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Classifying phase transitions is a fundamental and complex challenge in condensed matter physics. This work proposes a framework for identifying quantum phase transitions by combining classical shadows with unsupervised machine learning. We use the axial next-nearest neighbor Ising model as our benchmark and extend the analysis to the Kitaev-Heisenberg model on a two-leg ladder. Even with few qubits, we can effectively distinguish between the different phases of the Hamiltonian models. Moreover, given that we only rely on two-point correlator functions, the classical shadows protocol enables the cost of the analysis to scale logarithmically with the number of qubits, making our approach a scalable and efficient way to study phase transitions in many-body systems., Comment: 12 pages, 16 figures

According to recent lore, supercooled first-order phase transitions (FOPTs) cannot explain the evidence for a stochastic gravitational wave background obtained by pulsar timing arrays. We demonstrate that supercooled FOPTs in Majoron-like U(1)' models with a conformal dark sector easily explain the nHz signal at NANOGrav in conjunction with the observed pattern of active neutrino masses and mixing. We find that models with a U(1)' charge of the Higgs doublet close to that of the B-L model are favored., Comment: 5 pages, 3 figures, 2 tables

Electrons trapped on the surface of liquid helium is an extremely clean system which holds promise for a scalable qubit platform. However, the superfluid surface is not free from fluctuations which might cause the decay and dephasing of the electrons quantized states. Understanding and mitigating these fluctuations is essential for the advancement of electrons-on-helium (eHe) qubit technology. Some work has been recently done to investigate surface oscillations due to the mechanical vibration of the cryostat using a superconducting coplanar waveguide (CPW) resonator. In the present work, we focus on a sub-hertz frequency range and observe a strong effect of surface oscillations on the temporal dynamics of the Rydberg transition of electrons confined in a microchannel trapping device. We suggest possible origin of such oscillations and find a reasonable agreement between the corresponding estimation of the oscillation frequency and the observed result., Comment: 10 pages, 4 figures

Fairness testing is increasingly recognized as fundamental in software engineering, especially in the domain of data-driven systems powered by artificial intelligence. However, its practical integration into software development may pose challenges, given its overlapping boundaries with usability and accessibility testing. In this tertiary study, we explore these complexities using insights from 12 systematic reviews published in the past decade, shedding light on the nuanced interactions among fairness, usability, and accessibility testing and how they intersect within contemporary software development practices.

Electrons trapped on the surface of superfluid helium have been thought of as a useful resource for quantum computing. Such electrons show long coherence of their surface-bound (Rydberg) states combined with their easy electrostatic manipulation. Recent proposals explored the possibility of coupling the spin state of an electron and the state of its quantized motion with a promise of a highly scalable 2D architecture for a quantum computer. However, despite recent progress in the detection of quantized lateral motion of electrons using a circuit-QED setup, the manipulation of a small number of electrons and their quantum state detection remains a challenging problem. Here, we report on the detection of the Rydberg transition of electrons on superfluid helium in an on-chip microchannel device in which electrons are moved and trapped by a set of electrostatic gates. A highly sensitive image-charge detection system allows us not only to resolve the transition spectra of such electrons, but also to perform the device characterization. The demonstrated sensitivity shows the feasibility of detecting the Rydberg transition of a single electron, which can open a new pathway for a non-destructive spin-state readout., Comment: 12 pages, 13 figures

Background. As artificial intelligence and AI-powered systems continue to grow, the role of data scientists has become essential in software development environments. Data scientists face challenges related to managing large volumes of data and addressing the societal impacts of AI algorithms, which require a broad range of soft skills. Goal. This study aims to identify the key soft skills that data scientists need when working on AI-powered projects, with a particular focus on addressing biases that affect society. Method. We conducted a thematic analysis of 87 job postings on LinkedIn and 11 interviews with industry practitioners. The job postings came from companies in 12 countries and covered various experience levels. The interviews featured professionals from diverse backgrounds, including different genders, ethnicities, and sexual orientations, who worked with clients from South America, North America, and Europe. Results. While data scientists share many skills with other software practitioners -- such as those related to coordination, engineering, and management -- there is a growing emphasis on innovation and social responsibility. These include soft skills like curiosity, critical thinking, empathy, and ethical awareness, which are essential for addressing the ethical and societal implications of AI. Conclusion. Our findings indicate that data scientists working on AI-powered projects require not only technical expertise but also a solid foundation in soft skills that enable them to build AI systems responsibly, with fairness and inclusivity. These insights have important implications for recruitment and training within software companies and for ensuring the long-term success of AI-powered systems and their broader societal impact.

