applications in nanoparticle characterisation, water monitoring, and beyond.

Degree heterogeneity and latent geometry, also referred to as popularity and similarity, are key explanatory components underlying the structure of real-world networks. The relationship between these components and the statistical complexity of networks is not well understood. We introduce a parsimonious normalised measure of statistical complexity for networks. The measure is trivially 0 in regular graphs and we prove that this measure tends to 0 in Erd\"os-R\'enyi random graphs in the thermodynamic limit. We go on to demonstrate that greater complexity arises from the combination of heterogeneous and geometric components to the network structure than either on their own. Further, the levels of complexity achieved are similar to those found in many real-world networks. However, we also find that real-world networks establish connections in a way which increases complexity and which our null models fail to explain. We study this using ten link growth mechanisms and find that only one mechanism successfully and consistently replicates this phenomenon -- probabilities proportional to the exponential of the number of common neighbours between two nodes. Common neighbours is a mechanism which implicitly accounts for degree heterogeneity and latent geometry. This explains how a simple mechanism facilitates the growth of statistical complexity in real-world networks., Comment: 20 pages, 6 figures

Searching the vast chemical space for drug-like molecules that bind with a protein pocket is a challenging task in drug discovery. Recently, structure-based generative models have been introduced which promise to be more efficient by learning to generate molecules for any given protein structure. However, since they learn the distribution of a limited protein-ligand complex dataset, structure-based methods do not yet outperform optimization-based methods that generate binding molecules for just one pocket. To overcome limitations on data while leveraging learning across protein targets, we choose to model the reward distribution conditioned on pocket structure, instead of the training data distribution. We design TacoGFN, a novel GFlowNet-based approach for structure-based drug design, which can generate molecules conditioned on any protein pocket structure with probabilities proportional to its affinity and property rewards. In the generative setting for CrossDocked2020 benchmark, TacoGFN attains a state-of-the-art success rate of $56.0\%$ and $-8.44$ kcal/mol in median Vina Dock score while improving the generation time by multiple orders of magnitude. Fine-tuning TacoGFN further improves the median Vina Dock score to $-10.93$ kcal/mol and the success rate to $88.8\%$, outperforming all optimization-based methods., Comment: Published in Transactions on Machine Learning Research (TMLR), 09/2024

Effective light extraction from optically active solid-state spin centres inside high-index semiconductor host crystals is an important factor in integrating these pseudo-atomic centres in wider quantum systems. Here we report increased fluorescent light collection efficiency from laser-written nitrogen vacancy centers (NV) in bulk diamond facilitated by micro-transfer printed GaN solid immersion lenses. Both laser-writing of NV centres and transfer printing of micro-lens structures are compatible with high spatial resolution, enabling deterministic fabrication routes towards future scalable systems development. The micro-lenses are integrated in a non-invasive manner, as they are added on top of the unstructured diamond surface and bond by Van-der-Waals forces. For emitters at 5 micrometer depth, we find approximately 2x improvement of fluorescent light collection using an air objective with a numerical aperture of NA = 0.95 in good agreement with simulations. Similarly, the solid immersion lenses strongly enhance light collection when using an objective with NA = 0.5, significantly improving the signal-to-noise ratio of the NV center emission while maintaining the NV's quantum properties after integration., Comment: 21 pages, 13 figures

Ultranarrow bandwidth single-photon sources operating at room-temperature are of vital importance for viable optical quantum technologies at scale, including quantum key distribution, cloud based quantum information processing networks, and quantum metrology. Here we show a room-temperature ultranarrow bandwidth single-photon source generating polarised photons at a rate of 5MHz based on an inorganic CsPbI3 perovskite quantum dot embedded in a tunable open-access optical microcavity. When coupled to an optical cavity mode, the quantum dot room-temperature emission becomes single-mode and the spectrum narrows down to just 1 nm. The low numerical aperture of the optical cavities enables efficient collection of high-purity single-mode single-photon emission at room-temperature, offering promising performance for photonic and quantum technology applications. We measure 94% pure single-photon emission into a single-mode under pulsed and continuous-wave (CW) excitation.

Through digital imaging, microscopy has evolved from primarily being a means for visual observation of life at the micro- and nano-scale, to a quantitative tool with ever-increasing resolution and throughput. Artificial intelligence, deep neural networks, and machine learning are all niche terms describing computational methods that have gained a pivotal role in microscopy-based research over the past decade. This Roadmap is written collectively by prominent researchers and encompasses selected aspects of how machine learning is applied to microscopy image data, with the aim of gaining scientific knowledge by improved image quality, automated detection, segmentation, classification and tracking of objects, and efficient merging of information from multiple imaging modalities. We aim to give the reader an overview of the key developments and an understanding of possibilities and limitations of machine learning for microscopy. It will be of interest to a wide cross-disciplinary audience in the physical sciences and life sciences.

