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We present strain tuning of excitonic emission in monolayer MoSe$_2$ by using a high-temperature physical vapor deposition (PVD). The use of two amorphous substrates, Si$_{3}$N$_{4}$ and SiO$_{2}$, provides two setpoints to induce distinct amounts of \textit{biaxial} tensile strain determined by a thermal expansion mismatch between the monolayer and the substrate. The tuning rate of the $A$-exciton transition energy is found to be 103 meV/\% by photoluminescence (PL), which represents the highest value realized by biaxial strain in transition metal dichalcogenides. The biaxial nature of the tensile strain is confirmed by polarization-resolved second harmonic generation, which reveals unperturbed in-plane three-fold symmetry of the monolayer. Furthermore, a softening of $A_\mathrm{1g}$ out-of-plane lattice vibration is identified in the Raman spectroscopy, which is known to be insignificant for uniaxial strain. Concomitantly, PL mapping of our PVD monolayers demonstrates (i) larger strain occurs in the interior of the mono-domain islands compared to the edges and (ii) the absence of island-size dependence in the magnitude of induced strain. Our results demonstrate an effective path towards strain engineering of excitons by using growth substrates, which holds great promise as a building block for future optoelectronic applications.

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.

Background and objective Risk prediction models aim at identifying people at higher risk of developing a target disease. Feature selection is particularly important to improve the prediction model performance avoiding overfitting and to identify the leading cancer risk (and protective) factors. Assessing the stability of feature selection/ranking algorithms becomes an important issue when the aim is to analyze the features with more prediction power. Methods This work is focused on colorectal cancer, assessing several feature ranking algorithms in terms of performance for a set of risk prediction models (Neural Networks, Support Vector Machines (SVM), Logistic Regression, k-Nearest Neighbors and Boosted Trees). Additionally, their robustness is evaluated following a conventional approach with scalar stability metrics and a visual approach proposed in this work to study both similarity among feature ranking techniques as well as their individual stability. A comparative analysis is carried out between the most relevant features found out in this study and features provided by the experts according to the state-of-the-art knowledge. Results The two best performance results in terms of Area Under the ROC Curve (AUC) are achieved with a SVM classifier using the top-41 features selected by the SVM wrapper approach (AUC=0.693) and Logistic Regression with the top-40 features selected by the Pearson (AUC=0.689). Experiments showed that performing feature selection contributes to classification performance with a 3.9% and 1.9% improvement in AUC for the SVM and Logistic Regression classifier, respectively, with respect to the results using the full feature set. The visual approach proposed in this work allows to see that the Neural Network-based wrapper ranking is the most unstable while the Random Forest is the most stable.

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.

Topology, like symmetry, is a fundamental concept in understanding general properties of physical systems. In condensed matter systems, non-trivial topology may manifest itself as singular features in the energy spectrum or the quantization of observable quantities such as electrical conductance and magnetic flux. Using microwave spectroscopy, we show that a superconducting circuit with three Josephson tunnel junctions in parallel can possess energy degeneracies indicative of $\textrm{\emph{intrinsic}}$ non-trivial topology. We identify three topological invariants, one of which is related to a hidden quantum mechanical supersymmetry. Depending on fabrication parameters, devices are gapless or not, and fall on a simple phase diagram which is shown to be robust to perturbations including junction imperfections, asymmetry, and inductance. Josephson tunnel junction circuits, which are readily fabricated with conventional microlithography techniques, allow access to a wide range of topological systems which have no condensed matter analog. Notable spectral features of these circuits, such as degeneracies and flat bands, may be leveraged for quantum information applications, whereas quantized transport properties could be useful for metrology applications., Comment: 20 pages, 12 figures, submitted version

The challenge of accessing specialized adhesives designed for strain gage applications has been highlighted due to failures in logistic chains, requiring the exploration of local alternatives. A direct simulation of strain gage bonding behavior with two steel plates is infeasible due to the unique construction of strain gages. Therefore, an indirect simulation method, comparing local alternatives to a widely accepted adhesive, Loctite 496, was employed in this study. Two potential replacements, Loctite 401 and Tekbond 793, were tested and matched against the benchmark adhesive, with a focus on the key mechanical properties: Proportional Shear Strain (PSS), Proportional Shear Stress (PSSt), and Apparent Shear Modulus (G*). Loctite 401 exhibited the highest G*, suggesting its potential use in strain gage installations if G* is considered most important. However, Tekbond 793 demonstrated superior PSS, Maximum Shear Stress (MSSt), and Rupture Shear Stress (RSSt) performance, displaying linear behavior even without an accelerator. Surface preparation considerations were also discussed, noting that hand abrading results in double the surface roughness than using an orbital sander. The study further identified two main regions concerning failure modes related to Ra, with values below 0.31 {\mu}m causing significant variations in observed mechanical properties, pointing towards factors beyond adhesive layer thickness affecting bond properties. Lastly, the general recommendation is the use of an accelerator for all tested adhesives, while the use of a surface conditioner and neutralizer was found to negatively impact adhesive performance., Comment: 44 pages, 24 figures

The sky-averaged cosmological 21 cm signal can improve our understanding of the evolution of the early Universe from the Dark Age to the end of the Epoch of Reionization. Although the EDGES experiment reported an absorption profile of this signal, there have been concerns about the plausibility of these results, motivating independent validation experiments. One of these initiatives is the Mapper of the IGM Spin Temperature (MIST), which is planned to be deployed at different remote locations around the world. One of its key features is that it seeks to comprehensively compensate for systematic uncertainties through detailed modeling and characterization of its different instrumental subsystems, particularly its antenna. Here we propose a novel optimizing scheme which can be used to design an antenna applied to MIST, improving bandwidth, return loss, and beam chromaticity. This new procedure combines the Particle Swarm Optimization (PSO) algorithm with a commercial electromagnetic simulation software (HFSS). We improved the performance of two antenna models: a rectangular blade antenna, similar to the one used in the EDGES experiment, and a trapezoidal bow-tie antenna. Although the performance of both antennas improved after applying our optimization method, we found that our bow-tie model outperforms the blade antenna by achieving lower reflection losses and beam chromaticity in the entire band of interest. To further validate the optimization process, we also built and characterized 1:20 scale models of both antenna types showing an excellent agreement with our simulations., Comment: 16 pages, published in the Journal of Astronomical Instrumentation