We study the first-order phase transitions and the emerging stochastic gravitational wave spectrum in a minimal leptoquark extension of the Standard Model that explains active neutrino oscillation data while satisfying current flavor physics constraints. This model exhibits diverse phase transition patterns, including color symmetry-breaking scenarios in the early Universe. Strong correlations between model parameters and gravitational-wave signals yield testable predictions for future experiments such as LISA, BBO, and DECIGO. Specifically, a detectable signal in the mHz$\unicode{x2013}$0.1~Hz frequency range features color-restoration and leptoquark masses near $1.5~\mathrm{TeV}$. With this article, we also present the first application in the literature of \texttt{Dratopi}. This is a soon-to-be-released tool for phase transition analysis using the dimensional reduction formalism, that interfaces the \texttt{DRalgo} package with \texttt{Python} and a slightly modified version of \texttt{CosmoTransitions}., Comment: 33 pages, 12 figures, 2 tables

The iterative bleaching extends multiplexity (IBEX) Knowledge-Base is a central portal for researchers adopting IBEX and related 2D and 3D immunofluorescence imaging methods. The design of the Knowledge-Base is modeled after efforts in the open-source software community and includes three facets: a development platform (GitHub), static website, and service for data archiving. The Knowledge-Base facilitates the practice of open science throughout the research life cycle by providing validation data for recommended and non-recommended reagents, e.g., primary and secondary antibodies. In addition to reporting negative data, the Knowledge-Base empowers method adoption and evolution by providing a venue for sharing protocols, videos, datasets, software, and publications. A dedicated discussion forum fosters a sense of community among researchers while addressing questions not covered in published manuscripts. Together, scientists from around the world are advancing scientific discovery at a faster pace, reducing wasted time and effort, and instilling greater confidence in the resulting data.

Responsible AI principles provide ethical guidelines for developing AI systems, yet their practical implementation in software engineering lacks thorough investigation. Therefore, this study explores the practices and challenges faced by software practitioners in aligning with these principles. Through semi-structured interviews with 25 practitioners, we investigated their methods, concerns, and strategies for addressing Responsible AI in software development. Our findings reveal that while practitioners frequently address fairness, inclusiveness, and reliability, principles such as transparency and accountability receive comparatively less attention in their practices. This scenario highlights gaps in current strategies and the need for more comprehensive frameworks to fully operationalize Responsible AI principles in software engineering.

The use of large language models (LLMs) for qualitative analysis is gaining attention in various fields, including software engineering, where qualitative methods are essential for understanding human and social factors. This study aimed to investigate how LLMs are currently used in qualitative analysis and their potential applications in software engineering research, focusing on the benefits, limitations, and practices associated with their use. A systematic mapping study was conducted, analyzing 21 relevant studies to explore reported uses of LLMs for qualitative analysis. The findings indicate that LLMs are primarily used for tasks such as coding, thematic analysis, and data categorization, offering benefits like increased efficiency and support for new researchers. However, limitations such as output variability, challenges in capturing nuanced perspectives, and ethical concerns related to privacy and transparency were also identified. The study emphasizes the need for structured strategies and guidelines to optimize LLM use in qualitative research within software engineering, enhancing their effectiveness while addressing ethical considerations. While LLMs show promise in supporting qualitative analysis, human expertise remains crucial for interpreting data, and ongoing exploration of best practices will be vital for their successful integration into empirical software engineering research.

This paper introduces a task-specific, model-agnostic framework for evaluating dataset similarity, providing a means to assess and compare dataset realism and quality. Such a framework is crucial for augmenting real-world data, improving benchmarking, and making informed retraining decisions when adapting to new deployment settings, such as different sites or frequency bands. The proposed framework is employed to design metrics based on UMAP topology-preserving dimensionality reduction, leveraging Wasserstein and Euclidean distances on latent space KNN clusters. The designed metrics show correlations above 0.85 between dataset distances and model performances on a channel state information compression unsupervised machine learning task leveraging autoencoder architectures. The results show that the designed metrics outperform traditional methods.