Spiking Neural Networks (SNNs) emerged as a promising solution in the field of Artificial Neural Networks (ANNs), attracting the attention of researchers due to their ability to mimic the human brain and process complex information with remarkable speed and accuracy. This research aimed to optimise the training process of Liquid State Machines (LSMs), a recurrent architecture of SNNs, by identifying the most effective weight range to be assigned in SNN to achieve the least difference between desired and actual output. The experimental results showed that by using spike metrics and a range of weights, the desired output and the actual output of spiking neurons could be effectively optimised, leading to improved performance of SNNs. The results were tested and confirmed using three different weight initialisation approaches, with the best results obtained using the Barabasi-Albert random graph method.

Characterising the time over which quantum coherence survives is critical for any implementation of quantum bits, memories and sensors. The usual method for determining a quantum system's decoherence rate involves a suite of experiments probing the entire expected range of this parameter, and extracting the resulting estimation in post-processing. Here we present an adaptive multi-parameter Bayesian approach, based on a simple analytical update rule, to estimate the key decoherence timescales ($T_1$, $T_2^*$ and $T_2$) and the corresponding decay exponent of a quantum system in real time, using information gained in preceding experiments. This approach reduces the time required to reach a given uncertainty by a factor up to an order of magnitude, depending on the specific experiment, compared to the standard protocol of curve fitting. A further speed-up of a factor $\sim 2$ can be realised by performing our optimisation with respect to sensitivity as opposed to variance., Comment: made a change of word in the title ("Online" to "Real-time") and rearranged figures and text to improve the readability of the manuscript

A multipath in a directed graph is a disjoint union of paths. The multipath complex of a directed graph ${\tt G}$ is the simplicial complex whose faces are the multipaths of ${\tt G}$. We compute the Euler characteristic, and associated generating function, of the multipath complex for some families of graphs, including transitive tournaments and complete bipartite graphs. Then, we compute the homotopy type of multipath complexes of linear graphs, polygons, small grids and transitive tournaments. We show that they are all contractible or wedges of spheres. We introduce a new technique for decomposing directed graphs into dynamical regions, which allows us to simplify the homotopy computations., Comment: 18 pages, 15 Figures. Comments are welcome!

We introduce a method for analyzing the physical properties of nanoparticles in fluids via the competition between viscous drag and optical forces. By flowing particles through a microfluidic device containing an optical microcavity which acts as a combined optical trap and sensor, the variation of the rate of trapping events with the different forces can be established. A clear threshold behaviour is observed which provides a measure of a parameter combining the dielectric polarizability and the hydrodynamic radius. This technique could be applied in combination with other analytic techniques to provide a detailed physical characterisation of particles in solution.

Engineering single atomic defects into wide bandgap materials has become an attractive field in recent years due to emerging applications such as solid-state quantum bits and sensors. The simplest atomic-scale defect is the lattice vacancy which is often a constituent part of more complex defects such as the nitrogen-vacancy (NV) centre in diamond, therefore an understanding of the formation mechanisms and precision engineering of vacancies is desirable. We present a theoretical and experimental study into the ultra-fast laser generation of vacancy-interstitial pairs (Frenkel defects) in diamond. The process is described by a set of coupled rate equations of the pulsed laser interaction with the material and of the non-equilibrium dynamics of charge carriers during and in the wake of the pulse. We find that a model for Frenkel defect generation via the recombination of a bound biexciton as the electron plasma cools provides good agreement with experimental data, reproducing an effective non-linearity of $\sim$ 40 for Frenkel defect generation with respect to laser pulse energy., Comment: 17 pages, 11 figures

This article reviews deep learning applications in biomedical optics with a particular emphasis on image formation. The review is organized by imaging domains within biomedical optics and includes microscopy, fluorescence lifetime imaging, in vivo microscopy, widefield endoscopy, optical coherence tomography, photoacoustic imaging, diffuse tomography, and functional optical brain imaging. For each of these domains, we summarize how deep learning has been applied and highlight methods by which deep learning can enable new capabilities for optics in medicine. Challenges and opportunities to improve translation and adoption of deep learning in biomedical optics are also summarized.

