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We introduce a novel optomechanical microgear cavity for both optical and mechanical isotropic materials, featuring a single etch configuration. The design leverages a conjunction of phononic and photonic crystal-like structures to achieve remarkable confinement of both optical and mechanical fields. The microgear cavity we designed in amorphous silicon nitride exhibits a mechanical resonance at 4.8 GHz, and whispering gallery modes in the near-infrared, with scattering-limited quality factors above the reported material limit of up $10^7$. Notably, the optomechanical photoelastic overlap contribution reaches 75% of the ideal configuration seen in a floating ring structure., Comment: 4 Figures, 1 Supplemental document

Single-photon sources are of fundamental importance to emergent quantum technologies. Nano-structured optomechanical crystals provide an attractive platform for single photon generation due to their unique engineering freedom and compatibility with on-chip silicon fabrication. However, optical absorption heating has thus far prevented these systems from being widely used in practical applications. Here, we overcome this limitation through the use of a quasi-two-dimensional optomechanical crystal structure and demonstrate an on-chip source of single photons natively at telecom wavelength. We verify the low thermal noise and resulting high purity of the generated single photons through a Hanbury Brown-Twiss experiment with $g^{(2)}(0)=0.35^{+0.10}_{-0.08}$. Furthermore, we perform Hong-Ou-Mandel interference of the emitted photons showcasing the indistinguishability and coherence of photons generated from our source with visibility $V=0.52 \pm 0.15$ after 1.43 km of fiber delay line. With the possibility of using the mechanical mode as a quantum memory, we can retrieve the single photons on-demand. Crucial for applications, the optomechanical interaction at the heart of our device allows the bandwidth of emitted single photons to be tuned over a large range from 100 kHz to several hundreds of MHz, which makes them directly compatible with leading quantum memory platforms.

We introduce a novel computational unit, termed PushPull-Conv, in the first layer of a ResNet architecture, inspired by the anti-phase inhibition phenomenon observed in the primary visual cortex. This unit redefines the traditional convolutional layer by implementing a pair of complementary filters: a trainable push kernel and its counterpart, the pull kernel. The push kernel (analogous to traditional convolution) learns to respond to specific stimuli, while the pull kernel reacts to the same stimuli but of opposite contrast. This configuration enhances stimulus selectivity and effectively inhibits response in regions lacking preferred stimuli. This effect is attributed to the push and pull kernels, which produce responses of comparable magnitude in such regions, thereby neutralizing each other. The incorporation of the PushPull-Conv into ResNets significantly increases their robustness to image corruption. Our experiments with benchmark corruption datasets show that the PushPull-Conv can be combined with other data augmentation techniques to further improve model robustness. We set a new robustness benchmark on ResNet50 achieving an $mCE$ of 49.95$\%$ on ImageNet-C when combining PRIME augmentation with PushPull inhibition., Comment: Accepted at ICPR 2024, code available at https://github.com/bgswaroop/pushpull-conv

Microresonator-based degenerate optical parametric oscillation (DOPO) has recently been explored as a compelling platform for all-optical computing and quantum information applications, such as truly random number generation and the production of squeezed states of light. Emerging research has highlighted the potential of coupled microresonators, or photonic molecules, as a novel avenue for spectral engineering, unlocking an extra degree of freedom for the optimization of four-wave mixing interactions. Here, we demonstrate DOPO within the coupled modes of a silicon nitride triple-state photonic molecule. Our design introduces a distinctive mechanism for spectral engineering, using microheaters to individually tune the resonance spectral positions, thus enabling dynamic local dispersion control within the coupled modes. We successfully generate a DOPO signal with active efficiency control and explore the optical mode spacing in the tens of gigahertz range to use native phase-locked optical pumps driven by a radio-frequency source.