We study supercooled first-order phase transitions above the QCD scale in a wide class of conformal Majoron-like U(1)' models that explain the totality of active neutrino oscillation data and produce a detectable stochastic gravitational wave background (SGWB) at LIGO, LISA and ET. We place constraints on the U(1)' breaking scale and gauge coupling using current LIGO-Virgo-Kagra data. We find that strong supercooling can be ruled out in large regions of parameter space if a SGWB is not detected by these experiments. A null signal at LIGO and ET will disfavor a type-I seesaw scale above $10^{14}$ GeV, while a positive signal is a signature of heavy right-handed neutrinos. On the other hand, LISA will be sensitive to seesaw scales as low as a TeV, and could detect a SGWB even if the right-handed neutrinos are decoupled., Comment: 50 pages, 25 figures, 3 tables. Version to appear in JHEP

The assumption of a flat universe that follows the cosmological principle, i.e., that the universe is statistically homogeneous and isotropic at large scales, comprises one of the core foundations of the standard cosmological model -- namely, the $\Lambda$CDM paradigm. Nevertheless, it has been seldom tested in the literature. In this work, we assess the validity of this hypothesis by reconstructing the cosmic curvature with currently available observations, such as Type Ia Supernova and Cosmic Chronometers. We do so by means of null tests, given by consistency relations within the standard model scenario, using a non-parametric method -- which allows us to circumvent prior assumptions on the underlying cosmology. We find no statistically significant departure from the cosmological principle and null curvature in our analysis. In addition, we show that future cosmological observations, such as those expected from Hubble parameter measurements from redshift surveys, along with gravitational wave observations as standard sirens, will be able to significantly reduce the uncertainties of current reconstructions., Comment: 7 pages, 3 figures and 1 appendix

Realizing a one-dimensional (1D) topological insulator and identifying the lower dimensional limit of two-dimensional (2D) behavior are crucial steps toward developing high-density quantum state networks, advancing topological quantum computing, and exploring dimensionality effects in topological materials. Although 2D topological insulators have been experimentally realized, their lower dimensional limit and 1D counterparts remain elusive. Here, we fabricated and characterized arrays of zigzag-terminated germanene nanoribbons, a 2D topological insulator with a large topological bulk gap. The electronic properties of these nanoribbons strongly depend on their width, with topological edge states persisting down to a critical width (approx. 2 nm), defining the limit of 2D topology. Below this threshold, contrary to the tenfold way classification, we observe zero-dimensional (0D) states localized at the ends of the ultrathin nanoribbons. These end states, topologically protected by time-reversal and mirror symmetries, mark the first realization of a 1D topological insulator with strong spin-orbit coupling. Our findings establish germanene nanoribbons as a platform for investigating 1D topology and dimensionality effects in topological materials.

Fractal geometries, characterized by self-similar patterns and non-integer dimensions, provide an intriguing platform for exploring topological phases of matter. In this work, we introduce a theoretical framework that leverages isospectral reduction to effectively simplify complex fractal structures, revealing the presence of topologically protected boundary and corner states. Our approach demonstrates that fractals can support topological phases, even in the absence of traditional driving mechanisms such as magnetic fields or spin-orbit coupling. The isospectral reduction not only elucidates the underlying topological features but also makes this framework broadly applicable to a variety of fractal systems. Furthermore, our findings suggest that these topological phases may naturally occur in materials with fractal structures found in nature. This work opens new avenues for designing fractal-based topological materials, advancing both theoretical understanding and experimental exploration of topology in complex, self-similar geometries.

Latent symmetries, which materialize after performing isospectral reductions, have recently been shown to be instrumental in revealing novel topological phases in one-dimensional systems, among many other applications. In this work, we explore how to construct a family of seemingly complicated two-dimensional models that result in energy-dependent Haldane models upon performing an isospectral reduction. In these models, we find energy-dependent latent Semenoff masses without introducing a staggered on-site potential. In addition, energy-dependent latent Haldane masses also emerge in decorated lattices with nearest-neighbor complex hoppings. Using the Haldane model's properties, we then predict the location of the topological gaps in the aforementioned family of models and construct phase diagrams to determine where the topological phases lie in parameter space. This idea yielded, for instance, useful insights in the case of a modified version of $\alpha$-graphyne and hexagonal plaquettes with additional decorations, where the gap-closing energies can be calculated using the ISR to predict topological phase transitions., Comment: 10 pages, 12 figures, regular article to be submitted to Physical Review B