A binary state on a graph means an assignment of binary values to its vertices. For example, if one encodes a network of spiking neurons as a directed graph, then the spikes produced by the neurons at an instant of time is a binary state on the encoding graph. Allowing time to vary and recording the spiking patterns of the neurons in the network produces an example of binary dynamics on the encoding graph, namely a one-parameter family of binary states on it. The central object of study in this article is the closed neighbourhood of a vertex $v$ in a graph $\mathcal{G}$, namely the subgraph of $\mathcal{G}$ that is induced by $v$ and all its neighbours in $\mathcal{G}$. We present a topological/graph theoretic method for extracting information out of binary dynamics on a graph, based on a selection of a relatively small number of vertices and their neighbourhoods. As a test case we demonstrate an application of the method to binary dynamics that arises from sample activity on the Blue Brain Project reconstruction of cortical tissue of a rat., Comment: 31 pages, 18 figures, 2 tables

To train algorithms for supervised author name disambiguation, many studies have relied on hand-labeled truth data that are very laborious to generate. This paper shows that labeled training data can be automatically generated using information features such as email address, coauthor names, and cited references that are available from publication records. For this purpose, high-precision rules for matching name instances on each feature are decided using an external-authority database. Then, selected name instances in target ambiguous data go through the process of pairwise matching based on the rules. Next, they are merged into clusters by a generic entity resolution algorithm. The clustering procedure is repeated over other features until further merging is impossible. Tested on 26,566 instances out of the population of 228K author name instances, this iterative clustering produced accurately labeled data with pairwise F1 = 0.99. The labeled data represented the population data in terms of name ethnicity and co-disambiguating name group size distributions. In addition, trained on the labeled data, machine learning algorithms disambiguated 24K names in test data with performance of pairwise F1 = 0.90 ~ 0.92. Several challenges are discussed for applying this method to resolving author name ambiguity in large-scale scholarly data., Comment: 25 pages

How can we evaluate the performance of a disambiguation method implemented on big bibliographic data? This study suggests that the open researcher profile system, ORCID, can be used as an authority source to label name instances at scale. This study demonstrates the potential by evaluating the disambiguation performances of Author-ity2009 (which algorithmically disambiguates author names in MEDLINE) using 3 million name instances that are automatically labeled through linkage to 5 million ORCID researcher profiles. Results show that although ORCID-linked labeled data do not effectively represent the population of name instances in Author-ity2009, they do effectively capture the 'high precision over high recall' performances of Author-ity2009. In addition, ORCID-linked labeled data can provide nuanced details about the Author-ity2009's performance when name instances are evaluated within and across ethnicity categories. As ORCID continues to be expanded to include more researchers, labeled data via ORCID-linkage can be improved in representing the population of a whole disambiguated data and updated on a regular basis. This can benefit author name disambiguation researchers and practitioners who need large-scale labeled data but lack resources for manual labeling or access to other authority sources for linkage-based labeling. The ORCID-linked labeled data for Author-tiy2009 are publicly available for validation and reuse., Comment: A pre-print of a paper accepted for publication in the journal Scientometrics

We present some results on the proportion of permutations of length $n$ containing certain mesh patterns as $n$ grows large, and give exact enumeration results in some cases. In particular, we focus on mesh patterns where entire rows and columns are shaded. We prove some general results which apply to mesh patterns of any length, and then consider mesh patterns of length four. An important consequence of these results is to show that the proportion of permutations containing a mesh pattern can take a wide range of values between $0$ and $1$.

Two-dimensional semiconducting transition metal dichalcogenides embedded in optical microcavities in the strong exciton-photon coupling regime may lead to promising applications in spin and valley addressable polaritonic logic gates and circuits. One significant obstacle for their realization is the inherent lack of scalability associated with the mechanical exfoliation commonly used for fabrication of two-dimensional materials and their heterostructures. Chemical vapor deposition offers an alternative scalable fabrication method for both monolayer semiconductors and other two-dimensional materials, such as hexagonal boron nitride. Observation of the strong light-matter coupling in chemical vapor grown transition metal dichalcogenides has been demonstrated so far in a handful of experiments with monolayer molybdenum disulfide and tungsten disulfide. Here we instead demonstrate the strong exciton-photon coupling in microcavities comprising large area transition metal dichalcogenide / hexagonal boron nitride heterostructures made from chemical vapor deposition grown molybdenum diselenide and tungsten diselenide encapsulated on one or both sides in continuous few-layer boron nitride films also grown by chemical vapor deposition. These transition metal dichalcogenide / hexagonal boron nitride heterostructures show high optical quality comparable with mechanically exfoliated samples, allowing operation in the strong coupling regime in a wide range of temperatures down to 4 Kelvin in tunable and monolithic microcavities, and demonstrating the possibility to successfully develop large area transition metal dichalcogenide based polariton devices.

Phase transitions, being the ultimate manifestation of collective behaviour, are typically features of many-particle systems only. Here, we describe the experimental observation of collective behaviour in small photonic condensates made up of only a few photons. Moreover, a wide range of both equilibrium and non-equilibrium regimes, including Bose-Einstein condensation or laser-like emission are identified. However, the small photon number and the presence of large relative fluctuations places major difficulties in identifying different phases and phase transitions. We overcome this limitation by employing unsupervised learning and fuzzy clustering algorithms to systematically construct the fuzzy phase diagram of our small photonic condensate. Our results thus demonstrate the rich and complex phase structure of even small collections of photons, making them an ideal platform to investigate equilibrium and non-equilibrium physics at the few particle level.