Integrated optomechanical systems are one of the leading platforms for manipulating, sensing, and distributing quantum information. The temperature increase due to residual optical absorption sets the ultimate limit on performance for these applications. In this work, we demonstrate a two-dimensional optomechanical crystal geometry, named \textbf{b-dagger}, that alleviates this problem through increased thermal anchoring to the surrounding material. Our mechanical mode operates at 7.4 GHz, well within the operation range of standard cryogenic microwave hardware and piezoelectric transducers. The enhanced thermalization combined with the large optomechanical coupling rates, $g_0/2\pi \approx 880~\mathrm{kHz}$, and high optical quality factors, $Q_\text{opt} = 2.4 \times 10^5$, enables the ground-state cooling of the acoustic mode to phononic occupancies as low as $n_\text{m} = 0.35$ from an initial temperature of 3 kelvin, as well as entering the optomechanical strong-coupling regime. Finally, we perform pulsed sideband asymmetry of our devices at a temperature below 10 millikelvin and demonstrate ground-state operation ($n_\text{m} < 0.45$) for repetition rates as high as 3 MHz. Our results extend the boundaries of optomechanical system capabilities and establish a robust foundation for the next generation of microwave-to-optical transducers with entanglement rates overcoming the decoherence rates of state-of-the-art superconducting qubits., Comment: 13 pages, 4 main figures

Modern high-sensitivity radio telescopes are discovering an increased number of resolved sources with intricate radio structures and fainter radio emissions. These sources often present a challenge because source detectors might identify them as separate radio sources rather than components belonging to the same physically connected radio source. Currently, there are no reliable automatic methods to determine which radio components are single radio sources or part of multi-component sources. We propose a deep learning classifier to identify those sources that are part of a multi-component system and require component association on data from the LOFAR Two-Metre Sky Survey (LoTSS). We combine different types of input data using multi-modal deep learning to extract spatial and local information about the radio source components: a convolutional neural network component that processes radio images is combined with a neural network component that uses parameters measured from the radio sources and their nearest neighbours. Our model retrieves 94 per cent of the sources with multiple components on a balanced test set with 2,683 sources and achieves almost 97 per cent accuracy in the real imbalanced data (323,103 sources). The approach holds potential for integration into pipelines for automatic radio component association and cross-identification. Our work demonstrates how deep learning can be used to integrate different types of data and create an effective solution for managing modern radio surveys., Comment: 20 pages, 14 figures, accepted for publication in MNRAS

Large-scale sky surveys at low frequencies, like the LOFAR Two-metre Sky Survey (LoTSS), allow for the detection and characterisation of unprecedented numbers of giant radio galaxies (GRGs, or 'giants'). In this work, by automating the creation of radio--optical catalogues, we aim to significantly expand the census of known giants. We then combine this sample with a forward model to constrain GRG properties of cosmological interest. In particular, we automate radio source component association through machine learning and optical host identification for resolved radio sources. We create a radio--optical catalogue for the full LoTSS Data Release 2 (DR2) and select all possible giants. We combine our candidates with an existing catalogue of LoTSS DR2 crowd-sourced GRG candidates and visually confirm or reject them. To infer intrinsic GRG properties from GRG observations, we develop further a population-based forward model that takes into account selection effects and constrain its parameters using Bayesian inference. We confirm 5,647 previously unknown giants from the crowd-sourced catalogue and 2,597 previously unknown giants from the ML-driven catalogue. Our confirmations and discoveries bring the total number of known giants to at least 11,585. We predict a comoving GRG number density $n_\mathrm{GRG} = 13 \pm 10\ (100\ \mathrm{Mpc})^{-3}$, close to a recent estimate of the number density of luminous non-giant radio galaxies. We derive a current-day GRG lobe volume-filling fraction $V_\mathrm{GRG-CW}(z = 0) = 1.4 \pm 1.1 \cdot 10^{-5}$ in clusters and filaments of the Cosmic Web. Our analysis suggests that giants are more common than previously thought. Moreover, tentative results imply that it is possible that magnetic fields once contained in giants pervade a significant ($\gtrsim 10\%$) fraction of today's Cosmic Web., Comment: 25 pages, 15 figures, 3 tables, accepted for publication in Astronomy & Astrophysics