As businesses increasingly adopt cloud technologies, they also need to be aware of new security challenges, such as server-side script attacks, to ensure the integrity of their systems and data. These scripts can steal data, compromise credentials, and disrupt operations. Unlike executables with standardized formats (e.g., ELF, PE), scripts are plaintext files with diverse syntax, making them harder to detect using traditional methods. As a result, more sophisticated approaches are needed to protect cloud infrastructures from these evolving threats. In this paper, we propose novel feature extraction and deep learning (DL)-based approaches for static script malware detection, targeting server-side threats. We extract features from plain-text code using two techniques: syntactic code highlighting (SCH) and abstract syntax tree (AST) construction. SCH leverages complex regexes to parse syntactic elements of code, such as keywords, variable names, etc. ASTs generate a hierarchical representation of a program's syntactic structure. We then propose a sequential and a graph-based model that exploits these feature representations to detect script malware. We evaluate our approach on more than 400K server-side scripts in Bash, Python and Perl. We use a balanced dataset of 90K scripts for training, validation, and testing, with the remaining from 400K reserved for further analysis. Experiments show that our method achieves a true positive rate (TPR) up to 81% higher than leading signature-based antivirus solutions, while maintaining a low false positive rate (FPR) of 0.17%. Moreover, our approach outperforms various neural network-based detectors, demonstrating its effectiveness in learning code maliciousness for accurate detection of script malware.

In this paper, we present a semi-empirical calibration between the oxygen abundance and the $N2$ emission-line ratio for Low Ionization Nuclear Emission Regions (LINERs). This relation was derived by comparing the optical spectroscopic data of 118 nuclear spaxels classified as LINERs using three different BPT diagrams from the Mapping Nearby Galaxies survey (MaNGA) and sub-classified as weak (wAGN, 84 objects) and strong (sAGN, 34 objects) AGN (active galactic nucleus) from the WHAN diagnostic diagram and photoionization model results obtained with the {\sc cloudy} code assuming gas accretion into a black hole (representing an AGN). We found that our wAGN LINERs exhibit an oxygen abundance in the range of $8.50 \lesssim \mathrm{12+\log(O/H)} \lesssim 8.90 $, with an average value of $\mathrm{12+\log(O/H)}=8.68$, while our sAGN LINERs exhibit an oxygen abundance in the range of $8.51 \lesssim \: \mathrm{12+\log(O/H)} \: \lesssim \: 8.81 $, with an average value of $\mathrm{12+\log(O/H)}=8.65$. Our abundance estimations are in good agreement with those derived for another two different samples one of them with 463 Seyfert 2 objects and the other with 43 LINERs galaxies ionized by post-AGB stars, showing that the assumptions of our models are likely suitable for wAGN and sAGN LINERs. A relation between the equivalent width of the observed H$\alpha$ emission-line and the estimated ionization parameter provided by models was obtained. Our results also suggest that LINERs does not show a clear correlation between oxygen abundances and the stellar mass of the hosting galaxies., Comment: Accepted for publication in PASA

We report on the reactions $\psi(3770)\to \bar{\Omega}^+ \bar{K} \Xi $ and $\psi(3770)\to \bar{\Omega}^+ \bar{K}\Xi^*(1530)~(\Xi^*(1530)\to \pi\Xi $), and calculate the mass distributions $\frac{{\rm d}\Gamma}{{\rm d}M_\text{inv}(\bar{K}\Xi)}$ and $\frac{{\rm d}\Gamma}{{\rm d}M_\text{inv}(\bar{K}\Xi^*)}$, respectively. We obtain clear peaks corresponding to the $\Omega(2012)$. From the decay of $\psi(3770)\to \bar{\Omega}^+ \bar{K}\Xi^*$, we also get a second, broader, peak around $2035\,\rm MeV$, which comes from the $\Omega(2012)$ decay to $\bar{K}\Xi^*$. This second peak is closely tied to the molecular picture of the $\Omega(2012)$ with the $\bar{K}\Xi^*(1530)$ and $\eta\Omega$ components. Its observation would provide a boost to the molecular picture of the $\Omega(2012)$., Comment: 6 pages, 5 figures;V2: new reference and footnote added;v3: discussion added, references added, version to appear in Eur.Phys.J.C