Quantum control allows us to address the problem of engineering quantum dynamics for special purposes. While recently the field of quantum batteries has attracted much attention, optimization of their charging has not benefited from the quantum control methods. Here we fill this gap by using an optimization method. We apply for the first time this convergent iterative method for the control of the population of a bipartite quantum system in two cases, starting with a qubit-qubit case. The quantum charger-battery system is considered here, where the energy is pumped into the charger by an external classical electromagnetic field. Secondly, we systematically develop the original formulation of the method for two harmonic oscillators in the Gaussian regime. In both cases, the charger is considered to be an open dissipative system. Our optimization takes into account experimentally viable problem of turning-on and off of the charging external field. Optimising the shape of the pulse significantly boosts both the power and efficiency of the charging process in comparison to the sinusoidal drive. The harmonic oscillator setting of quantum batteries is of a particular interest, as the optimal driving pulse remains so independently of the temperature of environment., Comment: Detailed analysis of methods performance for the task of one single qubit battery charging added, minor mistakes removed

Radiative shocks (RS) are complex phenomena which are ubiquitous in astrophysical environments. The study of such hypersonic shocks in the laboratory, under controlled conditions, is of primary interest to understand the physics at play and also to check the ability of numerical simulations to reproduce the experimental results. In this context, we conducted, at the Prague Asterix Laser System facility (PALS), the first experiments dedicated to the study of two counter-propagating radiative shocks propagating at non-equal speeds up to 25-50 km/s in noble gases at pressures ranging between 0.1 and 0.6 bar. These experiments highlighted the interaction between the two radiative precursors. This interaction is qualitatively but not quantitatively described by 1D simulations. Preliminary results obtained with XUV spectroscopy leading to the estimation of shock temperature and ion charge of the plasma are also presented.

Imaging using millimeter waves (mmWs) has many advantages including the ability to penetrate obscurants such as clothes and polymers. After having explored shape information retrieved from mmW images for person recognition, in this work we aim to gain some insight about the potential of using mmW texture information for the same task, considering not only the mmW face, but also mmW torso and mmW wholebody. We report experimental results using the mmW TNO database consisting of 50 individuals based on both hand-crafted and learned features from Alexnet and VGG-face pretrained Convolutional Neural Networks (CNN) models. First, we analyze the individual performance of three mmW body parts, concluding that: i) mmW torso region is more discriminative than mmW face and the whole body, ii) CNN features produce better results compared to hand-crafted features on mmW faces and the entire body, and iii) hand-crafted features slightly outperform CNN features on mmW torso. In the second part of this work, we analyze different multi-algorithmic and multi-modal techniques, including a novel CNN-based fusion technique, improving verification results to 2% EER and identification rank-1 results up to 99%. Comparative analyses with mmW body shape information and face recognition in the visible and NIR spectral bands are also reported., Comment: Published at IEEE Transactions on Biometrics, Behavior, and Identity Science

In this work we study the monodromy group of covers $\varphi \circ \psi$ of curves \linebreak $\mathcal{Y}\xrightarrow {\quad {\psi}} \mathcal{X} \xrightarrow {\quad \varphi} \mathbb{P}^{1}$, where $\psi$ is a $q$-fold cyclic \'etale cover and $\varphi$ is a totally ramified $p$-fold cover, with $p$ and $q$ different prime numbers with $p$ odd. We show that the Galois group $\mathcal{G}$ of the Galois closure $\mathcal{Z}$ of $\varphi \circ \psi$ is of the form $ \mathcal{G} = \mathbb{Z}_q^s \rtimes \mathcal{U}$, where $0 \leq s \leq p-1$ and $\mathcal{U}$ is a simple transitive permutation group of degree $p$. Since the simple transitive permutation group of prime degree $p$ are known, and we construct examples of such covers with these Galois groups, the result is very different from the previously known case when the cover $\varphi$ was assumed to be cyclic, in which case the Galois group is of the form $ \mathcal{G} = \mathbb{Z}_q^s \rtimes \mathbb{Z}_p$. Furthermore, we are able to characterize the subgroups $\mathcal{H}$ and $\mathcal{N}$ of $\mathcal{G}$ such that $\mathcal{Y} = \mathcal{Z}/\mathcal{N}$ and $X = \mathcal{Z}/\mathcal{H}$.

This paper considers cooperative Multi-Agent Reinforcement Learning, focusing on emergent communication in settings where multiple pairs of independent learners interact at varying frequencies. In this context, multiple distinct and incompatible languages can emerge. When an agent encounters a speaker of an alternative language, there is a requirement for a period of adaptation before they can efficiently converse. This adaptation results in the emergence of a new language and the forgetting of the previous language. In principle, this is an example of the Catastrophic Forgetting problem which can be mitigated by enabling the agents to learn and maintain multiple languages. We take inspiration from the Continual Learning literature and equip our agents with multi-headed neural networks which enable our agents to be multi-lingual. Our method is empirically validated within a referential MNIST based communication game and is shown to be able to maintain multiple languages where existing approaches cannot., Comment: Cooperative AI workshop NeurIPS 2021