This work presents a set of optimal machine learning (ML) models to represent the temporal degradation suffered by the power conversion efficiency (PCE) of polymeric organic solar cells (OSCs) with a multilayer structure ITO/PEDOT:PSS/P3HT:PCBM/Al. To that aim, we generated a database with 996 entries, which includes up to 7 variables regarding both the manufacturing process and environmental conditions for more than 180 days. Then, we relied on a software framework that brings together a conglomeration of automated ML protocols that execute sequentially against our database by simply command-line interface. This easily permits hyper-optimizing and randomizing seeds of the ML models through exhaustive benchmarking so that optimal models are obtained. The accuracy achieved reaches values of the coefficient determination (R2) widely exceeding 0.90, whereas the root mean squared error (RMSE), sum of squared error (SSE), and mean absolute error (MAE)>1% of the target value, the PCE. Additionally, we contribute with validated models able to screen the behavior of OSCs never seen in the database. In that case, R2~0.96-0.97 and RMSE~1%, thus confirming the reliability of the proposal to predict. For comparative purposes, classical Bayesian regression fitting based on non-linear mean squares (LMS) are also presented, which only perform sufficiently for univariate cases of single OSCs. Hence they fail to outperform the breadth of the capabilities shown by the ML models. Finally, thanks to the standardized results offered by the ML framework, we study the dependencies between the variables of the dataset and their implications for the optimal performance and stability of the OSCs. Reproducibility is ensured by a standardized report altogether with the dataset, which are publicly available at Github.

Spam emails are unsolicited, annoying and sometimes harmful messages which may contain malware, phishing or hoaxes. Unlike most studies that address the design of efficient anti-spam filters, we approach the spam email problem from a different and novel perspective. Focusing on the needs of cybersecurity units, we follow a topic-based approach for addressing the classification of spam email into multiple categories. We propose SPEMC-15K-E and SPEMC-15K-S, two novel datasets with approximately 15K emails each in English and Spanish, respectively, and we label them using agglomerative hierarchical clustering into 11 classes. We evaluate 16 pipelines, combining four text representation techniques -Term Frequency-Inverse Document Frequency (TF-IDF), Bag of Words, Word2Vec and BERT- and four classifiers: Support Vector Machine, N\"aive Bayes, Random Forest and Logistic Regression. Experimental results show that the highest performance is achieved with TF-IDF and LR for the English dataset, with a F1 score of 0.953 and an accuracy of 94.6%, and while for the Spanish dataset, TF-IDF with NB yields a F1 score of 0.945 and 98.5% accuracy. Regarding the processing time, TF-IDF with LR leads to the fastest classification, processing an English and Spanish spam email in and on average, respectively.

In this work we propose a new online, low cost and fast approach based on computer vision and machine learning to determine whether cutting tools used in edge profile milling processes are serviceable or disposable based on their wear level. We created a new dataset of 254 images of edge profile cutting heads which is, to the best of our knowledge, the first publicly available dataset with enough quality for this purpose. All the inserts were segmented and their cutting edges were cropped, obtaining 577 images of cutting edges: 301 functional and 276 disposable. The proposed method is based on (1) dividing the cutting edge image in different regions, called Wear Patches (WP), (2) characterising each one as worn or serviceable using texture descriptors based on different variants of Local Binary Patterns (LBP) and (3) determine, based on the state of these WP, if the cutting edge (and, therefore, the tool) is serviceable or disposable. We proposed and assessed five different patch division configurations. The individual WP were classified by a Support Vector Machine (SVM) with an intersection kernel. The best patch division configuration and texture descriptor for the WP achieves an accuracy of 90.26% in the detection of the disposable cutting edges. These results show a very promising opportunity for automatic wear monitoring in edge profile milling processes.