A driven linear oscillator and a feedback mechanism are two necessary elements of any classical periodic clock. Here, we introduce a novel, fully quantum clock using a driven oscillator in the quantum regime and coherent quantum feedback. We show that if we treat the model semiclassically, this system supports limit cycles, or self-sustained oscillations, as needed for a periodic clock. We then analyse the noise of the system quantum mechanically and prove that the accuracy of this clock is higher compared to the clock implemented with the classical measurement feedback. We experimentally implement the model using two superconducting cavities with incorporated Josephson junctions and microwave circulators for the realisation of the quantum feedback. We confirm the appearance of the limit cycle and study the clock accuracy both in frequency and time domains. Under specific conditions of noisy driving, we observe that the clock oscillations are more coherent than the drive, pointing towards the implementation of a quantum autonomous clock.

We study the Higgs boson decay into dark matter (DM) in the framework of freeze-in at stronger coupling. Even though the Higgs-DM coupling is significant, up to order one, DM does not thermalize due to the Boltzmann suppression of its production at low temperatures. We find that this mechanism leads to observable Higgs decay into invisible final states with the branching fraction of 10% and below, while producing the correct DM relic abundance. This applies to the DM masses down to the MeV scale, which requires a careful treatment of the hadronic production modes. For DM masses below the muon threshold, the Boltzmann suppression is not operative and the freeze-in nature of the production mechanism is instead guaranteed by the smallness of the electron Yukawa coupling. As a result, MeV DM with a significant coupling to the Higgs boson remains non-thermal as long as the reheating temperature does not exceed $\mathcal{O}(100)\;$MeV. Our findings indicate that there are good prospects for observing light non-thermal DM via invisible Higgs decay at the LHC and FCC., Comment: 23 pages, 9 figures, formulas corrected

Contragenic functions are defined to be reduced-quaternion-valued harmonic functions which are orthogonal to all monogenic and antimonogenic functions in the $L^2$ norm of a given domain. The parallelism between the spaces of contragenic functions in the interior and exterior of the unit sphere in $\R^3$ is described in detail. Bergman reproducing kernels for the spaces of contragenic functions are given, mirroring the corresponding kernels for the spaces of vector parts of monogenic functions. Numerical examples are given showing the accuracy of truncations of the integral kernels. A striking duality is observed between the basic interior contragenic functions and the vector parts of exterior monogenic functions, and vice versa., Comment: 10 pages, 3 figures

We analyze the staggered entry of rideshare services across U.S. metropolitan areas, estimating its effect on the spatial redistribution and real outcomes of residents. Ridesharing services gentrify urban areas-especially those with ex-ante lower housing values-causing housing prices to rise 9 percent, with the in-migration of rich-younger individuals more than offsetting the out-migration of incumbent residents and reduced in-migration of poorer individuals. Impact on incumbent residents is conditional on ex-ante homeownership. For homeowners, there is no displacement and a decline in delinquency rates. For non-homeowners, displacement and delinquency rates rise 11 percent and 42 percent, respectively. Our study emphasizes how the private provision of high-end transportation technologies can increase urbanization and exacerbate inequality.

We analyze the latest LHCb data on the $\pi^+\pi^-$ spectrum in the isospin-violating $X(3872)\to J/\psi \pi^+\pi^-$ decay, based on dispersion theory to deal with the $\pi\pi$ final state interactions. Additionally, the isospin breaking effects are properly introduced, allowing for a reliable and accurate extraction of the ratio, $R_X$, between the $X(3872)$ couplings to the $J/\psi \rho$ and $J/\psi \omega$ channels from the data. We find very good agreement with the LHCb data for the whole range of the $\pi^+\pi^-$ invariant mass, and $R_X$ is determined to be {$0.26\pm 0.03$}. Using this value, we make predictions for the $\pi^+\pi^0\pi^-$ mass distribution in the $X(3872)\to J/\psi \pi^+\pi^0\pi^-$ process, which is currently accessible by the BESIII Collaboration, and update a prediction for the pole positions of the isovector partner states of the $X(3872)$, $W_{c1}$, with $I(J^{PC})=1(1^{++})$., Comment: 9 pages, 1 table and 2 figures. Version to be published in Phys. Rev. D; results updated with a mistake on the rho-omega mixing corrected and a new appendix explaining how the mixing was obtained