Complete digraphs are referred to in the combinatorics literature as tournaments. We consider a family of semi-simplicial complexes, that we refer to as "tournaplexes", whose simplices are tournaments. In particular, given a digraph $\mathcal{G}$, we associate with it a "flag tournaplex" which is a tournaplex containing the directed flag complex of $\mathcal{G}$, but also the geometric realisation of cliques that are not directed. We define several types of filtrations on tournaplexes, and exploiting persistent homology, we observe that flag tournaplexes provide finer means of distinguishing graph dynamics than the directed flag complex. We then demonstrate the power of these ideas by applying them to graph data arising from the Blue Brain Project's digital reconstruction of a rat's neocortex., Comment: 22 pages, 9 figures

The fluorescent nitrogen-vacancy (NV) defect in diamond has remarkable photophysical properties, including high photostability, which allows stable fluorescence emission for hours; as a result, there has been much interest in using nanodiamonds (NDs) for quantum optics and biological imaging. Such applications have been limited by the complexity and heterogeneity of ND photophysics. Photophysics of the NV center in NDs have been studied before, but the lack of a sensitive and high-throughput method has limited the characterization of NDs. Here is reported a systematic analysis of NDs using two-color wide-field epifluorescence imaging coupled to high-throughput single-particle detection of single NVs in NDs with sizes down to 5-10 nm. By using fluorescence intensity ratios, the charge conversion of single NV center (NV- or NV0) is observed and the lifetimes of different NV charge states in NDs is measured. The discovery of reversible manipulation of NV charge states by pH is also presented. In addition to provide another mechanism to control the NV charge state in nanodiamonds, our findings open the possibility to perform pH nanosensing with a non-photobleachable probe., Comment: Manuscript: 30 pages, 5 figures; supplementary information: 12 pages, 11 figures

Harriet Padberg wrote Computer-Composed Canon and Free Fugue as part of her 1964 dissertation in Mathematics and Music at Saint Louis University. This program is one of the earliest examples of text-to-music software and algorithmic composition, which are areas of great interest in the present-day field of music technology. This paper aims to analyze the technological innovation, aesthetic design process, and impact of Harriet Padberg's original 1964 thesis as well as the design of a modern recreation and utilization, in order to gain insight to the nature of revisiting older works. Here, we present our open source recreation of Padberg's program with a modern interface and, through its use as an artistic tool by three composers, show how historical works can be effectively used for new creative purposes in contemporary contexts. Not Even One by Molly Jones draws on the historical and social significance of Harriet Padberg through using her program in a piece about the lack of representation of women judges in composition competitions. Brevity by Anna Savery utilizes the original software design as a composition tool, and The Padberg Piano by Anthony Caulkins uses the melodic generation of the original to create a software instrument.

We present a new computing package Flagser, designed to construct the directed flag complex of a finite directed graph, and compute persistent homology for flexibly defined filtrations on the graph and the resulting complex. The persistent homology computation part of Flagser is based on the program Ripser [Bau18a], but is optimized specifically for large computations. The construction of the directed flag complex is done in a way that allows easy parallelization by arbitrarily many cores. Flagser also has the option of working with undirected graphs. For homology computations Flagser has an Approximate option, which shortens compute time with remarkable accuracy. We demonstrate the power of Flagser by applying it to the construction of the directed flag complex of digital reconstructions of brain microcircuitry by the Blue Brain Project and several other examples. In some instances we perform computation of homology. For a more complete performance analysis, we also apply Flagser to some other data collections. In all cases the hardware used in the computation, the use of memory and the compute time are recorded., Comment: 14 Pages, 12 figures

A permutation graph is a graph whose edges are given by inversions of a permutation. We study the Abelian sandpile model (ASM) on such graphs. We exhibit a bijection between recurrent configurations of the ASM on permutation graphs and the tiered trees introduced by Dugan et al. [10]. This bijection allows certain parameters of the recurrent configurations to be read on the corresponding tree. In particular, we show that the level of a recurrent configuration can be interpreted as the external activity of the corresponding tree, so that the bijection exhibited provides a new proof of a famous result linking the level polynomial of the ASM to the ubiquitous Tutte polynomial. We show that the set of minimal recurrent configurations is in bijection with the set of complete non-ambiguous binary trees introduced by Aval et al. [2], and introduce a multi-rooted generalization of these that we show to correspond to all recurrent configurations. In the case of permutations with a single descent, we recover some results from the case of Ferrers graphs presented in [11], while we also recover results of Perkinson et al. [16] in the case of threshold graphs., Comment: 21 pages