In this paper, a new system based on combinations of a shape descriptor and a contour descriptor has been proposed for classifying inserts in milling processes according to their wear level following a computer vision based approach. To describe the wear region shape we have proposed a new descriptor called ShapeFeat and its contour has been characterized using the method BORCHIZ that, to the best of our knowledge, achieves the best performance for tool wear monitoring following a computer vision-based approach. Results show that the combination of BORCHIZ with ShapeFeat using a late fusion method improves the classification performance significantly, obtaining an accuracy of 91.44% in the binary classification (i.e. the classification of the wear as high or low) and 82.90% using three target classes (i.e. classification of the wear as high, medium or low). These results outperform the ones obtained by both descriptors used on their own, which achieve accuracies of 88.70 and 80.67% for two and three classes, respectively, using ShapeFeat and 87.06 and 80.24% with B-ORCHIZ. This study yielded encouraging results for the manufacturing community in order to classify automatically the inserts in terms of their wear for milling processes.

In this paper, we propose Ranksum, an approach for extractive text summarization of single documents based on the rank fusion of four multi-dimensional sentence features extracted for each sentence: topic information, semantic content, significant keywords, and position. The Ranksum obtains the sentence saliency rankings corresponding to each feature in an unsupervised way followed by the weighted fusion of the four scores to rank the sentences according to their significance. The scores are generated in completely unsupervised way, and a labeled document set is required to learn the fusion weights. Since we found that the fusion weights can generalize to other datasets, we consider the Ranksum as an unsupervised approach. To determine topic rank, we employ probabilistic topic models whereas semantic information is captured using sentence embeddings. To derive rankings using sentence embeddings, we utilize Siamese networks to produce abstractive sentence representation and then we formulate a novel strategy to arrange them in their order of importance. A graph-based strategy is applied to find the significant keywords and related sentence rankings in the document. We also formulate a sentence novelty measure based on bigrams, trigrams, and sentence embeddings to eliminate redundant sentences from the summary. The ranks of all the sentences computed for each feature are finally fused to get the final score for each sentence in the document. We evaluate our approach on publicly available summarization datasets CNN/DailyMail and DUC 2002. Experimental results show that our approach outperforms other existing state-of-the-art summarization methods.

In many Reinforcement Learning (RL) papers, learning curves are useful indicators to measure the effectiveness of RL algorithms. However, the complete raw data of the learning curves are rarely available. As a result, it is usually necessary to reproduce the experiments from scratch, which can be time-consuming and error-prone. We present Open RL Benchmark, a set of fully tracked RL experiments, including not only the usual data such as episodic return, but also all algorithm-specific and system metrics. Open RL Benchmark is community-driven: anyone can download, use, and contribute to the data. At the time of writing, more than 25,000 runs have been tracked, for a cumulative duration of more than 8 years. Open RL Benchmark covers a wide range of RL libraries and reference implementations. Special care is taken to ensure that each experiment is precisely reproducible by providing not only the full parameters, but also the versions of the dependencies used to generate it. In addition, Open RL Benchmark comes with a command-line interface (CLI) for easy fetching and generating figures to present the results. In this document, we include two case studies to demonstrate the usefulness of Open RL Benchmark in practice. To the best of our knowledge, Open RL Benchmark is the first RL benchmark of its kind, and the authors hope that it will improve and facilitate the work of researchers in the field., Comment: Under review