Studies on human decision-making focused on humanitarian aid have found that cognitive biases can hinder the fair allocation of resources. However, few HCI and Information Visualization studies have explored ways to overcome those cognitive biases. This work investigates whether the design of interactive resource allocation tools can help to promote allocation fairness. We specifically study the effect of presentation format (using text or visualization) and a specific framing strategy (showing resources allocated to groups or individuals). In our three crowdsourced experiments, we provided different tool designs to split money between two fictional programs that benefit two distinct communities. Our main finding indicates that individual-framed visualizations and text may be able to curb unfair allocations caused by group-framed designs. This work opens new perspectives that can motivate research on how interactive tools and visualizations can be engineered to combat cognitive biases that lead to inequitable decisions., Comment: Published as a conference paper at CHI 2023

We measure the speed of light with current observations, such as Type Ia Supernova, galaxy ages, radial BAO mode, as well as simulations of forthcoming redshift surveys and gravitational waves as standard sirens. By means of a Gaussian Process reconstruction, we find that the precision of such measurements can be improved from roughly 6\% and to about $2-2.5\%$ when the gravitational wave simulations are considered, and to $1.5-2\%$ when redshift survey are included in the analysis as well. This result demonstrates that we will be able to perform a cosmological measurement of a fundamental physical constant with significantly improved precision, which will help us underpinning if its value is truly consistent with local measurements, as predicted by the standard model of Cosmology., Comment: 12 pages, 1 appendix, 9 figures. Accepted for publication in JCAP

The interacting SSH model provides an ideal ground to study the interplay between topologically insulating phases and electron-electron interactions. We study the polarization density as a topological invariant and provide an analytic treatment of its behavior in the low-energy sector of the one-dimensional interacting SSH model. By formulating the topological invariant in terms of Green's functions, we use the low-energy field theory of the Thirring model to derive the behavior of the polarization density. We show that the polarization density in the continuum theory describes the usual topological insulating phases. Still, it contains an extra factor from the fields' scaling dimensions in the low-energy quantum field theory. We interpret this as a measure of the modified charge of the new excitations in the system. We find two distinct contributions: a renormalization of the electronic charge $e$ of a Fermi liquid because of quasiparticle smearing and an additional contribution coming from the topological charge of the soliton arising in the bosonized version of the Thirring model, the sine-Gordon model., Comment: 18 pages, 6 figures to be submitted to PRB. Comments are welcome

We investigate the topological phases of the Haldane model on the Sierpi\'nski gasket. As a consequence of the fractal geometry, multiple fractal gaps arise. Additionally, a flat band appears, and due to a complex next-nearest neighbour hopping, this band splits and multiple topological flux-induced gaps emerge. Owing to the fractal nature of the model, conventional momentum-space topological invariants cannot be used. Therefore, we characterise the system's topology in terms of a real-space Chern number. In addition, we verify the robustness of the topological states to disorder. Finally, we present phase diagrams for both a fractal gap and a flux-induced gap. Previous work on a similar system claims that fractality "squeezes" the well-known Haldane phase diagram. However, this result arises because a doubled system was considered with two Sierpi\'nski gaskets glued together. We consider only a single copy of the Sierpi\'nski gasket, keeping global self-similarity. In contrast with these previous results, we find intricate and complex patterns in the phase diagram of this single fractal. Our work shows that the fractality of the model greatly influences the phase space of these structures, and can drive topological phases in the multitude of fractal and flux-induced gaps, providing a richer platform than a conventional integer dimensional geometry., Comment: 18 pages, 17 figures

Iterative Bleaching Extends multipleXity (IBEX) is a versatile method for highly multiplexed imaging of diverse tissues. Based on open science principles, we created the IBEX Knowledge-Base, a resource for reagents, protocols and more, to empower innovation., Comment: 8 pages, 1 figure, 9 references

This work introduces an on-the-fly (i.e., online) linear unmixing method which is able to sequentially analyze spectral data acquired on a spectrum-by-spectrum basis. After deriving a sequential counterpart of the conventional linear mixing model, the proposed approach recasts the linear unmixing problem into a linear state-space estimation framework. Under Gaussian noise and state models, the estimation of the pure spectra can be efficiently conducted by resorting to Kalman filtering. Interestingly, it is shown that this Kalman filter can operate in a lower-dimensional subspace while ensuring the nonnegativity constraint inherent to pure spectra. This dimensionality reduction allows significantly lightening the computational burden, while leveraging recent advances related to the representation of essential spectral information. The proposed method is evaluated through extensive numerical experiments conducted on synthetic and real Raman data sets. The results show that this Kalman filter-based method offers a convenient trade-off between unmixing accuracy and computational efficiency, which is crucial for operating in an on-the-fly setting. To the best of the authors' knowledge, this is the first operational method which is able to solve the spectral unmixing problem efficiently in a dynamic fashion. It also constitutes a valuable building block for benefiting from acquisition and processing frameworks recently proposed in the microscopy literature, which are motivated by practical issues such as reducing acquisition time and avoiding potential damages being inflicted to photosensitive samples.

We propose a comprehensive approach to increase the reliability and resilience of future power grids rich in distributed energy resources. Our distributed scheme combines federated learning-based attack detection with a local electricity market-based attack mitigation method. We validate the scheme by applying it to a real-world distribution grid rich in solar PV. Simulation results demonstrate that the approach is feasible and can successfully mitigate the grid impacts of cyber-physical attacks., Comment: Submitted to CIRED 2024 USA: Workshop on Resilience of Electric Distribution Systems

High data rate and low-latency vehicle-to-vehicle (V2V) communication are essential for future intelligent transport systems to enable coordination, enhance safety, and support distributed computing and intelligence requirements. Developing effective communication strategies, however, demands realistic test scenarios and datasets. This is important at the high-frequency bands where more spectrum is available, yet harvesting this bandwidth is challenged by the need for direction transmission and the sensitivity of signal propagation to blockages. This work presents the first large-scale multi-modal dataset for studying mmWave vehicle-to-vehicle communications. It presents a two-vehicle testbed that comprises data from a 360-degree camera, four radars, four 60 GHz phased arrays, a 3D lidar, and two precise GPSs. The dataset contains vehicles driving during the day and night for 120 km in intercity and rural settings, with speeds up to 100 km per hour. More than one million objects were detected across all images, from trucks to bicycles. This work further includes detailed dataset statistics that prove the coverage of various situations and highlights how this dataset can enable novel machine-learning applications., Comment: 14 pages, 15 figures, 2 tables. The dataset is available on the DeepSense6G website: https://deepsense6g.net/

Topological phase transitions are found in a variety of systems and were shown to be deeply related with a thermodynamic description through scaling relations. Here, we investigate the entanglement entropy, which is a quantity that captures the central charge of a critical model and the thermodynamics of the non-reciprocal Su-Schrieffer-Heeger (SSH) model. Although this model has been widely studied, the thermodynamic properties reveal interesting physics not explored so far. In order to analyze the boundary effects of the model, we use Hill's thermodynamics to split the grand potential in two contributions: the extensive one, related to the bulk, and the subdivision one, related to the boundaries. Then, we derive the thermodynamic entropy for both, the edges and the bulk and the heat capacity for the bulk at the topological phase transitions. The latter is related to the central charge when the underlying theory is a conformal field theory, whereas the first reveals the resilience of the topological edge states to finite temperatures. The phase transition between phases that are adiabatically connected with the Hermitian SSH model display the well-known behaviour of systems within the Dirac universality class, but the transition between phases with complex energies shows an unexpected critical behavior, which signals the emergence of an imaginary time crystal., Comment: 13 pages, 9 figures

Most Zero-shot Multi-speaker TTS (ZS-TTS) systems support only a single language. Although models like YourTTS, VALL-E X, Mega-TTS 2, and Voicebox explored Multilingual ZS-TTS they are limited to just a few high/medium resource languages, limiting the applications of these models in most of the low/medium resource languages. In this paper, we aim to alleviate this issue by proposing and making publicly available the XTTS system. Our method builds upon the Tortoise model and adds several novel modifications to enable multilingual training, improve voice cloning, and enable faster training and inference. XTTS was trained in 16 languages and achieved state-of-the-art (SOTA) results in most of them., Comment: Accepted at INTERSPEECH 2024