The rotation state of small asteroids is affected in the long term by perturbing torques of gravitational and radiative origin (the YORP effect). Direct observational evidence of the YORP effect is the primary goal of our work. We carried out photometric observations of five near-Earth asteroids: (1862) Apollo, (2100) Ra-Shalom, (85989) 1999 JD6, (138852) 2000 WN10, and (161989) Cacus. Then we applied the light-curve inversion method to all available data to determine the spin state and a convex shape model for each of the five studied asteroids. In the case of (2100) Ra-Shalom, the analysis required that the spin-axis precession due to the solar gravitational torque also be included. We obtained two new detections of the YORP effect: (i) $(2.9 \pm 2.0)\times 10^{-9}\,\mathrm{rad\,d}^{-2}$ for (2100) Ra-Shalom, and (ii) $(5.5\pm 0.7)\times 10^{-8}\,\mathrm{rad\,d}^{-2}$ for (138852) 2000 WN10. The analysis of Ra-Shalom also reveals a precession of the spin axis with a precession constant $\sim 3000''\,\mathrm{yr}^{-1}$. This is the first such detection from Earth-bound photometric data. For the other two asteroids, we improved the accuracy of the previously reported YORP detection: (i) $(4.94 \pm 0.09)\times 10^{-8}\,\mathrm{rad\,d}^{-2}$ for (1862) Apollo, and (ii) $(1.86\pm 0.09)\times 10^{-8}\,\mathrm{rad\,d}^{-2}$ for (161989) Cacus. Despite the recent report of a detected YORP effect for (85989) 1999 JD6, we show that the model without YORP cannot be rejected statistically. Therefore, the detection of the YORP effect for this asteroid requires future observations. The spin-axis precession constant of Ra-Shalom determined from observations matches the theoretically expected value. The total number of asteroids with a YORP detection has increased to 12. In all cases, the rotation frequency increases in time.

In this work, the successful heteroepitaxial growth of boron carbide (B x C) on 4HSiC(0001) 4{\textdegree} off substrate using chemical vapor deposition (CVD) is reported. Towards this end, a two-step procedure was developed, involving the 4H-SiC substrate boridation under BCl 3 precursor at 1200{\textdegree}C, followed by conventional CVD under BCl 3 + C 3 H 8 at 1600{\textdegree}C. Such a procedure allowed obtaining reproducibly monocrystalline (0001) oriented films of B x C with a step flow morphology at a growth rate of 1.9 $\mu$m/h. Without the boridation step, the layers are systematically polycrystalline. The study of the epitaxial growth mechanism shows that a monocrystalline B x C layer is formed after boridation but covered with a B-and Si-containing amorphous layer. Upon heating up to 1600{\textdegree}C, under pure H 2 atmosphere, the amorphous layer was converted into epitaxial B x C and transient surface SiB x and Si crystallites. These crystallites disappear upon CVD growth.

This paper proposes minimum distance inference for a structural parameter of interest, which is robust to the lack of identification of other structural nuisance parameters. Some choices of the weighting matrix lead to asymptotic chi-squared distributions with degrees of freedom that can be consistently estimated from the data, even under partial identification. In any case, knowledge of the level of under-identification is not required. We study the power of our robust test. Several examples show the wide applicability of the procedure and a Monte Carlo investigates its finite sample performance. Our identification-robust inference method can be applied to make inferences on both calibrated (fixed) parameters and any other structural parameter of interest. We illustrate the method's usefulness by applying it to a structural model on the non-neutrality of monetary policy, as in \cite{nakamura2018high}, where we empirically evaluate the validity of the calibrated parameters and we carry out robust inference on the slope of the Phillips curve and the information effect.

The second data release of the LOFAR Two-Metre Sky Survey (LoTSS) covers 27% of the northern sky, with a total area of $\sim 5,700$ deg$^2$. The high angular resolution of LOFAR with Dutch baselines (6 arcsec) allows us to carry out optical identifications of a large fraction of the detected radio sources without further radio followup; however, the process is made more challenging by the many extended radio sources found in LOFAR images as a result of its excellent sensitivity to extended structure. In this paper we present source associations and identifications for sources in the second data release based on optical and near-infrared data, using a combination of a likelihood-ratio cross-match method developed for our first data release, our citizen science project Radio Galaxy Zoo: LOFAR, and new approaches to algorithmic optical identification, together with extensive visual inspection by astronomers. We also present spectroscopic or photometric redshifts for a large fraction of the optical identifications. In total 4,116,934 radio sources lie in the area with good optical data, of which 85% have an optical or infrared identification and 58% have a good redshift estimate. We demonstrate the quality of the dataset by comparing it with earlier optically identified radio surveys. This is by far the largest ever optically identified radio catalogue, and will permit robust statistical studies of star-forming and radio-loud active galaxies., Comment: 29 pages. Accepted by A&A; data products available at https://lofar-surveys.org/dr2_release.html